Automated vehicle collisions in California: Applying Bayesian latent class model
Automated vehicle collisions in California: Applying Bayesian latent class model
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DOI：
10.1016/j.iatssr.2020.03.001
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发表时间：
2020-12-01
期刊：
影响因子：
3.2
通讯作者：
Tsapakis, Ioannis
中科院分区：
文献类型：
--
作者：
Das, Subasish;Dutta, Anandi;Tsapakis, Ioannis
自动驾驶汽车（AV）的新兴技术一直在快速发展，并伴随着各种积极和消极的潜力。预计新技术将主要通过减少碰撞次数和旅行时间以及提高燃油效率和停车效益来影响成本。另一方面，AV部署的安全性结果是一个关键问题。确保自动驾驶汽车的安全需要采用多学科方法来监控这些车辆的各个方面。近年来，加州机动车管理局已强制要求公开自动驾驶汽车碰撞报告。本研究收集了不同制造商提交的扫描碰撞报告，这些制造商正在评估加州的自动驾驶汽车（2014年9月至2019年5月）。收集到的数据提供了有关无人驾驶汽车碰撞频率和相关影响因素的关键信息。本研究对关键变量进行了深入的探索性分析。我们证明了贝叶斯潜在类模型的变分推理算法。贝叶斯潜在类别模型确定了六类碰撞模式。与转弯、多车碰撞、路灯暗照明条件以及侧擦和追尾碰撞相关的类别也与较高比例的伤害严重程度水平相关。作者预计，这些结果将为AV和安全性结局领域做出重大贡献。(C)2020年国际交通与安全科学协会。制作和主办：Elsevier Ltd.
The emerging technology of automated vehicles (AV) has been rapidly advancing and is accompanied by various positive and negative potentials. The new technology is expected to affect costs mainly by reducing the number of collisions and travel time, as well as improving fuel efficiency and parking benefits. On the other hand, safety outcomes from AV deployment is a critical issue. Ensuring the safety of AVs requires a multi-disciplinary approach that monitors every aspect of these vehicles. The California Department of Motor Vehicles has mandated that AV collision reports be made public in recent years. This study collected the scanned collision reports filed by different manufacturers that are assessing AVs in California (September 2014 to May 2019). The collected data offers critical information on AV collision frequencies and associated contributing factors. This study provides an in-depth exploratory analysis of the critical variables. We demonstrated a variational inference algorithm for Bayesian latent class models. The Bayesian latent class model identified six classes of collision patterns. Classes associated with turning, multi-vehicle collisions, dark lighting conditions with streetlights, and sideswipe and rear-end collisions were also associated with a higher proportion of injury severity levels. The authors anticipate that these results will provide a significant contribution to the area of AV and safety outcomes. (C) 2020 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd.