Learning and Adapting Behavior of Autonomous Vehicles through Inverse Reinforcement Learning

The driving behavior of autonomous vehicles has a significant impact on safety for all traffic participants. Unlike current traffic participants, autonomous vehicles in the future will also need to adhere to safety standards and defined risk properties in order to achieve a high level of public acceptance. At the same time, successful autonomous vehicles must be able to interact with human drivers in mixed traffic in a way that enables traffic to flow. In this paper, we present a hybrid approach to trajectory planning that learns and adapts human driving behavior using inverse reinforcement learning. The proposed approach performs a large-scale simulation with HighD real-world scenarios to learn human driving behavior and domain-specific traffic-flow characteristics. The analysis of the work focuses on the influence of risk-taking, which provides information about driving style safety. The results show insights into the risk behavior of trajectory planning approaches compared to human risk assessment. The comparison to human trajectories is intended to ensure comparability and accurate classification of risk-taking. We recommend a hybrid method for adapting driving behavior, in order to maintain the explainability and safety of the trajectory planning algorithm.