Modelling personalised car-following behaviour: a memory-based deep reinforcement learning approach

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Modelling personalised car-following behaviour: a memory-based deep reinforcement learning approach

Liao, Yaping / Yu, Guizhen / Chen, Peng / Zhou, Bin / Li, Han

To adapt to human-driving habits, this study develops a personalised car-following model via a memory-based deep reinforcement learning approach. Specifically, Twin Delayed Deep Deterministic Policy Gradients (TD3) is integrated with a long short-term memory (LSTM) (abbreviated as LSTM-TD3). Using the NGSIM dataset, unsupervised learning-based clustering and data feature analyses are performed. The driving characteristics related to safety, efficiency and comfort are extracted for different driving styles, i.e. aggressive, common and conservative. Then, reward functions are constructed for different driving styles by incorporating their driving characteristics. By resorting to the TD3 policy within a recurrent actor–critic framework, LSTM-TD3 optimises the car-following behaviour via trial-and-error interactions according to the reward functions. Results show that compared with LSTM-DDPG and DDPG, LSTM-TD3 reproduces personalised car-following behaviour with desirable convergence speed and reward. It reveals that LSTM-TD3 can reflect the essential difference in safety, efficiency and comfort requirements among different driving styles.