Autonomous driving tends to be an imperative technology to facilitate smart transportation, where vehicular automation plays a prominent role. To accelerate its development, the Society of Automotive Engineers (SAE) regularized the automation to different SAE levels and successfully promoted the research and products to improve road safety, efficiency, and convenience. Meanwhile, advanced driver assistance systems (ADAS) and automated driving systems (ADS) are widely developed to support vehicular automation and achieve success in automation technology. However, safety risks always concern the developers and users and hinder the deployment of autonomous vehicles. Although the studies on the injury risk from autonomous vehicles are ongoing and extensive, limited current research compares ADAS and ADS, especially from a systematic perspective. We conduct this comparison study comprehensively. Different from existing works, we first incorporate multi-source data to ensure higher reliability of analysis results. Next, we conduct both descriptive statistics and statistical inference with random parameters multinomial logit model to analyze the interaction between investigated factors and observed crash data. Moreover, we compare the crash severity across different automation levels SAE L2-5 to further reveal the interaction between different factors. Given the analysis results, we find that different factors impact the injury severity between ADS and ADAS. The crashes from ADAS are more correlated to driver type, surface,object collided with, and the impact area. The ADS crashes are more associated with road type, pre-crash movement, impact area, and vehicle conditions. Our findings provide the insights into safety outcomes of current ADS and ADAS, which helps stakeholders better explore automated vehicle safety for accelerating the deployment of autonomous vehicles.

翻译：自动驾驶是推动智能交通发展的关键技术，其中车辆自动化扮演着重要角色。为加速其发展，美国汽车工程师学会（SAE）将自动化划分为不同SAE级别，并成功推动了提升道路安全、效率和便利性的研究及产品。与此同时，高级驾驶辅助系统（ADAS）和自动驾驶系统（ADS）被广泛开发以支持车辆自动化，并在自动化技术领域取得了成功。然而，安全风险始终是开发者和用户关注的焦点，也阻碍了自动驾驶汽车的部署。尽管关于自动驾驶车辆伤害风险的研究正在广泛开展，但当前从系统视角对ADAS与ADS进行比较的研究仍然有限。我们全面开展了这项对比研究。与现有工作不同，我们首先整合了多源数据以确保分析结果的更高可靠性。其次，我们采用随机参数多项逻辑回归模型进行描述性统计和统计推断，以分析研究因素与观测碰撞数据之间的相互作用。此外，我们比较了不同自动化级别（SAE L2-5）的碰撞严重程度，以进一步揭示不同因素间的交互作用。基于分析结果，我们发现影响ADS与ADAS伤害严重程度的因素不同。ADAS碰撞与驾驶员类型、路面状况、碰撞物体及碰撞区域的相关性更强；而ADS碰撞则与道路类型、碰撞前运动状态、碰撞区域及车辆状况更相关。我们的研究结果为当前ADS与ADAS的安全性能提供了见解，有助于利益相关方更好地探索自动驾驶汽车安全，从而加速自动驾驶汽车的部署。