Trajectory planners of autonomous vehicles usually rely on physical models to predict the vehicle behavior. However, despite their suitability, physical models have some shortcomings. On the one hand, simple models suffer from larger model errors and more restrictive assumptions. On the other hand, complex models are computationally more demanding and depend on environmental and operational parameters. In each case, the drawbacks can be associated to a certain degree to the physical modeling of the yaw rate dynamics. Therefore, this paper investigates the yaw rate prediction based on conditional neural processes (CNP), a data-driven meta-learning approach, to simultaneously achieve low errors, adequate complexity and robustness to varying parameters. Thus, physical models can be enhanced in a targeted manner to provide accurate and computationally efficient predictions to enable safe planning in autonomous vehicles. High fidelity simulations for a variety of driving scenarios and different types of cars show that CNP makes it possible to employ and transfer knowledge about the yaw rate based on current driving dynamics in a human-like manner, yielding robustness against changing environmental and operational conditions.

翻译：自动驾驶车辆的轨迹规划器通常依赖物理模型来预测车辆行为。然而，尽管物理模型具有适用性，但仍存在一些缺陷。一方面，简单模型存在较大的模型误差和更严格的假设条件；另一方面，复杂模型计算需求更高，且依赖于环境和运行参数。这两种情况下的缺点都在一定程度上与横摆角速度动态的物理建模相关。因此，本文研究基于条件神经过程（CNP）的横摆角速度预测方法——一种数据驱动的元学习途径，旨在同时实现低误差、适度复杂度和对变化参数的鲁棒性。通过这种方式，物理模型得以针对性增强，从而提供精确且计算高效的预测，为自动驾驶车辆实现安全规划提供支持。针对多种驾驶场景和不同类型汽车的高保真仿真表明，CNP能够以类人的方式基于当前驾驶动态运用并迁移横摆角速度相关知识，从而实现对变化的环境与运行条件的鲁棒性。