Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
This work addresses the challenge of making autonomous driving agents more efficient and suitable for human-robot interaction in traffic, though it appears incremental by modifying existing DRL methods with human data.
The paper tackled the problem of limited generalization in autonomous driving by proposing a two-stage DRL method that incorporates real-world human driving experience into a car-following agent, achieving superior performance compared to pure DRL agents.
In the autonomous driving field, fusion of human knowledge into Deep Reinforcement Learning (DRL) is often based on the human demonstration recorded in a simulated environment. This limits the generalization and the feasibility of application in real-world traffic. We propose a two-stage DRL method to train a car-following agent, that modifies the policy by leveraging the real-world human driving experience and achieves performance superior to the pure DRL agent. Training a DRL agent is done within CARLA framework with Robot Operating System (ROS). For evaluation, we designed different driving scenarios to compare the proposed two-stage DRL car-following agent with other agents. After extracting the "good" behavior from the human driver, the agent becomes more efficient and reasonable, which makes this autonomous agent more suitable for Human-Robot Interaction (HRI) traffic.