Personalized Highway Pilot Assist Considering Leading Vehicle's Lateral Behaviours

TL;DR

This paper introduces a personalized highway pilot assist system that adapts to individual driver behaviors, especially in lateral control, to enhance safety and user acceptance through simulation and driver-in-the-loop experiments.

Contribution

It proposes a novel personalized highway pilot algorithm incorporating lateral behavior modeling and driver clustering, improving safety and acceptance over traditional methods.

Findings

Personalized system reduces driver mental workload.

Improves user acceptance of driver assistance.

Effective in simulated and driver-in-the-loop tests.

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 analyse driver gaze and lane-keeping Behaviours in free-driving and following driving scenario. Drivers are clustered into two driving style groups referring to their driving Behaviours 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.