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Use CaseManufacturing & Industrials / Automobiles and parts

Visualise and recognise road layout, other vehicles and pedestrians and potential risks to empower predictive driving

Vision systems that enable risk and reality recognition are key to a host of autonomous and semi-autonomous vehicle use cases. Typically these rely on a battery of AI ranging from image recognition to predictive responses.

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Risk reduction
Risk reduction - Reduce environmental physical risk
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Risk reduction
Risk reduction - Real time awareness
Description

Visualise and recognise road layout, other vehicles and pedestrians and potential risks to empower predictive driving

Linked Case Studies
Case Study
Motor Vehicles Department Kerala
The Motor Vehicles Department in Kerala detects lack of seatbelt or helmet usage in motor vehicles in with 98% accuracy in trial
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Case Study
Tesla Inc
Drivers demonstrate Tesla''s Autopilot limitations in recreation of fatal Model X crash at exact same location
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Case Study
Uber
Uber''s self driving car failed to visualise and recognise a pedestrian and the potential risk in real-time resulting in a fatal accident
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Source: appliedai.com · Editor: original-sdg
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