The National Academies Press

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From page 87... ... B-1 This appendix provides analytical examples for applying the framework to three prominent ADS features that are envisioned, namely, conditional traffic jam assist, highway platooning, and fleet-operated automated driving system–dedicated vehicle (ADS-DV) Read the entire page →
From page 88... ... B-2 Framework for Assessing Potential Safety Impacts of Automated Driving Systems obstacle is the discrepancy among U.S. states in lane markings, signage, traffic signals, and road configuration. Read the entire page →
From page 89... ... Additional Example Scenarios B-3 the medium term (next 5 to 10 years) Read the entire page →
From page 90... ... B-4 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Timeline Operational Design Domain Level Additional Deployment Context Short term (low disruption) • Market share = 2% • Fleet share = 1% • VHT share = 1% • Urban and rural highways (divided) Read the entire page →
From page 91... ... Additional Example Scenarios B-5 • These algorithms are in early development and may have more errors than later, more mature technology, leading to lower safety performance and/or lower percentage of time operating in autonomous mode (high disengagement rate) Read the entire page →
From page 92... ... B-6 Framework for Assessing Potential Safety Impacts of Automated Driving Systems vehicles, or poor weather conditions. The dependence on surrounding cars to help navigate (i.e., the requirement to have a car in front and cars to the side) Read the entire page →
From page 93... ... Additional Example Scenarios B-7 – At low penetration rates, TJA-equipped vehicles could result in more aggressive and frequent lane-change maneuvers by following, nonautomated vehicles. This could increase the crash risk for sideswipe crashes and other crashes in the aggregate traffic stream due to disruptions in traffic flow. Read the entire page →
From page 94... ... B-8 Framework for Assessing Potential Safety Impacts of Automated Driving Systems While this provides an overview of potential safety impacts, it is important to perform a crash sequencing exercise to think through the contributing factors and precipitating events that lead to a crash. The following are a few examples related to TJA: 1. Read the entire page →
From page 95... ... Additional Example Scenarios B-9 To estimate the safety benefits or disbenefits of TJA, technical assumptions about the scenario were made in analyzing crash data. An "optimistic scenario" is assumed where there is a maximum crash reduction. Read the entire page →
From page 96... ... B-10 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Roadway data from Connect NCDOT (Road Characteristics Arcs; https://connect.ncdot.gov/ resources/gis/Pages/GIS-Data-Layers.aspx) were used to supplement the HSIS roadway data and provide additional variables for analysis, including road functional class. Read the entire page →
From page 97... ... Additional Example Scenarios B-11 A sensitivity analysis is used to explore various assumptions related to penetration rates and probabilities that the trailing vehicle has TJA, has it activated, and it functions properly. The change in crashes is calculated by subtracting the crashes TJA can reduce or increase from the total number of crashes for the given years. Read the entire page →
From page 98... ... B-12 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Fatal and suspected serious injury 16 5 4 Total 4,256 582 541 Severity Level Number of Crashes in Clear Weather Conditions Number of Crashes in Other Weather Conditions Number of Crashes in Rain Weather Conditions Blank 2 1 1 PDO 2,890 404 371 Possible injury 1,172 152 146 Suspected minor injury 176 20 19 Table B-7. Total crashes on urban, multilane, divided roadways during peak periods (2012–2017) Read the entire page →
From page 99... ... Additional Example Scenarios B-13 Severity Level Number of Crashes in Clear Weather Conditions Number of Crashes in Other Weather Conditions Number of Crashes in Rain Weather Conditions Blank 0 0 0 PDO 13 1 1 Possible injury 2 0 0 Suspected minor injury 0 0 0 Fatal and suspected serious injury 0 0 0 Total 15 1 1 Table B-10. Total crashes on urban, one-way roadways during peak periods (2012–2017) Read the entire page →
From page 100... ... B-14 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Divided, Multilane Freeways and Expressways During Peak Periods The third categorization of the total crashes included separating out urban, divided, multilane freeways and expressways during congested conditions (weekday peak periods) (Tables B-13, B-14, and B-15) Read the entire page →
From page 101... ... Additional Example Scenarios B-15 In addition to the percentage of VMT share shown above, other percentages of VMT share were used as a sensitivity analysis to account for two variants: (1) vehicles that have the ADS but not engaged, and (2) Read the entire page →
From page 102... ... B-16 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Severity Total Crashes on All Roads and in All Weather Conditions (2012–2017) Rear-End Crashes on Urban, Multilane, Divided Roadways during Peak Periods in Clear and Windy Weather Conditions (2012–2017) Read the entire page →
From page 103... ... Additional Example Scenarios B-17 Severity Total Crashes on All Roads in All Weather Conditions (2012– 2017) Rear-End Crashes on Urban, Multilane, Divided Roadways during Peak Periods in Clear, Windy, and Rainy Weather Conditions (2012–2017) Read the entire page →
From page 104... ... B-18 Framework for Assessing Potential Safety Impacts of Automated Driving Systems The percent crash reduction with respect to average total crashes per year is calculated below using a VMT share of 2%. = − −       × =Percent crash reduction per year 1 23,975 10.94 23,975 100 0.046% The percent crash reduction with respect to average total crashes per year is calculated below using a VMT share of 4%. Read the entire page →
From page 105... ... Additional Example Scenarios B-19 a point of reference. These numbers might increase due to conditional traffic jam assist. Read the entire page →
From page 106... ... B-20 Framework for Assessing Potential Safety Impacts of Automated Driving Systems However, assumptions were made to perform the analysis. The analysis and results could change slightly as the technology landscape changes, the ODD (or extent of the ODD) Read the entire page →
From page 107... ... Additional Example Scenarios B-21 market at $501 million in 2017 and projected it to grow to $4.6 billion by 2025 with a compound annual growth rate (CAGR) Read the entire page →
From page 108... ... B-22 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Operational Design Domain Level Timeline Additional Deployment Context Self-driving features of ADS-equipped trucks can be supplemented with platooning capabilities to further increase benefits. • Freeways (both urban and rural) Read the entire page →
From page 109... ... Additional Example Scenarios B-23 • This sensor package manifests as mature sensor fusion technology that is able to combine the sensing capabilities of multiple sensors, resulting in more reliable and robust perception with a broad sensing scope. To this end, the underlying perception algorithms for processing the data are more advanced and are capable of performing complex sensor fusion calculations enabling the operation in an expanded ODD and deployment context. Read the entire page →
From page 110... ... B-24 Framework for Assessing Potential Safety Impacts of Automated Driving Systems operating locally can swap trailers to autonomous tractors optimized for highway driving. Likewise, highway-optimized trucks can swap trailers to human-driven trucks for last-mile and urban delivery where driverless operations are more complex. Read the entire page →
From page 111... ... Additional Example Scenarios B-25 a driver may not be familiar with more than one machine-user interface and may fail to react safely in a request for control takeover initiated by the ADS. • The occupant injury risks in truck platooning are different from risks in single-passenger vehicle crashes. Read the entire page →
From page 112... ... B-26 Framework for Assessing Potential Safety Impacts of Automated Driving Systems • In the future, many trucks with ADS and platooning capabilities will likely be electric. In the United States, the transportation sector is responsible for almost 30% of annual greenhouse gas (GHG) Read the entire page →
From page 113... ... Additional Example Scenarios B-27 In summary, through the deployment of highway truck platooning, it is hypothesized that the frequency and severity of truck-involved and truck-related crashes will decrease. While other crash types such as merging and diverging actions at interchanges may increase, it is expected that the severity distribution will remain the same. Read the entire page →
From page 114... ... B-28 Framework for Assessing Potential Safety Impacts of Automated Driving Systems to crashes involving other vehicle(s) on the road] Read the entire page →
From page 115... ... Additional Example Scenarios B-29 Evaluation Method. The impact method is also similar to that of ADS-equipped trucks. Read the entire page →
From page 116... ... B-30 Framework for Assessing Potential Safety Impacts of Automated Driving Systems fleet to be automated and able to drive in platoons or improving infrastructure to allow highway truck platooning (e.g., constructing truck-only lanes or truck-bypass lanes) Read the entire page →
From page 117... ... Additional Example Scenarios B-31 Assumptions were made regarding facility conditions. Roads need to be well marked and well maintained for highway truck platooning to operate. Read the entire page →
From page 118... ... B-32 Framework for Assessing Potential Safety Impacts of Automated Driving Systems for this level of automation if they are operated as shared vehicles. If fleet-operated ADS-DVs are offered as gasoline engines, their operational costs will outweigh the benefits of shared mobility. Read the entire page →
From page 119... ... Additional Example Scenarios B-33 assumed to be geographically bounded, and the designated route network for the fleet-operated ADS-DV does not use all roads and intersections within the road network (e.g., highways, school zones) Read the entire page →
From page 120... ... B-34 Framework for Assessing Potential Safety Impacts of Automated Driving Systems • Cannot navigate temporary traffic zones (e.g., work zones, school zones) Read the entire page →
From page 121... ... Additional Example Scenarios B-35 • Suburban and urban geofenced area. • All streets and intersections within a suburb or a central business district that are fulfilling an ODD model set by the car manufacturer. Read the entire page →
From page 122... ... B-36 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Second Generation (Gen II) : Gen II is a more advanced stage of technology than first-generation models or systems. Read the entire page →
From page 123... ... Additional Example Scenarios B-37 • Another concern with the implementation of fleet-operated ADS-DVs could be the expected degradation of some driving skills -- depending on the ADS feature -- due to reliance on automation and lack of exposure to DDT. Skill degradation is not limited to psychomotor skills -- those pertaining to performing maneuvers -- but also include decision-making skills (Miller and Parasuraman, 2007) Read the entire page →
From page 124... ... B-38 Framework for Assessing Potential Safety Impacts of Automated Driving Systems conditions improves the vehicle's energy efficiency and motor lifetime. Funding recipients of the DOE NEXTCAR program are commercializing these types of technologies (ARPA-E, 2020) Read the entire page →
From page 125... ... Additional Example Scenarios B-39 1. Read the entire page →
From page 126... ... B-40 Framework for Assessing Potential Safety Impacts of Automated Driving Systems A summary of the components of the analysis is shown in Table B-35. It is assumed that fleet-operated ADS-DVs will not eliminate all crashes, including pedestrian and bicycle crashes, where the vehicles involved are vehicles that fleet-operated ADS-DVs are expected to replace. Read the entire page →
From page 127... ... Additional Example Scenarios B-41 Define Metrics The primary metric for assessing the safety impact of fleet-operated ADS-DVs is the change in crashes by frequency. While the change in crashes should be based on the crashes of interest for the deployment scenario, the NTD data are not sufficiently detailed to categorize and filter crashes based on area type, infrastructure conditions, speed requirements, and weather conditions. Read the entire page →
From page 128... ... B-42 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Results Statistics Table B-37 through Table B-39 display crash statistics for the NTD data for 2008 to 2018. The crashes are separated by events and collisions, fatalities, and injuries, respectively. Read the entire page →
From page 129... ... Additional Example Scenarios B-43 Short-term VMT share for sensitivity analysis = 5% Medium-term VMT share for the sensitivity analysis = 11% The estimated reductions for total collisions, fatalities, and injuries were calculated using the method previously described, where the rate is multiplied by the VMT share. The analysis considered pedestrian and bicyclist fatalities and injuries separately as well as collision with person. Read the entire page →
From page 130... ... B-44 Framework for Assessing Potential Safety Impacts of Automated Driving Systems every 100 million passenger miles traveled. An agency could perform a simple or more detailed benefit-cost analysis to compare these benefits with the cost to convert the vehicle fleet as part of the decision process. Read the entire page →
From page 131... ... Additional Example Scenarios B-45 Another assumption used in the analysis relates to the environmental conditions. Fleet-operated ADS-DVs can only operate in clear and windy weather conditions for the short-term timeline and in weather conditions consisting of no heavy rain or snow for the medium-term timeline. Read the entire page →

From page 87...

... B-1 This appendix provides analytical examples for applying the framework to three prominent ADS features that are envisioned, namely, conditional traffic jam assist, highway platooning, and fleet-operated automated driving system–dedicated vehicle (ADS-DV)