Sub-Goal Social Force Model for Collective Pedestrian Motion Under Vehicle Influence

TL;DR

This paper introduces the sub-goal social force model (SG-SFM) to accurately simulate collective pedestrian movement influenced by vehicles, aiding urban planning and autonomous vehicle navigation in mixed traffic environments.

Contribution

The paper presents a novel SG-SFM that integrates vehicle influence with pedestrian interactions using a sub-goal concept, applicable to various vehicle-pedestrian scenarios.

Findings

Successfully reproduces pedestrian motion across three datasets.

Effectively models diverse vehicle-pedestrian interaction patterns.

Demonstrates the model's effectiveness through qualitative and quantitative evaluations.

Abstract

In mixed traffic scenarios, a certain number of pedestrians might coexist in a small area while interacting with vehicles. In this situation, every pedestrian must simultaneously react to the surrounding pedestrians and vehicles. Analytical modeling of such collective pedestrian motion can benefit intelligent transportation practices like shared space design and urban autonomous driving. This work proposed the sub-goal social force model (SG-SFM) to describe the collective pedestrian motion under vehicle influence. The proposed model introduced a new design of vehicle influence on pedestrian motion, which was smoothly combined with the influence of surrounding pedestrians using the sub-goal concept. This model aims to describe generalized pedestrian motion, i.e., it is applicable to various vehicle-pedestrian interaction patterns. The generalization was verified by both quantitative and qualitative evaluation. The quantitative evaluation was conducted to reproduce pedestrian motion in three different datasets, HBS, CITR, and DUT. It also compared two different ways of calibrating the model parameters. The qualitative evaluation examined the simulation of collective pedestrian motion in a series of fundamental vehicle-pedestrian interaction scenarios. The above evaluation results demonstrated the effectiveness of the proposed model.