Comfort Improvement for Autonomous Vehicles Using Reinforcement Learning with In-Situ Human Feedback

In this paper, a reinforcement learning-based method is proposed to adapt autonomous vehicle passengers’ expectation of comfort through in-situ human-vehicle interaction. Ride comfort has a significant influence on the user’s experience and thus acceptance of autonomous vehicles. There is plenty of research about the motion planning and control of autonomous vehicles. However, limited studies have explicitly considered the comfort of passengers in autonomous vehicles. This paper studies the comfort of humans in autonomous vehicles longitudinal autonomous driving. The paper models and then improves passengers’ feelings about autonomous driving behaviors. This proposed approach builds a control and adaptation strategy based on reinforcement learning using human’s in-situ feedback on autonomous driving. It also proposes an adaptation of humans to autonomous vehicles to account for improper human driving expectations. The proposed approaches are implemented and tested with human-in-the-loop experiments and the results demonstrate that the proposed approaches can successfully adapt the vehicle behaviors, improve the ride comfort of humans in autonomous vehicles, and also correct improper human driving expectations.