Self-Aware Trajectory Prediction for Safe Autonomous Driving
Wenbo Shao Jun Li Hong Wang
Trajectory prediction is one of the key components of the autonomous driving
software stack. Accurate prediction for the future movement of surrounding
traffic participants is an important prerequisite for ensuring the driving
efficiency and safety of intelligent vehicles. Trajectory prediction algorithms
based on artificial intelligence have been widely studied and applied in recent
years and have achieved remarkable results. However, complex artificial
intelligence models are uncertain and difficult to explain, so they may face
unintended failures when applied in the real world. In this paper, a self-aware
trajectory prediction method is proposed. By introducing a self-awareness
module and a two-stage training process, the original trajectory prediction
module's performance is estimated online, to facilitate the system to deal with
the possible scenario of insufficient prediction function in time, and create
conditions for the realization of safe and reliable autonomous driving.
Comprehensive experiments and analysis are performed, and the proposed method
performed well in terms of self-awareness, memory footprint, and real-time
performance, showing that it may serve as a promising paradigm for safe
autonomous driving.