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：自动驾驶车辆依赖准确的轨迹预测来指导导航和避障相关的决策过程。然而，当前的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，对评估和改进轨迹预测模型鲁棒性的文献中定义与策略进行分类与评估。该框架详细探讨了多种方法，包括数据切片方法、扰动技术、模型架构变更及训练后调整。文献中涌现出许多提升鲁棒性的有效方法，这些方法对于实现安全可靠的自动驾驶不可或缺。