Framework for Assessing Potential Safety Impacts of Automated Driving Systems (2022) / Chapter Skim
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Pages 43-75
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43 Framework Proof of Concept with MnDOT -- Rochester Automated Shuttle Pilot Minnesota Department of Transportation (MnDOT) has many planned automated driving system (ADS)
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44 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Figure 12. Proposed route for the AV shuttle (Source: MnDOT)
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Direct Application of the Framework 45 The scope of the Rochester Autonomous Shuttle Pilot is to assess the expected impacts of the project on the future of transportation safety (which is consistent with the title and scope of NCHRP Project 17-91)
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46 Framework for Assessing Potential Safety Impacts of Automated Driving Systems scale and timeframe of safety impacts in the area. It is important to note that MnDOT and its partners in the ADS shuttle pilot are not anticipating a mode shift, and the intent of the pilot is not to enhance shuttle service.
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Direct Application of the Framework 47 more aggressive and frequent lane-change maneuvers by the following nonautomated vehicles. This could increase the crash risk for the aggregate traffic stream.
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48 Framework for Assessing Potential Safety Impacts of Automated Driving Systems In defining the hypotheses and related questions, the team documented the expected deployment timeline, which could include multiple timeframes depending on the certainty in deployment and penetration rates. Finally, the team demonstrated how to map these hypotheses and findings to plans, policies, and procedures.
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Direct Application of the Framework 49 Surrounding Land Use The desired elements for surrounding land use include the zoning and types of businesses within and adjacent to the study area. The intent of this information is to identify potential origins and destinations of transit riders, pedestrians, and bicyclists.
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50 Framework for Assessing Potential Safety Impacts of Automated Driving Systems The project team obtained roadway data and surrounding area characteristics for the IHSDM analysis through a desktop data collection effort using Google Earth. Roadway information included alignment type, lane width, median width, median type, number of driveways, presence of on-street parking, and lighting.
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Direct Application of the Framework 51 Evaluation Method The project team used the IHSDM Crash Prediction Module (CPM) to predict crashes along the shuttle loop for existing conditions, calculate the expected crashes using historical crash data, and predict crashes for two scenarios that involve a shuttle.
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52 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Figure 14. Road network of the shuttle loop as it appears in IHSDM (Source: IHSDM project output)
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Direct Application of the Framework 53 Figure 15. Existing pedestrian activity at intersections in the study area (Source: © 2021 Google modified by the authors)
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54 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Figure 16. Hypothetical pedestrian activity due to shuttle at intersections in the study area (Source: © 2021 Google modified by the authors)
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Direct Application of the Framework 55 Location Predicted Total Crash Frequency (crashes/yr) Predicted Fatal+Injury Crash Frequency (crashes/yr)
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56 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Table 22 displays the expected crash frequency from IHSDM by intersection for the existing conditions. There are a total of 38.6 expected crashes per year, 11.9 expected fatal plus injury crashes per year, and 26.6 expected property damage only crashes at the 16 intersections in the study area for the existing conditions.
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Direct Application of the Framework 57 multiple-vehicle crashes along segments in the study area compared to single-vehicle collisions. Rear-end collisions are the crash type with the highest expected crash frequency (22.5 expected crashes for the 5-year study period)
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58 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Crash Type Fatal and Injury Property Damage Only Total Crashes % Crashes % Crashes % Collision with animal 0.0 0.0 0.0 0.0 0.0 0.0 Collision with bicycle 2.9 3.8 0.0 0.0 2.9 1.1 Collision with fixed object 2.1 2.8 7.6 4.2 9.8 3.8 Non-collision 0.5 0.6 0.3 0.1 0.8 0.3 Collision with other object 0.2 0.3 0.6 0.3 0.8 0.3 Other single-vehicle collision 0.2 0.2 0.5 0.2 0.6 0.2 Collision with parked vehicle 0.0 0.0 0.0 0.0 0.0 0.0 Collision with pedestrian 15.8 20.8 0.0 0.0 15.8 6.1 Total intersection single-vehicle crashes 21.6 28.6 9.0 4.9 30.6 11.8 Angle collision 15.7 20.7 39.3 21.5 55.0 21.3 Head-on collision 2.0 2.6 3.9 2.1 5.9 2.3 Other multivehicle collision 2.3 3.1 27.4 14.9 29.7 11.5 Rear-end collision 19.0 25.1 65.5 35.8 84.5 32.7 Sideswipe 4.0 5.3 6.0 3.3 10.0 3.9 Total intersection multivehicle crashes 43.1 56.8 142.1 77.6 185.2 71.5 Total intersection crashes 64.7 85.4 151.1 82.6 215.8 83.4 Table 24. Expected crash type distribution for intersections for the 5-year study period for the existing conditions.
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Direct Application of the Framework 59 Location Predicted Total Crash Frequency (crashes/yr) for Existing Conditions Predicted Total Crash Frequency (crashes/yr)
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60 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Crash Type Total Expected Crashes for Existing Conditions (2021−2026) Total Predicted Crashes for Scenario 1 (2021−2026)
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Direct Application of the Framework 61 Crash Type Total Expected Crashes for Existing Conditions (2021–2026) Total Predicted Crashes for Scenario 1 Total Predicted Crashes for Scenario 2 (2021–2026)
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62 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Source: I-81 Corridor Improvement Plan, VDOT, December 2018 Figure 17. Elevation along the I-81 corridor (Source: VDOT)
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Direct Application of the Framework 63 Step 1 -- Identify ADS Application(s) of Interest The selected applications included in the proof of concept are ADS-equipped trucks and vehicles with forward collision avoidance.
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64 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Operational Design Domain Level Timeline Additional Deployment Context • Freeways (both urban and rural)
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Direct Application of the Framework 65 and inertial measurement units (gyroscopes and accelerometers) with a priori digital maps (lane-level detail)
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66 Framework for Assessing Potential Safety Impacts of Automated Driving Systems 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.
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Direct Application of the Framework 67 Cameras are an important part of perceiving the road structure and signage and classifying objects. Cameras do not perform well in precipitation and fog and are dependent on ambient light to detect infrastructure components.
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68 Framework for Assessing Potential Safety Impacts of Automated Driving Systems parking lots outside congested urban areas. ATPs not only allow trailer switching, but also provide driver facilities and refueling and charging stations.
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Direct Application of the Framework 69 is the potential to reduce crashes in which the truck is not one of the vehicles involved in the crash. Conversely, if ADS-equipped trucks can detect and react to situations faster than human-driven vehicles, this could lead to a potential increase in rear-end crashes, particularly if the large trucks limit forward sight distance for following vehicles.
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70 Framework for Assessing Potential Safety Impacts of Automated Driving Systems Year Fatal Injury Suspected Serious Injury Suspected Minor Injury Possible Injury Property Damage Only Total 2014 1 20 53 7 282 363 2015 2 17 62 10 327 418 2016 4 22 67 11 370 474 2017 5 21 67 12 363 468 2018 3 25 82 17 468 595 2019 4 16 86 7 447 560 2020 7 15 49 10 319 400 Total 26 136 466 74 2,576 3,278 Table 33. Crashes along I-81 from milepost 110 to 150 by year and severity (2014–2020)
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Direct Application of the Framework 71 Weather Condition Total Crashes Total TruckInvolved Crashes No adverse condition (clear/cloudy) 2,492 763 Fog 19 7 Mist 36 9 Rain 510 136 Snow 162 51 Sleet/hail 57 13 Other 1 0 Severe crosswinds 1 0 Total 3,278 979 Table 35.
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72 Framework for Assessing Potential Safety Impacts of Automated Driving Systems the autonomous feature is activated and functions properly. Similarly, this serves as a sensitivity analysis to explore assumptions related to penetration rates and probabilities that a passenger vehicle is equipped with forward collision avoidance, and if it is equipped, that the feature is activated and functioning properly.
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Direct Application of the Framework 73 et al., 2020)
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74 Framework for Assessing Potential Safety Impacts of Automated Driving Systems crashes that could be the result of drivers trying to avoid a rear-end crash with a truck should be included, but this level of detail is not readily available in the current data (i.e., no information that a passenger car was following a large truck before the vehicle left the road)
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Direct Application of the Framework 75 However, to test the hypothesis and related questions, assumptions were made to estimate the safety impacts of ADS-equipped trucks. Regarding the ODD facility conditions, the technology requires dedicated or separated trucking lanes.
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