Ma, Lin (Southwestern University of Finance and Economics), Chen, Longrui (Imperial college london), Zhang, Yan (University of Melbourne), Chu, Mengdi (Tsinghua University), Jiang, Wenjie (Institute for AI Industry Research, Tsinghua University), Shen, Jiahao (University of Science and Technology Beijing), Li, Chuxuan (Tsinghua University), Shi, Yifeng (baidu), Luo, Nairui (Baidu, Inc.), Yuan, Jirui (Tsinghua University), ZHOU, Guyue (Tsinghua University), Gong, Jiangtao (Tsinghua University)

Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework

Scheduled for presentation during the Poster Session "Modeling, Simulation, and Control of Pedestrians and Cyclists II" (ThAT14), Thursday, September 26, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

Pedestrians’safety is a crucial factor in assessing autonomous driving scenarios. However, pedestrian safety evaluation is rarely considered by existing autonomous driving simulation platforms. This paper proposes a pedestrian safety evaluation method for autonomous driving, in which not only the collision events but also the conflict events together with the characteristics of pedestrians are fully considered. Moreover, to apply the pedestrian safety evaluation system, we construct a high-fidelity simulation framework embedded with pedestrian safety-critical characteristics. We demonstrate our simulation framework and pedestrian safety evaluation with a comparative experiment with two kinds of autonomous driving perception algorithms---single-vehicle perception and vehicle. to-infrastructure (V2l) cooperative perception, The results show that our framework can evaluate different autonomous driving algorithms with detailed and quantitative pedestrian safetyindexes. To this end, the proposed simulation method and framework can be used to access different autonomous driving algorithms and evaluate pedestrians' safety performance in future autonomous driving simulations, which can inspire more pedestrian-friendly autonomous driving algorithms.