Real Crash Research Articles

Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

Objective Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers’ crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. Methods Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. Results The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). Conclusions The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Effect of Chain-Link Fence Attachment on the Crash Performance of an H1 Containment Level Safety Barrier

Longitudinal barriers are among the road safety equipment used to prevent vehicles from leaving the roadway. These systems are designed to be lightweight for economic reasons without compromising their structural adequacy. In this study, the effect of chain-link fence on impact severity and structural performance of a longitudinal barrier was investigated through finite element (FE) analyses. An H1 containment level longitudinal barrier FE model was validated using real crash test results. After modifying the validated system to reduce its weight, crash test simulations (TB11 and TB42) were conducted on the modified system, both with and without chain-link fence attachment. The chain-link fence was placed below the rail and in traffic side of post, in a manner that had not been applied before. FE analyses found that the chain-link fence minimally altered TB11 test performance. In TB42 simulations without a chain-link fence, the vehicle climbed over the rail, resulting in test failure. However, when chain-link fence is used, same barrier contained and redirected the vehicle, leading to a successful test. It was concluded that using chain-link fences can enhance the crash performance of longitudinal barriers by limiting the barrier lateral deformation. Further detailed studies, supported by real crash tests, on the placement of fences in barriers are recommended.

Enhanced Modeling of Crumple Zone in Vehicle Crash Simulation Using Modified Kamal Model Optimized with Gravitational Search Algorithm

The effectiveness of a vehicle crash system depends on how well it can simulate the behavior of a real vehicle in a crash scenario and accurately identifies the correct working limits of the model parameters, including mass, spring, and damper. Therefore, this study explores the modelling vehicle front crumple zone to represent the behaviors of real crash scenario. The modelling process using Kamal approach is used to develop a precise vehicle crash model for analyzing the impact of a collision on both the vehicle and its passengers. In this study, a complex mass-spring-damper system representing the front crumple zone of an actual car is re-designed to modify the existing vehicle crash model. The gravitational search algorithm (GSA) is implemented in the simulation model's code to obtain optimized values of damping coefficient (c) and spring constant (k). The simulation results show that the deformation response of crumple zone and the deceleration response of vehicle body match the experimental results, indicating the model's accuracy. Additionally, this study investigates the effects of varying the GSA parameters' number of agents (N), the beta parameter (β), and the gravitational constant (G) to improve the model's accuracy by minimizing the root mean square error (RMSE) between model response and crash test data. The optimal GSA parameter chosen in this study were N = 50, β = 0.3, and G = 20 with the lowest RMSE of 22.3874, 22.26664, and 23.86638 respectively.

A novel one-vs-rest consensus learning method for crash severity prediction

Research in crash severity prediction is necessary to allow safety planners to take precautionary measures and enable first aiders to remain prepared for assisting the injured. Existing literature in the field of crash severity prediction is mostly focused on generating the attributes for predicting the severity. However, in reality, not all features are discriminating, and certain classes are challenging to detect even employing the entire feature set. Although to tackle these problems several techniques are developed in the Machine Learning (ML) literature. But their application to crash severity prediction and an optimal strategy for the best combination of features or classifiers for achieving high accuracy is a less studied area. To address these problems, this work first provides a comparison of widely used classifiers for predicting crash severity; and secondly, by combining class-wise majority voting with One-vs-Rest (OvR) approach, a novel classification framework named, OvR consensus learning (OvRCL) is proposed. The proposed method avail a feature selection technique, Mutual information (MI), to acquire the most relevant feature set regarding the output class (i.e. severity). Moreover, to differentiate each class in the multi-class data, OvRCL iteratively runs ML algorithms as binary classifiers in an ensemble framework to significantly ameliorate classification performance. In our experiments, we use four classifiers, namely, the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Bagging classifier, to get the consensus. Analysis was done using a real crash dataset obtained from an open data source of Leeds city council. A four-year crash data (2015–2018) is used for training and the OvRCL is tested on the 2019 data. Moreover, to validate the performance of OvRCL, this study also utilizes two more datasets with high-class imbalance. In contrast to conventional ML algorithms, our experiments depictthat the OvRCL is a potent method for forecasting crash severity levels on the data under test.

Aberrant driving behaviors as mediators in the relationship between driving anger patterns and crashes among taxi drivers: An investigation in a complex cultural context

Objective Taxis have become an integrated component of Qatar’s urban transportation network due to their convenience, comfort, and flexibility. Qatar has seen an uptick in the demand for professional taxi drivers. Most Qatari taxi drivers come from developing countries with poor awareness of road safety; therefore, they regularly engage in aberrant driving behavior, leading to traffic violations and crashes. For taxi rides to be safer, it is essential to determine the association between driving aberration and road traffic crashes (RTCs), with an emphasis on the underlying factors that trigger these behaviors. Methods To this end, we collected the data from taxi drivers relying on standard questionnaires, namely the Driving Anger Scale (DAS) and the Driver Behavior Questionnaire (DBQ), together with the real crash data of the same taxi drivers obtained from the police department. We relied on factor analysis to identify the main factors of these tools and then structural equation modeling to predict their causal relationship with RTCs. Results The results indicated that the component of DAS, namely “illegal driving”, triggered all dimensions of aberrant driving behaviors, whereas hostile gestures had a positive correlation with lapses. In addition, the factor “error” was identified as a significant direct predictor, while the factor “illegal driving” was identified as a significant indirect predictor for RTCs. Regarding demographic characteristics, professional driving experience was found to be negatively associated with RTCs. Conclusion Driving aberration mediated the impact of driving anger on RTCs. The findings from this study could help road safety practitioners and researchers better understand these relations. In addition, these results could also be very helpful for driving instructors to train taxi drivers in a way to cope with provoking situations.

Finite Element Analysis and Investigation of Critical Impact Point of Steel Guardrails Affecting Safety and Structural Performance

After the guardrails are designed, the structural adequacy and safety criteria are determined by the relevant standards and full-scale crash tests. One of the widely used standards is European Norm 1317 (EN1317). Guardrail systems generally consist of rails and posts. The guardrails are more rigid around the posts, which are mounted on the ground or embedded in soil at certain intervals. Therefore, it is important for driver/passenger and roadside safety to determine the most critical point in terms of structural and safety performance and design according to the most unfavourable situation. With this motivation, in this study, the effect of different impact points on the structural and safety performance of the H1W4 guardrail was investigated by finite element (FE) analysis. For this purpose, first of all, the finite element models of the H1W4-A system were calibrated and validated with real crash test data. Then, with the help of the validated models, analyses were completed for different impact points as 0.5, 1.0, 1.5 and 2.0 meters with a half-meter difference for the standard 2-meter post spacing. In the light of the measured safety parameters such as Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV) and structural performance criteria such as working width (W) and exit angle (α), the critical impact point for the guardrail was determined. Contrary to what is generally known, crashing vehicles into flexible points (0.5 and 1.0 m) rather than impacting rigid points (1.5 and 2.0 m) creates a more negative situation in crash tests.

Comparison of Volunteers' Head Displacement with Computer Simulation-Crash Test with Low Speed of 20 km/h.

Recently, the automotive industry has used simulation programs much more often than experimental research. Computer simulations are more and more often used due to the repeatability of simulation conditions and the possibility of making modifications in simulation objects. Experimental and simulation studies carried out are aimed at developing a model of a simulation dummy adapted to both frontal and rear crash tests, taking into account changes in the moment of resistance in individual joints. The main purpose of the article is to reproduce a real crash test at a low speed of 20 km/h in a simulation program. For this purpose, a series of experimental crash tests with the participation of volunteers was carried out, and then a crash test with a dummy was simulated in the MSC ADAMS program. The experimental studies involved 100 volunteers who were divided into three percentile groups (C5, C50, C95). With the help of force sensors and a high-speed camera, crash tests of volunteers were recorded. The collected data from the force sensors made it possible to map the force in the seat belts. For low-speed crash tests, the displacement and acceleration of individual body parts of the dummy and volunteers can be measured using vision systems. The article identified head displacements of volunteers in the TEMA program based on a video analysis of a crash test film with a frequency of up to 2500 frames per second. The displacement of the simulation dummy's head in the MSC ADAMS program in the considered crash time interval from 0.0 to 0.4 s for all three percentile groups coincided with the head displacement of the volunteers during the experimental crash test.

Alignment of highly resolved time-dependent experimental and simulated crash test data

We investigate for car and component crash tests the comparison of highly resolved experimental data with corresponding simulation data. Due to recent advances for optical measurement systems, one can nowadays obtain surface measurement data from a real crash experiment with high resolution in space and time. These advances call for new data processing methods that allow an alignment of this experimental data with numerical simulation results. We propose an approach based on a data representation stemming from a discrete Laplace–Beltrami operator, which allows such an alignment as well as a joint visual comparative analysis of both data sources. The method enables the identification of the best corresponding simulation among several numerical results, which also allows inferring physical quantities that cannot be measured in experiments. We evaluate the procedure on synthetic and real experimental data from two different crashworthiness setups.

Real-time crash identification using connected electric vehicle operation data

As the automobile market gradually develops towards intelligence, networking, and information-orientated, intelligent identification based on connected vehicle data becomes a key technology. Specifically, real-time crash identification using vehicle operation data can enable automotive companies to obtain timely information on the safety of user vehicle usage so that timely customer service and roadside rescue can be provided. In this paper, an accurate vehicle crash identification algorithm is developed based on machine learning techniques using electric vehicles’ operation data provided by SAIC-GM-Wuling. The point of battery disconnection is identified as a potential crash event. Data before and after the battery disconnection is retrieved for feature extraction. Two different feature extraction methods are used: one directly extracts the descriptive statistical features of various variables, and the other directly unfolds the multivariate time series data. The AdaBoost algorithm is used to classify whether a potential crash event is a real crash using the constructed features. Models trained with the two different features are fused for the final outputs. The results show that the final model is simple, effective, and has a fast inference speed. The model has an F1 score of 0.98 on testing data for crash classification, and the identified crash times are all within 10 s around the true crash times. All data and code are available at https://github.com/MeixinZhu/vehicle-crash-identification.

An online hazard perception training course reduces heavy braking, speeding, and over-revving rates during everyday driving

In essence, driver training involves learning the skills required to drive safely and avoid dangerous events. However, in traditional on-road driver instruction, drivers virtually never accrue experience of the most significant types of events that they are learning to avoid: crashes. One means of providing this experience safely is to present novice drivers with video clips of real crashes, as part of structured learning exercises. A six-week automated online hazard perception training course for drivers, incorporating evidence-based training methods and over a hundred crash clips, was previously found to improve novice drivers’ hazard perception skill, which is known to be an important attribute for avoiding crashes. However, since hazard perception was measured using computer-based methods, the possibility remained that the training effect might not transfer to actual driving. We report a randomized control trial in which novice drivers were recruited to assess everyday driving behaviour objectively, using g-force triggered dashcams and GPS trackers installed in their vehicles. On-road data were collected for a one-month baseline period, and for a further two months after half of the sample completed the hazard perception training course. Drivers who completed the course significantly reduced their rate of heavy-braking events, their speeding behaviour, and their rate of over-revving events. These findings support the proposal that a relatively inexpensive and highly scalable hazard perception training intervention can improve on-road driving behaviour, with the clear potential to impact real-world driver safety.

Crashworthiness analysis of shrink circular tube energy absorbers with anti-climbers under multiple loading cases

Energy-absorbing structures with anti-climbers for railway vehicles rarely experience pure axial loading in real crash events, but rather a combination of axial and off-axial loads. In this perspective, it is critical to understand the non-centric collision process of energy absorbers. To address this issue, this paper aims to investigate the crashworthiness of shrink circular tube energy absorbers with anti-climbers under vertical, horizontal eccentric and oblique loads comprehensively. Finite element (FE) models of the shrink circular tube energy absorber with anti-climbers are then developed and validated by a physical impact experiment. The numerical results show that the different eccentric distances and oblique angles affect the crashworthiness of the proposed energy absorber significantly. The energy-absorbing structure exhibits a stable and controlled deformation pattern as well as a very high energy absorption efficiency in offset collisions at different vertical heights. However, when the horizontal offset distance exceeds half of the anti-climber width, the energy-absorbing circular tube suffers an overall destabilizing deformation, failing in the energy-absorbing capacity. Additionally, the proposed energy absorber is suitable for oblique load collision scenarios that the oblique angle is no more than 10°. Finally, the response surface modeling technique and non-dominated sorting genetic algorithm are employed to optimize the key structural parameters of shrink circular tube energy absorbers under multiple loading cases. The results exhibit that an optimized shrink circular tube energy absorber is more competent in crashworthiness for multiple load cases.

Dynamics of frontal crash in/without the presence of passive safety systems

The paper aims to debate on experimental retrieved accelerations from real crash tests, based on different scenarios, to enable assessment on driver potential injury. Scenarios included the presence of passive safety systems (e.g., airbag, seat belt) and test data are being processed to estimate the head injury criteria (HIC) followed by its correlation with abbreviated injury scale values (AIS). A dummy, average male resemblance, was developed in-situ and used in the experimental configuration. Signals were being acquired by few accelerometers disposed on the dummy’s head and thorax using a Pic DAQ system (DSD, Linz, Austria). Data processing and values of injury criteria underline the importance of passive safety systems proven HIC experienced a consistent decrease in the presence of both air-bag and seatbelt.

Estimated crash avoidance with the hypothetical introduction of automated vehicles: a simulation based on experts\u2019 assessment from French in-depth data

ObjectiveWe aimed to quantify, through simulations using real crash data, the number of potentially avoided crashes following different replacement levels of light vehicles by level-5 automated light vehicles (AVs).MethodsSince level-5 AVs are not on the road yet, or are too rare, we simulated their introduction into traffic using a national database of all fatal crashes and 5% of injury crashes observed in France in 2011. We fictitiously replaced a certain proportion of light vehicles (LVs) involved in crashes by level-5 AVs, and applied crash avoidance probabilities estimated by a number of experts regarding the capabilities of AVs depending on specific configurations. Estimates of the percentage of avoided crashes per user configuration and according to three selected (10%, 50%, 100%) replacement levels were made, as well as estimates taking into account the relative weight of these crash configurations, and considering fatal and injury crashes separately.ResultsOur simulation suggests that a reduction of almost half of fatal crashes (56%) and injury crashes (46%) could be expected by replacing all LVs on the road with level-5 AVs. The introduction of AVs would be the least effective for crashes involving a vulnerable road user, especially motorcyclists.ConclusionThis result represents encouraging prospects for the introduction of automated vehicles into traffic, while making it clear that, even with all light vehicles replaced with level 5-AVs, all issues would not be solved, especially for crashes involving motorcyclists, cyclists and pedestrians.

Effects of Deceleration on Secondary Collisions between Adult Occupants and the Vehicle in Frontal Crash Accidents

The paper seeks to highlight and analyze the relationship between the occupants’ displacements of chest and pelvis and the deceleration of vehicle in frontal crash accidents. A testing scheme including 5 groups of dynamic tests was devised and conducted, and totally 5 kinds of acceleration pulses were employed to simulate the real crash. The experimental finding indicates that the integral values and shapes of vehicle’s deceleration pulses can influence the occupants’ chest and pelvis displacements to some extent, thus having effects on the risks of secondary collisions between occupants and the vehicle. How the deceleration pulses of vehicle influence the secondary collision is also clarified in the paper by a comprehensive comparison of testing results between different groups. Further research can be carried out on the optimization of deceleration pulses of vehicle in the frontal collisions and on how to reduce the risks of secondary collisions based on the findings.

Learner drivers (and their parent-supervisors) benefit from an online hazard perception course incorporating evidence-based training strategies and extensive crash footage

We previously found that a six-session online hazard perception training course, which incorporates evidence-based learning strategies and footage of over a hundred real crashes, improved hazard perception skill and reduced risk-taking intentions in novice drivers who had passed their on-road driving test within the previous three years. However, one issue with targeting crash-prevention training at individuals who are already driving unsupervised is that drivers are at their highest crash risk immediately after they pass their on-road driving test. That is, the training may arrive too late to protect drivers while they are at their most vulnerable. It is also possible that it may prove difficult to persuade drivers to complete an unsupervised training course if they are already licensed to drive independently. Given that learner drivers cannot drive unsupervised, and that they are typically supervised by a parent, one potential strategy is to target the training at learners and to ask their parents to provide one-on-one mentoring throughout the course. We therefore recruited learner driver/parent-supervisor dyads to participate in a randomized control study, with the objective of examining the effects of the hazard perception training course on aspects of driving behaviour associated with crash risk (as measured using validated computer-based tests). Outcome measures included two hazard perception skill assessments (a response time hazard perception test and a verbal response hazard prediction test), and three tests assessing aspects of risk-taking propensity in driving (speed choice, following distance, and gap acceptance). Learners who completed the course (N = 26) significantly improved their scores on both hazard perception skill measures, and also chose safer following distances, compared with a waitlist control group (N = 23). However, the training did not significantly reduce learners’ speed choice or gap acceptance propensity. The hazard perception skill of parent-supervisors, who observed the course but did not complete it, also improved on both hazard perception measures, relative to controls. Additionally, both learners and their parent-supervisors reported a range of positive effects on the learners’ real-world driving performance. These results suggest that this type of hazard perception training could be beneficial if deployed during the learner phase of driver licensing.

A probabilistic approach to online classification of bicycle crashes

When a bicycle crash takes place, it is paramount for an emergency center to recognize the physical state of the cyclist as early as possible. However, an injured bicyclist may be incapable of making a phone call to the emergency center. In this study, we propose an online approach to classify bicycle crashes based on signals from an onboard inertial measurement unit (IMU), which can be used as a trigger function for an automatic emergency system. For this purpose, we define several bicycle crash features according to the kinematic properties of bicycle accidents. The input signals (variables) influencing the individual crash features are determined by the ANOVA method (analysis of variance). With the determined input signals, probabilistic models for each crash feature are trained on the base of logit models and 20,000 km naturalistic driving data including 20 real crashes. In addition, further crash and corner case data has been collected for the model training. A decision tree describing all probabilistic crash features is used to classify different kinematic events and crash scenarios. A series of driving tests with a crash-dummy and a crash-car are performed to verify the proposed crash classification approach, showing a sensitivity of 96.8%, a specificity of 99.6% and an accuracy of 99.5% of the trained model.

Energy absorption of arched thin-walled structures under transverse loading

Thin-walled beams are more frequently subjected to transverse or oblique loads instead of axial loads in real crash accidents. However, the energy absorption capacity of them under transverse loading is very much lower than that under axial loading. When subjected to transverse loads, arched structures tend to switch the transverse forces to axial forces, and are very promising to significantly improve the energy absorption and crashworthiness performance of structures. The present work is aimed at exploring the energy absorption characteristics of arched thin-walled structures under transverse loading. The static and dynamic three-point bending tests are first carried out for arched and straight thin-walled beams with a circular section. The force responses and deformation of the specimens are analyzed and compared. Numerical analyses are then performed to simulate the experiments, and the results are compared with experiments. The influences of various factors on the responses of arched structures are investigated numerically. To explore the optimal profile of arched thin-walled structures under transverse loading, a crashworthiness optimization problem is solved, and the optimal design shows excellent performances. Finally, partial foam filling is attempted to further improve the performances of the optimal design.

Vehicle occupant safety development with finite element method

AbstractCrash tests of vehicles are specified by government programs. This laws are includes only minimum requirements for individual components. Therefore additional consumer protection load cases have been developed by independent private institutes. Finite element method simulations can reduce development periods and the number of cost-intensive real crash tests. The goals of the calculations are that the early detection of component failure, the protection of occupants or pedestrians. The biggest challenge of the future, in the field of vehicle occupant safety is the interaction of the airbags and belt system with dummy by the electric vehicles, which have the concept of autonomous driving function. The aim of the research is to investigate this area using a simulation model.

Determining the Behavior of Door Impact Beam Tubes Under Three Point Bending Loading

In real crash cases, simple bending behavior is not common. In this case, using the three-point bending test to examine the bending behavior provides a closer simulation in terms of the representation of the structure under loading. It is seen in the literature that products with tube sections are generally used as door im-pact beams. On the other hand, when examining the three-point bending behavior of developed high strength steels, it is seen that the studies on tube profiles are only evaluated in terms of hydroforming. In this study, dual-phase steel tubes used as door impact beams are investigated by means of bending loading with experimental and numerical studies. For this purpose, DP500 and DP600 steel grades are used as materials. Three-point bending tests are performed for both materials then force-stroke curves, and springback values are obtained experi-mentally. In the second stage of the study, finite element analyses are performed using Hill-48 plasticity model. Force-stroke curves, the amount of springback, and the product forms are compared with the experimental results. It is seen that force-stroke curves and product forms are obtained highly compatible with the experimental results however the amount of springback values in finite element analyses are determined higher than the experimental results.

A thousand years of crash experience in three hours: An online hazard perception training course for drivers

A key goal of driver training is to teach drivers to avoid crashes. However, in traditional driver training, drivers are unlikely to see even a single example of the class of event that we want them to learn to avoid. We developed a six-session automated online hazard perception training course for drivers, which incorporates a range of evidence-based strategies and employs extensive video footage of real crashes. We evaluated this course in a randomized control trial by examining its effects on previously-validated computer-based measures of hazard perception, hazard prediction, speed choice, following distance, and gap acceptance propensity, as well as self-rated measures of driver skill, safety, and real world transfer. We found that the course resulted in significant improvements in hazard perception response time and hazard prediction scores, and significantly longer vehicle following distances. Additionally, all participants in the trained group reported that their real world driving behaviour had improved. No significant training effects were found for the other measures. The results suggest that the course can improve key behaviours associated with crash risk.