Generative Modeling of Pedestrian Behavior: A Receding Horizon Optimization-Based Trajectory Planning Approach

作     者：Gupta, Saumya Zaki, Mohamed H. Vela, Adan 

作者机构：Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2022年第10卷

页      面：81624-81641页

核心收录：

主　　题：Behavioral sciences Trajectory Legged locomotion Safety Roads Transportation Planning Optimization mixed integer linear programming shared space social interaction rules trajectory-planning vulnerable road users urban design and planning 

摘      要：Urbanization is bringing together various modes of transport, and with that, there are challenges to maintaining the safety of all road users, especially vulnerable road users (VRUs). There is a need for street designs that encourages cooperation between road users. Shared space is a street design approach that softens the demarcation of vehicles and pedestrian traffic by reducing traffic rules, traffic signals, road marking, and regulations. Understanding the interactions and trajectory formations of various VRUs will facilitate the design of safer shared spaces. In line with this goal, this paper aims to develop a methodology for generating VRUs trajectories that accounts for behaviors and social interactions. We develop a receding horizon optimization-based pedestrian trajectory planning algorithm capable of modeling pedestrian trajectories in a variety of shared space scenarios. Focusing on three scenarios-group interactions, unidirectional interaction, and fixed obstacle interaction-case studies are performed to demonstrate the strengths of the resulting generative model. Additionally, generated trajectories are validated using two benchmark datasets - DUT and TrajNet++. The three case studies are shown to yield low or near-zero Mean Euclidean Distance and Final Displacement Error values supporting the performance validity of the models. We also analyze gait parameters (step length and step frequency) to further demonstrate the model s capability at generating realistic pedestrian trajectories.