Inverse Reinforcement Learning Based: Segmented Lane-Change Trajectory Planning With Consideration of Interactive Driving Intention

One of the most challenging problems in autonomous driving is trajectory planning for lane changes. Conventional trajectory planning is generally realized by optimizing a specific cost function. However, the model performance required for different scenarios makes it repetitive and time-consuming to tune the cost function. Furthermore, to ensure driving safety, comfort as well as traffic efficiency, it is essential to interact with other vehicles by predicting their driving intentions. Therefore, this paper proposes a segmented-updated lane-change trajectory planning method for autonomous vehicles on closed highways. The maximum entropy inverse reinforcement learning (IRL) algorithm for human demonstration learning and the long short-term memory and Bayesian network (LSTM-BN)-based model for target vehicles' driving intentions are applied. Three contributions are made in this work: 1) The LSTM-BN is used to judge the intentions of surrounding vehicles based on its historical information to inform lane-change trajectory planning. 2) Different feature-based cost functions of the IRL algorithm are designed according to different lane-change scenarios, which enhances its adaptability to different scenarios. 3) Segmented-updated trajectory planning is used to dynamically optimize trajectories via an online optimization process, which reduces the model's limitations and improves overall lane-change performance. Finally, simulation experiments demonstrate that the proposed method has strong generalization ability, reliable accuracy, and can adjust lane-change trajectories reasonably according to the driving intentions of surrounding vehicles.