Xin, Haojie (Xi'an Jiaotong University), Zhang, Xiaodong (Xidian University), Tang, Renzhi (ShanghaiTech University), Yan, Songyang (Xi'an Jiaotong University), Zhao, Qianrui (Xi'an Jiaotong University), Yang, Chunze (Xi'an Jiaotong University), Cui, Wen (Xidian Univerisity; Institute for Interdisciplinary Information), Yang, Zijiang (Xian Jiaotong University)

LitSim: A Conflict-Aware Policy for Long-Term Interactive Traffic Simulation

Scheduled for presentation during the Regular Session "Multi-autonomous Vehicle Studies, Models, Techniques and Simulations II" (FrAT10), Friday, September 27, 2024, 10:50−11:10, Salon 18

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages of high efficiency and low cost compared to on-road testing. Bridging the gap between simulation and the real world requires realistic agent behaviors. However, the existing works have the following shortcomings in achieving this goal: (1) log replay offers realistic scenarios but often leads to collisions due to the absence of dynamic interactions, and (2) both heuristic-based and data-based solutions, which are parameterized and trained on real-world datasets, encourage interactions but often deviate from real-world data over long horizons. In this work, we propose LitSim, a long-term interactive simulation approach that maximizes realism by minimizing the interventions in the log. Specifically, our approach primarily uses log replay to ensure realism and intervenes only when necessary to prevent potential conflicts. We then encourage interactions among the agents and resolve the conflicts, thereby reducing the risk of unrealistic behaviors. We train and validate our model on the real-world dataset NGSIM, and the experimental results demonstrate that LitSim outperforms the currently popular approaches in terms of realism and reactivity.