Comfort-oriented Haptic Guidance Steering via Deep Reinforcement Learning for Individualized Lane Keeping Assist

One challenge of haptic guidance steering system is to be both efficient on reducing lane departure risk and as comfort as possible for driver acceptance. This paper focuses on design and evaluation of a comfort-oriented haptic guidance steering system via deep reinforcement learning for lane keeping purpose by numerical simulation. The design is based on a Deep Q Network to learn an end-to-end mapping from environmental observation and driver input to agent action values, with task reward as the only form of supervision. The evaluation is based on lane keeping accuracy measured by mean absolute lateral error from the centerline of lane and steering control activity measured by mean absolute steering wheel angle. By comparison, other two types of haptic guidance steering, namely continuous haptic guidance with constant gain and threshold-based haptic guidance are addressed. The results indicate that haptic guidance via reinforcement learning has the best performance on reducing steering control activity while remaining a high lane keeping accuracy. Furthermore, the proposed system shows its capability of being adaptive to individualized drivers. This paper suggests the potential of using deep reinforcement learning to design haptic guidance steering system for improving individualized driver safety and comfort.