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Abstract

Highway pilot assist has become the front line of competition in advanced driver assistance systems. The increasing requirements on safety and user acceptance are calling for personalization in the development process of such systems. Inspired by a finding on drivers’ car-following preferences on lateral direction, a personalized highway pilot assist algorithm is proposed, which consists of an Intelligent Driver Model (IDM) based speed control model and a novel lane-keeping model considering the leading vehicle’s lateral movement. A simulated driving experiment is conducted to analyze drivers’ gaze and lane-keeping behaviors in free-driving and following driving scenario. Drivers are clustered into two driving style groups referring to their driving behaviors affected by the leading vehicle, and then the personalization parameters for every specific subject driver are optimized. The proposed algorithm is validated through driver-in-the-loop experiment based on a moving-base simulator. Results show that, compared with the un-personalized algorithms, the personalized highway pilot algorithm can significantly reduce the mental workload and improve user acceptance of the assist functions.

Keywords

Personalized driving automation highway pilot driving style clustering car-following mental workload driving simulator

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Personalized highway pilot assist considering leading vehicle’s lateral behaviors

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