Pre-crash Scenario Research Articles

A Precrash Scenario Analysis Comparing Safety Performance across Autonomous Vehicle Driving Modes

Precrash scenario analysis for autonomous vehicles (AVs) is critical for improving the safety of autonomous driving, yet the scenario differences between different driving modes are unexplored. Using the precrash scenario typology of the USDOT, this study classified 484 AV crash reports from the California DMV from 2018 to 2022, revealing the differences in the scenario proportions of the three modes of autonomous driving, driving takeover, and conventional driving in 34 types of scenarios. The results showed that there were significant differences in the proportion of six scenarios such as “Lead AV stopped” and “Lead AV decelerating” among different driving modes p<0.05. To analyze the relative risk of different driving modes in specific scenarios, an evaluation model of the risk level of AV precrash scenarios was established using the analytic hierarchy process (AHP). The findings indicated that ​ autonomous driving has the highest risk rating and poses the greatest danger in Scenario 1, while conventional driving is associated with Scenario 2b, and driving takeover corresponds to Scenario 3, respectively. In-depth analysis of the crash characteristics and causes of these three typical scenarios was conducted, and suggestions were made from the perspectives of autonomous driving system (ADS) and drivers to reduce the severity of crashes. This study compared precrash scenarios of AV by different driving modes, providing references for the optimization of ADS and the safety of human-machine codriving.

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers’ collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers’ collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers’ TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Causation analysis of crashes and near crashes using naturalistic driving data

Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework’s road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.

Effects of Automated Emergency Braking and Seatbelt Pre-Pretensioning on Occupant Injury Risks in High-Severity Frontal Crashes

In high-severity crashes, occupant protection is challenging. Automated Emergency Braking (AEB) and seatbelt pre-pretensioning (PPT) are means to improve occupant protection; the purpose of this study was to quantify their effects on occupant injury risks in high-severity full-frontal crashes by Finite Element (FE) simulations. The SAFER Active average male Human Body Model was used as an occupant substitute. The crash pulses used were from separate full-frontal crash simulations using a Honda Accord FE model. The vehicle interior model comprised a seat, an instrument panel, a three-point pretensioned seatbelt system with a load-limiter of 3.1 kN force level, and a frontal passenger airbag. The effects of AEB and PPT were evaluated by simulating a 1 g pre-crash braking scenario for 0.5 s, with and without AEB, for three different PPT force levels: 0, 300, and 600 N. The impact speed of 80 km/h was reduced to 69 km/h by AEB. When neither system was activated, the predicted risk for an occupant to sustain two or more fractured ribs (NFR2+) was 100% for both 45- and 65-year-old male occupants. The risks were reduced when the AEB was activated, particularly for the 45-year-old occupant. When the AEB was activated, the risks of concussion and rib fractures were reduced; upper neck tension forces, pelvis Anterior Superior Iliac Spine (ASIS) forces, and lower extremity forces were also reduced. Increasing the PPT forces reduced the rib fracture risk further (to about 48% for a 45-year-old occupant with 600 N PPT force). The reduced speed due to AEB resulted in a lower concussion risk (from 71.3% to 31%). However, the concussion risk increased slightly with increased PPT forces.

Pedestrian safety system with crash prediction

Every year nearly 1.5 million people are dying in traffic collisions around the world, due to the unexpected behavior of pedestrians while crossing the road. To address this problem an augmentation function for predicting the crash risk of the active pedestrian is proposed. The augmentation function has several functions like pre-crash scenario, vehicle trajectory, and pedestrian trajectory. In a Pre-Crash scenario, pedestrian movement such as entering the road boundary or not is detected. The input comes from the sensor which is located at the head of the car. After the pre-crash scenario vehicle trajectory is used to control the speed of the vehicle. Then the Markov IRW model-based pedestrian trajectory finding is used to predict the state of each pedestrian. The states are categorized into three types: Running, Walking and Standing. In this model, the pedestrian types whether the pedestrian is a child or young or old aged people is predicted. And the crash risk is evaluated based on the Monte Carlo algorithm that calculates the minimum detection range for active pedestrians. If the crash risk is exceeded the threshold limit then an augmentation function is activating the evasive action.

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology

Data-based research approaches to generate crash scenarios have mainly relied on conventional vehicle crashes and naturalistic driving data, and have not considered differences between the autonomous vehicle (AV) and conventional vehicle crashes. As the AV’s presence on roadways continues to grow, its crash scenarios take on new importance for traffic safety. This study therefore obtained crash patterns using the United States Department of Transportation pre-crash scenario typology, and used statistical analysis to determine the differences between AV and conventional vehicle pre-crash scenarios. Analysis of 122 AV crashes and 2084 conventional vehicle crashes revealed 15 types of scenario for AVs and 26 for conventional vehicles. The two groups showed differences in type of scenario, and differed in the proportion of crashes when the scenario was the same. The most frequent AV pre-crash scenarios were rear-end collisions (52.46%) and lane change collisions (18.85%), with the proportion of AVs rear-ended by conventional vehicles occurring with a frequency 1.6 times that of conventional vehicles. An in-depth crash investigation was conducted of the characteristics and causes of four AV pre-crash scenarios, summarized from the perspectives of perception and path planning. The perception-reaction time (PRT) difference between AVs and human drivers, AV’s inaccurate identification of the intention of other vehicles to change lanes, and AV’s insufficient path planning combining time and space dimensions were found to be important causes for the AV crashes. By increasing understanding of the complex characteristics of AV pre-crash scenarios, this analysis will encourage cooperation with vehicle manufacturers and AV technology companies for further study of crash causation toward the goals of improved test scenario construction and optimization of the AV’s automated driving system (ADS).

Safety challenges for autonomous vehicles in the absence of connectivity

Autonomous vehicles (AVs) are promoted as a technology that will create a future with effortless driving and virtually no traffic accidents. AV companies claim that, when fully developed, the technology will eliminate 94% of all accidents that are caused by human error. Indeed, these AVs will likely avoid the large number of crashes caused by impaired, distracted or reckless drivers. But there remains a significant proportion of crashes for which no driver is directly responsible. In particular, the absence of connectivity of an AV with its neighboring vehicles (V2V) and the infrastructure (I2V) leads to a lack of information that can induce such crashes. Since AV designs today do not require such connectivity, these crashes would persist in the future. Using prototypical examples motivated by the NHTSA pre-crash scenario typology, we show that fully autonomous vehicles cannot guarantee safety in the absence of connectivity. Combining theoretical models and empirical data, we also argue that such hazardous scenarios will occur with a significantly high probability. This suggests that incorporating connectivity is an essential step on the path towards safe AV technology.

Research of fatal car-to-pedestrian precrash scenarios for the testing of the active safety system in China

Road safety remains a challenge with numerous Vulnerable Road Users (VRUs) suffering from injuries and death every year. Pedestrian protection using active safety systems, such as Automated Emergency Braking (AEB), is an effective measure to combat the situation. Furthermore, the perception of precrash scenarios plays an important role in active safety research. It is essential to understand and define precrash scenarios. This study aimed to apply the obtained typical car-to-pedestrian precrash scenarios from Chinese severely injured pedestrian traffic accidents to develop and test active safety systems. The National Automobile Accident In-Depth Investigation System (NAIS) recorded 467 cases from 2011 to 2018 in China, and 12 items were selected from the NAIS database as description variables for the precrash scenario. The items were divided into four categories: car, pedestrian, road, and environment. Group decision theory was applied to evaluate the importance of each variable in its category. A total of 34 basic scenarios were defined and obtained according to the extracted significant variables. These basic scenarios represented diverse fatal scenarios in China which are crucial for autonomous driving. The frequency distribution of the scenarios demonstrated that the top five scenarios covered 85.3 % of the total. Five scenarios were identified to have the common characteristic of cars going straight. Additionally, 13 detailed scenarios were obtained from the five basic scenarios by using cluster and frequency analyses. In contrast to the New Car Assessment Program (NCAP) test scenarios, weather and lighting conditions were considered in these 13 scenarios, and the driving speed before the crash were mostly distributed in the range of 40–80 km/h (20–60 km/h in the NCAP). Meanwhile, both walking and running were commonly recorded for pedestrians to cross the street from the nearside, compared with records of walking only to cross from the nearside in the NCAP. These results contribute to a reference for test scenarios of pedestrian AEB or Forward Collision Warning (FCW) in China.

Video from user-generated content as a source of pre-crash scenario naturalistic driving data

Objective The objective of this study was to investigate the use of public video from internet user-generated content as a means of collecting naturalistic driving data. Methods A convenience sample of 38 videos comprised of 203 events was extracted from publicly available channels on the YouTube™ platform. Each event was manually reviewed and pseudo-coded according to a subset of current CRSS variables. Pre-crash scenarios were coded using categories developed for prior NHTSA analysis. Results Crashes represented 67% of the reviewed cases. Collisions with motor vehicles accounted for 84% of all crashes in the sample. Pre-crash scenarios were able to be determined for all crashes and near-crashes. The most prevalent pre-crash scenario types in the video data were Crossing Paths (41%), Rear End (21%), and Lane Change (17%). The top pre-crash scenarios from Swanson et al., were Rear End (31%), Crossing Paths (21%), and Lane Change (12%). The most prevalent pre-near crash scenario types in the video data were Crossing Paths (32%), Lane Change (30%), and Pedestrian (12%). Conclusions The most prevalent pre-crash scenarios in the video data were similar to those in data from FARS and NASS-GES. Though not nationally representative, this preliminary study demonstrated that user-generated content may be useful as a source of inexpensive naturalistic data and provides sufficient detail to capture important pre-crash, near-crash and crash information.

Mining and comparative analysis of typical pre-crash scenarios from IGLAD

Scenario-based testing is crucial for considering the intended functional safety of automated driving vehicles. For the first time, pre-crash scenario mining research was conducted using worldwide accident data obtained from the Initiative for the Global Harmonization of Accident Data (IGLAD). First, data from the IGLAD database were analyzed and divided into four categories based on differences in traffic environments among countries and regions. Second, according to actual accident characteristics, fields and methods of clustering were selected, and 21 typical pre-crash scenarios were obtained using clustering and analysis. Finally, the typical scenarios were analyzed and compared in detail.Four conclusions were drawn as follows: 1. Considerable differences exist in traffic participant types, accident forms, and typical scenarios across countries and regions. 2. The third group of countries (3-G, represented by China and Brazil) in which accidents and pre-crash scenarios are the most representative and diverse is an ideal data source for the international scenario research. 3. The typical scenarios mined through clustering were highly consistent with the new test scenarios added in the Euro-NCAP 2025 Roadmap, but a few typical scenario elements which are critical for safety evaluations were still not covered in Roadmap. 4. Data from the IGLAD database still lacks a few important pieces of information for scenario research, such as obstruction of visual field due to obstacles, and the data representativeness need to be improved, therefore we recommend that IGLAD database adds some new data parameters to fit the further scenario research, and propose distribution requirements of accident data considering scenario elements. The analysis methods and conclusions presented used in this study could serve as guidelines or references for automated vehicle safety evaluations.

Influence of pedestrian-to-vehicle technology on drivers’ response and safety benefits considering pre-crash conditions

Pedestrian-to-vehicle (P2V) technology may offer a promising approach to reducing pedestrian crashes. However, its influences on both driver response and safety benefits have been little studied in previous research, particularly in regard to the variation of influences between different pre-crash scenarios. To investigate these influences, this study designed three pre-crash scenarios based on pedestrian crash contributing factors identified from crash reports, and collected 44 drivers’ driving simulator experiments’ data. The results clarified how using P2V technology to warn drivers of an impending collision improves safety by causing a series of changes for both brake operation and braking profile. These series of changes were further demonstrated to vary between scenarios. The study showed that P2V technology may be particularly useful in scenarios in which a pedestrian’s crossing intention is unclear; specifically, in this type of scenario, the P2V warning had changed the braking process from a panic brake of “slow reaction-hard brake” to a comfortable brake of “quick reaction-gentle brake.” In addition, the P2V warning may be less effective in “low-risk” level scenarios where a driver is confident that he/she can handle the situation through a more conservative evasive action and don’t need to react strongly to a warning. Moreover, depending on the pre-crash scenario, the P2V warning may be mostly beneficial for drivers who had a crash/citation in the past five years and working-aged drivers.

In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention

IntroductionA pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns. Method: In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well. Results: Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development.

Opportunities for crash and injury reduction: A multiharm approach for crash data analysis

ABSTRACTObjective: A multiharm approach for analyzing crash and injury data was developed for the ultimate purpose of getting a richer picture of motor vehicle crash outcomes for identifying research opportunities in crash safety.Methods: Methods were illustrated using a retrospective analysis of 69,597 occupant cases from NASS CDS from 2005 to 2015. Occupant cases were analyzed by frequency and severity of outcome: fatality, injury by Abbreviated Injury Scale (AIS), number of cases, attributable fatality, disability, and injury costs. Comparative analysis variables included precrash scenario, impact type, and injured body region.Results: Crash and injury prevention opportunities vary depending on the search parameters. For example, occupants in rear-end crash scenarios were more frequent than in any other precrash configuration, yet there were significantly more fatalities and serious injury cases in control loss, road departure, and opposite direction crashes. Fatality is most frequently associated with head and thorax injury, and disability is primarily associated with extremity injury. Costs attributed to specific body regions are more evenly distributed, dominated by injuries to the head, thorax, and extremities but with contributions from all body regions. Though AIS 3+ can be used as a single measure of harm, an analysis based on multiple measures of harm gives a much more detailed picture of the risk presented by a particular injury or set of crash conditions.Conclusions: The developed methods represent a new approach to crash data mining that is expected to be useful for the identification of research priorities and opportunities for reduction of crashes and injuries. As the pace of crash safety improvement accelerates with innovations in both active and passive safety, these techniques for combining outcome measures for insights beyond fatality and serious injury will be increasingly valuable.

Potential Safety Benefit of the Blind Spot Detection System for Large Trucks on the Vulnerable Road Users in Taiwan

Considering motorcyclists, pedestrians and bicyclists as vulnerable road users (VRUs), more than 75 percent of the victims of fatal crashes involving large trucks in Taiwan are VRUs. Most crashes occurred at or were due to the blind spots of large trucks because of the size and traveling locations of the VRUs. This study applies typology and statistical methods to estimate the potential safety benefit of blind spot detection (BSD) systems for large trucks on VRUs. The pre-crash scenarios associated with the blind spots of large trucks were derived by counting the maneuvers of large trucks and VRUs, prior to crashes, the truck drivers’ improper behaviors (cause of crashes), and the crash types. The number of crashes and fatalities were counted for the pre-crash scenario relevant to the BSD systems. A value of 0.8 of human machine interface factor (HMIF) based on a previous study was applied to estimate the potential safety benefits of the BSD system. The results show that the implementation of BSD systems on all large trucks could help avoid about 24, 10, and 11 percent of large truck-involved crashes with pedestrians, bicycles, and motorcycles, respectively. The BSD systems could also save 5 pedestrians, 3 bicyclists, and 15 motorcyclists per year from crashes involving large trucks.