Abstract:

Obtaining autonomous driving decision-making strategies by using human driver data is a hot spot in current research on autonomous driving technology. Most of the classic reinforcement learning decision-making methods artificially construct reward functions by designing formulas related to safety， comfort， and economy， which leads to a big gap between decision-making strategies and human drivers. This paper uses the maximum margin inverse reinforcement learning algorithm. Taking the driver’s driving data as expert demonstration data， a reward function is established， and the longitudinal autonomous driving decision-making by imitating the driver is realized. The simulation test results show that compared with the reinforcement learning method， the reward function of the inverse reinforcement learning method is automatically extracted from the driver's data， which reduces the difficulty of establishing the reward function， and the obtained decision-making strategy has higher consistency with the driver’s behavior.

Key words: autonomous driving, decision-making algorithm, reinforcement learning, inverse reinforcement learning（IRL）