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：自动驾驶车辆依赖于准确的轨迹预测来指导与导航和碰撞避免相关的决策过程。然而，当前的轨迹预测模型表现出过拟合的迹象，可能导致不安全或次优行为。为应对这些挑战，本文提出了一个全面框架，对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和评估。这包括对多种方法的详细探讨，例如数据切片方法、扰动技术、模型架构变更及训练后调整。在文献中，我们观察到许多有希望提升鲁棒性的方法，这些方法对于安全可靠的自动驾驶至关重要。