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：自动驾驶旨在成为推动智慧交通的关键技术，其中车辆自动化发挥着重要作用。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与产品。与此同时，高级驾驶辅助系统（ADAS）和自动驾驶系统（ADS）被广泛开发以支撑车辆自动化，并在自动化技术领域取得突破。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。尽管针对自动驾驶车辆伤害风险的研究仍在持续且广泛开展，但现有研究中系统对比ADAS与ADS的成果有限。我们全面开展了此项对比研究。与现有工作不同，我们首先整合多源数据以确保分析结果的更高可靠性。其次，采用随机参数多项Logit模型进行描述性统计与统计推断，分析所研究因素与观测碰撞数据之间的交互作用。此外，我们对比了不同SAE L2-5级别的碰撞严重程度，以进一步揭示不同因素间的相互作用。基于分析结果发现，ADS与ADAS的碰撞伤害程度受不同因素影响。ADAS相关碰撞与驾驶员类型、路面状况、碰撞物体及碰撞区域关联更密切，而ADS相关碰撞则与道路类型、碰撞前运动状态、碰撞区域及车辆状况关联更紧密。研究结果揭示了当前ADS与ADAS的安全性能特征，有助于利益相关方更深入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。