Human injury-based safety decision of automated vehicles
Summary: Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protecti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-08-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004222009750 |
| _version_ | 1828763273103671296 |
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| author | Qingfan Wang Qing Zhou Miao Lin Bingbing Nie |
| author_facet | Qingfan Wang Qing Zhou Miao Lin Bingbing Nie |
| author_sort | Qingfan Wang |
| collection | DOAJ |
| description | Summary: Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protection, we propose an injury risk mitigation-based decision-making algorithm. The algorithm is guided by a real-time, data-driven human injury prediction model and is assessed using detailed first-hand information collected from real-world crashes. The results demonstrate that integrating injury prediction into decision-making is promising for reducing traffic casualties. Because safety decisions involve harm distribution for different participants, we further analyze the potential ethical issues quantitatively, providing a technically critical step closer to settling such dilemmas. This work demonstrates the feasibility of applying mining tools to identify the underlying mechanisms embedded in crash data accumulated over time and opens the way for future AVs to facilitate optimal road traffic safety. |
| first_indexed | 2024-12-11T01:57:59Z |
| format | Article |
| id | doaj.art-e8492d8d4ff943aea0a83bef8a210561 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-11T01:57:59Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-e8492d8d4ff943aea0a83bef8a2105612022-12-22T01:24:35ZengElsevieriScience2589-00422022-08-01258104703Human injury-based safety decision of automated vehiclesQingfan Wang0Qing Zhou1Miao Lin2Bingbing Nie3State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaChina Automotive Technology & Research Center (CATARC), Tianjin 300399, ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China; Corresponding authorSummary: Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protection, we propose an injury risk mitigation-based decision-making algorithm. The algorithm is guided by a real-time, data-driven human injury prediction model and is assessed using detailed first-hand information collected from real-world crashes. The results demonstrate that integrating injury prediction into decision-making is promising for reducing traffic casualties. Because safety decisions involve harm distribution for different participants, we further analyze the potential ethical issues quantitatively, providing a technically critical step closer to settling such dilemmas. This work demonstrates the feasibility of applying mining tools to identify the underlying mechanisms embedded in crash data accumulated over time and opens the way for future AVs to facilitate optimal road traffic safety.http://www.sciencedirect.com/science/article/pii/S2589004222009750InjuryApplied computingHuman-computer interactionEngineering |
| spellingShingle | Qingfan Wang Qing Zhou Miao Lin Bingbing Nie Human injury-based safety decision of automated vehicles iScience Injury Applied computing Human-computer interaction Engineering |
| title | Human injury-based safety decision of automated vehicles |
| title_full | Human injury-based safety decision of automated vehicles |
| title_fullStr | Human injury-based safety decision of automated vehicles |
| title_full_unstemmed | Human injury-based safety decision of automated vehicles |
| title_short | Human injury-based safety decision of automated vehicles |
| title_sort | human injury based safety decision of automated vehicles |
| topic | Injury Applied computing Human-computer interaction Engineering |
| url | http://www.sciencedirect.com/science/article/pii/S2589004222009750 |
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