Reinforcement Learning approach for decision-making in driver control shifting for semi-autonomous driving

Abstract

Semi-autonomous driving innovations aim to bridge the gap to fully autonomous driving by co-operating with human drivers to lead to optimal choices on who should drive in different scenarios by offering different automation levels. However, in the present day, known semi-autonomous driving solutions do not generalise to every complex case of driver and AI interaction. This limitation prompted research in attempting to solve the problem using artificial intelligence and machine learning techniques. This paper focuses on providing a reinforcement learning approach to solve one specific decision-making scenario of the driver initiating a shift of control to a different automation level. The decision problem was formulated as a Markov Decision Process, and the problem was solved both by a baseline handcrafted decision tree and a learned reinforcement learning policy using the DQN algorithm. The two policies were compared based on safety, comfort and efficiency metrics in a simulated driving environment. The results were indicative that a reinforcement learning policy generally ensured safety \& comfort and has shown increased efficiency over the baseline policy, however, it faced efficiency & comfort issues in outlier cases.