Framework for Assessing Potential Safety Impacts of Automated Driving Systems (2022)

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1   Quickly advancing automated driving system (ADS) technologies (typically, SAE auto­ mation Levels 3 through 5) are expected to positively affect transportation safety. ADS includes a plethora of applications that affect safety, mobility, human factors, and environmental aspects of driving. One anticipated disruption is the type of ADS­related crash (both in likelihood and severity) when compared to traditional vehicle crashes. For example, unlike human drivers, ADSs are projected to have a lower probability of certain crashes in traffic due to lower susceptibility to performance issues a human operator may face (distracted driving, drowsiness, etc.). While ADSs have the potential to improve safety, humans still have advan­ tages in certain roadway conditions, such as operating on roads with degraded lane markings and during adverse weather (e.g., heavy rain, snow, ice), two conditions with which the ADS has challenges. Analysis of the effects of ADS on safety, traffic flow, and other considerations depends on available data. Data sources revealing ADS safety performance are not publicly available, and this lack of data makes this analysis challenging. However, there are other data sources (typi­ cally, SAE Levels 1 and 2 automation) that can be used as a strong proxy in the analysis. To forecast safety impacts of ADS with limited data, it is important to understand the underlying factors that influence safety, such as enabling technologies (e.g., sensors, communications), human­machine interaction, and vehicle­to­infrastructure interactions. A framework can help state and local agencies assess when their traditional safety processes and procedures may be affected and characterize the safety impacts of competing options. Such a framework will sup­ port a smoother transition to ADS transportation. This framework should include processes and procedures to facilitate the safe, phased integration of ADS under different contexts, time­ frames, risks, and opportunities. This report describes a framework to help state and local agencies assess the safety impact of ADS. It will guide state and local agencies on how to adapt the framework for a variety of scenarios. It starts with an introduction and a high­level background on various technology terms in Chapter 1. Chapter 2 covers the effects of ADS on transportation safety by discussing aspects of the current safety landscape, crash data, and ADS performance. Chapter 3 delves into the six steps of the framework and the key steps that state and local agencies can follow to assess the safety impact of ADS applications. Chapter 4 presents the results of pilot studies of the framework with two state departments of transportation. Finally, Chapter 5 concludes the report with the key points that need to be considered when applying the framework. The first step in the framework’s process identifies ADS features that agencies would like to assess based on their safety concerns, strategic highway safety plans, and commercial deploy­ ment plans. This is followed by a thorough understanding of functionalities of the ADS appli­ cations of interest. It also suggests estimating the expected market penetration rates of the S U M M A R Y Framework for Assessing Potential Safety Impacts of Automated Driving Systems

2 Framework for Assessing Potential Safety Impacts of Automated Driving Systems ADS under assessment to better estimate potential impacts. In most cases, the ADS devel­ oper describes the physical and environmental boundaries within which a particular function is designed to work. In other words, the developer specifies the operational design domain (ODD) of the feature. This ODD helps to define the deployment scenarios in the third step. Once the deployment scenarios are defined, it is important to understand the technology and its infrastructure dependencies, which helps to recognize the risks and opportunities involved. With the scenarios identified, the fourth step is to define safety goals and the asso­ ciated hypotheses. Step 5 is identifying data sources and metrics to evaluate the hypotheses formulated in the fourth step. The analysis method is also chosen during this step to derive insights from the data. Upon completion of the analysis, Step 6 is to communicate the results and share the safety impact of the ADS feature to support decision­making. Chapter 4 shows the practical application of the framework by summarizing the results of a proof­of­concept study that involved piloting the framework in partnership with two state departments of transportation. One study evaluated a low­speed shuttle scenario in Minnesota, and the other evaluated the potential deployment of ADS­equipped trucks in Virginia’s I­81 corridor. This framework was refined throughout the course of this project based on feedback from stakeholder engagement (obtained in research Phase 3) and the proof­of­concept pilots (obtained in research Phase 4).