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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2407.09466 |
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| _version_ | 1866913428951531520 |
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| author | Thalapanane, Sandeep Kumar, Sandip Sharan Senthil Peethambari, Guru Nandhan Appiya Dilipkumar SriHari, Sourang Zheng, Laura Poveda, Julio Lin, Ming C. |
| author_facet | Thalapanane, Sandeep Kumar, Sandip Sharan Senthil Peethambari, Guru Nandhan Appiya Dilipkumar SriHari, Sourang Zheng, Laura Poveda, Julio Lin, Ming C. |
| contents | Data for training learning-enabled self-driving cars in the physical world are typically collected in a safe, normal environment. Such data distribution often engenders a strong bias towards safe driving, making self-driving cars unprepared when encountering adversarial scenarios like unexpected accidents. Due to a dearth of such adverse data that is unrealistic for drivers to collect, autonomous vehicles can perform poorly when experiencing such rare events. This work addresses much-needed research by having participants drive a VR vehicle simulator going through simulated traffic with various types of accidental scenarios. It aims to understand human responses and behaviors in simulated accidents, contributing to our understanding of driving dynamics and safety. The simulation framework adopts a robust traffic simulation and is rendered using the Unity Game Engine. Furthermore, the simulation framework is built with portable, light-weight immersive driving simulator hardware, lowering the resource barrier for studies in autonomous driving research.
Keywords: Rare Events, Traffic Simulation, Autonomous Driving, Virtual Reality, User Studies |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TRAVERSE: Traffic-Responsive Autonomous Vehicle Experience & Rare-event Simulation for Enhanced safety Thalapanane, Sandeep Kumar, Sandip Sharan Senthil Peethambari, Guru Nandhan Appiya Dilipkumar SriHari, Sourang Zheng, Laura Poveda, Julio Lin, Ming C. Robotics Graphics Data for training learning-enabled self-driving cars in the physical world are typically collected in a safe, normal environment. Such data distribution often engenders a strong bias towards safe driving, making self-driving cars unprepared when encountering adversarial scenarios like unexpected accidents. Due to a dearth of such adverse data that is unrealistic for drivers to collect, autonomous vehicles can perform poorly when experiencing such rare events. This work addresses much-needed research by having participants drive a VR vehicle simulator going through simulated traffic with various types of accidental scenarios. It aims to understand human responses and behaviors in simulated accidents, contributing to our understanding of driving dynamics and safety. The simulation framework adopts a robust traffic simulation and is rendered using the Unity Game Engine. Furthermore, the simulation framework is built with portable, light-weight immersive driving simulator hardware, lowering the resource barrier for studies in autonomous driving research. Keywords: Rare Events, Traffic Simulation, Autonomous Driving, Virtual Reality, User Studies |
| title | TRAVERSE: Traffic-Responsive Autonomous Vehicle Experience & Rare-event Simulation for Enhanced safety |
| topic | Robotics Graphics |
| url | https://arxiv.org/abs/2407.09466 |