Self-Aware Trajectory Prediction for Safe Autonomous Driving

TL;DR

This paper introduces a self-aware trajectory prediction method for autonomous vehicles, enhancing safety by estimating prediction reliability online, thus addressing AI uncertainty and failure risks in real-world driving scenarios.

Contribution

The paper proposes a novel self-awareness module and a two-stage training process to improve trajectory prediction reliability and safety in autonomous driving systems.

Findings

Effective self-awareness estimation improves prediction reliability.

Method maintains low memory footprint and real-time performance.

Enhanced safety and robustness demonstrated through experiments.

Abstract

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.