Real-world Crashes Research Articles

Motor vehicle crash risk after cardioverter-defibrillator implantation: a population-based cohort study

BackgroundLimited empirical evidence informs driving restrictions after implantable cardioverter-defibrillator (ICD) implantation. We sought to evaluate real-world motor vehicle crash risks after ICD implantation.MethodsWe performed a retrospective cohort study using 22...

Validation of a generic finite element vehicle buck model for near-side crashes

Objective Finite element (FE) reconstructions of motor vehicle crashes using human body models are effective tools for developing a better understanding of occupant kinematics and injuries in real-world lateral crash conditions, but current near-side reconstruction methods are limited by the paucity of full-scale FE vehicle models. The objective of this study was to validate a generic vehicle model equipped with left-side airbags and intrusion capability by simulating a series of near-side crash tests for a range of vehicles and assessing model accuracy using objective evaluation methods. Methods Moving deformable barrier crash tests were reconstructed for five common vehicle classifications (compact passenger, mid-size passenger, sport utility vehicle, pickup truck, and van) using an updated version of a previously developed simplified vehicle model. Unknown vehicle and intrusion properties (pretensioner force, seatback airbag pressure, curtain airbag pressure, door panel stiffness, ratio of dynamic-to-static intrusion, intrusion velocity, and intrusion scaling factor) were estimated by parameterizing them across 224 simulations per crash test using a Latin hypercube design of experiments. Model accuracy was assessed for 13 anthropomorphic test device signals using the Correlation and Analysis (CORA) objective rating method and injury metric comparisons. Results Maximum ratings of 0.69, 0.67, 0.52, 0.52, and 0.62 were achieved for the compact passenger, midsize passenger, sport utility vehicle, pickup truck, and van classifications, respectively. On average, the abdomen displayed the most accurate behavior (0.51 ± 0.12), followed by the thorax (0.50 ± 0.10) and head (0.50 ± 0.07). The pelvis displayed the least accurate behavior (0.46 ± 0.18) of any region. Reconstructions overpraedicted injury metrics in all cases. Conclusions All vehicles achieved “fair” biofidelity ratings and the compact passenger and midsize passenger vehicles achieved “good” biofidelity ratings, validating them for kinematic evaluations with vehicle-to-vehicle nearside crash reconstructions. Regression models were developed for injuries and CORA ratings and can be used to optimize vehicle parameters in future studies.

Evaluation of a novel head and neck restraint for harness-restrained children

Motor Vehicle Crashes (MVCs) are a common source of neck injury and, although rare, catastrophic neck injury occurs in restrained children up to age 10 years old. Recently, a head/neck support (HNS) that cradles the head during sleep was developed. The objective of this study was to determine if the HNS improves injury criteria measured on anthropomorphic test devices (ATDs) in front facing 5-point harness child restraint systems (FFCRS). Nineteen matched pair frontal impact sled tests were conducted with ATDs representing the 12 month old, or 3 or 6 year old child in FFCRS. HNS reduced HIC36 by median 22.2%, maximum resultant head acceleration (3ms clip) by 16.2%, head excursion by 8.2%, and neck tension by 12.2%. Providing supplemental restraint directly to the head has the potential to improve injury outcomes while not adding to harm during common misuse scenarios, but further research is needed to determine if these benefits persist in real-world crashes.

MODELING AND 3D PRINTING OF TWO WHEELER CRASH GUARDS

The crash guard is a protective device installed on vehicles to minimize damage in case of accidents. It helps to protect the front or rear of the vehicle from collisions.3D printing is designing crash guards for two-wheelers, aiming to enhance safety and customization. By employing solid works software and simulations, complex geometries are optimized for impact resistance. Advanced materials with high strength-to-weight ratios are selected to ensure performance and lightweight characteristics. This delves into novel materials and ergonomic considerations for rider comfort. The optimizing crash guard structures for impact resistance but also on ensuring economic feasibility and scalability for mass production. It explores the potential for widespread adoption within the automotive industry. Additionally, it emphasizes the importance of comprehensive real-world crash simulations to validate the effectiveness of the proposed approach. By considering a holistic approach that blends advanced numerical analyses, sophisticated 3D printing methodologies, and user experience factors, the research aims to pave the way for a new era in two-wheeler crash guard design and manufacturing. Keywords: Solidworks, Ansys, Flash Forge Pro

Adjusting vehicle secondary safety ratings to account for crash-avoidance technology fitment using real-world crash and injury data

Objectives Publicizing safety ratings of vehicles can motivate manufacturers to prioritize safety and help consumers choose safer vehicles, leading to safer fleets. The benefits of primary safety technologies that prevent crash occurrence are not currently incorporated in current ratings in a way that values their safety benefits consistently. We aimed to propose a method for assigning weights for each safety technology to account for established safety benefits using published effectiveness and prevalence from real-life data. Methods To illustrate this method, we present a worked example calculated using crash and injury data from Australia and New Zealand. The method proposed attenuates the weights for given safety technologies where two or more safety technologies fitted to the same vehicle are effective for the same types of crashes. Results In the worked example using Australasian data, large SUVs were estimated to have the largest safety increment from the fitment of all the technologies considered compared to vehicles without these primary safety technologies, with an almost 17% reduction in crash occurrence. Cars with all the technologies fitted had estimated average crash reduction of between 11% and 12%. Conclusions Different market groups have different crash patterns, so the safety attributable to safety technology fitment differs at the market group level. This study presents an approach for providing a summary measure of crash avoidance according to the fitment of safety technologies. If this measure is combined with an estimate of secondary safety (whether derived from existing crash and injury data or from new car crash assessment programs), the combined estimate then represents the important elements of safety provided by the vehicle. The methods presented here form a rational basis for assigning safety ratings to represent the benefits of swiftly developing safety technologies.

How would autonomous vehicles behave in real-world crash scenarios?

Autonomous Vehicles (AVs) have the potential to revolutionize transportation systems by enhancing traffic safety. Safety testing is undoubtedly a critical step for enabling large-scale deployment of AVs. High-risk scenarios are particularly important as they pose significant challenges and provide valuable insights into the driving capabilities of AVs. This study presents a novel approach to assess the safety of AVs using in-depth crash data, with a particular focus on real-world crash scenarios. First, based on the high-definition video recording of the whole process prior to the crash occurrences, 453 real-world crashes involving 596 passenger cars from China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database were reconstructed. Pertinent static and dynamic elements needed for the construction of the testing scenarios were extracted. Subsequently, 596 testing scenarios were created via each passenger car’s perspective within the simulation platform. Following this, each of the crash-involved passenger cars was replaced with Baidu Apollo, a famous automated driving system (ADS), for counterfactual simulation. Lastly, the safety performance of the AV was assessed using the simulation results. A logit model was utilized to identify the fifteen crucial scenario elements that have significant impacts on the test results. The findings demonstrated that the AV could avoid 363 real-world crashes, accounting for approximately 60.91% of the total, and effectively mitigated injuries in the remaining 233 unavoidable scenarios compared to a human driver. Moreover, the AV maintain a smoother speed in most of the scenarios. The common feature of these unavoidable scenarios is that the AV is in a passive state, and the crashes are not caused by the AV violating traffic rules, but rather caused by abnormal behavior exhibited by the human drivers. Additionally, seven specific scenarios have been identified wherein AVs are unable to avoid a crash. These findings demonstrate that, compared to human drivers, AVs can avoid crashes that are difficult for humans to avoid, thereby enhancing traffic safety.

Examination of Crash Injury Risk as a Function of Occupant Demographics.

The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries. The most problematic of these are AIS 2+ and AIS 3+ upper-extremity and lower-extremity injuries. These are among the most frequently observed injuries for females, and their incidence is consistently greater than for males. Overall, the odds of females sustaining MAIS 3+ (or fatality) are 4.5% higher than the odds for males, while the odds of females sustaining MAIS 2+ (or fatality) are 33.9% higher than those for males. The analyses highlight the need to carefully control for both the vehicle occupied, and the other involved vehicle, when calculating risk ratios by occupant sex. Female driver preferences in terms of vehicle class/size differ significantly from those of males, with females favoring smaller, lighter vehicles.

PREDICTING THE RELIABILITY OF SOFTWARE SYSTEMS USING RECURRENT NEURAL NETWORKS: LSTM MODEL

The dynamics and complexity of processes occurring in complex software systems, as well as the emergence of new types of malicious threats, further complicate the issues of ensuring software reliability. Despite the development of hundreds of models for increasing the reliability of software systems, this issue still remains relevant. Research shows that the use of neural networks in predicting the reliability of software systems allows one to obtain more accurate results. In this paper, to predict reliability, we used a neural network model with long short-term memory, which is a type of recurrent neural networks. Seven real-world software crash datasets were used to test the model's performance. The experiments were carried out in Python. Both parametric and nonparametric models were taken for comparison. The experimental results showed the practical significance of using the proposed model in predicting the reliability of software systems.

Evaluation of AIS3+ car occupant injuries using deterministic and probabilistic methods in frontal crashes

Computational modelling was used to assess the capability of a deterministic and a probabilistic method to predict the incidence of AIS3+ injuries in passenger car occupants by comparing the predictions of the methods to the actual injuries observed in real-world crashes. The likelihood of sustaining an injury was first calculated using a computer model for a selected set of injury criteria in different impact conditions based on real-world crashes; AIS3+ injuries were then predicted using each method separately. Regardless of the method, the number of serious injuries was over-predicted. It was also noted that the used injury criteria suggested the occurrence of specific injuries that were not observed in the real world. Although both methods are susceptible to be adapted to improve their predictions, the question of the suitability of using some of the most commonly accepted injury criteria used with crash test dummies for injury assessment with human body models deserves further research.

Demographic adjustments to driver death rates by vehicle type and size

Objective Driver death rates per million registered vehicles are often used to compare the real-world crash experiences of vehicles of different types and sizes. However, these rates are affected by the risky behavior of drivers (e.g., speeding, impaired driving, lack of restraint use) that differs by age and gender. This paper presents a method for adjusting driver death rates to account for differences in driver age and gender. Methods Driver death rates per million registered vehicles per year were calculated for passenger vehicles of model year 2020. To account for potential differences in driver exposure by age and gender, which can affect motor vehicle crash and injury experience, an algebraic method was used to standardize these rates to a common distribution of female drivers ages 25–64. The standardization depends upon an independent estimate of the relative fatality risk for female drivers ages 25–64. Results The smallest vehicles tended to have higher driver death rates compared with larger vehicles. However, for sports cars, this trend was reversed; larger sports cars had higher driver death rates. Vehicle types popular with male drivers, such as sports cars and pickups, had standardized death rates that were much lower than the raw rates. Conclusions The adjustment for driver age and gender greatly reduced the variability of driver death rates among vehicle types. The procedure used here to adjust driver death rates for driver age and gender can be extended to any situation where it is desired to compare the crash rates of two or more subgroups of a population.

Predicting head injury metrics during low- to moderate-speed frontal collisions using computational simulations

ObjectivesSubjective reports of mild traumatic brain injury (mTBI) are common following low-energy motor-vehicle collisions. Biomechanical analyses are useful in providing a quantitative means for determining the likelihood of sustaining mTBI. While occupant dynamics in low-speed rear impacts have been extensively investigated, peer-reviewed studies on occupant dynamics during low-speed frontal collisions are sparse. The objective of this study is to present a validated computational method to quantify the biomechanical response of the head in low- and moderate-speed frontal collisions. Data and methodsThis study used data from a previously-published series of four instrumented in-line front-to-rear staged collisions using 2014 Honda Accord sedans at closing velocities of approximately 7.4 kph (test L1), 12.7 kph (test L2), 21.7 kph (test L3), and 33.6 kph (test L4) kph. A model of the test vehicle occupant compartment was created using the MAthematical DYnamic MOdeling (MADYMO) software using methods previously described. Crash pulse data from L4 were applied to the MADYMO model. Seat belt parameters were optimized to achieve reasonable agreement between simulation results and test data for relevant head injury metrics (linear head acceleration [LHA], angular head acceleration [AHA], and HIC15). Crash pulses from the other tests in the series (L1, L2, and L3) were then applied to the model and peak values for LHA, AHA, and HIC15 were compared to the physical test data to demonstrate validation. ResultsThe optimization of seat belt and seat parameters within the MADYMO model resulted in accurate prediction of ATD dynamics demonstrated in Test L4. The simulation-predicted peak LHA was within 0.2 g of the test value, peak AHA was within 64 rad/s2, and HIC15 was within 0.46. When applied to the remainder of the tests (L1, L2, and L3), the optimized model showed excellent accuracy in predicting peak LHA and HIC15. When compared to the physical test data, the simulation-predicted values for LHA were within 0.4 g or less and the HIC15 values were within 0.4 or less across all tests. The model generally over-predicted AHA, particularly for the lower-severity collisions (L1 and L2). ConclusionsWe have demonstrated a reliable methodology for developing a biomechanical computational model to predict head injury metrics in low- to moderate-speed frontal collisions. This approach can be particularly valuable in forensic investigations of real-world crashes. Pre-existing crash test data can be used in conjunction with exemplar vehicle information to validate a MADYMO model. Appropriate crash pulse data from classical accident reconstruction techniques, event data recorders, or simulations can then be applied to the model to accurately predict head dynamics for real-world vehicle occupants without the need for full-scale staged crash tests.

Insights Into Wire Rope Safety Barrier Crashes Based on Police-Reported Statistics and Narratives

Wire rope safety barrier (WRSB) is known to be highly effective in reducing the severity of off-road or off-path crashes, however fatal and serious injury outcomes still occur in a small percentage of WRSB-involved crashes. Yet, there is little research on why some WRSB crashes result in severe injury while the majority of crashes involve minor injury outcomes. This paper includes detailed analysis of off-path crashes involving WRSB to better understand associated factors that contribute to severe injury outcomes. Three datasets were analysed: four years of NSW police-reported crash data (2014-2017); ten years of Victorian police-reported crash data and police narratives (2008-2017); five years of police narratives from the US (2001-2005). Results supported earlier research that indicated a relatively low percentage of WRSB crashes involved fatal and serious injury outcomes. Yet, adverse vehicle dynamics were also observed, such as vehicle ricochetting and barrier override, suggesting barrier safety performance could be improved. Some crashes also involved impact angles greater than those used in crash tests, supporting earlier, more detailed research that the full range of real-world crash conditions involving WRSB are not covered in barrier test protocols, and these might be the conditions contributing to severe injury outcomes. The findings provide insight into the potential factors associated with the serious injury outcomes of crashes and can guide further improvements in this effective highway crash countermeasure. Future research should include detailed analysis of real-world crashes to identify factors contributing to adverse crash dynamics, injury outcomes and subsequent implications for barrier design.

Holistic pedestrian safety assessment for average males and females.

An integrated assessment framework that enables holistic safety evaluations addressing vulnerable road users (VRU) is introduced and applied in the current study. The developed method enables consideration of both active and passive safety measures and distributions of real-world crash scenario parameters. The likelihood of a specific virtual testing scenario occurring in real life has been derived from accident databases scaled to European level. Based on pre-crash simulations, it is determined how likely it is that scenarios could be avoided by a specific Autonomous Emergency Braking (AEB) system. For the unavoidable cases, probabilities for specific collision scenarios are determined, and the injury risk for these is determined, subsequently, from in-crash simulations with the VIVA+ Human Body Models combined with the created metamodel for an average male and female model. The integrated assessment framework was applied for the holistic assessment of car-related pedestrian protection using a generic car model to assess the safety benefits of a generic AEB system combined with current passive safety structures. In total, 61,914 virtual testing scenarios have been derived from the different car-pedestrian cases based on real-world crash scenario parameters. Considering the occurrence probability of the virtual testing scenarios, by implementing an AEB, a total crash risk reduction of 81.70% was achieved based on pre-crash simulations. It was shown that 50 in-crash simulations per load case are sufficient to create a metamodel for injury prediction. For the in-crash simulations with the generic vehicle, it was also shown that the injury risk can be reduced by implementing an AEB, as compared to the baseline scenarios. Moreover, as seen in the unavoidable cases, the injury risk for the average male and female is the same for brain injuries and femoral shaft fractures. The average male has a higher risk of skull fractures and fractures of more than three ribs compared to the average female. The average female has a higher risk of proximal femoral fractures than the average male. A novel methodology was developed which allows for movement away from the exclusive use of standard-load case assessments, thus helping to bridge the gap between active and passive safety evaluations.

Converting post-crash deformation into energy equivalent speed (EES): Toward an objective measure of crash severity

In this paper, an objective method for the conversion of post-crash vehicle deformation to crash energy is presented. While the crash severity assessment for standard crash test configurations is relatively robust, bias in real-world crash reconstruction is a well-known problem. A method to reduce the bias in crash severity reconstruction is constructed by focusing on post-crash deformation, the principle direction of force ( PDOF), and vehicle type ( VT) only. The analysis was restricted to 12 PDOF and six VT. A voxel model, onto which the depth information of the post-crash deformations was mapped, was constructed for every PDOF and VT. Standardized crash tests were used to normalize the deformation energy assigned to each voxel. The plastic deformation energy was computed by integrating this energy across all deformed voxels. This energy is expressed as the “energy equivalent speed” ( EES). The method allows for a retrospective calculation of the EES from deformations. Thus, objective EES values can be computed for crash databases like CISS or GIDAS and even for legacy databases like NASSCDS. The results from the EES model will become part of the standard GIDAS database. The EES model was tested using a car-to-car crash test, a 40% offset frontal-frontal collision of an Audi Q7 against a Fiat 500.

Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k-means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.

Evaluation of full-face, open-face, and airbag-equipped helmets for facial impact protection

ObjectiveTwo-wheeler riders frequently sustain injuries to the head and face in real-world crashes, including traumatic brain injury, basilar skull fracture, and facial fracture. Different types of helmets exist today, which are recognized as preventing head injuries in general; however, their efficacy and limitations in facial impact protection are underexplored. Biofidelic surrogate test devices and assessment criteria are lacking in current helmet standards. This study addresses these gaps by applying a new, more biofidelic test method to evaluate conventional full-face helmets and a novel airbag-equipped helmet design. Ultimately, this study aims to contribute to better helmet design and testing standards. MethodsFacial impact tests at two locations, mid-face and lower face, were conducted with a complete THOR dummy. Forces applied to the face and at the junction of the head and neck were measured. Brain strain was predicted by a finite element head model taking both linear and rotational head kinematics as input. Four helmet types were evaluated: full-face motorcycle and bike helmets, a novel design called a face airbag (an inflatable structure integrated into an open-face motorcycle helmet), and an open-face motorcycle helmet. The unpaired, two-sided student’s t-test was performed between the open-face helmet and the others, which featured face-protective designs. ResultsA substantial reduction in brain strain and facial forces was found with the full-face motorcycle helmet and face airbag. Upper neck tensile forces increased slightly with both full-face motorcycle (14.4%, p >.05) and bike helmets (21.7%, p =.039). The full-face bike helmet reduced the brain strain and facial forces for lower-face impacts, but not for mid-face impacts. The motorcycle helmet reduced mid-face impact forces while slightly increasing forces in the lower face. Significance of resultsThe chin guards of full-face helmets and the face airbag protect by reducing facial load and brain strain for lower face impact; however, the full-face helmets’ influence on neck tension and increased risk for basilar skull fracture need further investigation. The motorcycle helmet’s visor re-directed mid-face impact forces to the forehead and lower face via the helmet’s upper rim and chin guard: a thus-far undescribed protective mechanism. Given the significance of the visor for facial protection, an impact test procedure should be included in helmet standards, and the use of helmet visors promoted. A simplified, yet biofidelic, facial impact test method should be included in future helmet standards to ensure a minimum level of protection performance.

The Effectiveness of Two Driving Interventions on Real-World Driving Events in Combat Veterans

Abstract Date Presented 04/20/2023 The study findings will inform clinicians on efficacious driving interventions that mitigate the occurrence of real-world traffic violations and crashes for at-risk returning combat veterans. Primary Author and Speaker: Isabelle Wandenkolk Additional Authors and Speakers: Sherrilene Classen Contributing Authors: Abraham Yarney, Mary Jeghers, Sandra Winter, Justin Mason

The effect of seatbelt pre-tensioners and load limiters in the reduction of MAIS 2+, MAIS 3+, and fatal injuries in real-world frontal crashes

Contemporary research has pointed out that while newer cars are contributing to the decrease of AIS2+ and AIS3+ injuries in several body regions, this effect is not shown for thoracic injuries like rib or sternal fractures. The objective of this study is to assess the effectiveness of advanced seat belt systems incorporating pre-tensioners and load limiters in the prevention of fatal, AIS2+ and AIS3+ injuries overall and then focus only on the head-face-neck and thoracic areas. Data from the NASS CDS database between 2000 and 2015 was augmented with specific vehicle information taken from NHTSA’s NCAP tests to identify the characteristics of the seat belt of each vehicle involved in a collision. Multivariate logistic regressions were developed to assess the likelihood of injuries for belted front seat occupants in frontal impacts. The presence of pre-tensioners and load limiters with a low load limiter (<4.5 kN) was significantly associated with a decreased risk of fatal and AIS3+ in the whole body (OR = 0.31 (p < 0.05) and OR = 0.70 (p < 0.1)), while high load limiters were significant in the prevention of fatal injuries (OR = 0.42). These effects should be considered always in combination with the delta-v of the collision, as the interaction term between delta-v and advanced seat belt features was significant. In the crashes considered, the model predicted a higher risk of injury for women compared to men, controlling for other occupant and crash factors. Impacts with a slightly oblique component increased the risk of injury compared to pure frontal impacts. After controlling for the presence of pre-tensioners and load limiters, the vehicle model year variable was found to be insignificant in any of the regression models. This study shows that the real-world effectiveness of advanced seat belts still requires further analysis. Other effects like age or impact direction might be more influential in the injury outcome than these seat belt features.

A Clustering Regression Approach to Explore the Heterogeneous Effects of Risk Factors Associated with Teen Driver Crash Severity

Teen drivers remain one of the most long-standing traffic safety concerns, as they continue to be overrepresented in the fatal and injury crash statistics. No previous studies have explored significant variations in the effect of risk attributes contributing to teen driver collisions in multiple crash circumstances by degrees of crash severity. Therefore, substantial exploration of teen driver crashes is expected to facilitate the strategic employment of countermeasures effectively. This study aimed to analyze teen driver crashes to investigate the heterogeneous effect of contributing factors on crash severity outcomes. Three years (2017 to 2019) of police-investigated crash information was used for the state of Alabama. This research first applied latent class clustering to minimize the heterogeneity in the extracted dataset by dividing the data into meaningful clusters (subgroups of the whole data). Then, multinomial logit models were constructed to illustrate the significant risk factors influencing the severity outcomes in different crash scenarios. Marginal effects were computed to understand the impacts of variable categories better. The findings suggested that the significance and estimated impact of variables varied within clusters and between crash severity levels in the same cluster. The results of latent class segmented submodels represented real-world crash patterns demonstrating the cumulative effect of variable attributes. Such contextual understandings of underlying risk factors could help to strengthen existing teen driver educational interventions. In addition, the study outcomes could assist practitioners and policy makers in developing safety improvement strategies to reduce the causalities associated with teen driver crashes in distinct circumstances.

Investigating the Potential of a Scenario Catalogue for Automated Driving Safety Evaluation to Cover Real-World Crashes

Investigating the Potential of a Scenario Catalogue for Automated Driving Safety Evaluation to Cover Real-World Crashes