Physics-informed deep learning for robust trajectory prediction in automated driving

Engineering notes

Key topics: autonomous driving, trajectory prediction, prediction, planning. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Planning a safe, comfortable, and energy-efficient ego-vehicle trajectory is fundamental for automated driving. Efficiency reduces emissions, while safety protects occupants and vulnerable road users. Purely data-driven models, however, struggle with robustness and out-of-distribution scenarios, especially for rare edge cases which are underrepresented in the training data. If the data is furthermore affected by sensor noise, the model performance can suffer drastically. Physics-Informed Neural Networks incorporate expert knowledge in form of physical laws or governing equations, enhancing generalisation and transparency compared to standard data-driven models. Only a few studies have applied such networks to the use case automated driving because of lack of reliable physical models for the complex human-driving patterns. This paper investigates the benefit of injecting a physical model, which describes the motion patterns only to a certain extent. It is demonstrated that it still enables a highly accurate extrapolation to OOD scenarios and yields robust performance even with limited data. The root mean squared error on the predicted trajectories is reduced up to a factor of 10.

Links and sources

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