Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866917736388493312 |
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| author | Ma, Lin Chen, Longrui Zhang, Yan Chu, Mengdi Jiang, Wenjie Shen, Jiahao Li, Chuxuan Shi, Yifeng Luo, Nairui Yuan, Jirui Zhou, Guyue Gong, Jiangtao |
| author_facet | Ma, Lin Chen, Longrui Zhang, Yan Chu, Mengdi Jiang, Wenjie Shen, Jiahao Li, Chuxuan Shi, Yifeng Luo, Nairui Yuan, Jirui Zhou, Guyue Gong, Jiangtao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_08731 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework Ma, Lin Chen, Longrui Zhang, Yan Chu, Mengdi Jiang, Wenjie Shen, Jiahao Li, Chuxuan Shi, Yifeng Luo, Nairui Yuan, Jirui Zhou, Guyue Gong, Jiangtao Artificial Intelligence Human-Computer Interaction Robotics 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. |
| title | Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework |
| topic | Artificial Intelligence Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2210.08731 |