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.

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).

The effect of precrash velocity reduction on occupant response using a human body finite element model

OBJECTIVE

The objective of this study is to use a validated finite element model of the human body and a certified model of an anthropomorphic test dummy (ATD) to evaluate the effect of simulated precrash braking on driver kinematics, restraint loads, body loads, and computed injury criteria in 4 commonly injured body regions.

METHODS

The Global Human Body Models Consortium (GHBMC) 50th percentile male occupant (M50-O) and the Humanetics Hybrid III 50th percentile models were gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and driver airbag. Fifteen simulations per model (30 total) were conducted, including 4 scenarios at 3 severity levels: median, severe, and the U.S. New Car Assessment Program (U.S.-NCAP) and 3 extra per model with high-intensity braking. The 4 scenarios were no precollision system (no PCS), forward collision warning (FCW), FCW with prebraking assist (FCW+PBA), and FCW and PBA with autonomous precrash braking (FCW + PBA + PB). The baseline ΔV was 17, 34, and 56.4 kph for median, severe, and U.S.-NCAP scenarios, respectively, and were based on crash reconstructions from NASS/CDS. Pulses were then developed based on the assumed precrash systems equipped. Restraint properties and the generic pulse used were based on literature.

RESULTS

In median crash severity cases, little to no risk (<10% risk for Abbreviated injury Scale [AIS] 3+) was found for all injury measures for both models. In the severe set of cases, little to no risk for AIS 3+ injury was also found for all injury measures. In NCAP cases, highest risk was typically found with No PCS and lowest with FCW + PBA + PB. In the higher intensity braking cases (1.0-1.4 g), head injury criterion (HIC), brain injury criterion (BrIC), and chest deflection injury measures increased with increased braking intensity. All other measures for these cases tended to decrease. The ATD also predicted and trended similar to the human body models predictions for both the median, severe, and NCAP cases. Forward excursion for both models decreased across median, severe, and NCAP cases and diverged from each other in cases above 1.0 g of braking intensity.

CONCLUSIONS

The addition of precrash systems simulated through reduced precrash speeds caused reductions in some injury criteria, whereas others (chest deflection, HIC, and BrIC) increased due to a modified occupant position. The human model and ATD models trended similarly in nearly all cases with greater risk indicated in the human model. These results suggest the need for integrated safety systems that have restraints that optimize the occupant's position during precrash braking and prior to impact.

Population distributions of time to collision at brake application during car following from naturalistic driving data

PROBLEM

Forward collision warning (FCW) systems are designed to mitigate the effects of rear-end collisions. Driver acceptance of these systems is crucial to their success, as perceived "nuisance" alarms may cause drivers to disable the systems. In order to make customizable FCW thresholds, system designers need to quantify the variation in braking behavior in the driving population. The objective of this study was to quantify the time to collision (TTC) that drivers applied the brakes during car following scenarios from a large scale naturalistic driving study (NDS).

METHODS

Because of the large amount of data generated by NDS, an automated algorithm was developed to identify lead vehicles using radar data recorded as part of the study. Using the search algorithm, all trips from 64 drivers from the 100-Car NDS were analyzed. A comparison of the algorithm to 7135 brake applications where the presence of a lead vehicle was manually identified found that the algorithm agreed with the human review 90.6% of the time.

RESULTS

This study examined 72,123 trips that resulted in 2.6 million brake applications. Population distributions of the minimum, 1st, and 10th percentiles were computed for each driver in speed ranges between 3 and 60 mph in 10 mph increments. As speed increased, so did the minimum TTC experience by drivers as well as variance in TTC. Younger drivers (18-30) had lower TTC at brake application compared to older drivers (30-51+), especially at speeds between 40 mph and 60 mph.

DISCUSSION

This is one of the first studies to use large scale NDS data to quantify braking behavior during car following. The results of this study can be used to design and evaluate FCW systems and calibrate traffic simulation models.

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.

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

OBJECTIVE

Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human-machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.

METHODS

The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.

RESULTS

Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40-50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.

CONCLUSIONS

This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.

Comparison of Expected Crash and Injury Reduction from Production Forward Collision and Lane Departure Warning Systems

OBJECTIVES

The U.S. New Car Assessment Program (NCAP) now tests for forward collision warning (FCW) and lane departure warning (LDW). The design of these warnings differs greatly between vehicles and can result in different real-world field performance in preventing or mitigating the effects of collisions. The objective of this study was to compare the expected number of crashes and injured drivers that could be prevented if all vehicles in the fleet were equipped with the FCW and LDW systems tested under the U.S. NCAP.

METHODS

To predict the potential crashes and serious injury that could be prevented, our approach was to computationally model the U.S. crash population. The models simulated all rear-end and single-vehicle road departure collisions that occurred in a nationally representative crash database (NASS-CDS). A sample of 478 single-vehicle crashes from NASS-CDS 2012 was the basis for 24,822 simulations for LDW. A sample of 1,042 rear-end collisions from NASS-CDS years 1997-2013 was the basis for 7,616 simulations for FCW. For each crash, 2 simulations were performed: (1) without the system present and (2) with the system present. Models of each production safety system were based on 54 model year 2010-2014 vehicles that were evaluated under the NCAP confirmation procedure for LDW and/or FCW. NCAP performed 40 LDW and 45 FCW tests of these vehicles.

RESULTS

The design of the FCW systems had a dramatic impact on their potential to prevent crashes and injuries. Between 0 and 67% of crashes and 2 and 69% of moderately to fatally injured drivers in rear-end impacts could have been prevented if all vehicles were equipped with the FCW systems. Earlier warning times resulted in increased benefits. The largest effect on benefits, however, was the lower operating speed threshold of the systems. Systems that only operated at speeds above 20 mph were less than half as effective as those that operated above 5 mph with similar warning times. The production LDW systems could have prevented between 11 and 23% of drift-out-of-lane crashes and 13 and 22% of seriously to fatally injured drivers. A majority of the tested LDW systems delivered warnings near the point when the vehicle first touched the lane line, leading to similar benefits. Minimum operating speed also greatly affected LDW effectiveness.

CONCLUSIONS

The results of this study show that the expected field performance of FCW and LDW systems are highly dependent on the design and system limitations. Systems that delivered warnings earlier and operated at lower speeds may prevent far more crashes and injuries than systems that warn late and operate only at high speeds. These results suggest that future FCW and LDW evaluation should prioritize early warnings and full-speed range operation. A limitation of this study is that additional crash avoidance features that may also mitigate collisions-for example, brake assist, automated braking, or lane-keeping assistance-were not evaluated during the NCAP tests or in our benefits models. The potential additional mitigating effects of these systems were not quantified in this study.

Comprehensive target populations for current active safety systems using national crash databases

OBJECTIVE

The objective of active safety systems is to prevent or mitigate collisions. A critical component in the design of active safety systems is the identification of the target population for a proposed system. The target population for an active safety system is that set of crashes that a proposed system could prevent or mitigate. Target crashes have scenarios in which the sensors and algorithms would likely activate. For example, the rear-end crash scenario, where the front of one vehicle contacts another vehicle traveling in the same direction and in the same lane as the striking vehicle, is one scenario for which forward collision warning (FCW) would be most effective in mitigating or preventing. This article presents a novel set of precrash scenarios based on coded variables from NHTSA's nationally representative crash databases in the United States.

METHODS

Using 4 databases (National Automotive Sampling System-General Estimates System [NASS-GES], NASS Crashworthiness Data System [NASS-CDS], Fatality Analysis Reporting System [FARS], and National Motor Vehicle Crash Causation Survey [NMVCCS]) the scenarios developed in this study can be used to quantify the number of police-reported crashes, seriously injured occupants, and fatalities that are applicable to proposed active safety systems. In this article, we use the precrash scenarios to identify the target populations for FCW, pedestrian crash avoidance systems (PCAS), lane departure warning (LDW), and vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) systems. Crash scenarios were derived using precrash variables (critical event, accident type, precrash movement) present in all 4 data sources.

RESULTS AND CONCLUSIONS

This study found that these active safety systems could potentially mitigate approximately 1 in 5 of all severity and serious injury crashes in the United States and 26 percent of fatal crashes. Annually, this corresponds to 1.2 million all severity, 14,353 serious injury (MAIS 3+), and 7412 fatal crashes. In addition, we provide the source code for the crash scenarios as an appendix (see online supplement) to this article so that researchers can use the crash scenarios in future research.

Age and gender differences in time to collision at braking from the 100-Car Naturalistic Driving Study

OBJECTIVE

Forward collision warning (FCW) is an active safety system that aims to mitigate the effect of forward collisions by warning the driver of objects in front of the vehicle. Success of FCW relies on how drivers react to the alerts. Drivers who receive too many warnings that they deem as unnecessary-that is, nuisance alarms-may grow to distrust and turn the system off. To reduce the perception of nuisance alarms, FCW systems can be tailored to individual driving styles, but these driving styles must first be characterized. The objective of this study was to characterize differences in braking behavior between age and gender groups in car-following scenarios using data from the 100-Car Naturalistic Driving Study.

METHODS

The data source for this study was the 100-Car Naturalistic Driving Study, which recorded the driving of 108 primary drivers for approximately a year. Braking behavior was characterized in terms of time to collision (TTC) at brake application, a common metric used in the design of warning thresholds of FCW. Because of the large volume of data analyzed, the TTC at which drivers braked during car-following situations was collected via an automated search algorithm. The minimum TTC for each vehicle speed 10 mph increment from 10 mph to 80 mph was recorded for each driver. Mixed model analysis of variance was used to examine the differences between age and gender groups.

RESULTS

In total, 527,861 brake applications contained in 11,503 trips were analyzed. Differences in TTC at braking were statistically significant for age and gender (P<.01 for both cases). Males age 18-20 (n=7) had the lowest average minimum TTC at braking of 2.5±0.8 s, and females age 31-50 (n=6) had the highest average minimum TTC at braking of 6.4±0.9 s. On average, women (n=32) braked at a TTC 1.3 s higher than men (n=52). Age was a statistically significant factor for TTC at braking between participants under 30 (n=42) and participants over 30 (n=42), with the latter braking 1.7 s on average before the former. No statistical significance was found between ages 18-20 (n=15) and 21-30 (n=27) or between ages 31-50 (n=23) and 50+(n=19).

CONCLUSIONS

There are clear statistical differences in TTC at braking for both gender and those over 30 vs. those under 30. Designers of FCW systems can use the data found in this study to tailor alert timings to the target demographic of a vehicle when designing forward collision warning systems. Appropriate alert timings for FCW systems will maximize effectiveness in collision reduction and mitigation.

Methodology for using advanced event data recorders to reconstruct vehicle trajectories for use in safety impact methodologies (SIM)

OBJECTIVE

Safety impact methodologies (SIMs) have the goal of estimating safety benefits for proposed active safety systems. Because the precrash movements of vehicles involved in real-world crashes are often unknown, previous SIMs have taken the approach to reconstruct collisions from incomplete information sources, such as scaled scene diagrams and photographic evidence. The objective of this study is to introduce a novel methodology for reconstructing the precrash vehicle trajectories using data from advanced event data recorders (EDRs).

METHODS

Some EDRs from model year 2009 and newer Ford vehicles can record steering wheel angle in addition to precrash vehicle speed, accelerator pedal, and throttle input prior to the crash. A model was constructed using these precrash records and a vehicle model developed in the simulation software PreScan. The model was validated using the yaw rate and longitudinal and lateral accelerations also recorded by this type of Ford EDR but not used to develop the models.

RESULTS

In general, the model was able to approximate the dynamics recorded on the EDR. The model did not match the observed dynamics when either the vehicle departed the paved surface or when electronic stability control was active. Modifying the surface friction at the estimated point at which the vehicle departed the road produced better simulation results. The developed trajectories were used to simulate 2 road departure crashes, one into a fixed object and one into a vehicle traveling in the opposite direction, as if the departing vehicle were equipped with a lane departure warning (LDW) system. This example application demonstrates the utility of this method and its potential application to a SIM.

CONCLUSIONS

This study demonstrated a novel method for crash reconstruction that can be applied to a SIM for active safety systems. Benefits of this method are that the driver inputs do not need to be inferred from other reconstructions because they are recorded directly by the EDR. Currently, there are too few cases with the advanced EDR data to estimate fleet-wide benefits of a system. Because of recent regulation (49 CFR Part 563), EDRs are likely to be downloaded in more real-world crashes, making this method a potentially valuable and low-cost method for developing SIMs in the future.

Potential Occupant Injury Reduction in Pre-Crash System Equipped Vehicles in the Striking Vehicle of Rear-end Crashes

To mitigate the severity of rear-end and other collisions, Pre-Crash Systems (PCS) are being developed. These active safety systems utilize radar and/or video cameras to determine when a frontal crash, such as a front-to-back rear-end collisions, is imminent and can brake autonomously, even with no driver input. Of these PCS features, the effects of autonomous pre-crash braking are estimated. To estimate the maximum potential for injury reduction due to autonomous pre-crash braking in the striking vehicle of rear-end crashes, a methodology is presented for determining 1) the reduction in vehicle crash change in velocity (ΔV) due to PCS braking and 2) the number of injuries that could be prevented due to the reduction in collision severity. Injury reduction was only performed for belted drivers, as unbelted drivers have an unknown risk of being thrown out of position. The study was based on 1,406 rear-end striking vehicles from NASS / CDS years 1993 to 2008. PCS parameters were selected from realistic values and varied to examine the effect on system performance. PCS braking authority was varied from 0.5 G's to 0.8 G's while time to collision (TTC) was held at 0.45 seconds. TTC was then varied from 0.3 second to 0.6 seconds while braking authority was held constant at 0.6 G's. A constant braking pulse (step function) and ramp-up braking pulse were used. The study found that automated PCS braking could reduce the crash ΔV in rear-end striking vehicles by an average of 12% - 50% and avoid 0% - 14% of collisions, depending on PCS parameters. Autonomous PCS braking could potentially reduce the number of injured drivers who are belted by 19% to 57%.