一种分层的人行道行为模型以在交通模拟中生成逼真的人类行为
A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation
AI总结：
本文提出一种分层行人行为模型，通过行为树和适应后的社会力模型生成逼真的人类行为，用于交通模拟中的自动驾驶测试。
摘要：
在自动驾驶车辆的发展和测试中，建模行人行为至关重要。在本工作中，我们提出了一种分层的人行道行为模型，通过使用行为树生成高层决策，以产生由适应后的社会力模型驱动的低层运动规划器执行的机动动作。我们工作的完整实现被集成到GeoScenario Server中，一个场景定义和执行引擎，扩展了其车辆模拟能力，加入了行人模拟。扩展的环境允许模拟同时涉及车辆和行人的情景，以协助自动驾驶车辆的基于场景的测试过程。所提出的分层模型在两个不同地点收集的真实世界数据集上进行了评估。我们的模型在仅使用每个行人的高层路线信息的情况下，能够以高保真度复制真实世界的行人轨迹，并表现出98%或更高的决策准确性。
英文摘要：
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles. In this work, we present a hierarchical pedestrian behavior model that generates high-level decisions through the use of behavior trees, in order to produce maneuvers executed by a low-level motion planner using an adapted Social Force model. A full implementation of our work is integrated into GeoScenario Server, a scenario definition and execution engine, extending its vehicle simulation capabilities with pedestrian simulation. The extended environment allows simulating test scenarios involving both vehicles and pedestrians to assist in the scenario-based testing process of autonomous vehicles. The presented hierarchical model is evaluated on two real-world data sets collected at separate locations with different road structures. Our model is shown to replicate the real-world pedestrians' trajectories with a high degree of fidelity and a decision-making accuracy of 98% or better, given only high-level routing information for each pedestrian.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
