Online Maneuver Learning and Its Real-Time Application to Automated Driving System for Obstacles Avoidance

Abstract

Learning methods to adapt the planned path to individual drivers have been proposed to improve the comfort and trust provided by automated driving systems (ADSs). However, existing methods apply offline learning, even if they can accept the request to learn while driving (on-demand learning). Although several online learning methods are available, their learning results have not been applied in real-time for vehicle maneuvering. Focusing on obstacle avoidance, we propose on-demand online learning of preferred paths for individual drivers and investigate whether the proposed method improves the comfort and trust after learning. Unlike the existing methods, the proposed ADS can smoothly and arbitrarily transition between automated and manual driving, thereby learning preferred maneuvers whose results can be applied in real-time to obstacle avoidance. Accordingly, the proposed ADS includes mutual transfer of steering authority between the ADS and driver and curve modification according to the error between the planned path and actual vehicle trajectory. Experimental results show that the proposed ADS method improves the comfort and trust of drivers. In addition, the proposed ADS method learns the paths preferred by individual drivers during avoidance maneuvers to some extent and gradually adjusts the path according to the drivers preference through repeated learning.