Efficient Safety Verification of Autonomous Vehicles with Neural Network Operator
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917124611506176 |
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| author | Fan, Lingxiang He, Linxuan Ji, Haoyuan Feng, Shuo |
| author_facet | Fan, Lingxiang He, Linxuan Ji, Haoyuan Feng, Shuo |
| contents | When autonomous vehicles encounter untrained scenarios, ensuring safety hinges on effective safety verification to prevent accidents stemming from unexpected model decisions. Reachability analysis, a method of safety verification, offers relatively high precision but at the cost of significant computational complexity. Our method leverages end-to-end neural network operators to compute reachable sets, replacing traditional mathematical set operators, thereby achieving higher efficiency in safety verification without substantially compromising accuracy or increasing conservativeness. We define vehicle dynamics on discrete time series and detail the safety verification process and safety standard based on reachable sets. Experimental evaluations conducted in several typical road driving scenarios demonstrate the superior efficiency performance of our proposed operator over classical methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04557 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Safety Verification of Autonomous Vehicles with Neural Network Operator Fan, Lingxiang He, Linxuan Ji, Haoyuan Feng, Shuo Systems and Control When autonomous vehicles encounter untrained scenarios, ensuring safety hinges on effective safety verification to prevent accidents stemming from unexpected model decisions. Reachability analysis, a method of safety verification, offers relatively high precision but at the cost of significant computational complexity. Our method leverages end-to-end neural network operators to compute reachable sets, replacing traditional mathematical set operators, thereby achieving higher efficiency in safety verification without substantially compromising accuracy or increasing conservativeness. We define vehicle dynamics on discrete time series and detail the safety verification process and safety standard based on reachable sets. Experimental evaluations conducted in several typical road driving scenarios demonstrate the superior efficiency performance of our proposed operator over classical methods. |
| title | Efficient Safety Verification of Autonomous Vehicles with Neural Network Operator |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.04557 |