融合物理规则的混合模型，让自动驾驶轨迹预测更真实安全
Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction
- 用深度学习+运动学模型联合预测加速度和转向角
- 约束动作空间后，预测轨迹更符合车辆物理限制
- 适合需要高安全性的自动驾驶规划系统使用
轨迹预测对提升自动驾驶的安全性与效率至关重要。尽管基于深度学习的端到端模型潜力巨大，但通常忽略车辆动力学约束，导致预测结果不现实。为此，本文提出一种新型混合模型，结合深度学习与运动学模型，能够预测加速度、航向角等物体属性，并基于此生成轨迹。核心创新在于将专家知识融入深度学习目标函数，从而约束可用动作空间，实现物理可行的属性与轨迹预测，提升安全性与鲁棒性。该模型增强可解释性，有助于建立对深度学习方法的信任，推动安全路径规划发展。在公开的真实世界Argoverse数据集上的实验表明，模型能生成符合实际驾驶行为的轨迹，基准对比与消融实验均显示优异性能。
原文摘要 · Abstract (English)
Trajectory prediction is crucial to advance autonomous driving, improving safety, and efficiency. Although end-to-end models based on deep learning have great potential, they often do not consider vehicle dynamic limitations, leading to unrealistic predictions. To address this problem, this work introduces a novel hybrid model that combines deep learning with a kinematic motion model. It is able to predict object attributes such as acceleration and yaw rate and generate trajectories based on them. A key contribution is the incorporation of expert knowledge into the learning objective of the deep learning model. This results in the constraint of the available action space, thus enabling the prediction of physically feasible object attributes and trajectories, thereby increasing safety and robustness. The proposed hybrid model facilitates enhanced interpretability, thereby reinforcing the trustworthiness of deep learning methods and promoting the development of safe planning solutions. Experiments conducted on the publicly available real-world Argoverse dataset demonstrate realistic driving behaviour, with benchmark comparisons and ablation studies showing promising results.
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