Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles. However, current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements. Addressing these limitations, this study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss. These functions are designed to keep predicted paths within driving area boundaries, aligned with traffic directions, and cover a wider variety of plausible driving scenarios. As all prediction modes should adhere to road rules and conditions, this work overcomes the shortcomings of traditional "winner takes all" training methods by applying the loss functions to all prediction modes. These loss functions not only improve model training but can also serve as metrics for evaluating the realism and diversity of trajectory predictions. Extensive validation on the nuScenes and Argoverse 2 datasets with leading baseline models demonstrates that our approach not only maintains accuracy but significantly improves safety and robustness, reducing offroad errors on average by 47% on original and by 37% on attacked scenes. This work sets a new benchmark for trajectory prediction in autonomous driving, offering substantial improvements in navigating complex environments. Our code is available at https://github.com/vita-epfl/stay-on-track .

翻译：轨迹预测对于自动驾驶车辆规划的安全性与效率至关重要。然而，现有模型往往难以充分捕捉复杂的交通规则及车辆潜在运动的完整范围。针对这些局限性，本研究引入了三种新颖的损失函数：离路损失、方向一致性误差与多样性损失。这些函数旨在将预测路径约束在可行驶区域边界内，保持与交通方向的一致性，并覆盖更广泛的合理驾驶场景。由于所有预测模式均应遵循道路规则与条件，本研究通过将损失函数应用于所有预测模式，克服了传统“赢者通吃”训练方法的缺陷。这些损失函数不仅提升了模型训练效果，亦可作为评估轨迹预测真实性与多样性的度量指标。在 nuScenes 和 Argoverse 2 数据集上使用主流基线模型进行的广泛验证表明，我们的方法在保持准确性的同时，显著提升了安全性与鲁棒性，在原始场景中平均减少离路误差47%，在受攻击场景中平均减少37%。此项工作为自动驾驶轨迹预测设立了新基准，在复杂环境导航方面实现了实质性改进。我们的代码公开于 https://github.com/vita-epfl/stay-on-track。