Interaction Aware Decision Making for Automated Vehicles Based on Reinforcement Learning

Sammanfattning

Decision-making is one of the key challenges blocking full autonomy of automated vehicles. In highway scenarios, automated vehicles are expected to be aware of their surroundings and make decisions by interacting with other road participants to drive safely and efficiently. In this thesis, one and multistep lookahead rollout algorithm and its variants are applied to address this problem. The results are evaluated using metrics related to safety and efficiency and compared with the DQN baseline. To improve the collision-avoidance performance of the ego-vehicle, I combine the idea of fortified rollout and rollout with multiple heuristics and propose the safe rollout method for the decision-making problem of automated vehicles. The experimental results show that the rollout agents have decent decision-making performance and can outperform the DQN baseline by collecting higher total reward. Experiments are also conducted to investigate the agent’s ability to adapt to varying behaviour of surrounding vehicles, as well as the impact of different horizon and reward function setting. The difference between deterministic and stochastic problems and its impact on the performance of different rollout agents is discussed. Two approaches to implement data-driven simulation are presented, and the feasibility of utilizing these data-driven simulator as control and decision support is investigated.