Reinforcement Learning for Switching of Semiautomated Vehicles Under Uncomfortable Driving Situations

Abstract

Over the last two decades, autonomous driving has progressed from science fiction to a real possibility and rapidly developing. However, autonomous driving technology has significant weaknesses and is not safe in unexpected conditions. As a result, automobile manufacturers insist that the driver remains in the driver's seat even while the vehicle is in autonomous mode. Semi-autonomous driving helps in this situation. Semi-autonomous vehicles require minimum human intervention and cooperate with human drivers. It provides multiple levels of automation to the driver to give the optimal decision about who should be in charge in a particular scenario. However, it has limitations and does not work perfectly in all complex scenarios. This paper focuses on this limitation and provides a reinforcement learning strategy to solve an existing complex scenario. In this specific scenario, Mediator initiates a shift of control to a different automation level when uncomfortable driving situations are detected. Markov Decision Process strategy used to formulate the decision problem, and the reinforcement learning strategy compared with a decision-tree based baseline strategy for the evaluation. The outcome was collected using driver safety and comfort metrics. The outcome supports the hypothesis, demonstrating that a learned reinforcement learning strategy can be used to solve complex decision-making scenarios in semi-autonomous driving.