Transportation Logistics

Solution space path planning for supporting en-route air traffic control

arXiv:2607.00064v1 Announce Type: new Abstract: As technology advances, many path-planning algorithms have been proposed for Air Traffic Management, yet their operational adoption in tactical control remains limited, revealing a misalignment between algorithmic design priorities and air traffic controllers' needs. This underscores the need for decision-support solutions that are inherently interpretable, computationally efficient, and explicitly designed for human use. Focusing on this design challenge, this study develops a conflict-free path-planning algorithm for en-route Air Traffic Control (ATC) designed to be compatible with two guiding considerations: (1) the interpretability and flexibility offered by solution-space displays, which motivate constructing an algorithm that exposes all feasible safe actions and accommodates shifting optimization goals; and (2) the decision logic controllers naturally apply when enforcing operational constraints, such as separation standards, maneuverability limits, waypoint minimization, and routing practicality. Centered on these principles, the algorithm integrates three intent-based conflict detection methods -- distance-based, time-interval-based, and zone-based -- within a solution-space framework to identify conflict-free paths in computationally efficient ways. Additionally, vertex-based and edge-based search nodes are proposed for solution space path planning (SSPP), resulting in two variants -- SSPPV and SSPPE, respectively, which are evaluated in terms of computational speed and solution quality. Empirical results show that SSPPV paired with zone-based conflict detection achieves the best performance, computing paths in 3.69 ms on average in operational-relevant scenarios based on the Delta sector of the Maastricht Upper Area Control Centre (MUAC) using a 5 nmi grid.

Nexar and Nauto merge in stock deal to form global physical AI company focused on real-world driving systems | CTech

The combined platform will use billions of miles of data to train autonomous and fleet intelligence models.

Can AI help avoid an air traffic control crisis — and would we trust it?

The technology has potential to assist controllers with an increasing flight load but many are wary

TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction

arXiv:2606.28716v1 Announce Type: new Abstract: The robustness of trajectory prediction models is crucial for developing safe autonomous driving systems. Adversarial attacks on trajectory prediction can significantly impair the accuracy of predicted trajectories, leading to hazardous driving behaviors. While heuristic defense strategies have been implemented to enhance the robustness of trajectory prediction models, these measures often fail against more sophisticated, targeted adversarial attacks. Hence, there is a pressing need to establish verifiable safety assurances for trajectory prediction models. In this paper, we extend the traditional Randomized Smoothing framework to "TrajRS", which provides a certified robust radius for smoothed trajectory predictors. We clarify and expand the formal definitions of robustness in trajectory prediction and tailor the practical TrajRS scheme specifically to "robustness for the optimal prediction" and "robustness for all possible predictions". An extensive set of experiments demonstrates that TrajRS effectively achieves robustness certification for all smoothed pedestrian trajectory predictors in this work.

GM-Backed Self-Driving Firm Momenta Seeks $752 Million From Hong Kong Listing

Autonomous-driving firm Momenta Global Ltd. started taking investor orders for its Hong Kong initial public offering to raise HK$5.9 billion ($752 million), pouncing on a multiyear high for share sales in the city.

Ministers likely to support law change to allow delivery robots on England’s paths

Exclusive: Safety campaigners concerned about plan for widespread deployment on already crowded pavements Large numbers of autonomous delivery robots could be coming to towns and cities across England after ministers signalled they were likely to support a change in the law allowing their use, prompting concern from safety campaigners. Low-speed robots, which mainly deliver groceries or takeaway food, are already in use in a handful of places but they operate in a regulatory grey area. The 1835 Highways Act bans “carriages” from pavements. Continue reading...

US auto regulators want to kill robotaxi brake pedals

Requiring driverless vehicles to keep human brake controls impedes innovation, the NHTSA says.

The AI Startup Challenging Tesla and Waymo in the Race to Automate Driving

Wayve is emerging as a go-to partner for traditional automakers trying to keep up with Silicon Valley.

Drone Startup Elroy Air Is Said to Near $800 Million SPAC Deal

Elroy Air Inc., a cargo drone startup aiming to replace delivery trucks, is in advanced talks to go public through a merger with a blank-check vehicle that will create a company with an enterprise value of about $1 billion.

From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities

arXiv:2606.25484v1 Announce Type: cross Abstract: Existing approaches to e-scooter mobility hub planning lack city-type-specific causal evidence. Demand models are typically correlational, built on proprietary trip data, and do not distinguish how driver profiles vary across urban typologies. This paper presents a three-phase agentic AI framework that constructs a Causal Template Library from public GBFS data across 29 German cities, encoding which environmental features causally drive hotspot demand for each combination of city type (large, university, industrial, hilly) and cluster type (core, peripheral). A large language model (LLM) orchestrated causal discovery pipeline adapts algorithm selection to local data conditions across 57 city-cluster units. The library reveals systematic variation. Core demand is driven by activity access and transit proximity, while peripheral demand responds to built form, with city-type-specific patterns supporting transferable siting templates. A planning tool built on the library scores candidate sites, calibrates infrastructure recommendations to local demographics, and generates practitioner-ready reports. In Heilbronn, Germany, two hub sites informed by the framework's causal evidence are currently under construction, illustrating how the outputs can support real-world siting decisions.

Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs

arXiv:2606.23938v1 Announce Type: new Abstract: Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision semantics needed to keep the rationale causally connected to the planned motion. We introduce Neuro-Symbolic Drive, a neuro-symbolic driving framework that supervises a driving VLA with rule-grounded reasoning traces extracted directly from classical rule-based planners. Our key observation is that rule-based planners are symbolic AI systems that already function as executable reasoning engines: they reason about active safety constraints, search over candidate maneuvers, and select a final trajectory. We instrument these planners in simulation to capture both the executed trajectory and the internal decision trace at each rule-evaluation step. Each trace is serialized into structured rule-grounded reasoning and paired with the trajectory to fine-tune Qwen3.5-4B as a driving VLA. Because these traces are derived directly from the planner states that determine the action, they ensure reasoning is structurally coupled to motion generation by construction, rather than by post-hoc alignment. On our simulator-generated benchmark, detailed rule-grounded reasoning reduces ADE@3s from 0.47 to 0.26 and miss rate from 8.30% to 6.40% under three-camera perception, and from 0.54 to 0.26 and 10.13% to 5.99% under eight-camera perception. Neuro-Symbolic Drive thus converts neuro-symbolic planning logic into structured supervision. Code base: https://github.com/XiangboGaoBarry/Neural-Symbolic-Drive.

Tesla Crash That Killed a Texas Woman Will be Investigated by Federal Regulators

The car’s driver-assistance system was in use when the crash killed a woman on Friday, the police said.

Waymo hits the brakes after robotaxis keep missing the signs for freeway construction zones

Nearly 4,000 vehicles recalled for driving past closure warnings and between cones marking shut lanes.

AI adoption in supply chains hampered by change management, not technology - The Loadstar

New survey finds AI adoption in supply chain is slowed by change management, integration challenges and a growing boardroom-frontline divide.

TechCrunch Mobility: A new robotaxi scorecard shows China’s dominance

A new industry scorecard highlights China's leading position in the global robotaxi market.

LATAM Airlines Leverages AI for Major Digital Overhaul, Boosting Efficiency and Customer Experience

LATAM Airlines launched an AI-driven digital transformation on June 20, 2026, to improve routing, maintenance, and customer service operations.

Go eyes robotaxis and acquisitions after Japan’s biggest IPO of 2026

Following a major IPO, the company Go is expanding its focus toward robotaxi services and strategic acquisitions.

Manna pauses drone delivery in Ireland over lack of clear policy

Pause not a permanent withdrawal from drone delivery operations in Ireland, Manna said. Read more: Manna pauses drone delivery in Ireland over lack of clear policy

ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

arXiv:2606.18803v1 Announce Type: new Abstract: Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem. Production matching pipelines remain dominated by structured numerical features, yet decisive behavioral signals (e.g., a driver's habitual aversion to certain regions) are inherently contextual and naturally expressible as LLM-generated user profiles. However, scaling such profiling to a live, millisecond-latency dispatcher faces three intertwined constraints rarely addressed together: on a platform with millions of daily orders, logs exceed any LLM's context window by orders of magnitude; most users are long-tail, with too few interactions for per-user profiling; and surface-fluent profiles do not necessarily improve downstream prediction utility. We present ProfiLLM, an agentic LLM data pipeline that operationalizes utility-aligned user profiling for production matching systems through two modules. (1) Tool-Augmented Global Knowledge Mining equips an LLM agent with 27 analytical tools to mine platform-scale data, producing reusable global knowledge, adaptive user clustering rules, and region-level supply-demand priors. (2) Utility-Aligned Profile Exploration generates multiple candidate profiles per cluster, evaluates them via a lightweight downstream utility proxy, iteratively refines the best candidates and constructs preference pairs for DPO fine-tuning. Deployed on DiDi's production dispatcher, ProfiLLM achieves up to +6.14% relative AUC improvement in outcome prediction, up to +4.35% GMV gain in dispatching simulation, and consistent improvements in a 14-day online A/B test including +0.47% GMV, +0.33% Completion Rate, and -0.82% Cancel-Before-Accept rate.

A Branch-Price-Cut-And-Switch Approach for Optimizing Team Formation and Routing for Airport Baggage Handling Tasks with Stochastic Travel Times

arXiv:2405.20912v5 Announce Type: replace Abstract: In airport operations, optimally using dedicated personnel for baggage handling tasks plays a crucial role in the design of resource-efficient processes. Teams of workers with different qualifications must be formed, and loading or unloading tasks must be assigned to them. Each task has a time window within which it can be started and should be finished. Violating these temporal restrictions incurs severe financial penalties for the operator. In practice, various components of this process are subject to uncertainties. We consider the aforementioned problem under the assumption of time-dependent stochastic travel times across the apron. We present two binary program formulations to model the problem at hand and propose a novel solution approach that we call Branch-Price-Cut-and-Switch, in which we dynamically switch between two master problem formulations. Furthermore, we use an exact separation method to identify violated rank-1 Chv\'atal-Gomory cuts and utilize an efficient branching rule relying on task finish times. We test the algorithm on instances generated based on real-world data from a major European hub airport with a planning horizon of up to two hours, 30 flights per hour, and three available task execution modes to choose from. Our results indicate that our algorithm is able to significantly outperform existing solution approaches. Moreover, an explicit consideration of stochastic travel times allows for solutions that utilize the available workforce more efficiently, while simultaneously guaranteeing a stable service level for the baggage handling operator.

Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

arXiv:2606.17530v1 Announce Type: cross Abstract: New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions. Because such policies generate spillovers across modes and locations, credible control groups are difficult to construct. We address this challenge using time series foundation models to generate probabilistic counterfactual demand forecasts with calibrated uncertainty. Applying this framework to bus, subway, and aggregate trip volume data, we find that post-policy bus and subway ridership increased significantly relative to expected no-policy demand, while overall travel demand decreased modestly. The effects are spatially heterogeneous: while reductions in overall travel demand are concentrated within the Congestion Relief Zone, transit gains extend beyond Manhattan's core. Socio-demographic analyses further reveal uneven adaptation across neighborhoods, highlighting spatial equity implications. Our framework provides a scalable approach for the uncertainty-aware evaluation of system-wide urban interventions when clean control groups are unavailable.

Competing firms, competing regulators: The strategic cost of fragmented climate policy

arXiv:2606.17290v1 Announce Type: new Abstract: Climate policy in global network industries is implemented across fragmented jurisdictions, yet firms respond through integrated operational networks. We develop a two-stage game-theoretic framework to analyze how firm-level responses interact with alternative governance structures. Regulators first choose emissions charges. Firms subsequently compete through pricing, service capacity and capital deployment decisions. The analytical results demonstrate that uniform global regulation maximizes welfare in symmetric markets. However, in sufficiently asymmetric markets, a uniform global charge is dominated by decentralized regimes. Multiple regulatory instruments better accommodate region-specific market externalities. We apply this framework to a calibrated case study of North American, Western European and transatlantic aviation markets. The numerical results establish that a globally coordinated regulator setting region-specific charges achieves the highest aggregate welfare. These aggregate gains nonetheless mask substantial distributional disparities across jurisdictions. Effective climate governance in network industries therefore requires more than determining an efficient emissions charge. Policy instruments ought to accommodate regional heterogeneity and transfer mechanisms will be necessary to ensure efficient, politically stable cooperation.

AI in Supply Chain Management Market Size, Trend & Growth | 2030

The AI in supply chain management market size was valued at USD 3.5 billion & is likely to grow at a CAGR of 30.3% during 2024-2030.

Irish fleet safety tech company CameraMatics raises €49m

The Dublin-headquartered company uses AI and analytics to improve road safety for commercial fleets. Read more: Irish fleet safety tech company CameraMatics raises €49m

How fleets can use agentic AI without risking maintenance decisions | FleetOwner

Agentic AI could streamline fleet maintenance workflows, but human oversight remains essential for critical decisions.

Supply chain roles requiring AI skills outpacing overall labor market - Truck News

Demand for supply chain roles requiring artificial intelligence skills has increased 387% in just three years, significantly outpacing overall labor

The ‘AI superstar’ CEO behind a self-driving truck unicorn on why Gen Z is a better hiring bet than industry veterans

Raquel Urtasun, co-founder of autonomous truck startup Waabi, talks to Fortune about unicorn hiring in the AI age: “Fear can paralyze your ability to embrace that change.”

Waymo Premier Launches: Subscription Offers Perks

Waymo introduces Premier, a $29.99 monthly ride subscription, offering perks like priority pickups and ride credits.

PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation

arXiv:2606.12616v1 Announce Type: new Abstract: Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or by learned models trained toward a single behavioral mode. Recent work introduces style variation through post-hoc labels on observational data or LLM-inferred reward weights, but these signals act as proxies for what a style should reward rather than demonstrations of humans explicitly asked to drive in that style. We introduce PersonaDrive, a pipeline that conditions a vision-language-action (VLA) driving agent on retrieved demonstrations from a style-instructed human driving dataset, in which participants drive CARLA leaderboard routes under aggressive, neutral, and conservative instructions on a driver-in-the-loop rig. The pipeline has three stages: (i) offline triplet mining over per-style human driving data using a combined image-text similarity score; (ii) training a lightweight retrieval head that fuses frozen visual features with a small control encoder over per-style databases; and (iii) fine-tuning a single VLA backbone to treat retrieved context points as in-context behavioral demonstrations during waypoint prediction. At inference, the same backbone is conditioned on any style by swapping which per-style database the retrieval head queries, so selecting a style requires no per-style retraining while enabling human-style, style-diverse non-ego agents for closed-loop simulation. On Bench2Drive, PersonaDrive (no style) improves the driving score by 4.6% over SimLingo and 2.5% over HiP-AD, and under style conditioning attains the highest driving score in every style within a roughly 2% band (its weakest style surpassing the strongest baseline, DMW, by 5.4%), while average speed and acceleration rise by 18% and 25% from the conservative to the aggressive instruction.

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

arXiv:2606.12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation. Existing LLM-based generators typically rely on either prompt engineering, which preserves zero-shot reasoning but lacks fine-grained spatiotemporal grounding, or trajectory-level fine-tuning, which improves statistical precision but incurs substantial computational cost and may weaken general reasoning. We propose TrajGenAgent, a semantic-aware hierarchical LLM-agent framework for human mobility trajectory generation without model fine-tuning. TrajGenAgent uses a two-stage orchestrator-worker design: an LLM first synthesizes an individual- and weekday-conditioned activity chain from historical evidence via in-context learning, and a deterministic workflow then grounds each activity into a complete visit using personalized POI retrieval, distance-aware location selection, kinematics-aware travel-time propagation, and LLM-based duration estimation. To evaluate realism beyond aggregate spatiotemporal statistics, we introduce an anomaly-detection-based evaluation framework using two complementary detectors to assess behavioral and semantic plausibility. Experiments on benchmark and large-scale simulation datasets show that TrajGenAgent improves spatiotemporal fidelity, semantic coherence, and individual-specific behavioral realism over representative neural and LLM-based baselines, while avoiding parameter updates.

SpaceX goes 19% higher in first day of trading

SpaceX shares rose 19% on their first day of trading, ending with a $2.1 trillion market cap and marking the largest IPO in U.S. history.

Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints

arXiv:2606.10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth datasets. Despite the availability of several real-world and simulated human trajectory collections, these datasets exclusively capture normal mobility patterns and lack annotated anomalies. This specific scarcity is fundamentally driven by the inherent statistical rarity of anomalous events, precluding the feasibility of conventional observational methods. Compounding this challenge, the systematic acquisition of large-scale mobility data is strictly bottlenecked by prohibitive costs and stringent privacy regulations. To overcome these fundamental limitations and establish a reliable human trajectory anomalies dataset with annotated ground truth, we introduce a novel, end-to-end generative framework designed to synthesize realistic trajectory anomalies at scale. Our architecture bridges the gap between purely synthetic mobility data and complex real-world physical constraints by operating directly on baseline simulated trajectories. We employ Large Language Model (LLM) agents to systematically inject semantically meaningful behavioral anomalies such as irregular out-of-distribution check-ins and skipped routine visits. To ensure rigorous spatial validity, the system leverages map-constrained routing reconstruction to recalculate the physical transitions between these LLM agent-modified staypoints. Moreover, to narrow the simulation-to-reality gap, we augment the resulting trajectories with a context-aware spatial noise model, parameterized by environmental and location-specific variables, to accurately emulate heterogeneous GPS sensor degradation.

Risk-Aware Planning for Transit Desert Remediation Under Demand Uncertainty

arXiv:2606.08371v1 Announce Type: new Abstract: Transit deserts are areas where public transportation is inadequate despite evidence of travel demand, a condition that affects tens of millions of residents across the Americas. Planning for these areas is difficult because the usual demand signal is missing: ridership cannot be observed before service exists. To address that setting, we formulate risk-aware transit desert remediation as a partially observable Markov decision process with Conditional Value-at-Risk constraints for financial tail risk. The model uses demographic, land-use, and employment data to set a prior over latent demand, then updates that prior as new service deployments produce ridership observations. A myopic belief-aware planner is evaluated on 25 cities using a unified financial model for operating cost, capital expenditure, fare revenue, and net subsidy. After five years, the planner remediates a median of 53.6% of transit-desert tracts and improves on static optimization by 5.0 percentage points on average, with gains in 16 of 25 cities. Gains are largest at moderate budgets (+9.9 points at baseline) and persist under 50% prior-demand miscalibration, while population density and existing transit density are the strongest structural predictors of remediation cost ($R^2\!=\!0.41$ on per-tract cost)

Driverless Trucks Are Here—and They’re Delivering Bags of Doritos

PepsiCo has 41 trucks on the road in Arizona, Texas and Arkansas, bringing the technology into the mainstream.

Uber Opens London Waitlist for Wayve Robotaxis Ahead of Launch

Uber Technologies Inc. has set up a waitlist for customers interested in riding in a robotaxi in London, ahead of a planned commercial debut in the UK this year.

CARVE-Q: Quantum-Proposed, Classically Certified Interactive Driving Repair

arXiv:2606.06531v1 Announce Type: new Abstract: The critical question after a correct driving veto is not only whether a maneuver is unsafe, but whether the blocked interaction admits a lawful, auditable, and responsibility-bounded repair. Prediction and game-theoretic planners can suggest plausible cooperation, yet they do not return a proof that the repair respects hard rules, right-of-way, cost allocation, and ego fallback. We introduce CARVE, Certified Affordable Repair of Vetoed maneuvers via Envelopes, a certificate architecture for prediction-free interactive repair. Given a vetoed maneuver, CARVE constructs a finite repair lattice and emits a structured certificate recording the binding rule, selected joint repair, right-of-way-scaled cooperation envelope, responsibility-weighted cost split, and ego-only fallback. This certificate view reveals the algorithmic bottleneck: multi-owner repair induces a product lattice $M = \prod_j |\mathcal{A}_j|$. We therefore introduce CARVE-Q, a verifier-shielded quantum-AI search layer that applies quantum minimum finding only to this black-box lattice while leaving all safety authority classical. In the conservative verifier-oracle model, exact classical minimum finding requires $\Theta(M)$ queries in the worst case, whereas Durr-Hoyer/Grover minimum finding uses $O(\sqrt{M})$ oracle queries with high probability. We prove verifier-shielded certificate soundness, priority non-elicitation, black-box query separation, and finite-precision reversible-oracle constructibility. We then demonstrate state-vector minimum finding on CARVE repair oracles up to 65,536 assignments and validate certificate preservation on Lanelet2-grounded INTERACTION replay with 100% right-of-way respect, 100% blame consistency, and zero priority false positives. The result is a trust-bounded quantum-AI pattern for certified autonomy: quantum proposes; CARVE certifies.

Brit maritime agency heralds fresh global rules for crewless cargo ships

If you thought driverless cars were bad, imagine a 200,000 ton container ship

Electric truck startup pitches containerized data center on wheels

A Belgium-based electric truck startup is pitching a mobile containerized data center concept. Earlier this year, Windrose Electric founder and CEO Wen Han outlined plans for a new containerized AI and energy offering that could be wheeled into place – using the company’s R700 electric semi-truck. – Windrose – LiFE-Younger “We are introducing a new […]

Uber's AI Spending Cap Reveals the Hidden Economics of Employee AI Tools - FourWeekMBA

When Uber had to cap employee AI spending after burning through its budget in just four months, it exposed a brewing crisis in how companies are pricing internal AI adoption. The ride-sharing giant’s predicament reveals a fundamental shift in enterprise software economics that could reshape ...

Uber, Autobrains, and Nvidia Launch Robotaxi Pilot in Munich

Uber is partnering with Autobrains and Nvidia to launch a robotaxi service in Munich, utilizing a cost-efficient, camera-based AI system.

TransResAI: A Compound AI System for Coastal Transportation Resilience

arXiv:2606.00042v1 Announce Type: new Abstract: Coastal flooding increasingly threatens transportation infrastructure, yet the analytical tools needed for resilience management remain difficult for many non-specialist practitioners to use. This study presents TransResAI, a compound AI system that supports analysis of flood-aware transportation resilience via natural-language interactions. The sys

VinFast, Autobrains, NVIDIA Launch Level 4 Autonomous Driving

The companies are collaborating to develop a Level 4 autonomous driving system for Southeast Asia, leveraging NVIDIA DRIVE Hyperion for scalable robotaxi operations.

Fescaro Warns of Generative AI-Driven Vehicle Cybersecurity Threats < IT·Gaming < 기사본문 - The Elec Inc.

Fescaro Chief Executive Officer ... vehicle cybersecurity landscape shaped by AI advancement, focusing on generative AI-based vehicle hacking attempts and aftermarket security threat cases. In particular, Hong pointed to generative AI-driven vulnerability analysis and attack automation technologies as emerging security risks...

African Startup Spiro Raises $215 Million, Nearing Unicorn Value

African electric-mobility startup Spiro raised $215 million backed by European and African investors as it nears $1 billion in value, or so-called unicorn status, founder Gagan Gupta said.

How Verizon Connect Scaled Agentic AI to 100,000 Users

Verizon Connect scaled agentic AI to turn massive vehicle-fleet data streams into actionable insights. The project demonstrated impressive scale and cost optimization across models.

Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

arXiv:2605.30576v1 Announce Type: new Abstract: Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages expert advice to guide exploration while avoiding long-term dependence. Advice is triggered when epistemic or aleatoric uncertainty exceeds adaptive thresholds derived from rolling buffers, ensuring advice evolves with the agent's confidence. A commitment-cooldown strategy with a stochastic early-stop heuristic regulates the duration and frequency of guidance, exposing the agent to coherent maneuvers without exhausting the advice budget. Expert and agent experiences are combined in a shared replay buffer within an off-policy implicit quantile network (IQN) backbone, enabling efficient reuse of expert trajectories. Experiments in CARLA show that our method outperforms the IQN baseline, improving success by 5-7% and reducing failures, demonstrating that risk-sensitive uncertainty coupled with regulated expert integration enables safer and more efficient exploration for sensor-based RL policy learning in unsignalized intersection navigation.

Joby Demonstrated its Air Taxi in Manhattan, but You Can’t Fly in It Yet

Aviation start-ups and the Trump administration want to replace helicopters with electric aircraft, but the new vehicles still have to pass arduous tests before the public can use them.

How Lyft Built a Self-Serve AI Agent Platform for Customer Support with LangGraph and LangSmith

Lyft developed a platform allowing non-engineers to create customer support agents, emphasizing the importance of observability, semantic layers, and domain-expert involvement.

Why Tesla’s AI trainers don’t trust its self-driving tech – or its safety stats | Reuters

Those efforts, which haven’t been previously reported, undermine Musk’s long-stated claim that Tesla’s self-driving technology will soon work anywhere globally and doesn’t require the same laborious local mapping of roads and hazards employed by rivals. Musk has said Tesla takes a simpler approach, relying solely on cameras and AI , that will allow it to scale up its robotaxi service at “hyperexponential” speed and offer current Tesla owners full autonomy through software updates.

I Think AI-Forward Car Companies Like Rivian Are Going to Get Their Clocks Cleaned: READ LISTEN OF THE DAY

Rivian Assistant is presented by Wassym as a deeply integrated, AI -powered voice agent that serves as the connective tissue of this new architecture, controlling most vehicle functions, orchestrating apps and services via an “agentic” framework, and increasingly run on powerful local (edge) compute, and that this approach justifies Rivian’s refusal to support Apple CarPlay or Android Auto.

Travelport, Cognizant and Anthropic Collaborate to Power Travel Technology for the AI Era

Travelport, Cognizant and Anthropic Collaborate to Power Travel Technology for the AI Era Accessibility Statement Skip Navigation Cognizant Logo Travelport - Travelport, Cognizant and Anthropic are building an AI travel ecosystem to modernize how travel technology is built, tested and maintained - Together they are closing the critical gap in AI-driven travel: connecting systems that can reason and plan with platforms that can actually transact TEANECK, N.J., May 27, 2026 /PRNewswire/ -- Cognizant (NASDAQ: CTSH) is working with Travelport on a strategic AI transformation that will deploy Anthropic's Claude to modernize the way Travelport builds, tests and maintains software across its travel retailing and distribution platforms. The collaboration aims to accelerate the delivery of AI-led innovation to airlines, hoteliers, travel management companies and online travel agencies worldwi

Uber adds to its Delivery Hero stake at €12bn valuation

Share purchase from Aspex Management steps up US ride-hailing group’s pursuit of German food delivery company

TMV's $200M Fund Targets Maritime Innovation to Tackle Global Shipping Challenges

TMV has unveiled a $200 million venture fund dedicated to advancing maritime infrastructure and logistics technologies to modernize global supply chains.

Pony AI Lifts 2026 Robotaxi Fleet Goal on Faster Growth

Pony AI Inc. raised its robotaxi fleet target for this year by 500 vehicles to 3,500 after reporting stronger-than-expected first-quarter revenue.

Logistics Firm Stord Nabs $250 Million to Help Brands Take On Amazon

Stord Inc., a logistics technology startup, raised $250 million in a new funding round to expand its fulfillment operation and help merchants better compete with Amazon.com Inc.

Ransomware Attacks on Automotive and Smart Mobility More Than Doubled in 2025, According to New Research by Upstream Security | The Manila Times

Upstream's report finds that the rapid adoption of Physical AI, with autonomous vehicles among the first production-ready systems in real-world operation, is expanding attack surfaces and accelerating attacker capabilities, creating large-scale cyber risks with massive impact potential.

Delivery robots are spreading across LA. Residents ‘both pity and hate them’

A region known for its lack of walkability now has more obstacles for pedestrians to contend with Robots have taken over Los Angeles. It’s not just the AI-generated videos that have caused angst in Hollywood. Our streets are full of driverless Waymo vehicles, covered in more sensors and gadgets than the Batmobile. And our walkways are home to fleets of boxes on wheels, hurrying past pedestrians and navigating outdoor bar-hoppers as the robots deliver smoothies and keto-friendly salads. Continue reading...

COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space

arXiv:2605.20618v1 Announce Type: new Abstract: Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditional heuristics rely on handcrafted rules for local improvements and occasional \textit{jumps} to escape local minima, but often struggle to generalize across diverse instances. We introduce \textbf{COAgents}, a cooperative multi-agent framework that models the search process as a graph: nodes represent solutions, and edges correspond to either local refinements or large perturbations for diversification (i.e., jumps). A \textit{Partial Search Graph} (PSG) is dynamically constructed during search, enabling COAgents to train a Node Selection Agent and a Move Selection Agent to guide intensification, and a Jump Agent to trigger well-timed explorations of new regions. Unlike end-to-end learning approaches, COAgents cleanly separates problem-agnostic search control from compact domain-specific encoding, facilitating adaptability across tasks. Extensive experiments on the CVRP and VRPTW benchmarks show that COAgents remains competitive with several learn-to-search baselines on CVRP and sets a new state of the art among learning-based methods on the more challenging VRPTW instances, reducing the gap to the best-known solutions by 14\% at $N\!=\!100$ and 44\% at $N\!=\!50$ relative to the strongest neural solver (POMO), and by 21\% and 40\% respectively relative to ALNS. Code is available at https://github.com/mahdims/COAgents.

Norway’s Roboxi lands €13 million to transform airport airside operations with automation and robotics

Roboxi, a Stavanger-based startup specialising in airport airside automation and autonomy, has announced the completion of a share issue raising approximately €13 million in new equity. According to the company, the share issue generated significant interest from both new and existing shareholders. The primary investors are prominent ones based in the Rogaland region of Norway. […]

Waymo pauses robotaxis in five US cities after cars drive into flooded roads

A Waymo spokesperson said it had expanded the temporary pause "out of an abundance of caution".

Tesla's Full Self-Driving Expands to Lithuania, Faces EU Regulatory Hurdles

Tesla's FSD Supervised has launched in Lithuania, becoming the second EU nation to authorize its on-road use. A shift to subscription models is set for May 21, 2026.

The David Rubenstein Show: FedEx President and CEO Raj Subramaniam

FedEx President and CEO Raj Subramaniam discusses how the company moves nearly $2 trillion worth of goods annually, its use of AI and data analytics, autonomous trucking, and the massive transformation underway inside FedEx. He also shares his personal journey from Kerala, India to becoming CEO of one of the world's largest transportation companies, including lessons from founder Fred Smith and the culture that continues to drive FedEx forward. Subramaniam is on this week's episode of "The David Rubenstein Show: Peer to Peer Conversations." This interview was recorded April 29 at the Economic Club of Washington DC. (Source: Bloomberg)

The NetMob26 Dataset: A High-Resolution Multi-Source View of Public Bus Mobility in Niter\'oi

arXiv:2605.20263v1 Announce Type: cross Abstract: The NetMob Data Challenge releases a comprehensive public transportation dataset from Niter\'oi, addressing the lack of high-quality mobility and passenger demand data. Based on operational records from March 2026, the dataset combines four main sources: GPS telemetry from buses, approximately 7.2 million ticketing transactions, auxiliary transit data (routes, stops, and weather), and urban infrastructure and socio-demographic information. Together, these sources provide a detailed view of both transit supply and passenger demand. The data were preprocessed, cleaned, and anonymized to preserve privacy and improve reliability, including the removal of operational inconsistencies and anonymization of passenger identifiers. Access is restricted to challenge participants who accept the Terms and Conditions and sign an NDA. The paper describes the data collection and preprocessing pipeline, dataset organization, and mobility patterns observed in the system. The dataset supports research on topics such as public transportation efficiency, demand forecasting, accessibility analysis, service reliability, and the influence of external factors like weather on urban mobility.

Grab bets on new delivery robots to fix Singapore’s ‘supply-constrained markets’ and solve the last-mile problem

The Southeast Asian tech company will launch a pilot of its first delivery robot in Singapore’s Punggol district in late 2026.

LG Innotek, Kakao Mobility Partner on Autonomous Driving - Seoul Economic Daily

LG Innotek and Kakao Mobility signed an MOU to co-develop autonomous driving AI, while Woori Bank advances financial sector AX with AI agents across 29 operations.

A Global-Local Graph Attention Network for Traffic Forecasting

arXiv:2605.16726v1 Announce Type: new Abstract: Traffic forecasting is a significant part of intelligent transportation systems. One of the critical challenges of traffic forecasting is to find spatio-temporal correlations. In recent years, graph convolutional networks and graph attention networks have replaced traditional statistical models to predict future traffic. However, it is complicated for both of them to allow vertices to have far different characters. To address this, we propose the Global-Local Graph Attention Network (GLGAT) with pairwise encoding and the event-based adjacency matrix. The GLGAT allows vertices to have a global attention matrix set for the whole graph and assigns local attention matrix sets to each vertex. Experiments on two real-world traffic datasets show that GLGAT can effectively capture spatio-temporal correlations and has competitive performance against other state-of-the-art baselines.

agentic ai: Beyond automation: Why logistics firms are betting on agentic AI - The Economic Times

Industry officials say agentic AI is helping supply chains move from “systems of record” to “systems of action”.

Revealing Interpretable Failure Modes of VLMs

arXiv:2605.12674v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to generalize with minimal task-specific engineering. Despite these advantages, they can exhibit catastrophic failures in specific real-world situations, constituting failure modes. We introduce REVELIO, a framework for systematically uncovering interpretable failure modes in VLMs. We define a failure mode as a composition of interpretable, domain-relevant concepts-such as pedestrian proximity or adverse weather conditions-under which a target VLM consistently behaves incorrectly. Identifying such failures requires searching over an exponentially large discrete combinatorial space. To address this challenge, REVELIO combines two search procedures: a diversity-aware beam search that efficiently maps the failure landscape, and a Gaussian-process Thompson Sampling strategy that enables broader exploration of complex failure modes. We apply REVELIO to autonomous driving and indoor robotics domains, uncovering previously unreported vulnerabilities in state-of-the-art VLMs. In driving environments, the models often demonstrate weak spatial grounding and fail to account for major obstructions, leading to recommendations that would result in simulated crashes. In indoor robotics tasks, VLMs either miss safety hazards or behave excessively conservatively, producing false alarms and reducing operational efficiency. By identifying structured and interpretable failure modes, REVELIO offers actionable insights that can support targeted VLM safety improvements.

LISA: Cognitive Arbitration for Signal-Free Autonomous Intersection Management

arXiv:2605.12321v1 Announce Type: cross Abstract: Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoning and multi-agent coordination. These capabilities make them well suited for cooperative driving, where rule-based approaches struggle in complex and dynamic traffic environments. Intersection management remains especially challenging due to conflicting right-of-way demands, heterogeneous vehicle priorities, and vehicle-specific kinematic constraints that must be resolved in real time. However, existing approaches typically use LLMs as auxiliary components on top of signal-based systems rather than as primary decision-makers. Signal controllers remain vehicle-agnostic, reservation-based methods lack intent awareness, and recent LLM-based systems still depend on signal infrastructure. In addition, LLM inference latency limits their use in sub-second control settings. We propose LISA (LLM-Based Intent-Driven Speed Advisory), a signal-free cognitive arbitration framework for autonomous intersection management. LISA uses an LLM to reason over declared vehicle intents, incorporating priority classes, queue pressure, and energy preferences. We evaluate LISA against fixed-cycle control, SCATS, AIM, and GLOSA across varying traffic loads. Results show that LISA reduces mean control delay by up to 89.1% and maintains Level of Service C while all non-LLM baselines degrade to Level of Service F. Under near-saturated demand, LISA reduces mean waiting time by 93% and peak queue length by 60.6% relative to fixed-cycle control. It also lowers fuel consumption by up to 48.8% and achieves 86.2% intent satisfaction, compared to 61.2% for the best non-LLM method. These results demonstrate that LLM-based reasoning can enable real-time, signal-free intersection management.

** Tesla vs BYD: Two Opposite EV Business Models - FourWeekMBA

Tesla vs BYD: Two Opposite EV Business Models Tesla and BYD represent fundamentally different approaches to electric vehicle dominance, with Tesla pursuing vertical integr — as explored in how AI is restructuring the traditional value chain — ation and software monetization while BYD leverages ...

Uber Uses OpenAI To Help People Earn Smarter and Book Faster

Uber is integrating OpenAI models to improve driver guidance and ride booking, demonstrating agentic AI at scale.

TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents

arXiv:2605.10440v1 Announce Type: new Abstract: Online travel agents (Booking, Trip.com, Expedia) have replaced ranked-list interfaces with conversational LLM agents that compress many options into one sentence of advice. Each booking earns the OTA commission and different suppliers pay different rates: the agent has a structural incentive to favor higher-margin recommendations. Whether any deployed agent does this, and by how much, no one can currently measure. Disclosure banners, conversion A/B testing, UI dark-pattern taxonomies, and generic LLM safety scores were built for older interfaces and miss the prose-recommendation surface where the steering happens. We propose TourMart, an applied intelligent-system audit instrument for LLM-OTA commission governance. Two governance levers -- lambda (gain on message-induced perception in the traveler's accept/reject decision) and kappa (budget-normalized cap on how far the message can shift perceived welfare) -- drive a paired counterfactual: holding the traveler and bundle fixed, the steering delta is read off between a commission-aware prompt and a minimum-disclosure factual template. A symmetric six-gate producer audit separates LLM-engineering failures (template collapse, refusal, internal-ID leakage) from genuine commercial steering. At deployed (lambda=1, kappa=0.05), a Qwen-14B reader shows +7.69pp steering (exact McNemar p=0.003); a Llama-3.1-8B reader shows +3.50pp in the same direction at n=143, with an extended-n supplement (n=270) confirming significance (+2.96pp, p=0.008). Across the (lambda, kappa) grid both arms pass family-wise scenario-clustered correction (p<0.001 / p=0.008). TourMart outputs a sentence a compliance report can quote: "at this deployment, 7.7 extra commission-steered recommendations per 100 paired traveler sessions."

Do City Delivery Drones Make Sense? No One Knows, but They're Flying Over NYC

A look at the current state of drone delivery services in New York City and the uncertainty surrounding their long-term viability and impact.

FAA Trials AI System to Predict and Prevent Air Traffic Congestion Weeks Ahead

The FAA is testing the SMART AI system to manage air traffic congestion weeks in advance, aiming to improve operational efficiency.

Grab-Foodpanda deal raises security concerns in Taiwan over Alibaba, Huawei ties

Taiwan's transport ministry will raise national security concerns over Grab's ties to Alibaba and Huawei regarding its proposed acquisition of Foodpanda's local operations.

Intelligent CCTV for Urban Design: AI-Based Analysis of Soft Infrastructure at Intersections

arXiv:2605.05402v1 Announce Type: new Abstract: Artificial intelligence (AI) and computer vision are transforming transportation data collection. This study introduces an AI-enabled analytics framework leveraging existing CCTV infrastructure to evaluate the impact of soft interventions, such as temporary pedestrian refuges and curb extensions, on vehicle speed and safety. Using deep learning and perspective-based speed estimation, we evaluated driver behavior before and after interventions, with repeated post-installation monitoring in Week 1 and Week 2, in Minneapolis. Findings reveal that at unsignalized intersections, mean and 85th-percentile speeds fell by up to 18.75% and 16.56%, respectively, while pass-through traffic decreased by as much as 12.2%. Signalized intersections showed comparable reductions except one location, with mean and 85th-percentile speeds dropping by up to 20.0% and 17.19%. These results demonstrate the traffic-calming effectiveness of soft infrastructure and underscore the utility of AI-powered methods for rapid, low-cost, and evidence-based transport policy evaluation.

Waymo vs Wayve: The Self-Driving Showdown Coming to London

Waymo leads with sensors and maps. Wayve bets on pure AI and scale. Both are launching in London—who wins the future of driving? Bloomberg's Tom Mackenzie reports (Source: Bloomberg)

Autonomous Driving Showdown: Who Will Win the Self-Driving Race? | Bloomberg Tech: Europe 5/08/2026

In this episode of Bloomberg Tech: Europe, Bloomberg’s Tom Mackenzie explores the accelerating race for autonomous driving. From Waymo’s sensor-driven precision to Wayve’s mapless, end-to-end AI approach, the battle for the future of mobility is intensifying. Which model will win out? We also hear from BYD, examine Vay’s vision for driverless transport, and see how Einride is reshaping freight with autonomous, electric trucks. Who will lead the driverless future? "Bloomberg Tech: Europe" spotlights the biggest names and trends shaping the region's technology ecosystem as the global competition heats up. This monthly, 30-minute magazine-style show features in-depth interviews with top technology leaders, as well as major investors and policymakers - giving you a compelling A to Z of the most consequential innovations, opportunities and challenges. (Source: Bloomberg)

Wayve CEO on Tesla, Waymo, Future of Self-Driving Cars

Wayve CEO Alex Kendall explains end-to-end autonomous driving, how Wayve's approach differs to those of Tesla and Waymo, and why artificial intelligence licensing could scale fastest. He speaks to Bloomberg's Tom Mackenzie. (Source: Bloomberg)

AI Is Reshaping Self-Driving Cars, Wayve CEO Says

Wayve CEO Alex Kendall explains how an artificial intelligence-driven approach to self-driving -- using onboard intelligence and real-world learning -- is reshaping how autonomous cars and robotaxis are built and scaled. Kendall speaks to Bloomberg's Tom Mackenzie. (Source: Bloomberg)

How Waymo Builds Self-Driving Cars

Srikanth Thirumalai, vice president of onboard software at Waymo, breaks down the company's approach to autonomous cars. He speaks to Bloomberg's Tom Mackenzie. (Source: Bloomberg)

From Review to Design: Ethical Multimodal Driver Monitoring Systems for Risk Mitigation, Incident Response, and Accountability in Automated Vehicles

arXiv:2605.06439v1 Announce Type: new Abstract: As vehicles transition toward higher levels of automation, Driver Monitoring Systems (DMS) have become essential for ensuring human oversight, safety, and regulatory compliance in a vehicle. These systems rely on multimodal sensing and AI-driven inference to assess driver attention, cognitive state, and readiness to take control. While technologically promising, their deployment introduces a complex set of ethical and legal challenges - ranging from privacy and consent to data ownership and algorithmic fairness. While overarching frameworks such as the GDPR, EU AI Act, and IEEE standards offer important guidance, they lack the specificity required for addressing the unique risks posed by in-cabin sensing technologies. This paper adopts a review-to-design perspective, critically examining existing regulatory instruments and ethical frameworks -- such as the GDPR, the EU AI Act, and IEEE guidelines -- and identifying gaps in their applicability to the distinctive risks posed by multimodal, AI-enabled in-cabin monitoring. Building on this review, we propose a modular ethical design framework tailored specifically to Driver Monitoring Systems. The framework translates high-level principles into actionable design and deployment guidance, including user-configurable consent mechanisms, fairness-aware model development, transparency and explainability tools, and safeguards for driver emotional well-being. Finally, the paper outlines a risk analysis and failure mitigation strategy, emphasizing proactive incident response and accountability mechanisms tailored to the DMS context. Together, these contributions aim to inform the development of transparent, trustworthy, and human-centered driver monitoring systems for next-generation autonomous vehicles.

Revisiting the Travel Planning Capabilities of Large Language Models

arXiv:2605.03308v1 Announce Type: new Abstract: Travel planning serves as a critical task for long-horizon reasoning, exposing significant deficits in LLMs. However, existing benchmarks and evaluations primarily assess final plans in an end-to-end manner, which lacks interpretability and makes it difficult to analyze the root causes of failures. To bridge this gap, we decompose travel planning into five constituent atomic sub-capabilities, including \emph{Constraint Extraction}, \emph{Tool Use}, \emph{Plan Generation}, \emph{Error Identification}, and \emph{Error Correction}. We implement a decoupled evaluation protocol leveraging oracle intermediate contexts to rigorously isolate these components, thereby measuring the atomic performance boundary without the noise of cascading errors. Our results highlight a clear contrast in performance: while LLMs are proficient in extracting explicit constraints, they struggle to infer implicit, open-world requirements. Furthermore, they exhibit structural biases in plan generation and suffer from ineffective self-correction, characterized by excessive sensitivity and erroneous persistence. These findings offer precise directions for improving LLM reasoning and planning abilities.

MoveOD: Synthesizing Origin-Destination Commute Distribution from U.S. Census Data

arXiv:2510.18858v2 Announce Type: replace Abstract: High-resolution origin-destination (OD) tables are essential for a wide spectrum of transportation applications, from modeling traffic and signal timing optimization to congestion pricing and vehicle routing. However, outside a handful of data rich cities, such data is rarely available. We introduce MOVEOD, an open-source pipeline that synthesizes public data into commuter OD flows with fine-grained spatial and temporal departure times for any county in the United States. MOVEOD combines five open data sources: American Community Survey (ACS) departure time and travel time distributions, Longitudinal Employer-Household Dynamics (LODES) residence-to-workplace flows, county geometries, road network information from OpenStreetMap (OSM), and building footprints from OSM and Microsoft, into a single OD dataset. We use a constrained sampling and integer-programming method to reconcile the OD dataset with data from ACS and LODES. Our approach involves: (1) matching commuter totals per origin zone, (2) aligning workplace destinations with employment distributions, and (3) calibrating travel durations to ACS-reported commute times. This ensures the OD data accurately reflects commuting patterns. We demonstrate the framework on Hamilton County, Tennessee, where we generate roughly 150,000 synthetic trips in minutes, which we feed into a benchmark suite of classical and learning-based vehicle-routing algorithms. The MOVEOD pipeline is an end-to-end automated system, enabling users to easily apply it across the United States by giving only a county and a year; and it can be adapted to other countries with comparable census datasets. The source code and a lightweight browser interface are publicly available.

Uber Shares What Happens When 1,500 AI Agents Hit Production

Uber details the operational challenges and outcomes of deploying 1,500 AI agents into their production environment.

Why the Collapse of Spirit Airlines Means Higher Fares for Everyone

The defunct budget airline had long been a competitive force on lower-cost tickets.

Confusion in Strait of Hormuz Leaves Shipping Firms Guessing

The U.S. vowed to help tankers navigate the perilous conditions that have kept them stranded in the Persian Gulf, but it remained unclear if companies would try to get out.

Penske Launches AI-Driven Platform

Penske Logistics has unveiled Supply Chain Insight, a robust platform offering real-time, comprehensive visibility across transportation and warehousing systems.

Agentic AI for Trip Planning Optimization Application

arXiv:2605.00276v1 Announce Type: new Abstract: Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting factors such as travel time, energy consumption, and traffic conditions directly affect plan quality. Yet existing systems are largely designed for feasibility-oriented planning, and current benchmarks provide only reference answers without ground truth, preventing objective evaluation of optimization performance. In our paper, we address these limitations with an agentic AI framework that enables dynamic refinement through an orchestration agent coordinating specialized agents for traffic, charging, and points of interest, and with the Trip-planning Optimization Problems Dataset, which supplies definitive optimal solutions and category-level task structure for fine-grained analysis. Experiments show that our system achieves 77.4\% accuracy on the TOP Benchmark, significantly outperforming single-agent and workflow-based multi-agent baselines, demonstrating the importance of orchestrated agentic reasoning for robust trip planning optimization.

Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

arXiv:2605.00572v1 Announce Type: new Abstract: Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particularly evident in the Electric Capacitated Vehicle Routing Problem, where instances differ in structure, demand patterns, and energy constraints. This paper investigates instance-aware parameter configuration for Bilevel Late Acceptance Hill Climbing, a state-of-the-art metaheuristic for the Electric Capacitated Vehicle Routing Problem. An offline tuning procedure is used to obtain instance-specific parameter labels, which are then mapped from instance features via a regression model to enable parameter prediction for unseen instances prior to execution. Experimental results on the IEEE WCCI 2020 benchmark and its extensions show that the proposed approach achieves an average objective value reduction of $0.28\%$ across eight held-out test instances relative to a globally tuned configuration. This corresponds to a significant cost reduction in multimillion-dollar transportation operations.

Nuro receives driverless testing permit ahead of Uber robotaxi service launch

Nuro has secured a permit for driverless testing, positioning itself ahead of the upcoming launch of Uber's robotaxi service.

Penske Launches AI-Driven Platform for Real-Time Supply Chain Visibility and Efficiency

Penske Logistics has unveiled Supply Chain Insight, a cloud-native platform offering real-time visibility and AI-driven decision-making for supply chain performance.

Amazon Expands Logistics Arm to Outside Companies

The company said its shipping, fulfillment and delivery services would be offered to other businesses. Several large corporations have already signed on.

Spirit Airlines’ Demise Could Help Other Airlines

Even in its reduced state, the company played an important role in forcing other airlines to keep fares low, some experts said.

Linking Behaviour and Perception to Evaluate Meaningful Human Control over Partially Automated Driving

arXiv:2605.00556v1 Announce Type: cross Abstract: Partial driving automation creates a tension: drivers remain legally responsible for vehicle behaviour, yet their active control is significantly reduced. This reduction undermines the engagement and sense of agency needed to intervene safely. Meaningful human control (MHC) has been proposed as a normative framework to address this tension. However, empirical methods for evaluating whether existing systems actually provide MHC remain underdeveloped. In this study, we investigated the extent to which drivers experience MHC when interacting with partially automated driving systems. Twenty-four drivers completed a simulator study involving silent automation failures under two modes - haptic shared control (HSC) and traded control (TC). We derived behavioural metrics from telemetry data, subjective perception scores from post-trial surveys and used them to test hypothesised relations between them derived from the properties of systems under MHC. The confirmatory analysis showed a significant negative correlation between the perception of the automated vehicle (AV) understanding the driver and conflict in steering torques. An exploratory analysis also revealed a surprising positive correlation between reaction times and the perception of sufficient control. Qualitative feedback from open-ended post-experiment questionnaires revealed that mismatches in intentions between the driver and automation, lack of safety, and resistance to driver inputs contribute to the reduction of perceived MHC, while subtle haptic guidance aligned with driver intent had a positive effect. These findings suggest that future designs should prioritise effortless driver interventions, transparent communication of automation intent, and context-sensitive authority allocation to strengthen meaningful human control in partially automated driving.

An Adaptive Variable Neighborhood Search for a Family of Set Covering Routing Problems with an Application in Disaster Relief Operations

arXiv:2605.00131v1 Announce Type: cross Abstract: This paper studies a variant of the Set Covering Routing Problem (SCRP) motivated by post-disaster humanitarian logistics. We consider a hybrid distribution concept in which the majority of transportation is performed by helicopters, while ground transport is limited to the last mile, addressing severe accessibility constraints in disaster-affected regions. The resulting problem integrates landing site location, routing, and covering decisions, incorporating features of the Multi-Vehicle Covering Tour Problem (m-CTP) and the Vehicle Routing with Demand Allocation Problem (VRDAP) in a facility-capacitated, multi-depot setting. Due to the computational complexity of the problem, we develop an Adaptive Variable Neighborhood Search (AVNS) that combines established routing operators with novel mechanisms for covering decisions. The performance of the proposed approach is evaluated on benchmark instances for the related m-CTP and VRDAP problems, demonstrating competitive solution quality compared to problem-specific state-of-the-art approaches. Furthermore, we apply our AVNS to a real-world case study based on the 2024 flash floods in Afghanistan. The results highlight the practical relevance of the proposed framework and provide managerial insights into effective distribution strategies for disaster response operations.

As Formula One evolves, AI becomes part of the race | Reuters

AI has been innovative in sifting through administrative tasks and interpreting key rules within sporting and technical regulations, helping engineers take ‌swifter decisions ⁠during on-track situations which were impossible decades ago.

The Fuel-Price Crunch That’s Turning Into a Disaster for Airlines

The higher costs that took down Spirit are squeezing an entire industry, especially the budget carriers.

California to begin ticketing driverless cars that violate traffic laws

California regulators are moving to issue traffic citations to autonomous vehicles that commit traffic violations.

TechCrunch Mobility: How do you issue a ticket to a robotaxi?

An exploration of the legal and logistical challenges of enforcing traffic violations for autonomous robotaxis.

Spirit Airlines Cancels All Flights, Stranding Passengers

The budget carrier abruptly canceled flights early on Saturday, leaving passengers to rush to make other plans. “Even if they go back into business, never again,” one traveler said.

NYT

Spirit Airlines Shuts Down After Years of Struggle

WSJ

Spirit Airlines Is Shutting Down. Here’s What to Do If You Had Tickets.

China’s self-driving truck leaders say AI breakthroughs won’t accelerate rollout — here’s why

01 May 2026 01:10 UTC - by Evelyn Cheng · Rapid advances in AI for coding and chatbots doesn't change the timeline for getting self-driving vehicles on the road, industry executives in China said

Maritime Connectivity Vulnerability Index: Construction, Patterns, and Validation Across 185 Economies, 2006-2025

arXiv:2604.18767v2 Announce Type: replace-cross Abstract: Recent disruptions at major maritime chokepoints have exposed the structural fragility of liner shipping networks. Existing indicators measure connectivity, but none quantify its structural vulnerability from a supply-side perspective. We propose the Maritime Connectivity Vulnerability Index (MCVI), capturing three dimensions mapped to distinct UNCTAD sources: low overall connectivity (LSCI), weak bilateral integration (LSBCI), and port infrastructure concentration (PLSCI). The index covers 185 economies over 2006-2025 using pooled fractional rank normalization and equal-weight aggregation from publicly available data. SIDS exhibit a mean vulnerability 0.234 points above non-SIDS economies, with the gap widening from 0.232 to 0.249 over two decades. A modest global decline of 4.2% is observed. Port concentration dominates for nearly 40% of economies (72 of 185), establishing infrastructure diversification as a distinct policy priority. Rankings are highly stable across alternative weighting schemes, normalization methods (Spearman rho = 0.97-0.999), and PCA-derived weights; Monte Carlo simulation (1,000 replications) confirms rho > 0.95 in every realization. External validation shows strong negative correlation with the World Bank Logistics Performance Index (rho = -0.61 across seven waves) and positive correlation with ad valorem maritime freight rates (rho = +0.32). Panel regressions reveal a vulnerability paradox whereby small trade-dependent economies are simultaneously the most trade-open and the most vulnerable. Pre-crisis MCVI predicts trade losses during the COVID-19 supply shock (rho = -0.25, p < 0.005), while the contrasting positive correlation during the 2008-2009 demand shock (rho = +0.23, p = 0.01) validates the supply-side specificity of the index.

How AI is powering the next generation of robotaxis

Technological advances have propelled self-driving cars from small-scale testing to rapid global expansion

Bloomberg: China pauses AV permits after Baidu disruption

Baidu’s robotaxi operations in Wuhan have been suspended, sources tell the publication. Read more: Bloomberg: China pauses AV permits after Baidu disruption

Enterprises Evolution in the New Agentic Era - CXO Outlook

Dr. Ashwani Dev is the Vice President of Digital Business and Innovation for Crowley Maritime Corporation. He leads the digital transformation and innovation execution to enhance and scale business agility and competitiveness. Dr. Dev brings more than two decades experience in AI leadership ...

Japan’s Mitsui O.S.K. Planning REIT to Boost Property Gains

The Japanese shipping company owns prime properties in cities such as London, Sydney, Osaka, and Tokyo.

Humanoid robots to become baggage handlers in Japan airport experiment

Japan Airlines will introduce the robots for trial run at a Tokyo airport amid country’s surge in inbound tourism and worsening labour shortages Japan’s famously conscientious but overburdened baggage handlers will soon be joined by extra staff at Tokyo’s Haneda airport – although their new colleagues will need to take regular recharging breaks. Japan Airlines will introduce humanoid robots on a trial basis from the beginning of May, with a view to deploying them permanently as a solution to the country’s chronic labour shortage. Continue reading...

How Spreadsheets Quietly Cost Supply Chains Millions

An analysis of how over-reliance on spreadsheets can lead to significant financial inefficiencies in supply chain management.

Why supply chains are the proving ground for automation-led iPaaS

Supply chains are increasingly becoming the primary testing ground for automation-driven integration platforms.

UGAF-ITS: A Standards Harmonization Framework and Validation Tool for Multi-Framework AI Governance in Distributed Intelligent Transportation Systems

arXiv:2604.22789v1 Announce Type: new Abstract: Organizations deploying AI-enabled Intelligent Transportation Systems face fragmented governance: ISO/IEC 42001 demands a certifiable management system, the EU AI Act imposes binding high-risk obligations from August 2026, and the NIST AI Risk Management Framework structures voluntary practice. Each instrument is internally coherent, yet they drive different control vocabularies, evidence expectations, and audit rhythms. In distributed ITS deployments where vehicle manufacturers, roadside integrators, and cloud operators each hold partial evidence and partial accountability, this fragmentation multiplies compliance effort and obscures incident traceability. This paper introduces UGAF-ITS, a standards harmonization framework that consolidates 154 source obligations from the three instruments into 12 unified controls across eight governance domains through a reproducible five-phase crosswalk methodology. A three-tier operating model allocates each control to the vehicle, edge, or cloud tier where enforcement and defensible evidence production are feasible. An evidence backbone of 20 versioned artifacts supports a single audit package across all three frameworks without duplicating content. We validate UGAF-ITS through an open-source governance engine evaluated across four architecturally distinct ITS deployment scenarios. The engine encodes the complete crosswalk catalog and executes eight compliance computations. Three-tier deployments achieve 91.7% average framework coverage with 45.9% evidence reduction, complete bidirectional traceability, and 80% of artifacts serving all three frameworks simultaneously. Partial deployments degrade gracefully: coverage and reduction scale with architectural complexity. The tool, scenarios, and all reported results are publicly available for independent replication.

An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement

arXiv:2604.22777v1 Announce Type: new Abstract: Fault diagnosis of general aviation aircraft faces challenges including scarce real fault data, diverse fault types, and weak fault signatures. This paper proposes an intelligent fault diagnosis framework based on multi-fidelity digital twin, integrating four modules: high-fidelity flight dynamics simulation, FMEA-driven fault injection, multi-fidelity residual feature extraction, and large language model (LLM)-enhanced interpretable report generation. A digital twin is constructed using the JSBSim six-degree-of-freedom (6-DoF) flight dynamics engine, generating 23-channel engine health monitoring data via semi-empirical sensor synthesis equations. A three-layer fault injection engine based on failure mode and effects analysis (FMEA) models the physical causal propagation of 19 engine fault types. A multi-fidelity residual computation framework comprising paired-mirror residuals and GRU surrogate prediction residuals is proposed: the high-fidelity path obtains clean fault deviation signals using nominal mirror trajectories with identical initial conditions, while the low-fidelity path achieves online real-time residual computation through a multi-step prediction GRU surrogate model. A 1D-CNN classifier performs end-to-end diagnosis of 20 fault classes. An LLM diagnostic report engine enhanced with FMEA knowledge fuses classification results, residual evidence, and domain causal knowledge to generate interpretable natural language reports. Experiments show the paired-mirror residual scheme achieves a Macro-F1 of 96.2% on the 20-class task, while the GRU surrogate scheme achieves 4.3x inference acceleration at only 0.6% performance cost. Comparison across 24 schemes reveals that residual feature quality contributes approximately 5x more to diagnostic performance than classifier architecture, establishing the "residual quality first" design principle.

Budget Airlines Ask Trump Administration for Billions as Fuel Costs Rise

A trade group for the airlines is seeking $2. 5 billion to help offset the big jump in jet fuel costs since the start of U. S.

Auto & Transport Roundup: Market Talk

Find insight on consumer travel plans, United Airlines and more in the latest Market Talks covering Auto and Transport.

Budget Airlines Pitch Trump Administration on $2.5 Billion Relief Plan

While Spirit’s talks with officials continue, industry executives see window to negotiate for financial assistance and tax relief.

Relational Archetypes: A Comparative Analysis of AV-Human and Agent-Human Interactions

arXiv:2604.22564v1 Announce Type: new Abstract: Over the last couple of years, AI Agents have gained significant traction due to substantial progress in the capabilities of underlying General Purpose AI (GPAI) models, enhanced scaffolding techniques, and the promise to drive societal transformation. Companies, researchers, and policy makers have started to consider the different effects that AI agents may have across different dimensions of our lives. However, the literature exploring the broader effects of human-agent interactions is still underdeveloped. In this paper, we review the problem of traffic modulation by autonomous vehicles (AVs) in mixed traffic flows and extrapolate the learnings to the different modes of interaction between humans and AVs to the pair humans-AI agents. In doing so, we propose a preliminary taxonomy of relational archetypes based on literature on Human-Computer Interaction (HCI) and AV-human interaction and tentatively explore how the resulting framework may lead to new questions regarding human-agent interactions. Our effort is aimed at strengthening existing bridges between these two research communities, which share similar traits: autonomy, fast adoption, high impact, and great potential for economic transformation. Building on previous analogies between AI Agents and AVs (e.g., regarding autonomy levels), we anticipate this paper to spark scholarly debate on the different types of impact that agents may have on our societies, while inviting other researchers to expand the scope of their comparative analysis regarding AI Agents.

Aircraft Technicians Make Six Figures and Airlines Can’t Find Enough of Them

More than 40% of the technicians who keep America’s planes flying are approaching retirement age. The industry is scrambling to recruit more.

United’s Card-Counting CEO Made a Huge Bet—and It’s Paying Off

The airline went all-in on premium features and brand loyalty. Scott Kirby’s strategy is lifting United into Delta’s airspace.

How Jet Fuel Shortages Could Affect Summer Travel to Europe and Beyond

Facing sky-high fuel costs linked to the war in Iran, airlines are cutting routes and raising prices. European vacations are looking a lot less affordable.

‘Look, no hands’: China chases the driverless dream at Beijing car show

As domestic sales slow, manufacturers are investing in AI and seeking growth in technology and in overseas markets At the world’s biggest car fair, which opened in Beijing on Friday, there were hundreds of manufacturers, more than 1,000 vehicles, hundreds of thousands of enthusiasts – and hardly anyone behind a wheel. China’s car companies have cornered the domestic electric vehicle market, and are increasingly visible on the global stage. Now they are turning their attention to what they are betting is the future of mobility: autonomous driving.

Trump Administration Nears Loan Deal to Rescue Spirit Airlines

The Trump administration is negotiating a deal that would provide as much as $500 million to the struggling company, which is in its second bankruptcy in two years.

Tesla’s Cooling AI Hype Overshadows Blowout Earnings Forecasts

Tesla Inc. investors are in for a rare treat Wednesday afternoon: an earnings report that analysts say should be a blowout. The trouble is the actual numbers are likely to get overlooked as Wall Street seeks evidence that Elon Musk’s artificial intelligence and robotics ventures justify the stock’s sky-high valuation.

Tesla registers its AI-driven voice assistant in Shanghai

Tesla registers its AI-driven voice assistant in Shanghai

How AI is helping Birmingham logistics firm make deliveries

Mobile People Powered Logistics has increased revenue from £5m to £20m after using AI to streamline deliveries

JetBlue pressed by US lawmakers over suspected surveillance pricing

Deleted airline post on social media suggested a customer could see better airfare by clearing browser history

nuVizz Holding Seeds AI-First Logistics Venture to Extend Platform Intelligence Across Enterprise Operations

– New entity will develop purpose-built solutions connecting nuVizz platform data, APIs, and intelligence with customers' broader business ecosystems –...

Agentic AI in Logistics: When Your Supply Chain Starts Making Decisions Without You

The supply chain doesn’t just respond to events. It anticipates and prepares for them. Human roles at full deployment: strategic orchestrators who set policy boundaries, manage carrier and supplier relationships, handle regulatory and customer escalations, and continuously refine the governance framework as operations scale. BCG data confirms: logistics firms adopting AI ...

Struggling Spirit Airlines in Talks With Trump Administration on Government Investment

Florida-based Spirit has been working to sell some planes and refocus operations on core cities.

Uber Invests $10 Billion in Robotaxi Fleets, Shifts Strategy Toward Asset Ownership in Autonomous Mobility

Uber is pivoting towards an asset-heavy model in autonomous mobility, planning to invest $10 billion in robotaxi fleets and related services.

Driving Assistance System for Ambulances to Minimise the Vibrations in Patient Cabin

arXiv:2604.16047v1 Announce Type: cross Abstract: The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, and tested a system to assess driving in ambulance services. The proposed system is composed of a sensor node which measures the vehicle vibrations using an accelerometer. It also includes a GPS sensor, a battery, a display, and a speaker. When two possible routes reach the same destination point, the system compares the two routes based on previously classified data and calculates an index and a score. Thus, the index balances the possible routes in terms of time to reach the destination and the vibrations suffered in the patient cabin to recommend the route that minimises those vibrations. Three datasets are used to train, validate, and test the system. Based on an Artificial Neural network (ANN), the classification model is trained with tagged data classified as low, medium, and high vibrations, and 97% accuracy is achieved. Then, the obtained model is validated using data from three routes of another region. Finally, the system is tested in two new scenarios with two possible routes to reach the destination. The results indicate that the route with less vibration is preferred when there are low time differences (less than 6%) between the two possible routes. Nonetheless, with the current weighting factors, the shortest route is preferred when time differences between routes are higher than 20%, regardless of the higher vibrations in the shortest route.

Watching Trade from Space: Nowcasting and Spatial Extrapolation of Port-Level Maritime Trade Using Satellite Imagery

arXiv:2604.15444v1 Announce Type: new Abstract: Satellite data are increasingly used to measure economic activity, yet port-level trade remains largely unmeasured from space. This paper combines synthetic aperture radar imagery, nighttime lights, and port characteristics to measure monthly port-level maritime trade using only publicly available data. The model achieves strong out-of-sample accuracy for U.S. ports, with satellite signals and port attributes playing complementary roles. While absolute levels are difficult to extrapolate beyond the training domain, percentage changes are reliably recovered, as we confirm through a leave-one-region-out exercise and Monte Carlo simulation. Applying the framework to Russian ports after the 2022 sanctions, we detect shifts consistent with trade reorientation toward the Far East. The approach complements AIS-based methods by remaining robust to strategic signal manipulation.

AI continues to drive major disruptions in supply chain field, according to MHI’s Annual Industry Report | DC Velocity

Industry association also finds that tech investment will increase in spite of economic uncertainty.

While Others Are Testing AI, EASE Logistics Is Running It | The Manila Times

- Proprietary AI platform, AMMI executes over 40,000 tasks per week across every load, lane, and facility EASE serves. -

Wiliot, Velociti partner to scale real-time supply chain visibility | Fleet Maintenance

The partnership aims give fleets real-time asset visibility without adding maintenance burden.

Tesla brings its robotaxi service to Dallas and Houston

Tesla expands its autonomous robotaxi service to the Texas cities of Dallas and Houston.

Low-Cost System for Automatic Recognition of Driving Pattern in Assessing Interurban Mobility using Geo-Information

arXiv:2604.15216v1 Announce Type: cross Abstract: Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.

Palantir, Thales Among Companies Competing on FAA AI Tool

The US Federal Aviation Administration has brought in Palantir Technologies Inc., Thales SA and Air Space Intelligence Inc. to compete on a new artificial intelligence tool for air traffic management, according to a person familiar with the matter.

Uber commits $10bn to robotaxis in strategy shift

Ride-hailing app races to make up lost ground with equity investments and vehicle order commitments

French AviationTech startup Donecle bags €10 million to grow drone-based inspection platform

Donecle, a startup out of Toulouse specialising in automated aircraft inspection using autonomous drones and artificial intelligence, announced a new €10 million funding round to accelerate its international expansion, particularly in Europe and the United States. The round was led jointly by IRDI Capital Investissement and SWEN Capital Partners, with the participation of GSO Innovation […]

Waymo's self-driving cars face their toughest test yet: London

Google sibling takes on the Big Smoke – with a human hand on the wheel Waymo has started letting its software take the wheel on London streets, with trained specialists on standby as it gradually accelerates toward a fully driverless ride-hailing launch.…

China summons online-travel platforms, warns against ticket-grabbing bots

China's Cyberspace Administration and the National Railway Administration have summoned seven major online-travel platforms over train-ticket sales practices, warning against the use of automated ticket-grabbing tools.

PilotBench: A Benchmark for General Aviation Agents with Safety Constraints

arXiv:2604.08987v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained on text corpora reliably reason about complex physics while adhering to safety constraints? We address this through PilotBench, a benchmark evaluating LLMs on safety-critical flight trajectory and attitude prediction. Built from 708 real-world general aviation trajectories spanning nine operationally distinct flight phases with synchronized 34-channel telemetry, PilotBench systematically probes the intersection of semantic understanding and physics-governed prediction through comparative analysis of LLMs and traditional forecasters. We introduce Pilot-Score, a composite metric balancing 60% regression accuracy with 40% instruction adherence and safety compliance. Comparative evaluation across 41 models uncovers a Precision-Controllability Dichotomy: traditional forecasters achieve superior MAE of 7.01 but lack semantic reasoning capabilities, while LLMs gain controllability with 86--89% instruction-following at the cost of 11--14 MAE precision. Phase-stratified analysis further exposes a Dynamic Complexity Gap-LLM performance degrades sharply in high-workload phases such as Climb and Approach, suggesting brittle implicit physics models. These empirical discoveries motivate hybrid architectures combining LLMs' symbolic reasoning with specialized forecasters' numerical precision. PilotBench provides a rigorous foundation for advancing embodied AI in safety-constrained domains.

Uber, Nuro, San Francisco testing premium robotaxi service

Uber and Nuro are collaborating on a pilot program in San Francisco to test a new premium robotaxi service.

TechCrunch Mobility: Who is poaching all the self-driving vehicle talent?

An investigation into the competitive landscape for hiring experts in the autonomous vehicle industry.

European Airports Warn of Jet Fuel Shortages if Strait of Hormuz Remains Shut

An association of airports told European Union officials that fuel shipments through the Strait of Hormuz had to restart within three weeks to avoid a “systemic” shortage.

Jet Fuel Crunch Is Getting Severe With No Reprieve in Sight for Airlines

Countries in Asia and Europe are starting to run out of jet fuel, and it could take months for supplies to recover.

The Unconventional Logic Behind SpaceX's $1.75 Trillion Price Tag

The unconventional logic behind SpaceX's $1.75 trillion price tag is explained.

SpaceX Posted Nearly $5 Billion Loss in 2025, The Information Reports

SpaceX posted a nearly $5 billion loss in 2025, according to The Information.

Cyngn Accelerates Autonomous Vehicle Adoption in 2026

In 2025, the company tripled DriveMod ... while deployments at customers including Vann Family Orchards, G&J Pepsi, and Coats moved beyond initial pilots into fuller production environments. Across its installed base, early single-route automations are evolving into multi-vehicle, multi-workflow systems.

Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin

To implement the intelligent transportation digital twin (ITDT), unmanned aerial vehicles (UAVs) are scheduled to process the sensing data from the roadside sensors. At this time, generative artificial intelligence (GAI) technologies such as diffusion models are deployed on the UAVs to transform the raw sensing data into the high-quality and valuable. Therefore, we propose the GAI-empowered ITDT.

Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times

arXiv:2604.06251v1 Announce Type: new Abstract: This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the prediction of service requirements and container dwell times. We develop and evaluate machine learning models that leverage historical operational data to anticipate which containers will require pre-clearance handling services prior to cargo release and to estimate how long they are expected to remain in the terminal. As part of the data preparation process, we implement a classification system for cargo descriptions and perform deduplication of consignee records to improve data consistency and feature quality. These predictive capabilities provide valuable inputs for strategic planning and resource allocation in yard operations. Across multiple temporal validation periods, the proposed models consistently outperform existing rule-based heuristics and random baselines in precision and recall. These results demonstrate the practical value of predictive analytics for improving operational efficiency and supporting data-driven decision-making in container terminal logistics.

From Isolated Alerts To Contextual Intelligence: Agentic Maritime Anomaly Analysis with Generative AI

An AWS case study detailing how generative AI and agentic systems are being used to automate maritime anomaly detection and improve contextual reasoning.

Auto & Transport Roundup: Market Talk

Find insight on Delta Air Lines and more in the latest Market Talks covering the auto and transport sector.

SpaceX Isn’t Even Public Yet. Investors Are Already Abuzz About a Tesla Merger.

With Elon Musk focused on artificial intelligence, investors and analysts are discussing a merger of his biggest companies.

Grab to lean on scale, AI to navigate rising fuel costs, CEO ...

Grab to lean on scale, AI to navigate rising fuel costs, CEO says | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv Item 1 of 2 The helmet of a Grab bike rider is seen during rush hour traffic in Jakarta, Indonesia, July 18, 2016. Picture taken July 18, 2016. REUTERS/Iqro Rinaldi [1/2]The helmet of a Grab bike rider is seen during rush hour traffic in Jakarta, Indonesia, July 18, 2016. Picture taken July 18, 2016. REUTERS/Iqro Rinaldi Purchase Licensing Rights, opens new tab - Summary - Companies - CEO says its AI product strategy is paying off - New AI

Uber deploys AWS custom chips to scale AI and cut compute costs

US ride-hailing platform Uber has announced a partnership with Amazon Web Services (AWS) to deploy its in-house custom chips, aiming to improve the speed and efficiency of artificial intelligence (AI) model training and inference. The move is expected to strengthen Uber's real-time computing ...

Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation

arXiv:2604.05070v1 Announce Type: new Abstract: Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms increasingly leveraging dynamics such as wheel steering or door opening, realistic simulation requires animatable vehicle representations. Existing CAD-based pipelines are limited by library coverage and fixed templates, preventing faithful reconstruction of in-the-wild instances. We propose a generative framework that, from a single image or sparse multi-view input, synthesizes an animatable 3D Gaussian vehicle. Our method addresses two challenges: (i) large 3D asset generators are optimized for static quality but not articulation, leading to distortions at part boundaries when animated; and (ii) segmentation alone cannot provide the kinematic parameters required for motion. To overcome this, we introduce a part-edge refinement module that enforces exclusive Gaussian ownership and a kinematic reasoning head that predicts joint positions and hinge axes of movable parts. Together, these components enable faithful part-aware simulation, bridging the gap between static generation and animatable vehicle models.

Auto & Transport Roundup: Market Talk

Find insight on U.S. airlines, Genco Shipping & Trading, Uber and more in the latest Market Talks covering the auto and transport sector.

Uber bets on Amazon's custom chips to boost AI efforts | Reuters

The ⁠deal expands the companies&#x27; existing ​cloud partnership by enabling Uber ​to use Amazon Web Services&#x27; Graviton chips to support smoother rides ​and deliveries and Trainium ​processors to train AI models that power ‌its ⁠apps.

Delta’s CEO says AI’s biggest opportunity in aviation isn’t inside the plane—it’s air traffic control

Partly because of air traffic control issues, it takes longer to fly Atlanta to New York today than it did in the 1950s, Bastian said.

How FM Logistic Tackled the Traveling Salesman Problem at Warehouse Scale With AlphaEvolve

A case study on how FM Logistic used AlphaEvolve and Gemini to optimize warehouse routing, resulting in a 10.4% efficiency gain.

Scaled Adoption of AI Remains Limited | MHL News

Scaled Adoption of AI Remains Limited | MHL News Technology & Automation # Scaled Adoption of AI Remains Limited "The next challenge is less about access to technology and more about execution, integration, and building the capabilities to capture measurable value," says Boston Consulting Group. 3 min read - - - - - - #216959725@Melpomenem|Dreamstime AI in logistics continues to make inroads, as more than 40% of shippers now take logistics providers' AI capabilities into account when selecting partners. However, less than 10% currently treat AI as a mandatory criterion, according to a new report by Boston Consul

Tesla's South Korean sales up more than 300% to 11,134 vehicles in March | Reuters

Tesla's South Korean sales up more than 300% to 11,134 vehicles in March | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv Tesla electric vehicles for test driving are parked in Hanam, South Korea, July 6, 2020. REUTERS/Kim Hong-Ji/File Photo Purchase Licensing Rights, opens new tab - Companies Tesla Inc Follow SEOUL, April 6 (Reuters) - Tesla (TSLA.O), opens new tab saw ​its car registrations ‌in South Korea rise ​330% ​to 11,134 vehicles in ⁠March ​from year earlier, ​

Why Graph Reinforcement Learning, Not LLMs, Will Fix Logistics AI | Medium

Sign up Get app Sign up Press enter or click to view image in full size # Southwest Airlines Lost Track of Its Own Pilots. That's When I Knew Chatbots Wouldn't Save Logistics. 12 min read 20 hours ago -- 2 Listen Share The phone call that changed how I think about AI wasn’t from a customer or an investor. It was from a friend — a pilot — who spent Christmas 2022 sleeping on the floor of Denver International Airport. He wasn’t stranded because of the weather. The storm had passed. He was stranded because Southwest Airlines had literally lost track of where he was. The airline’s crew scheduling system — a legacy optimizer called SkySolver — was computing recovery plans based on crew positions that were hours out of date. It was generating schedules for a phantom airline. My friend called the scheduling hotline and waited on hold for eight hours. By the time someone picked up, t

Exclusive and Shared Electric Flying Taxis: Evidence on Modal Shares, Stated Reasons, and Modal Shifts

arXiv:2604.03166v1 Announce Type: new Abstract: This study examines travelers' preferences for electric flying taxi services in the United Arab Emirates (UAE) under varying travel conditions and service configurations. A stated preference (SP) survey of 213 respondents was conducted to analyze behavior across multiple transport alternatives, including private vehicles, public transport, ground taxis, and both shared and exclusive flying taxi services. The analysis considered key attributes such as travel time and cost, along with contextual factors including travel distance, congestion conditions, day of travel, and trip purpose. In addition, follow-up questions were used to capture the underlying reasons for mode choice and to assess potential modal shifts under changes in travel conditions. The results show that flying taxi services account for 22.6% of total responses, with higher shares under congested conditions and declining shares as travel distance increases. Clear differences are observed between shared and exclusive services. Shared flying taxis achieve higher modal shares and exhibit greater responsiveness to travel conditions, particularly at moderate distances, during weekdays, and for leisure trips. In contrast, exclusive flying taxis maintain lower modal shares, decline with increasing travel distance, and are more associated with business and weekend travel. The modal shift analysis further indicates that ground taxi users exhibit the highest propensity to switch to shared flying taxi services, particularly under cost increases. These findings highlight the importance of pricing and service design in promoting the adoption of shared flying taxi services as a more sustainable mobility option. In particular, maintaining affordable shared services, ensuring clear price differentiation from exclusive services, and prioritizing deployment in congested corridors and medium-distance travel markets can enhance adoption. Topic group: Adoption & Impact

US ends probe into Tesla remote driving feature after software updates | Reuters

US ends probe into Tesla remote driving feature after software updates | Reuters Exclusive news, data and analytics for financial market professionalsLearn more aboutRefinitiv A 2025 Tesla Model 3 self-drives on the streets of Los Angeles, California, U.S., November 6, 2025. REUTERS/Mike Blake Purchase Licensing Rights, opens new tab - Companies Tesla Inc Follow Carparts.Com Inc Follow WASHINGTON, April 6 (Reuters) - The U.S. National Highway Traffic Safety Administration said on Monday ​it closed a probe into nearly 2.6 million Tesla

Google Unveils Gemini AI for Android Auto

Google is integrating its AI, Gemini, into Android Auto, enhancing hands-free control of maps, messaging, and media for a safer driving experience.

TechCrunch Mobility: A stunning lack of transparency

The latest edition of TechCrunch Mobility examines the ongoing issues regarding transparency within the transportation and mobility sector.

Over 45 pct of Russia's transport firms adopt AI: official-Xinhua

Over 45 pct of Russia's transport firms adopt AI: official-Xinhua # Over 45 pct of Russia's transport firms adopt AI: official Source: Xinhua| 2026-04-04 01:23:30|Editor: huaxia ST. PETERSBURG, April 3 (Xinhua) -- Over 45 percent of companies in Russia's transport sector have adopted artificial intelligence (AI) technologies, a senior official said Friday. Boris Tashimov, deputy transport minister of Russia, made the remarks while addressing the International Transport and Logistics Forum, which was held in St. Petersburg from Wednesday to Friday. According to the Ministry of Transport and the Digital Transport and Logistics Association, the most common applications of AI in the sector include processing unstructured data, machine vision, predictive modeling and optimization, predictive analytics, and speech recognition. Tashimov noted that Russia is moving toward comprehensive r Topic group: Adoption & Impact

Tesla’s Sales Miss, Next Stage of NASA’s Moon Mission | Bloomberg Tech 4/2/2026

Bloomberg’s Tim Stenovec discusses the roller coaster for tech stocks as the market reacts to the ongoing conflict with Iran. Plus, Tesla posts one of its worst sales quarters in years, disappointing

Robotaxi Outage in China Leaves Passengers Stranded on Highways

Will.i.am's AI-Powered Vehicle

Will.i.am's Trinity three-wheeled electric vehicle aims to be an AI agent, helping its driver with tasks such as email and strategy.

Mass robotaxi malfunction halts traffic in Chinese city

Baidu has not responded to a request for comment about the outage, which affected at least 100 cars. Topic group: Adoption & Impact

Irish drone delivery firm Manna confirms $50m raise, plans 400 new jobs

The 400 new jobs are understood to break down as 300 in Ireland and 100 in the US, bringing total headcount to more than 570. Read more: Irish drone delivery firm Manna confirms $50m raise, plans 400 new jobs Topic group: Adoption & Impact

Google Maps Unveils AI-Powered EV Trip Planning

Google Maps on Android Auto introduces AI-powered EV trip planning, enhancing route efficiency with charging stop suggestions and battery estimates to ease range anxiety.

The karaoke company, the penny stock investor and the $17bn trucking rout

Controversial trader John Fife invested in Algorhythm months before its AI announcement triggered a surge in its share price and a market panic.

Taiwan FTC To Scrutinize Uber's Stake In Grab's $600m Foodpanda Deal

Taiwan's Fair Trade Commission said it is preparing to scrutinize Grab's planned acquisition of Foodpanda's Taiwan operations, as lawmakers raised concerns over Uber's stake in Grab and potential monopoly risks in the food delivery sector. During a legislative hearing on Thursday, the regulator also said it is probing disruptions in plastic bag supplies and ramping up scrutiny of emerging competition risks linked to artificial intelligence.

Cambridge Mobile Telematics: $350 Million Raised To Scale Global Road Safety Platform

Cambridge Mobile Telematics announced a $350 million strategic investment led by TPG and Allianz X, with participation from State Farm, to expand its AI-driven telematics platform and accelerate global road safety initiatives. The post Cambridge Mobile Telematics: $350 Million Raised To Scale Global Road Safety Platform appeared first on Pulse 2.0.

Safety Experts Considered LaGuardia Challenging but Not an Outlier

Regulators, pilots and others in aviation have worried about the kind of runway accident that happened at LaGuardia Airport on Sunday.

Pony AI CEO: Middle East Market Remains a Priority

Pony AI has reported its first profitable quarter. Founder and CEO James Peng says the company plans to expand its robotaxi business both domestically and overseas, citing strong demand. Peng adds that the Middle East remains a key priority market for Pony AI.

Uber Launches Robotaxi Service

Uber, in collaboration with Pony AI and Verne, has launched Europe's first commercial robotaxi service in Zagreb, Croatia, with plans for broader market expansion.

Pony AI Breaks Even, Aims to Launch Robotaxis in 20 Cities

Pony AI Inc. delivered its first ever profitable quarter, bolstered by a windfall from an early investment rather than its main robotaxi business.

Moviton Raises $2M

Moviton, a Dubai, UAE-based web3 decentralized logistics platform provider, raised $2 million in a community pre-sale round to enhance its AI-driven compliance systems.

Vuelo Raises €64M

Vuelo has raised €64 million in seed funding to build an AI-powered travel booking platform led by Backed VC and Play Ventures with debt funding from Viola Credit.

Can Flights Get Any Worse? Travelers Deal With TSA Lines, High Ticket Prices and Anxiety.

Travelers are waiting hours at security checkpoints, paying top dollar for tickets and worrying about safety after a deadly crash at LaGuardia.

Autonomous Driving Firm DeepRoute.ai Is Said to Consider Hong Kong IPO

Chinese autonomous driving company DeepRoute.ai is considering an initial public offering in Hong Kong, people familiar with the matter said, joining a growing list of tech firms selling shares in the Asian hub.

NoTraffic: $90 Million Raised For AI Traffic Management Platform Expansion Across North America

NoTraffic, an AI-powered mobility platform focused on modernizing traffic management, announced it has raised $90 million in a Series C funding round to accelerate growth and expand its footprint across North America. The round was led by PSG Equity, with participation from M&G Investments, Grove Ventures, LifeX Ventures, Meitav Investment House, and Next Gear Ventures. The post NoTraffic: $90 Million Raised For AI Traffic Management Platform Expansion Across North America appeared first on Pulse 2.

Cambridge Mobile Telematics: $350 Million Raised To Expand AI-Driven Road Safety Platform

Cambridge Mobile Telematics, a telematics and AI company focused on safer mobility, announced a $350 million strategic investment led by TPG and Allianz X, with participation from State Farm. The funding is aimed at accelerating the company’s global expansion and advancing its AI-powered road safety technologies. The post Cambridge Mobile Telematics: $350 Million Raised To Expand AI-Driven Road Safety Platform appeared first on Pulse 2.

How Pokémon Go Is Helping Robots Deliver Pizza On Time

How Pokémon Go is giving delivery robots an inch-perfect view of the world

Cambridge Mobile Telematics Raises $350M

Cambridge Mobile Telematics, a telematics and AI company focused on safer mobility, has secured a $350 million strategic investment led by TPG and Allianz X, with participation from State Farm.

Notraffic Raises $90M

Notraffic, an AI-powered mobility platform, has raised $90 million in a Series C funding round to enhance traffic management solutions and expand its presence in North America.

Exclusive: Cambridge Mobile Telematics secures $350 million from TPG, Allianz to make driving safer

The investment highlights how insurers are using real-time driving data and AI to price risk, prevent accidents, and improve road safety at scale.

Navi AI Raises $6.7M for Pilot Training

Navi AI Inc., a startup focused on generative artificial intelligence for pilot training, has secured $6.7 million in funding to enhance its AI platform that analyzes flight data, aiming to improve training efficiency and safety.

Hyground Secures €3M for AI-Powered Incident Management

Hyground, a startup based in Hamburg, has secured €3M in funding to address the costly gap between alerts and resolution in incident management. The company aims to enhance the efficiency of AI SRE agents within Deutsche Bahn.

Qcraft Raises $100M for Autonomous Driving

qcraft, a leader in autonomous driving AI, has completed a $100 million Series D funding round to enhance its development of physical-world AI and expand its global mobility presence.

Hamilton AI Raises $7.5M for Private Aviation

Hamilton AI, a New York City-based AI provider for private aviation, raised $7.5 million in seed funding.

Rivr Acquired by Amazon

Amazon has acquired robotics company RIVR to enhance its physical AI systems for last-mile delivery.

Uber fights to stay in the robotaxi race

Ride-hailing company must make up lost ground on driverless vehicles, or risk being left in the rear-view mirror