Mitigating Vulnerable Road Users Occlusion Risk Via Collective Perception: An Empirical Analysis
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913310295719936 |
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| author | Wolff, Vincent Albert Xhoxhi, Edmir |
| author_facet | Wolff, Vincent Albert Xhoxhi, Edmir |
| contents | Recent reports from the World Health Organization highlight that Vulnerable Road Users (VRUs) have been involved in over half of the road fatalities in recent years, with occlusion risk - a scenario where VRUs are hidden from drivers' view by obstacles like parked vehicles - being a critical contributing factor. To address this, we present a novel algorithm that quantifies occlusion risk based on the dynamics of both vehicles and VRUs. This algorithm has undergone testing and evaluation using a real-world dataset from German intersections. Additionally, we introduce the concept of Maximum Tracking Loss (MTL), which measures the longest consecutive duration a VRU remains untracked by any vehicle in a given scenario. Our study extends to examining the role of the Collective Perception Service (CPS) in VRU safety. CPS enhances safety by enabling vehicles to share sensor information, thereby potentially reducing occlusion risks. Our analysis reveals that a 25% market penetration of CPS-equipped vehicles can substantially diminish occlusion risks and significantly curtail MTL. These findings demonstrate how various scenarios pose different levels of risk to VRUs and how the deployment of Collective Perception can markedly improve their safety. Furthermore, they underline the efficacy of our proposed metrics to capture occlusion risk as a safety factor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07753 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating Vulnerable Road Users Occlusion Risk Via Collective Perception: An Empirical Analysis Wolff, Vincent Albert Xhoxhi, Edmir Networking and Internet Architecture Machine Learning Robotics Recent reports from the World Health Organization highlight that Vulnerable Road Users (VRUs) have been involved in over half of the road fatalities in recent years, with occlusion risk - a scenario where VRUs are hidden from drivers' view by obstacles like parked vehicles - being a critical contributing factor. To address this, we present a novel algorithm that quantifies occlusion risk based on the dynamics of both vehicles and VRUs. This algorithm has undergone testing and evaluation using a real-world dataset from German intersections. Additionally, we introduce the concept of Maximum Tracking Loss (MTL), which measures the longest consecutive duration a VRU remains untracked by any vehicle in a given scenario. Our study extends to examining the role of the Collective Perception Service (CPS) in VRU safety. CPS enhances safety by enabling vehicles to share sensor information, thereby potentially reducing occlusion risks. Our analysis reveals that a 25% market penetration of CPS-equipped vehicles can substantially diminish occlusion risks and significantly curtail MTL. These findings demonstrate how various scenarios pose different levels of risk to VRUs and how the deployment of Collective Perception can markedly improve their safety. Furthermore, they underline the efficacy of our proposed metrics to capture occlusion risk as a safety factor. |
| title | Mitigating Vulnerable Road Users Occlusion Risk Via Collective Perception: An Empirical Analysis |
| topic | Networking and Internet Architecture Machine Learning Robotics |
| url | https://arxiv.org/abs/2404.07753 |