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.

翻译：轨迹预测是自动驾驶软件栈中的关键组成部分之一。准确预测周围交通参与者的未来运动状态，是确保智能车辆行驶效率和安全性的重要前提。基于人工智能的轨迹预测算法近年来得到了广泛研究和应用，并取得了显著成果。然而，复杂的人工智能模型具有不确定性和难以解释性，因此在真实世界应用中可能面临意外故障。本文提出了一种自我感知轨迹预测方法。通过引入自我感知模块和两阶段训练过程，在线估计原始轨迹预测模块的性能，以便系统及时处理预测功能不足的可能场景，为实现安全可靠的自动驾驶创造条件。综合实验与分析结果表明，所提方法在自我感知能力、内存占用和实时性能方面表现良好，展现了其作为安全自动驾驶有前景范式的潜力。