Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for automated driving systems. The design of driving strategies for such systems usually starts with verifying VPI in simulation. This work proposed an improved framework for the study of VPI in uncontrolled pedestrian crossing scenarios. The framework admits the mutual effect between the pedestrian and the vehicle. A multi-state social force based pedestrian motion model was designed to describe the microscopic motion of the pedestrian crossing behavior. The pedestrian model considers major interaction factors such as the accepted gap of the pedestrian's decision on when to start crossing, the desired speed of the pedestrian, and the effect of the vehicle on the pedestrian while the pedestrian is crossing the road. Vehicle driving strategies focus on the longitudinal motion control, for which the feedback obstacle avoidance control and the model predictive control were tested and compared in the framework. The simulation results verified that the proposed framework can generate a variety of VPI scenarios, consisting of either the pedestrian yielding to the vehicle or the vehicle yielding to the pedestrian. The framework can be easily extended to apply different approaches to the VPI problems.

中文翻译：

---

一种基于多状态社会力的非受控人行横道场景中车-行人交互框架

车辆与行人交互 (VPI) 是自动驾驶系统最具挑战性的任务之一。此类系统的驱动策略设计通常从在仿真中验证 VPI 开始。这项工作提出了一个改进的框架，用于研究不受控制的人行横道场景中的 VPI。该框架承认行人和车辆之间的相互影响。设计了一种基于多状态社会力的行人运动模型来描述行人过街行为的微观运动。行人模型考虑了主要的交互因素，例如行人决定何时开始过马路的可接受差距、行人的期望速度以及行人过马路时车辆对行人的影响。车辆驾驶策略侧重于纵向运动控制，为此在框架内对反馈避障控制和模型预测控制进行了测试和比较。仿真结果验证了所提出的框架可以生成各种 VPI 场景，包括行人向车辆让行或车辆向行人让行。该框架可以轻松扩展以将不同的方法应用于 VPI 问题。