Determination of functional scenarios for intersection collisions.

INTRODUCTION: The National Highway Traffic Safety Administration (NHTSA) estimated that in 2019, intersection crashes accounted for $179 billion of economic damages and $639 billion in societal damages. Intersection advanced driver assist systems (I-ADASs) and automated driving systems (ADS) are designed and have been actively deployed to avoid or mitigate these intersection crash scenarios. Given the indeterminate parameter space for describing collision scenarios, evaluators, and designers are all challenged with condensing the possible intersection crash configurations into digestible, executable conditions for scenario-based simulation testing. The objective of this study is to identify functional intersection crash configurations for I-ADAS and ADS safety evaluation. METHODS: Real-world intersection crash characteristics are important considerations for scenario testing as these features can directly correlate to or influence causality, controllability, and potential injury severity. To identify functional intersection crash types, similar crash scenarios were grouped together by identified critical features using an unsupervised decision tree model. A key advantage of this approach was that the implemented cluster crash scenarios would be understandable and interpretable by users. Unsupervised decision trees work by generating uniformly distributed synthetic data with features from real data and classifying all the data as real or synthetic. Long, non-diverging branches were manually pruned to reduce overfitting and improve model performance. Feature importance values were computed based on how effective a given variable grouped the crashes together. DATA SOURCES: This analysis selected intersection cases that only involved two vehicles from the Crash Investigation Sampling System (CISS) spanning 2017 to 2020. Crash features such as road geometry, intersection signal, and vehicle configuration were important to consider for scenario generation. CISS contained the traffic device, device functionality, vehicle intended pre-event movement, road alignment, road profile, trafficway flow, number of lanes, and crash type for each crash case. Intersection geometry, intersecting road angle, each vehicles' legal moves, and the presence of a two-way-left-turn-lane (TWLTL), channelized roads, bike lanes, crosswalks, street parking, slip lanes, and visual obstructions were manually recorded from the scene diagram. RESULTS: The tree identified 44 functional intersection crash configurations after pruning. These clusters have five main sections: Straight-crossing path (SCP) crashes at 4-legged intersections, Left-Turn-Across-Path/Opposite Direction (LTAP/OD) crashes at 4-legged intersections, other crash types at 4-legged intersections, roundabout and multileg intersections, and 3-legged intersection crashes. The features that best split the data were TWLTL, lane travel direction violation, and traffic control device functionality. The largest cluster was SCP crashes at 4-legged, undivided intersections where the traffic control device was working and both vehicles did not violate the direction of their lane of travel. This cluster was adjacent to 32 vehicles in similar SCP crashes except a vehicle performed an unexpected maneuver based on their lane position. CONCLUSION: These 44 identified crash configurations could be useful in bolstering the robustness of I-ADAS and ADS intersection scenario testing as they are a compact representation of all the police reported intersection crashes where a vehicle was towed. Future studies could generate logical scenarios with distributions of initial conditions and behaviors from these clusters that could be used to evaluate an I-ADAS or ADS.

Passenger and heavy vehicle collisions with pedestrians: Assessment of injury mechanisms and risk.

OBJECTIVE: Automated Driving System (ADS) fleets are currently being deployed in several dense-urban operational design domains within the United States. In these dense-urban areas, pedestrians have historically comprised a significant portion, and sometimes the majority, of injury and fatal collisions. An expanded understanding of the injury risk in collision events involving pedestrians and human-driven vehicles can inform continued ADS development and safety benefits evaluation. There is no current systematic investigation of United States pedestrian collisions, so this study used reconstruction data from the German In-Depth Accident Study (GIDAS) to develop mechanistic injury risk models for pedestrians involved in collisions with vehicles. DATA SOURCE: The study queried the GIDAS database for cases from 1999 to 2021 involving passenger vehicle or heavy vehicle collisions with pedestrians. METHODS: We describe the injury patterns and frequencies for passenger vehicle-to-pedestrian and heavy vehicle-to-pedestrian collisions, where heavy vehicles included heavy trucks and buses. Injury risk functions were developed at the AIS2+, 3+, 4+ and 5+ levels for pedestrians involved in frontal collisions with passenger vehicles and separately for frontal collisions with heavy vehicles. Model predictors included mechanistic factors of collision speed, pedestrian age, sex, pedestrian height relative to vehicle bumper height, and vehicle acceleration before impact. Children (≤17 y.o.) and elderly (≥65 y.o.) pedestrians were included. We further conducted weighted and imputed analyses to understand the effects of missing data elements and of weighting towards the overall population of German pedestrian crashes. RESULTS: We identified 3,112 pedestrians involved in collisions with passenger vehicles, where 2,524 of those collisions were frontal vehicle strikes. Furthermore, we determined 154 pedestrians involved in collisions with heavy vehicles, where 87 of those identified collisions were frontal vehicle strikes. Children were found to be at higher risk of injury compared to young adults, and the highest risk of serious injuries (AIS 3+) existed for the oldest pedestrians in the dataset. Collisions with heavy vehicles were more likely to produce serious (AIS 3+) injuries at low speeds than collisions with passenger vehicles. Injury mechanisms differed between collisions with passenger vehicles and with heavy vehicles. The initial engagement caused 36% of pedestrians' most-severe injuries in passenger vehicle collisions, compared with 23% in heavy vehicles collisions. Conversely, the vehicle underside caused 6% of the most-severe injuries in passenger vehicle collisions and 20% in heavy vehicles collisions. SIGNIFICANCE: U.S. pedestrian fatalities have risen 59% since their recent recorded low in 2009. It is imperative that we understand and describe injury risk so that we can target effective strategies for injury and fatality reduction. This study builds on previous analyses by including the most modern vehicles, including children and elderly pedestrians, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using multiple imputation and weighting to better estimate these effects relative to the entire population of German pedestrian collisions. This study is the first to investigate the risk of injury to pedestrians in collisions with heavy vehicles based on field data.

Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain.

Preventing and mitigating high severity collisions is one of the main opportunities for Automated Driving Systems (ADS) to improve road safety. This study evaluated the Waymo Driver's performance within real-world fatal collision scenarios that occurred in a specific operational design domain (ODD). To address the rare nature of high-severity collisions, this paper describes the addition of novel techniques to established safety impact assessment methodologies. A census of fatal, human-involved collisions was examined for years 2008 through 2017 for Chandler, AZ, which overlaps the current geographic ODD of the Waymo One fully automated ride-hailing service. Crash reconstructions were performed on all available fatal collisions that involved a passenger vehicle as one of the first collision partners and an available map in this ODD to determine the pre-impact kinematics of the vehicles involved in the original crashes. The final dataset consisted of a total of 72 crashes and 91 vehicle actors (52 initiators and 39 responders) for simulations. Next, a novel counterfactual "what-if'' simulation method was developed to synthetically replace human-driven crash participants one at a time with the Waymo Driver. This study focused on the Waymo Driver's performance when replacing one of the first two collision partners. The results of these simulations showed that the Waymo Driver was successful in avoiding all collisions when replacing the crash initiator, that is, the road user who made the initial, unexpected maneuver leading to a collision. Replacing the driver reacting (the responder) to the actions of the crash initiator with the Waymo Driver resulted in an estimated 82% of simulations where a collision was prevented and an additional 10% of simulations where the collision severity was mitigated (reduction in crash-level serious injury risk). The remaining 8% of simulations with the Waymo Driver in the responder role had a similar outcome to the original collision. All of these "unchanged" collisions involved both the original vehicle and the Waymo Driver being struck in the rear in a front-to-rear configuration. These results demonstrate the potential of fully automated driving systems to improve traffic safety compared to the performance of the humans originally involved in the collisions. The findings also highlight the major importance of driving behaviors that prevent entering a conflict situation (e.g. maintaining safe time gaps and not surprising other road users). However, methodological challenges in performing single instance counterfactual simulations based solely on police report data and uncertainty in ADS performance may result in variable performance, requiring additional analysis and supplemental methodologies. This study's methods provide insights on rare, severe events that would otherwise only be experienced after operating in extreme real-world driving distances (many billions of driving miles).

An omni-directional model of injury risk in planar crashes with application for autonomous vehicles.

Objective: Automated driving systems (ADS) are actively being deployed within the driving fleet. ADS are designed to safely navigate roadways, which entails an expectation of encountering varying degrees of potential conflict with other road users. The ADS design and evaluation process benefits from estimating injury severity probabilities for collisions that may occur. Current regression models in the literature are typically bespoke analyses involving targeted principal directions of force (PDOFs) and occupant positions. It is preferable to rely on injury severity models derived from a single source to provide a continuous function of risk for all planar collisions, while also accounting for specific vehicle and occupant characteristics. The novel feature of the proposed models is continuous, parametric injury risk surfaces that encompass the full spectrum of available United States field data.Methods: We used years 2001-2015 of the National Automotive Sampling System, Crashworthiness Data System (NASS-CDS) and years 2017-2019 of the Crash Investigation Sampling System (CISS) to estimate injury risk at the maximum abbreviated injury scale (MAIS) 3 and higher (3+) and 5 and higher (5+) levels for all adult occupants traveling in 2002 or newer passenger vehicles which were less than 10 years old at the time of the crash. The models account for occupant, vehicle, and crash characteristics. Interactions with vulnerable road users (e.g., pedestrian, bicyclist) were not considered.Results: We present statistical models suitable to predict injury in all non-rollover crashes at the maximum MAIS3+ and 5+ levels, and show that these models can be comparable to similar single scenario (e.g., frontal) crash models. We discuss challenges with imputing missing field data, and discuss handling of covariates that may not be known at the time of the crash.Conclusions: Collision severity assessment is a vital component of the ADS design process. We developed a novel injury risk function that can assess occupant injury risks across the spectrum of foreseeable planar collisions. These models can provide insight on potential outcomes of counterfactual simulations, injury risk and crashworthiness considerations for human driven vehicles, and provide an evaluation tool that can be applied in ADS safety impact evaluation.

Injury mitigation estimates for an intersection driver assistance system in straight crossing path crashes in the United States.

OBJECTIVE: Accounting for one fifth of all crashes and one sixth of all fatal crashes in the United States, intersection crashes are among the most frequent and fatal crash modes. Intersection advanced driver assistance systems (I-ADAS) are emerging vehicle-based active safety systems that aim to help drivers safely navigate intersections. The objective of this study was to estimate the number of crashes and number of vehicles with a seriously injured driver (Maximum Abbreviated Injury Scale [MAIS] 3+) that could be prevented or reduced if, for every straight crossing path (SCP) intersection crash, one of the vehicles had been equipped with an I-ADAS. METHODS: This study retrospectively simulated 448 U.S. SCP crashes as if one of the vehicles had been equipped with I-ADAS. Crashes were reconstructed to determine the path and speeds traveled by the vehicles. Cases were then simulated with I-ADAS. A total of 30 variations of I-ADAS were considered in this study. These variations consisted of 5 separate activation timing thresholds, 3 separate computational latency times, and 2 different I-ADAS response modalities (i.e., a warning or autonomous braking). The likelihood of a serious driver injury was computed for every vehicle in every crash using impact delta-V. The results were then compiled across all crashes in order to estimate system effectiveness. RESULTS: The model predicted that an I-ADAS that delivers an alert to the driver has the potential to prevent 0-23% of SCP crashes and 0-25% of vehicles with a seriously injured driver. Conversely, an I-ADAS that autonomously brakes was found to have the potential to prevent 25-59% of crashes and 38-79% of vehicles with a seriously injured driver. I-ADAS effectiveness is a strong function of design. Increasing computational latency time from 0 to 0.5 s was found to reduce crash and injury prevention estimates by approximately one third. For an I-ADAS that delivers an alert, crash/injury prevention effectiveness was found to be very sensitive to changes in activation timing (warning delivered 1.0 to 3.0 s prior to impact). If autonomous braking was used, system effectiveness was found to largely plateau for activation timings greater than 1.5 s prior to impact. In general, the results of this study suggest that I-ADAS will be 2-3 times more effective if an autonomous braking system is utilized over a warning-based system. CONCLUSIONS: This study highlights the potential effectiveness of I-ADAS in the U.S. vehicle fleet, while also indicating the sensitivity of system effectiveness to design specifications. The results of this study should be considered by designers of I-ADAS and evaluators of this technology considering a future I-ADAS safety test.

Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system.

OBJECTIVE: Intersection crashes resulted in over 5,000 fatalities in the United States in 2014. Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that seek to help drivers safely traverse intersections. I-ADAS uses onboard sensors to detect oncoming vehicles and, in the event of an imminent crash, can either alert the driver or take autonomous evasive action. The objective of this study was to develop and evaluate a predictive model for detecting whether a stop sign violation was imminent. METHODS: Passenger vehicle intersection approaches were extracted from a data set of typical driver behavior (100-Car Naturalistic Driving Study) and violations (event data recorders downloaded from real-world crashes) and were assigned weighting factors based on real-world frequency. A k-fold cross-validation procedure was then used to develop and evaluate 3 hypothetical stop sign warning algorithms (i.e., early, intermediate, and delayed) for detecting an impending violation during the intersection approach. Violation detection models were developed using logistic regression models that evaluate likelihood of a violation at various locations along the intersection approach. Two potential indicators of driver intent to stop-that is, required deceleration parameter (RDP) and brake application-were used to develop the predictive models. The earliest violation detection opportunity was then evaluated for each detection algorithm in order to (1) evaluate the violation detection accuracy and (2) compare braking demand versus maximum braking capabilities. RESULTS: A total of 38 violating and 658 nonviolating approaches were used in the analysis. All 3 algorithms were able to detect a violation at some point during the intersection approach. The early detection algorithm, as designed, was able to detect violations earlier than all other algorithms during the intersection approach but gave false alarms for 22.3% of approaches. In contrast, the delayed detection algorithm sacrificed some time for detecting violations but was able to substantially reduce false alarms to only 3.3% of all nonviolating approaches. Given good surface conditions (maximum braking capabilities = 0.8 g) and maximum effort, most drivers (55.3-71.1%) would be able to stop the vehicle regardless of the detection algorithm. However, given poor surface conditions (maximum braking capabilities = 0.4 g), few drivers (10.5-26.3%) would be able to stop the vehicle. Automatic emergency braking (AEB) would allow for early braking prior to driver reaction. If equipped with an AEB system, the results suggest that, even for the poor surface conditions scenario, over one half (55.3-65.8%) of the vehicles could have been stopped. CONCLUSIONS: This study demonstrates the potential of I-ADAS to incorporate a stop sign violation detection algorithm. Repeating the analysis on a larger, more extensive data set will allow for the development of a more comprehensive algorithm to further validate the findings.

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders.

OBJECTIVE: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes. METHODS: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver. RESULTS: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle. CONCLUSIONS: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.