Superior safety is the main banner value of promoting autonomous vehicle (AV) technology, but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks would be overshadowed by technological limitations, regardless of their ability to mitigate or eliminate human error. This study aims to identify the key factors affecting crash severity by analyzing real-world AV crash data from the U.S. between 2015 and 2022. We integrated two open data sources from the California DMV and NHTSA. Mixed multinomial logit models incorporating the interaction effects were estimated using the crash severity level, addressing the observed and unobserved heterogeneities. Our results show that Advanced Driver Assistance System (ADAS) engagement is associated with a higher likelihood of slight injury crashes, whereas Automated Driving System (ADS) engagements show the opposite effect. In addition, we found that various environmental and road factors, including lighting conditions, weather, road type, and road surface conditions, significantly affect the severity of AV crashes. For instance, daylight conditions contribute to a lower likelihood of slight-injury crashes. On the other hand, driving under unfavorable weather conditions (cloudy, foggy, rainy, or snowy), on the highway, and on non-dry road surfaces are associated with an increase in the likelihood of severe injury crashes. Furthermore, several significant interaction effects were revealed. First, the mitigating effect of ADS engagement on the likelihood of slight injury crashes is reduced by the rear-end collision type. Second, the likelihood of slight injury crashes increases when AV interacts with heavy trucks on highways. Third, the likelihood of severe injuries increases when AVs collide with Vulnerable Road Users (VRUs) on urban streets. Overall, this research is expected to provide policymakers and AV manufacturers with valuable insights into AV safety, stressing that addressing the identified factors will lead to improved AV design and control algorithms.

中文翻译：

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从两个 AV 事故记录来源对影响自动驾驶汽车碰撞严重程度的因素的新见解


卓越的安全性是推广自动驾驶汽车 （AV） 技术的主要旗帜价值，但很难负责任地声称它。自动驾驶汽车降低碰撞和伤害风险的潜力将被技术限制所掩盖，无论它们减轻或消除人为错误的能力如何。本研究旨在通过分析 2015 年至 2022 年间美国的真实 AV 碰撞数据，确定影响碰撞严重程度的关键因素。我们集成了来自加州 DMV 和 NHTSA 的两个开放数据源。使用碰撞严重程度级别估计包含交互效应的混合多项式 logit 模型，解决观察到的和未观察到的异质性。我们的结果表明，高级驾驶辅助系统 （ADAS） 接合与轻微受伤碰撞的可能性较高相关，而自动驾驶系统 （ADS） 接合则显示出相反的效果。此外，我们发现各种环境和道路因素，包括照明条件、天气、道路类型和路面状况，都会显着影响 AV 碰撞的严重程度。例如，日光条件有助于降低轻微受伤碰撞的可能性。另一方面，在不利的天气条件（多云、雾、雨或雪）、高速公路和非干燥路面上驾驶与严重受伤事故的可能性增加有关。此外，还揭示了几个重要的交互效应。首先，追尾碰撞类型降低了 ADS 接合对轻微伤害碰撞可能性的缓解作用。其次，当 AV 在高速公路上与重型卡车互动时，发生轻伤事故的可能性会增加。 第三，当 AV 在城市街道上与弱势道路使用者 （VRU） 相撞时，重伤的可能性会增加。总体而言，这项研究有望为政策制定者和 AV 制造商提供有关 AV 安全的宝贵见解，强调解决已确定的因素将导致改进 AV 设计和控制算法。