提出三种新损失函数，让自动驾驶轨迹预测更安全、多样且符合交通规则。
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses
- 设计离路、方向一致性和多样性三类损失，约束路径在道路内并覆盖多种合理行为。
- 在nuScenes和Argoverse 2数据集上，离路错误平均降低47%（正常场景）和37%（攻击场景）。
- 适合关注自动驾驶安全与轨迹多样性的研究者和工程团队。
轨迹预测对自动驾驶的安全与效率至关重要。现有模型常无法充分捕捉复杂交通规则及车辆可能的全部运动模式。为此，本文提出三种新型损失函数：离路损失（Offroad Loss）、方向一致性误差（Direction Consistency Error）和多样性损失（Diversity Loss），分别用于确保预测路径在道路范围内、符合交通方向、覆盖更多合理驾驶场景。所有预测模式均应用这些损失，克服传统“胜者为王”训练方式的局限。该方法不仅提升模型训练效果，还可作为评估轨迹真实性和多样性的指标。在nuScenes和Argoverse 2数据集上，使用领先基线模型进行广泛验证，结果表明本方法在保持精度的同时显著提升安全性与鲁棒性，正常场景下离路错误平均减少47%，受攻击场景下减少37%。本工作为自动驾驶轨迹预测设立了新基准，显著改善复杂环境下的导航能力。代码已公开于https://github.com/vita-epfl/stay-on-track。
原文摘要 · Abstract (English)
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 .
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