Adaptive Performance Assessment For Drivers Through Behavioral Advantage
This addresses the challenge of performance analysis for drivers and autonomous systems in diverse conditions, though it appears incremental as it adapts existing concepts to a specific domain.
The paper tackles the problem of quantitatively assessing driver or autonomous agent performance across varying environmental conditions by proposing a method that uses behavioral advantage instead of absolute metrics. It demonstrates the method by evaluating and ranking over 100 truck drivers for fuel efficiency based on more than 90,000 trips averaging 300 miles each.
The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method that compares the performance of drivers or autonomous driving agents even if the environmental conditions are different, by using the driver behavioral advantage instead of absolute metrics, which efficiently removes the environmental factors. A concrete application of the method is also presented, where the performance of more than 100 truck drivers was evaluated and ranked in terms of fuel efficiency, covering more than 90,000 trips spanning an average of 300 miles in a variety of driving conditions and environments.