HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization

Abstract

The paper proposes a generative pedestrian trajectory modeling framework named HISS -Human Interactions in Shared Space. The trajectory modeling framework is based on a receding horizon optimization approach utilizing pedestrian behavior and interactions that seeks to capture pedestrian trajectory planning and execution. The benefit of the proposed dynamic optimization trajectory generation approach is that it requires minimal calibration data under a variety of traffic scenarios. In this paper, we formalize several pedestrian-pedestrian interaction scenarios and implement trajectories' conflict avoidance through mixed integer linear programming (MILP). We validate the proposed framework on two benchmark datasets -DUT and TrajNet++. The paper shows that when the framework's parameters are tuned to certain initial conditions and pedestrian behavior and interaction rules, the framework generates pedestrian trajectories similar to those observable in real-world scenarios, justifying the framework's capability to provide explanations and solutions to various traffic situations. This feature makes the proposed framework useful for modelers and urban city planners in making policy decisions.

A Hierarchical Forecasting Model of Pedestrian Crossing Behavior for Autonomous Vehicle

“…Finally, the theoretical hypothesis is grounded in real-world data. Our simulation environment is focused on its application and verification on shared spaces [8], [9] using the open data-set of [10], which is an interaction-focused dataset used by [11]- [14]. Thus, the model easily extends to unprotected crossroads, and lowspeed limited streets, commonly found in urban areas with traffic-calming measures [15].…”