Modeling Vehicle Behavior in Highway On-Ramps: A Game Theory and Deep Reinforcement Learning Method

With the improvement of autonomous driving technology, the testing methods for traditional vehicles can no longer meet autonomous driving needs. The simulation methods based on virtual scenario have become a current research hotpot. However, the background vehicles are often pre-set in most existing scenarios, making it difficult to interact with the tested autonomous vehicles and generate dynamic test scenarios that meet the characteristics of different drivers. Therefore, this study proposes a method combining game theory and deep reinforcement learning, and uses a data-driven approach to realistically simulate personalized driving behavior in highway on-ramps. The experimental results show that the proposed method can realistically simulate the speed change and lane-change actions during vehicle interaction. This study can provide a dynamic interaction test scenario with different driver style for autonomous vehicle virtual test in highway on-ramps and a more realistic environment for testing high-level autonomous vehicles.