Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.

翻译：自动驾驶车辆依赖精确的轨迹预测来支持导航与避障等相关决策过程。然而，现有轨迹预测模型表现出过拟合迹象，可能导致不安全或次优行为。为应对这些挑战，本文提出一个综合性框架，对现有文献中用于评估和提升轨迹预测模型鲁棒性的定义与策略进行分类与评估。该框架详细探讨了多种方法，包括数据切片技术、扰动方法、模型架构变更以及后训练调整。文献综述显示，诸多方法在提升鲁棒性方面展现出良好前景，这对实现安全可靠的自动驾驶至关重要。