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

Chapter: Chapter 2 - ADS Impacts on Safety

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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
×
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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
×
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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
×
Page 11
Page 12
Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
×
Page 12
Page 13
Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
×
Page 13
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Suggested Citation:"Chapter 2 - ADS Impacts on Safety." National Academies of Sciences, Engineering, and Medicine. 2022. Framework for Assessing Potential Safety Impacts of Automated Driving Systems. Washington, DC: The National Academies Press. doi: 10.17226/26791.
×
Page 14

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6 Roadway stakeholders must understand the potential safety impacts of an automated driving system (ADS) before widespread deployment. Insights on safety can help provide inputs into current transportation planning, design, and operations processes. Both the partial and full deployment of ADS will affect current safety practices. This section discusses the current safety methods and processes, followed by matching certain crash types to specific ADS technologies, and concludes with ways in which ADS performance could impact safety. Current Understanding of Safety Landscape To best understand the safety impacts of ADS, studying and understanding the current safety landscape can be helpful. ADS will influence both safety and safety processes. Automating driving decisions will force the scope of safety evaluation to assess new inputs from a more interconnected safety system. Mode shifts between walking/cycling and ride hailing, evaluation of service afford­ ability and equitability, and ethics of autonomous decision­making must be factored into a holistic definition of surface transportation safety. New and existing tools may be adapted to account for the partial and full deployment of ADS. Recent studies (Matthews, 2018; LaChance, 2022) suggest a link between existing advanced driver assistance systems (ADASs) and improved safety measures for vehicle testing. As many consider ADS­equipped vehicles as iterations of ADAS­featured vehicles, initial research suggests that ADS­equipped vehicles will likely yield safer test results. Current ADS data may not be available, but ADAS has a testing foundation with standard development organizations (IIHS­HLDI, 2020). The following subsections describe the roadway safety management process and project development process. The roadway safety management process is a focused approach to identify and address safety opportunities. The project development process is an opportunity to consider safety alongside other factors (e.g., project costs, traffic operations, mobility and accessibility, economic impacts, social equity, and envi­ ronmental impacts) when planning, designing, and maintaining roadway facilities. Both sec­ tions describe opportunities for considering the safety impacts of ADS and for adapting current safety processes to address ADS safety. Roadway Safety Management Process Figure 1 illustrates the six­step safety management process, starting with network screening and ending with safety effectiveness evaluation. 1: Network Screening Network screening is the process of analyzing the entire roadway network to identify poten­ tial sites or issues for further investigation. It is not possible to conduct a detailed assessment of C H A P T E R 2 ADS Impacts on Safety

ADS Impacts on Safety 7   the entire network, so network screening is used to pare down the network to a manageable list. There are two types of screening. 1. Site-specific: The objective of site­specific screening is to identify specific sites for further analysis (typically those with high crashes or overrepresented crashes). 2. Systemic: The objective of systemic screening is to identify factors that are common among crashes (typically a focus crash type) and are overrepresented in focus crashes compared to total crashes. While site­specific and systemic screening methods hold potential for identifying and addressing ADS­related safety opportunities, it may not be likely to find “hotspots” (i.e., specific locations with more crashes than expected) involving ADS­equipped vehicles. Instead, it seems more likely that crashes involving ADS­equipped vehicles could share common contributing factors. If this is the case, then there is the potential to employ systemic techniques to identify and address safety opportunities in the future. For example, if crashes involving ADS­equipped vehicles are overrepresented on curves with no signing or pavement markings (or degraded signs and pave­ ment markings), then this could be identified as a factor that increases the risk of ADS­related crashes. Once contributing factors are identified, agencies could search for similar locations (e.g., curves with absent or degraded signs or pavement markings) and address the factors accordingly (e.g., enhance signing and pavement markings or implement a new strategy that would satisfy the needs of the ADS technology). 2: Diagnosis Diagnosis is the process of further investigating the opportunities (e.g., sites or risk factors) iden­ tified in Step 1 (network screening). The objective of diagnosis is to identify existing and potential safety opportunities. Diagnosis often involves a review of the crash history, traffic operations, and general site conditions as well as a field visit to observe road­user behaviors (including behaviors of ADS­equipped vehicles). It is important to consider contributing factors related to the road user, vehicle, roadway, and environment during diagnosis. It is also important to diagnose the underlying crash contributing factors before developing potential countermeasures. One tool to support this process is the Haddon Matrix. The Haddon Matrix, originally devel­ oped for injury prevention, is also directly applicable to highway safety in both diagnosis and Network Screening Diagnosis Countermeasure Selection Economic Appraisal Project Prioritization Safety Effectiveness Evaluation Figure 1. Six-step safety management process (Source: FHWA).

8 Framework for Assessing Potential Safety Impacts of Automated Driving Systems countermeasure selection (Haddon, 1972). For diagnosis, the Haddon Matrix is useful in gaining a comprehensive understanding of the human, vehicle, roadway, and environmental factors contributing to the frequency and severity of crashes before, during, and after the crash event. Then analysts can identify targeted reactive and proactive countermeasures to address or mitigate the underlying contributing factors. The Haddon Matrix is typically composed of nine cells to identify human, vehicle, and roadway factors contributing to the target crash type or severity outcome before, during, and after the crash. This can be expanded to 12 cells to include environmental factors as another column, or these factors could be included with the roadway factors. Table 1 presents an example of the Haddon Matrix with 12 cells to identify crash contributing factors. The contributing factors originate from careful review of police crash reports, review of design drawings and traffic operations, and observations during field investigations. Examples of human factors include distraction, fatigue, and seat belt use. Examples of vehicle factors include worn brakes, headrest design, and airbag operation. With ADS­equipped vehicles, vehicle factors might also include some failure of the ADS technology. Examples of roadway factors include sharp curve, lack of curve signing, and steep grade. With ADS­equipped vehicles, roadway factors might also include the degraded quality of signs and pavement markings that are no longer detectable by the ADS. Examples of environmental factors include reduced pavement friction (e.g., wet or icy roads) and weather­limiting visibility (e.g., snow, fog, heavy rain). These factors would also apply to ADS­equipped vehicles. 3: Countermeasure Selection Countermeasure selection is the process of identifying and assessing ways to address or mitigate the underlying contributing factors identified in Step 2 (diagnosis). Countermeasures should directly target the contributing factors, and may include engineering, education, enforcement, and emergency medical service­related measures (i.e., the 4E approach). Several resources are available to help in selecting appropriate countermeasures, but they will need to be updated to reflect strategies for addressing ADS­related crashes. When selecting countermeasures for crashes involving traditional (non­ADS) vehicles, there is a need to consider the potential impacts on ADS­equipped vehicles. For example, stop ahead warning signs and pavement markings are one strategy to improve driver awareness of an approaching stop condition. Would this be expected to have a safety benefit, disbenefit, or no benefit for ADS­equipped vehicles? The likely answer is, “It depends.” Specifically, it depends on the ability and reliability of the ADS to detect and correctly interpret the meaning of the sign or pavement marking. If the ADS can perform this task reliably, then it may provide a benefit. If the ADS interprets the symbol for the stop ahead sign (Figure 2) as an actual stop sign, and performs a stop maneuver, then this could lead to a safety disbenefit (e.g., rear­end crashes if other drivers are not expecting the vehicle to stop at that location). If the ADS is preprogrammed with high­ fidelity maps of the roadway segments and intersections, then the sign may provide no additional benefit as the ADS already “knows” of the approaching intersection. Period Human Factors Vehicle Factors Roadway Factors Environmental Factors Before (causes of hazardous situation) During (causes of crash severity) After (factors of crash outcome) Table 1. Twelve-cell Haddon Matrix template.

ADS Impacts on Safety 9   4: Economic Appraisal Economic appraisal is the process of comparing the relative costs and benefits of the various alternatives. It is often not feasible or practical to implement all of the identified countermeasures. As such, it is necessary to estimate the cost and expected benefits of each potential countermeasure. The cost of projects is usually straightforward, but estimating the potential safety benefits is a new component of the data­driven and quantitative safety management process. The Highway Safety Manual (HSM) presents a predictive method to estimate the safety performance of a roadway facility under different conditions. Crash modification factors (CMFs) can be used in the pre­ dictive method to estimate the expected change in crashes after the implementation of a given countermeasure. The CMF Clearinghouse (www.cmfclearinghouse.org) is the primary source of CMFs, including those presented in the HSM. As ADS­equipped vehicles become more preva­ lent, the safety impact of common countermeasures may change. For example, widening the clear zone along the roadside may not have as great of an impact if there are fewer roadway departures as a result of lane­keeping technologies. In the future, CMFs will need to be updated to reflect the safety impact of a given countermeasure, considering the penetration rate of ADS­equipped vehicles in the fleet. Below, the “Project Development Process” section discusses the predictive method and opportunities to assess the impacts of ADS. 5: Project Prioritization Project prioritization is the process of developing a portfolio of projects for a given fiscal year. The final choice of projects is based on the available budget as well as other factors such as agency goals, political pressure, and public acceptance. Project prioritization should consider the safety benefits (and benefits of other factors) under different ADS deployment scenarios. For example, adding a dedicated lane for trucks may seem like a good investment to improve safety and mobility assuming the current vehicle fleet; however, automated trucks that can operate in mixed traffic lanes may provide similar benefits without the need for an added lane. Project Development Process The project development process is broader than the safety management process, but the two are related. Figure 3 (Gross et al., 2021) illustrates the relation between the two, whereby the six­ step safety management process is condensed to three basic components: planning (which includes Steps 1 through 5 of the safety management process), implementation (which includes the design and construction or implementation of projects), and evaluation (which is Step 6 of the safety Figure 2. Examples of stop ahead signs (Source: VHB).

10 Framework for Assessing Potential Safety Impacts of Automated Driving Systems management process). Once an agency plans projects and allocates resources to implement them, the project (safety­focused or otherwise) enters the broader project development process. Every phase of the project development process has potential impacts on safety performance. For example, during the planning phase, an agency might consider high­level alternatives such as different roadway cross sections (six­lane undivided, six­lane median­divided, or five­lane with two­way left­turn lane). During the design phase, an agency might consider the detailed aspects of one or more alternatives, including the lane and shoulder width, median width, presence and length of turn lanes, and presence and placement of signs and markings. Agencies can use the HSM and safety analysis tools, such as the Interactive Highway Safety Design Model (IHSDM) and AASHTO Ware Safety Analyst™, to assess the safety performance during the planning, design, and operations stages of a project. Specifically, the HSM provides a predictive method to estimate the frequency and severity of crashes based on the design and operations of the facility of inter­ est. Refer to “Evaluation Method” in Chapter 3 for more details on the potential use of IHSDM. Chapter 4 provides a proof of concept that demonstrates the use of IHSDM in the framework. While the HSM and related tools provide equations to predict crashes, these equations are based on data prior to 2010 and do not capture the impacts of ADS. The predictive methods can also incorporate historical crashes as a part of the prediction. Again, the historical crash data reflect a fleet of traditional vehicles with limited ADS features. With the widespread deploy­ ment of ADS, the existing models for predicting crashes may not be accurate and relevant for predicting future crashes under different ADS deployment scenarios. The predictive methods will need to be updated or calibrated to reflect different deployment scenarios with data or assumptions that represent the safety impacts of ADS­equipped vehicles. Until then, the frame­ work presented in this report can serve as a planning­level tool to help estimate the potential safety performance of ADS under different scenarios and inform related infrastructure invest­ ment decisions by IOOs. 6: Safety Effectiveness Evaluation Safety effectiveness evaluation is the process of estimating the safety impacts of implemented projects. This is the final step of the safety management process but provides a critical feedback link for future planning. Evaluation can and should be conducted at various levels. 1. Program Level: The objective of program evaluation is to determine the effectiveness of the overall safety program. The primary performance measures are the number and rate of crashes, injuries, and fatalities on the network. Program evaluation may also include the Figure 3. Relation between safety management and project development processes (Source: FHWA).

ADS Impacts on Safety 11   assessment of specific programs such as intersection safety, roadway departure, and pedes­ trian safety. If the agency is targeting roadway departure crashes, then it might be appropriate to compare the number and cost of related safety improvement projects each year to the number and trend in roadway departure crashes, injuries, and fatalities. 2. Project Level: The objective of project evaluation is to determine the effectiveness of individual projects or groups of similar projects. For example, the agency may have installed rumble strips on several sections of two­lane roads. A project­level evaluation could be conducted to determine the safety impacts of each individual rumble strip project. These projects could also be combined to determine the average impact of rumble strips on two­lane roads. This is how CMFs are developed. Regardless of the level of evaluation, it is important to account for other factors that change over time and could impact safety. For example, if ADS­equipped vehicles are becoming more prevalent and improving safety in general, then it would be important to account for this effect during the evaluation. Figure 4 illustrates a typical project evaluation and a hypothetical ADS effect that should be considered. If analysts only focus on the change in crashes before and after the project, they might erroneously conclude that the project was successful in improving safety at the project site. By considering the background trendline in ADS deployment and the cor­ responding trendline in crashes at other nearby locations, it may be more accurate to conclude that the safety improvement at the project site could be due, at least in part, to ADS deployment. Map Crash Data to ADS Functionality Specific ADS functionalities can be linked to increases or decreases of certain crash types. Crash likelihood and severity are expected to decrease; however, crash populations will change as ADS behaves differently than humans behave. These relationships between crashes and ADS functionality need to be identified in order to identify ADS impacts on safety, assess ADS perfor­ mance, and inform future investment decisions. ADS­equipped vehicles are thought to be safer than non­ADS­equipped vehicles, but this has not been rigorously assessed and uncertainty remains. Further, ADS technologies may be limited to operation in specific operational design domains (ODDs) and may target specific 15 13 14 7 6 5 0% 4% 8% 12% 16% 20% 0 10 20 30 40 50 60 70 80 90 100 2013 2014 2015 2016 2017 2018 2019 AD S Ve hi cl e Pe ne tr ati on (% o f fl ee t) Cr as he s Year Crashes at Project Site Before Crashes at Project Site After Crashes at Nearby Sites ADS Deployment Figure 4. Hypothetical safety evaluation (Source: VHB).

12 Framework for Assessing Potential Safety Impacts of Automated Driving Systems crash types. As such, we need to consider the crash types and contributing factors that could be addressed by each ADS technology, individually and in combination, within the facility types where the technology is most likely to operate and under different deployment scenarios. Table 2 indicates potential crash types impacted by different ADAS features and the expected direction of effect (i.e., downward arrow indicates expected reduction and upward arrow indi­ cates expected increase). These potential impacts are based on a single ADAS feature in the vehicle and are not based on a suite of ADAS features. They are potentially indicative of ADS safety performance. In some cases, ADAS features may be equipped and operated on ADS­equipped vehicles, so the safety benefits for an individual ADS feature may be difficult to determine directly. For example, lane­keeping assist is expected to reduce the frequency of roadway departure crashes. In some cases, the ADS technology could reduce certain crash types while potentially increasing others. For example, automatic emergency braking is expected to reduce rear­end crashes where the trailing vehicle is ADS­equipped but could increase rear­end crashes where the lead vehicle is ADS equipped and the trailing vehicle is not. In other cases, the ADS technology may only apply to certain vehicle types. For example, if autopilot is employed in a fleet of heavy trucks (i.e., ADS­equipped trucks), then the impact on crash types would include the subset of respective truck­related crashes. Note the information in Table 2 is based on current knowledge of ADS feature capabilities and judgment of the expected effects. This should be verified through pilots and supported by more empirical evidence. As more definitive research is conducted to quantify the relationships between ADS technologies and crash types or crash contributing factors, the information in this table should be updated. Table 2 does not explicitly address crash severity; however, the severity of a crash is related to the type of crash. Crashes involving higher speeds, high angles of impact, and more vulnerable road users are more likely to result in higher severity. The ADS technologies that are expected to reduce impact speeds, impact angles, and crashes with vulnerable road users will likely also ADAS Technology RE RA SS HO ROR PED B A P Active park assist Adaptive cruise control (ACC) Coordinated ACC Automatic emergency braking Autopilot Forward collision warning Lane-keeping assist Eco-approach/eco-departure Lane change warning Blind spot monitoring Backup warning Notes: RE = rear-end, RA = right angle, SS = sideswipe, HO = head-on, ROR = run-off-road, PED = pedestrian, B = bike, A = animal, and P = parking. — indicates no expected impact, indicates an expected reduction in the crash type, and an expected increase in the crash type. indicates Table 2. Potential safety impacts of ADAS features.

ADS Impacts on Safety 13   reduce the severity of those crashes as well. For example, automatic emergency braking, auto­ pilot, forward collision warning, and lane­keeping assist are associated with some of the more severe crash types (e.g., right­angle, head­on, run­off­road, pedestrian, and bicycle) and are therefore expected to reduce crash severity. Severity is also related to the age and frailty of the people involved in the crash and the vehicle safety rating. This will not necessarily have a disproportionate impact on crash types but could disproportionately impact safety for different driver populations. If higher­income families are more likely to own ADS­equipped vehicles, then these populations might recognize the greatest benefit. Beyond crash severity, ADS technologies may not operate in certain conditions (e.g., facility types or weather conditions). The information in Table 2 reflects the ideal conditions but does not indicate the performance and potential ineffectiveness of ADS technologies in certain condi­ tions. The next section, “ADS Performance,” provides more context in terms of the factors that may affect performance and could be used to identify subsets of crashes that are most applicable (or not applicable) to the specific technology. For example, lane­keeping assist is expected to reduce sideswipe, head­on, run­off­road, and bicycle crashes; however, if the technology does not work well in heavy rain or snow, then it would not have the potential to reduce crashes in those conditions. As such, the potential crash population would be a subset of sideswipe, head­on, run­off­road, and bicycle crashes (e.g., crashes in clear weather). ADS Performance ADS encapsulates hardware and software to perform the dynamic driving task (DDT). ADS uses a variety of sensor components and software to sense, model, plan, and act to operate an automobile with no input from the driver. This complex combination of activities to control the vehicle is performed by electronics and machinery that process inputs and control braking, acceleration, steering, and signaling instead of a human driver. As depicted in Figure 5, the SAE International standard defines six levels of driving automation (J3016). This standard has been adopted by the National Highway Traffic Safety Administration (SAE, 2018). ADS specifically includes Levels 3, 4, and 5 where Levels 3 and 4 are defined by an ODD. The ODD defines the operating conditions under which the ADS (system or feature) is designed to function and includes environmental, geographical, and time­of­day restrictions and/or the requisite presence or absence of certain traffic or roadway characteristics. ODD is typically defined by the ADS technology developer and original equipment manufacturer (OEM). The ODD would vary based on the ADS feature, maturity of technology, and readiness of infra­ structure (degraded signs or pavement markings). DDT fallback occurs when the ADS is unable to continue to perform the entire DDT (i.e., under normal operating conditions), For Level 3 ADS features, the human fallback­ready user is expected to respond to a request to intervene by either resuming manual driving if the vehicle remains drivable or achieving a minimal risk condition if the vehicle is not drivable. For a Level 4 or 5 ADS, the ADS feature or system performs the fallback by automatically achieving a minimal risk condition (SAE, 2018). For instance, if a Level 4 ADS feature is designed to operate a vehicle at high speeds on freeways and experiences a DDT performance­relevant system failure, then it would automatically remove the vehicle from active lanes and stop on the shoulder of the road. In Level 3 ADS features, drivers are no longer required to keep their hands on the steering wheel or continuously monitor the vehicle and the road. However, they must be alert and prepared to take over the task of driving when the system prompts them to do so. The literature review on human interactions during emergency takeover request indicates that the driver is susceptible to delayed decision­making following a warning for transition of control. Since the race of ADS

14 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Figure 5. SAE International automation levels with operational design domain (Source: SAE International). technology began, concerns have risen about the Level 3 automation. Specifically, it is expected that this level will have problems related to delineation in responsibility. For instance, Tesla’s Autopilot and General Motors’ Super Cruise are both Level 2 features that are offered in the market today, and it is clear that for these features the driver remains responsible for all driving tasks. In the Level 4 ADS features, responsibility is also clear: the drivers should have zero liability as long as the vehicle is within the specified ODD. Now, considering the Level 3 ADS feature, the situation is more complicated since the responsibility can be exchanged between human and ADS machine since humans are still required in case the system encounters a situation it cannot handle. Expectations of full and complete attentiveness of the driver in this model are unlikely. Several car manufacturers (including Volvo, Ford, and Google, among others) expected the safety and policy concerns associated with this level and decided to skip it and jump directly to full automation vehicles (Levels 4 and 5).

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Quickly advancing automated driving system (ADS) technologies are expected to positively affect transportation safety. ADS includes a plethora of applications that affect safety, mobility, human factors, and environmental aspects of driving.

TRB's joint publication of the National Cooperative Highway Research Program and the Behavioral Transportation Safety Cooperative Research Program is titled NCHRP Research Report 1001/BTSCRP Research Report 2: Framework for Assessing Potential Safety Impacts of Automated Driving Systems. The report describes a framework to help state and local agencies assess the safety impact of ADS and is designed to guide them on how to adapt the framework for a variety of scenarios.

Supplemental to the report are a Video describing the project’s assessment framework, a Proof of Concept Results Document, an Implementation Plan, and a Future Research Needs Document.

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