Force-based Heterogeneous Traffic Simulation for Autonomous Vehicle Testing

Abstract

Recent failures in real-world self-driving tests have suggested a paradigm shift from directly learning in real-world roads to building a high-fidelity driving simulator as an alternative, effective, and safe tool to handle intricate traffic environments in urban areas. To date, traffic simulation can construct virtual urban environments with various weather conditions, day and night, and traffic control for autonomous vehicle testing. However, mutual interactions between autonomous vehicles and pedestrians are rarely modeled in existing simulators. Besides vehicles and pedestrians, the usage of personal mobility devices is increasing in congested cities as an alternative to the traditional transport system. A simulator that considers all potential road-users in a realistic urban environment is urgently desired. In this work, we propose a novel, extensible, and microscopic method to build heterogenous traffic simulation using the force-based concept. This force-based approach can accurately replicate the sophisticated behaviors of various road users and their interactions through a simple and unified way. Furthermore, we validate our approach through simulation experiments and comparisons to the popular simulators currently used for research and development of autonomous vehicles.