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

• URL: http://arxiv.org/abs/2210.08731v4
• Date: Tue, 30 Jul 2024 00:01:37 GMT
• Title: Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework
• Authors: Lin Ma, Longrui Chen, Yan Zhang, Mengdi Chu, Wenjie Jiang, Jiahao Shen, Chuxuan Li, Yifeng Shi, Nairui Luo, Jirui Yuan, Guyue Zhou, Jiangtao Gong,
• Abstract summary: This paper proposes a pedestrian safety evaluation method for autonomous driving. We construct a high-fidelity simulation framework embedded with pedestrian safety-critical characteristics. The proposed simulation method and framework can be used to access different autonomous driving algorithms.
• Score: 21.456269382916062
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 (V2I) cooperative perception. The results show that our framework can evaluate different autonomous driving algorithms with detailed and quantitative pedestrian safety indexes. 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.

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