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 (L0 - L5) 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, the 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 lightning, surface, the object collided with, and the impact area. The ADS crashes are more associated with road type, drive type, impact area, and weather 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：自动驾驶正成为推动智能交通的关键技术，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等级（L0-L5），并成功推动了改善道路安全、效率和便利性的研究与产品。与此同时，先进驾驶辅助系统（ADAS）和自动驾驶系统（ADS）被广泛开发以支持车辆自动化，并在自动化技术方面取得了成功。然而，安全风险始终困扰着开发者和用户，并阻碍了自动驾驶汽车的部署。尽管关于自动驾驶汽车受伤风险的研究正在广泛开展，但目前从系统角度比较ADAS和ADS的文献有限。我们全面开展了这项比较研究。与现有工作不同，我们首先整合多源数据以确保分析结果具有更高的可靠性。其次，我们采用随机参数多项Logit模型进行描述性统计和统计推断，以分析所研究因素与观测碰撞数据之间的交互作用。此外，我们比较了不同SAE L2-5自动化等级间的碰撞严重性，以进一步揭示不同因素间的交互作用。基于分析结果，我们发现不同因素对ADS和ADAS造成的受伤严重性影响存在差异。ADAS相关碰撞与照明、路面、碰撞物体及碰撞区域的相关性更强；而ADS相关碰撞则与道路类型、驱动方式、碰撞区域和天气条件更为相关。我们的研究结果为当前ADS和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性，从而加速自动驾驶汽车的部署。