Long-Term Interactive Driving Simulation: MPC to the Rescue

Zhengxiao Han, Zhijie Yan, Yang Li, Pengfei Li, Yifeng Shi, Nairui Luo, Xu Gao, Yongliang Shi, Pengfei Huang, Jiangtao Gong, Guyue Zhou, Yilun Chen, Hang Zhao, Hao Zhao

Abstract: Simulation now plays an important role in the development of autonomous driving algorithms as it can significantly reduce the economical cost and ethical risk of real-world testing. However, building a high-quality driving simulator is not trivial as it calls for realistic interactive behaviors of road agents. Recently, several simulators employ interactive trajectory prediction models learnt in a data-driven manner. While they are successful in generating short-term interactive scenarios, the simulator quickly breaks down when the time horizon gets longer. We identify the reason behind: existing interactive trajectory predictors suffer from the out-of-domain (OOD) problem when recursively feeding predictions as the input back to the model. To this end, we propose to introduce a tailored model predictive control (MPC) module as a rescue into the state-of-the art interactive trajectory prediction model M2I, forming a new simulator named M\(^2\)Sim. Notably, M\(^2\)Sim can effectively address the OOD problem of long-term simulation by enforcing a flexible regularization that admits the replayed data, while still enjoying the diversity of data-driven predictions. We demonstrate the superiority of M\(^2\)Sim using both quantitative results and visualizations and release our data, code and models: https://github.com/0nhc/m2sim.