论文标题
在交通模拟中生成现实的人类行为的层次结构行为模型
A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation
论文作者
论文摘要
对行人行为进行建模对于自动驾驶汽车的开发和测试至关重要。在这项工作中，我们提出了一个层次的行人行为模型，该模型通过使用行为树来生成高级决策，以便使用适应性的社会力量模型产生低级运动计划者执行的动作。我们工作的完整实现已集成到Geoscenario服务器中，这是一种场景定义和执行引擎，通过行人模拟扩展了其车辆仿真功能。扩展的环境允许模拟涉及车辆和行人的测试场景，以协助自动驾驶汽车的基于方案的测试过程。在具有不同道路结构的单独位置收集的两个现实世界数据集上，评估了介绍的分层模型。我们的模型被证明可以以高度的保真度和98％或更高的决策精度复制现实世界行人的轨迹，只有每个行人的高级路由信息。
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.
