Rear-end Collisions Research Articles

Predominantly electric storage ring with nuclear spin control capability

A predominantly electric E&m storage ring, with weak superimposed magnetic bending, is shown to be capable of storing two different particle type bunches, such as helion (h) and deuteron (d), or h and electron (e -), co-traveling with different velocities on the same central orbit. Rear-end collisions occurring periodically in a full acceptance particle detector/polarimeter, allow the (previously inaccessible) direct measurement of the spin dependence of nuclear transmutation for center of mass (CM) kinetic energies (KE) ranging from hundreds of keV up toward pion production thresholds. With the nuclear process occurring in a semi-relativistic moving frame, all initial and final state particles have convenient laboratory frame KEs in the tens to hundreds of MeV. The rear-end collisions occur as faster stored bunches pass through slower bunches. An inexpensive facility capable of meeting these requirements is described, with several nuclear channels as examples. Especially noteworthy are the e+/--induced weak interaction triton (t) β-decay processes, t + e + → h + ν and h + e - → t + ν. Experimental capability of measurement of the spin dependence of the induced triton case is emphasized. For cosmological nuclear physics, the experimental improvement will be produced by the storage ring's capability to investigate the spin dependence of nuclear transmutation processes at reduced kinetic energies compared to what can be obtained with fixed target geometry.

New insights into factors affecting the severity of autonomous vehicle crashes from two sources of AV incident records

Superior safety is the main banner value of promoting autonomous vehicle (AV) technology, but it is difficult to responsibly claim it. The potential for AVs to reduce crash and injury risks would be overshadowed by technological limitations, regardless of their ability to mitigate or eliminate human error. This study aims to identify the key factors affecting crash severity by analyzing real-world AV crash data from the U.S. between 2015 and 2022. We integrated two open data sources from the California DMV and NHTSA. Mixed multinomial logit models incorporating the interaction effects were estimated using the crash severity level, addressing the observed and unobserved heterogeneities. Our results show that Advanced Driver Assistance System (ADAS) engagement is associated with a higher likelihood of slight injury crashes, whereas Automated Driving System (ADS) engagements show the opposite effect. In addition, we found that various environmental and road factors, including lighting conditions, weather, road type, and road surface conditions, significantly affect the severity of AV crashes. For instance, daylight conditions contribute to a lower likelihood of slight-injury crashes. On the other hand, driving under unfavorable weather conditions (cloudy, foggy, rainy, or snowy), on the highway, and on non-dry road surfaces are associated with an increase in the likelihood of severe injury crashes. Furthermore, several significant interaction effects were revealed. First, the mitigating effect of ADS engagement on the likelihood of slight injury crashes is reduced by the rear-end collision type. Second, the likelihood of slight injury crashes increases when AV interacts with heavy trucks on highways. Third, the likelihood of severe injuries increases when AVs collide with Vulnerable Road Users (VRUs) on urban streets. Overall, this research is expected to provide policymakers and AV manufacturers with valuable insights into AV safety, stressing that addressing the identified factors will lead to improved AV design and control algorithms.

Exploration of the Traffic Safety of Battery Electric Vehicles: A Case Study of Tesla Vehicle-Involved Crashes in Pennsylvania, USA

After decades of efforts, the number of battery electric vehicles (BEV) has increased greatly around the world. Meanwhile, their unique technical features also bring many new traffic safety challenges. Using traffic crash data from Pennsylvania from 2018 to 2021, this study aims to identify characteristics and trends of BEV crashes by comparing those crashes involving Tesla vehicles (the premier BEV brand in the auto market of the United States) with crashes involving internal combustion engine vehicles (ICEV). First, Tesla and ICEV crashes are compared for crash severity, collision type, spatial and temporal distributions, and environmental features. Tesla crashes show no significant difference from those of ICEVs in severity, but they have many more rear-end and angle collisions. In addition, Tesla crashes are found to concentrate in peak hours and around noon, occurring mainly in urban areas, at intersections, and on state roads. A logistic regression model is then built to identify the important factors influencing the severity of Tesla crashes. The findings are expected to provide new insights which will help researchers understand better traffic safety issues around BEVs.

Characteristics analysis and safety evaluation of vehicles turning over long distances

In order to explore the motion and safety characteristics of the U-turn vehicles at long-range U-turn intersections, based on the measured data of the U-turn vehicles at typical locations, we analyzed the characteristics of the U-turn vehicle's trajectories and traffic parameters with the help of probability theory and statistical knowledge, and established the U-turn trajectory model. We used Traffic Conflict Technology (TCT) and take Post Encroachment Time (PET) as the indicator to study the feature of vehicles security of - turn vehicles. Three conclusions are drawn from this study. First, there is a significant difference in the headway between long-range -turn vehicles and those at intersections without opening: the stability of the headway of long-range -turn vehicles is weak, and the release of vehicles is seriously disturbed by the turning flow; Secondly, when turning around at a long interval u-turn intersection, the starting position of vehicle turning is dispersed and the distribution of U-turn trajectories is quadratic distribution. Third, the setting of long interval turning lanes will increase the interference between traffic flows at intersections, thus increasing the number of conflicts with vehicles in the opposite direction. Cross-conflict accounts for the majority of conflicts in long interval U-turn intersections, and rear-end collision is the main conflict type in U-turn intersections without opening. The above research conclusions are helpful to improve the safety of long interval U-turn intersections.

Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm

This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes.

Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

Change in the collision avoidance performance of autonomous emergency braking systems

Objective This study quantifies the change in collision avoidance performance of autonomous emergency braking (AEB) systems for traffic accidents in Japan since 2015. Method This study used data on Japanese traffic accidents compiled by Japan’s National Police Agency. The data included only accidents involving loss of or injury to human life; accidents involving only property damage were excluded. We restricted our analysis to collisions between two 4-wheel vehicles and considered only collisions for which we could determine whether the primary party’s car was equipped with an AEB system. Both Poisson and negative binomial mixed-effects regression analyses were conducted using the data for 2021 and 2022 to measure the collision avoidance performance of first registered cars in 2015 to 2020 equipped with AEB systems compared with cars without AEB systems first registered in 2015. Collision avoidance performance was measured for 2 types of intervehicle collisions: rear-end collisions and right-turn collisions. Collision avoidance performance for rear-end collisions was also measured for each of the 3 car types—Standard, small, and light cars. Results The collision reduction rate for rear-end collisions increases with the year of first registration and for cars equipped with AEB systems first registered in 2020 compared with non-AEB-equipped cars first registered in 2015 is 69.2% (95% confidence interval [CI] 67.0%–71.1%), indicating that the performance of AEB systems has dramatically improved in terms of preventing rear-end collisions. For right-turn collisions, the rate increased to 20.4% (95% CI 5.9%–32.6%) for cars equipped with AEB systems first registered in 2019. However, no clear trend is observed. Conclusions This study evaluated a time series of the collision reduction performance of AEB systems using an original methodology. Japan’s New Car Assessment Program (JNCAP) has included AEB’s effectiveness in reducing damage from traffic collisions as an evaluation item since FY2014. The results could demonstrate the effectiveness of JNCAP.

Evaluating the Crashworthiness of Truck Rear-End Under-Run Bars: A Finite Element Analysis

Rear-end collisions involving trucks can have a direct impact on the safety of vehicle occupants, particularly due to the crashworthiness of the truck's rear-end under-run bar. These types of accidents, which occur between small vehicle and truck, contribute significantly to fatalities and serious injuries, presenting a significant public safety concern. This research paper aims to examine the collision effects on the rear-end under-run bar of a truck and its behaviour when it collides with another vehicle. The study employed the finite element method and simulated the under-run bar and impactor collisions using finite element software. The entire structure of the rear-end under-run bar had been designed, taking into account real parameters. The under-run bar structure was made of steel, and the impactor had a mass of 115kg, which resembled the mass of a motorcycle. A predefined field feature was utilized to apply a velocity of 30km/h to the impactor, which had a cylindrical shape. The element size of the mesh for the under-run bar was determined by using the mesh sensitivity technique. The simulation was tested a few times by using different sizes of elements. It is shown that the force reached a peak value of 28kN at a displacement of 68.25 mm, corresponding to the maximum under-run bar displacement. The data revealed that the energy absorbed increased from time 0.003s to 0.012s, reaching its peak value of 11.08kJ at 0.012s. The results show convincing results and prove the capability of rear-end under-run bar to be simulated and analysed using finite element simulation.

Enhancing work zone Safety: A psychological and behavioral analysis of Anti-Tailgating strategies using dynamic message signs and fixed warning signs

Rear-end collisions in work zones, particularly those resulting from tailgating, pose a significant safety challenge that has not been adequately addressed in road safety research. Existing studies have extensively investigated speeding, but the psychological triggers and behavioral patterns leading to tailgating remain largely unexplored. Bridging this critical gap, this research introduces an anti-tailgating strategy using dynamic and fixed message signs, designed with a deep understanding of driver psychology. The methodology involved a user-centric survey assessing the effect of positively toned, non-numerical anti-tailgating messages, which favored clearer comprehension and elicited favorable psychological responses from drivers. Field experiments demonstrated that these signs effectively increased headway and decreased tailgating, without the traffic disruptions associated with pavement markings or the technological limitations of in-vehicle alerts. These encouraging results underscore the potential of strategically placed message signs to positively influence driver behavior. This research not only fills a significant void by elucidating the behavioral factors of tailgating in work zones, but also paves the way for the implementation of behaviorally informed and nonintrusive road safety interventions. The use of dynamic and fixed message signs emerges as a promising, technologically inclusive, and psychologically effective tool for improving road safety.

Semiactive Car-Seat System for Rear-End Collisions

This study proposes and simulates a smart system that can be used in production car seats to decrease whiplash risk in rear-end crashes. A sliding seat incorporating a semiactively controlled magnetorheological (MR) damper model positioned under the seat-pan is simulated with a validated biofidelic human body model. Since this is the first study that demonstrates a computer controlled anti-whiplash car seat system to the best of the authors’ knowledge, a benchmark analysis is carried out to compare the proposed semiactive seat with a state-of-the-art passive anti-whiplash car seat using 23 different crash pulses, including the moderate and high severity crash pulses within the European New Car Assessment Program (EuroNCAP) whiplash risk assessment framework. The proposed semiactive design outperforms the passive seat design by further reducing the values of the critical EuroNCAP whiplash criteria, such as NIC and Nkm, together with the loads acting on the upper neck. The semiactive seat lowers the upper-neck shear force by an amount of 4 kg and 7 kg while lessening the NIC by 10% and 21% and Nkm by 9% and 56% for the EuroNCAP crash pulses, having a delta-V of 16 km/h and 24 km/h, respectively. The findings presented in this paper can aid in the design of car seats to further mitigate whiplash risk in rear-end crashes.

The implementation of a collision warning system in Formula racing

Advanced Emergency Braking (AEB) is used to reduce rear-end collisions in critical traffic scenarios. The racing environment contributes critical cases. In Formula races, critical collisions occur during cornering. Drivers may not concentrate on the boundary regions at high speeds. For this reason, an AEB system may support the drivers in such cases. On the other hand, this system is not permitted to be implemented in racing cars. Therefore, an AEB system may be implemented into the environment of the track by using several Long-Range Radars (LRR) at critical points. The originality of this study is the approach in the implementation of an AEB system into the racing environment as a collision warning system. The effectiveness of the implementation of the AEB system into the racing environment depends on the reaction time of the racing drivers. A fuzzy logic controller was proposed to control Time to Collision (TTC) during cornering according to the track geometry. The controller design was established in MATLAB/Simulink interface with the simulations in the IPG/CarMaker environment. According to the simulation results, the approach in the implementation of the AEB system prevented a pile-up.

The effect of taillight shapes and vehicle distance on rearward drivers’ hazard perception

Rear-end collisions are prevalent during night driving. Taillights are vital in conveying hazards between leading and following vehicles at night. Taillights highlight a vehicle’s presence, particularly for vehicles following from behind. Therefore, it is essential to design taillights that maximize the perception of hazards for drivers behind them. This study explored how different taillight shapes affect rear drivers’ hazard perceptions during nighttime driving. Two experiments were conducted using ERP measurements to study the effects of taillight shapes (three square types in Experiment 1: solid, array, and contour; two linear types in Experiment 2: through type and non-through type) and the distance to the leading vehicle on participants’ hazard perceptions. Participants responded to images of nighttime driving scenarios during the experiments while their ERP and behavioral data were recorded. The stimulus images showed the view from a driver looking at the vehicle ahead on a clear night. The neural process of hazard perception consists of two stages: automatic detection (early) and evaluation (later). In this study, P2 indicated attention bias to the stimulus during the detection stage, and LPP indicated the negative emotion triggered by the stimulus during the evaluation stage. Experiment 1 showed that solid-shaped taillights were perceived as more hazardous and processed faster than other square taillight shapes. Experiment 2 found that non-through-type and through-type linear taillights were perceived as more hazardous during the automatic detection and subjective evaluation stages, respectively. However, the behavioral data showed that through-type taillights were considered more hazardous and were associated with shorter response times. Thus, solid square taillights and through-type linear taillights can be optimal ergonomic solutions. These findings can serve as a reference for taillight designers, manufacturers, and potential car buyers regarding safety considerations.

Head injury mechanisms of the occupant under high-speed train rear-end collision

To improve the passive safety of high-speed trains, it is very important to understand the mechanism of head injury in high-speed train collisions. In this study, the head injury mechanisms of occupants in high-speed train rear-end collisions were investigated based on the occupant-seat coupling model, which included a dummy representing the Chinese 50th percentile adult male. The typical injury responses in terms of skull fractures, brain contusions, and diffuse axonal injury (DAI) were analyzed. Meanwhile, the influences of collision speed and seat parameters on head injury response were examined. The simulation results indicate that the skull fractures primarily occur at the skull base region due to excessive neck extension, while the brain contusions and DAI result from the relative displacement of different brain regions. The increase in collision speed will promote the probability of skull fracture, brain contusion, and DAI. Seat design modifications, such as reduced seat spacing, increased seat backrest angles, and selecting the appropriate cushion angle (76°) and friction coefficient (0.15), can effectively mitigate probably occupant's head injury.

Analyzing Rear-End Collision Risk Relevant to Autonomous Vehicles by Using a Humanlike Brake Model

Analyzing Rear-End Collision Risk Relevant to Autonomous Vehicles by Using a Humanlike Brake Model

Decision support for traffic safety: case of rear-end collision modelling

ABSTRACT This paper presents the decision problem of selecting a collision-warning activation algorithm for Rear-End Collision Alert Signal (RECAS) systems installed in motorcycles and other powered two-wheelers. Using Agent-Based Modelling, Discrete Event Simulation and System Dynamics, we have developed a hybrid simulation model of three scenarios of rear-end collisions to measure the performance of several algorithms found in the literature. The best-performing algorithm, by Hirst & Graham, has reduced the probability of collisions on average by 31.21% in comparison with the baseline scenario of using a standard brake light. Using this algorithm also resulted in up to 11.20% reduction in the estimated severity of injuries when a rear-end collision was not averted. As the Hirst & Graham algorithm was the most effective in all the tested emergency braking scenarios and across all the tested speed limits, we recommend this algorithm to be used in RECAS systems in powered two-wheelers.

The Effect of Changing the Angle of the Passenger Car Seat Backrest on the Head Trajectories of the 50th Percentile Male Dummy.

The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.

Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human–machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers’ responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers’ gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

Interaksi Faktor-Faktor Penyebab Kecelakaan di Jalan Tol dengan Kesenjangan Kecepatan sebagai Pemicu Kejadian Tabrak dari Belakang pada Malam Hari di Jalan Tol Cipali

Traffic accidents due to rear-end collisions are the highest contributor to the death rate in traffic accidents that occur on toll roads. It is suspected that vehicle, environmental, weather and driver factors as well as high speed gaps contribute to the occurrence of accidents. This study aims to determine the relationship between vehicle, environmental, weather and driver factors with the occurrence of speed gaps through observations of rear light brightness levels, vehicle visibility and speed. This study uses a descriptive observational method. The sample is a vehicle that passes through the Cipali Toll Road KM 188-108 towards Jakarta, on Sunday, January 29, 2024 at 19.30-21.00 WIB. The observer vehicle speed is 80-100 km/hour. The brightness level is measured using a luxmeter. The results of the study obtained dark weather, no rain, some toll roads did not have street lights, there were 316 minibus, bus and truck vehicles that passed or passed the researcher's route. A total of 240 vehicles (75%) of the minibus type had appropriate rear light brightness, visibility and speed. As many as 56 vehicles (23%) of the truck type had low rear light brightness, poor visibility, and speeds below regulations, resulting in a high speed gap which has a high potential to cause rear-end collisions.

Safety of cyclists interacting with autonomous vehicles: A combined microscopic simulation and SSAM analysis

The aim of this paper is to investigate the safety of cyclists while interacting with autonomous vehicles (AVs) in an uncontrolled T-intersection using PTV Vissim microsimulation combined with the Surrogate Safety Assessment Model (SSAM). For the microsimulation, three cycling behaviours were constructed (cautious, normal, and aggressive), and two AV behaviours (cautious and normal) were adopted. 87 h of microsimulation resulted in 230 conflicts. The type and nature of these conflicts, as well as two temporal indicators and four speed-related indicators were analysed. Nearly 96% of the conflicts were rear-end collisions, and almost 70% were conflicts where the cyclist was the first to arrive at the conflict point. Interestingly, the least involved cycling behaviour in conflicts was aggressive and the most involved was cautious. A significant relationship was found between severity clusters of temporal indicators and cycling behaviour. It is also concluded that an important future research direction would be the construction of a two-dimensional conflict diagram with severity thresholds specifically for bicycle-vehicle interactions.