FCUS: Traffic Rule-Aware Vehicle Trajectory Forecasting Using Continuous Unlikelihood and Signal Temporal Logic Feature

Sheng Wang, Ren Xin, Jie Cheng, Xiaodong Mei, Ming Liu

Abstract: Vehicle trajectory prediction is essential to autonomous driving tasks. Accurate trajectory prediction of other traffic agents can significantly improve the ability of scene understanding and eventually improve the driving safety level of autonomous vehicles. Popular trajectory prediction methods leverage traffic rules by considering maps or lane graphs as a part of the input. However, this implicit representation is insufficient and can not guarantee an accurate understanding of traffic rules through the model. In this paper, we introduce a novel framework called forecasting using continuous unlikelihood and signal temporal logic (FCUS), which incorporates traffic rules in vehicle motion forecasting by explicitly modeling them as continuous signal temporal logic (STL) features into a GAN-style neural network. By doing so, a strong rule prior can be introduced. Meanwhile, an auxiliary unlikelihood loss is adopted to make the model more rule-aware. Extensive experiments evaluated on the real-world driving dataset show that our framework enables state-of-the-art prediction models to improve 8.92% performance on MinADE 5 and 19.73% on avoiding rule violation. Demo videos and code are available at https://chantsss.github.io/FCUS/.