While highly automated driving relies most of the time on a smooth driving assumption, the possibility of a vehicle performing harsh maneuvers with high dynamic driving to face unexpected events is very likely. The modeling of the behavior of the vehicle in these events is crucial to proper planning and controlling; the used model should present accurate and computationally efficient properties. In this article, we propose an LSTM-based hybrid extended bicycle model able to present an accurate description of the state of the vehicle for both normal and aggressive situations. The introduced model is used in an MPPI framework for planning trajectories in high-dynamic scenarios where other simple models fail.

翻译：高度自动化驾驶大多依赖于平稳行驶的假设，但车辆在面对突发事件时很可能需执行剧烈操纵的高动态驾驶行为。对此类事件中车辆行为的建模，对于合理的规划与控制至关重要；所用模型应兼具精确性与计算效率双重特性。本文提出一种基于LSTM的混合扩展自行车模型，该模型能够对正常与激烈驾驶场景下的车辆状态进行精确描述。我们将该模型应用于MPPI框架中，用于规划其他简单模型难以胜任的高动态场景下的轨迹。