Personalized Highway Pilot Assist Considering Leading Vehicle's Lateral Behaviours
This work addresses the need for safer and more acceptable advanced driver assistance systems for highway drivers, though it is incremental as it builds on existing models like IDM.
The authors tackled the problem of improving highway pilot assist systems by personalizing them based on drivers' lateral car-following preferences, resulting in a significant reduction in mental workload and improved user acceptance compared to un-personalized algorithms.
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.