Automotive Grade Linux

This paper proposes a novel approach to measuring human driving performance by using the AI capabilities of automated driving systems, illustrated through three example scenarios. Traditionally, the assessment of human driving has followed a bottom-up methodology, where raw data are compared to fixed thresholds, yielding indicators such as the number of hard braking events. However, acceleration threshold exceedances are often heavily influenced by the driving context. We propose a top-down context-aware approach to driving assessments, in which recordings of human-driven vehicles are analyzed by an automated driving system. By comparing the human driver’s speed to the AI’s recommended speed, we derive a level of disagreement that can be used to distinguish between hard braking caused by aggressive driving and emergency braking in response to a critical event. The proposed method may serve as an alternative to the metrics currently used by some insurance companies and may serve as a template for future AI-based driver assessment.

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Automotive Grade Linux

Cited by 2 publications

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Parallel Driving OS: A Ubiquitous Operating System for Autonomous Driving in CPSS

Parallel Driving OS: A Ubiquitous Operating System for Autonomous Driving in CPSS

How AI from Automated Driving Systems Can Contribute to the Assessment of Human Driving Behavior

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