Rear-end Collisions Research Articles

Analysis of crash severity in rear-end and angle collisions on an urban roundabout in heterogeneous non-lane-based traffic conditions

ABSTRACT This study examines crash risk factors and develops a severity model for rear-end and angle collisions in roundabouts and its vicinity in non-lane-based heterogeneous traffic. The rear-end severity is primarily influenced by posted speed limits, violation type, collision partner, single-vehicle collision, and vehicle age. Whereas type of violation, collision partner, and presence of road surface condition are the critical influencing factors for determining the severity outcomes of angle collision. The marginal effects of angle collision results indicated that the probability of fatal injury increased by 28% due to heavy-vehicle-vulnerable road user collisions. In the rear-end case, risk factors such as collision partners (heavy vehicles-vulnerable road users) increased by 18% and single-vehicle collisions increased by 25%, highly influencing the severity of fatal injury at roundabouts. Notably, rear-end collisions exhibit higher severity compared to angle collisions. This study provides valuable insights for developing strategies to mitigate roundabout collision severity.

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. While previous research acknowledges that human drivers adjust their behavior when sharing the road with CAVs, the underlying reasons and the extent of associated risks are not fully understood yet. The study focuses on how CAV presence can diminish conflicts, employing surrogate safety measures and real-world mixed traffic data, and assesses the safety and performance of freeway interchange configurations in Italy and the US across diverse urban contexts. This research proposes tools for optimizing urban layouts to minimize conflicts in mixed traffic environments. Results reveal that adding auxiliary lanes enhances safety, particularly for CAVs and rear-end collisions. Along interchange ramps, an exclusive CAV stream performs similarly to human-driven ones in terms of longitudinal conflicts, but mixed traffic flows, consisting of both CAVs and human-driven vehicles, may result in more conflicts. Notably, when CAVs follow human-driven vehicles in near-identical conditions, more conflicts arise, emphasizing the complexity of CAV integration and the need for careful safety measures and roadway design considerations.

A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles

A Study on Machine Vision-based Emergency Collision Avoidance Method for Longitudinal Rear-end Collision of Intelligent Vehicles

Adaptive Advanced Emergency Braking on Combined Road Friction Coefficients

In this study, an Advanced Emergency Braking System (AEBS) is adopted to the combined road friction coefficients by proposing an emergency steering maneuver. AEBS is one of the significant driver assistant systems to avoid rear-end collisions. AEBS supports the drivers with acoustic and optical warnings before applying the full braking maneuver. However, in special cases, such as the sudden decrease of road friction coefficient during braking, a single hard braking maneuver without a proper steering assist may not be sufficient to prevent a rear-end collision. Therefore, AEBS may work with the autonomous emergency steering systems to prevent inevitable rear-end collisions. The originality of this study arises from the necessity of the adaptation of the AEBS to work in cooperation with the autonomous emergency steering systems. A predictive controller was used to support the emergency braking maneuver with an autonomous steering maneuver via observing the yaw rate of the vehicle. The predictive controller was developed in MATLAB software. The implementation of the controller was performed in a non-linear four-wheel vehicle model in the IPG/CarMaker simulation environment. The communication between the simulation environment and MATLAB software was established in the Simulink interface of MATLAB. The results proved that the predictive controller maintained the vehicle lateral stability without causing any type of collisions.

Classifying safe following distance for motorcycles to prevent rear-end collisions

This study aims to classify motorcycle (MC) following distance based on trajectory traffic data and identify the risks associated with MC following distances to prevent rear-end collisions. A total of 8,223 events of a MC following a vehicle were investigated in Pathum Thani, Thailand, and 41 cases of MC rear-end crashes were analyzed between 2017 and 2021. Time headway (TH), safe stopping distance (SSD) and time to collision (TTC) were applied to the proposed concept to determine safe following distance (SFD). Speed and following distance for actual rear-end crashes were applied to validate SFD. Results showed that the proposed SFD model identified the causes of MC rear-end collision events as mostly due to longitudinal critical area (38 cases, 92.68%), implying insufficient MC rider reaction and decision time for evasive action. The longitudinal warning area had relatively few chances for rear-end collisions to occur, with only 3 cases recorded. VDO clip extracts from MC rear-end crashes illustrated 11 cases (26.83%) of rider fatality. The study findings revealed that the SFD concept can help to prevent MC rear-end collision events by developing reminder systems when the rider reached the following distances of both warning and critical areas.

Evaluating the Effectiveness of Contact-Analog and Bounding Box Prototypes in Augmented Reality Head-Up Display Warning for Chinese Novice Drivers Under Various Collision Types and Traffic Density

Augmented Reality Head-Up Display (AR-HUD) is a promising solution to the current warning system distraction problem. However, how to effectively convey warnings through AR graphics is still unclear. This study examines the effectiveness of the contact-analog graphic compared to the bounding box graphic in various collision types and traffic densities. Forty-eight participants watched AR-augmented driving videos and were instructed to respond to critical events. Reaction time, response rate, and subjective evaluations were compared for rear-end and pedestrian collisions in different traffic densities under different warnings. Both bounding box and contact-analog warnings improved driving performance compared to the non-warning group. The contact-analog warning performed better for rear-end collisions, while the bounding box warning had a lower reaction time for pedestrian collisions, regardless of traffic density.

Investigating the impacts of autonomous vehicles on crash severity and traffic safety

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California’s Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper’s aims are multi-faceted: to understand AVs’ operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public’s safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified.

Risk analysis of rear-end collision scenario

Risk analysis of rear-end collision scenario

Analysis of following vehicle driver injury severity in rear-end collision on straight road based on LightGBM and SHAP

Rear-end accidents, as one of the common accident types, may cause serious injuries to the driver and passengers. There are mutual coupling effects among causal features of rear-end accidents, and traditional analytical methods may lead to modeling distortion due to assumption constraints. In this study, the Light Gradient Boosting Machine (LightGBM), a machine learning algorithm, is used to model the injury severity of the following vehicle driver in a rear-end crash accident on a straight road, based on the data from the China In-depth Accident Study data. The Shapley Additive Explanations (SHAP) method was used to interpret the results of the LightGBM model and to analyze the relationship between factors and driver injury severity. The results show that location familiarity, willingness to take risks, and driving time have a significant impact on the injury severity for following vehicle drivers involved in rear-end crashes. Cloudy conditions increase a driver’s risk of being involved in a fatal or injured rear-end collision. In rain, snow, hail, and foggy conditions, have a higher propensity to cause driver fatalities in the crash event. The rear-end crash of passenger cars resulted in a higher death probability of the following vehicle driver compared to sedan and truck, and female drivers are more likely to be involved in uninjured accidents compared to males. These results are informative for preventing rear-end accidents and reducing the extent of accidental injuries.

Evaluating the effectiveness of engineering countermeasures at yellow dilemma zone: a driving simulator-based study

ABSTRACT The indecisiveness of drivers at the yellow onset creates a dilemma zone (DZ) upstream of the signalized crossings. The yellow light dilemma negatively influences intersection safety, often leading to rear-end and right-angle collisions. This study explores the impact of upstream road marking and active in-vehicle warnings designed based on DZ boundaries. The prototype concepts of in-vehicle warning systems and upstream road marking were designed and implemented in the driving simulator environment. The driver responses from the experimental setup were captured to evaluate the effectiveness of the designed DZ countermeasures. Study results indicate that implemented DZ countermeasures (i.e. DZ road marking, in-vehicle display warning, and in-vehicle audio warning) significantly decreased the drivers’ crossing probability, resulting in a shorter dilemma zone. However, the proposed countermeasures did not show any improvement in the drivers’ red-light running (RLR) behavior. The driving simulator study showed promising results in reducing aggressive crossing responses at the yellow onset.

Conflict-Based safety evaluations at unsignalized intersections using surrogate safety measures

Conflict-based road safety assessments may provide a deeper insight into the processes leading to crashes compared to assessments solely based on field crash data. The evaluation of road safety is conducted on specific road segments using different surrogate measure of safety indicators, such as temporal, spatial, and kinematic proximity measures, depending on the relevant context and applicability of these measures. Therefore, this study endeavored to develop a methodology by adopting safety measures such as post encroachment time (PET) and conflicting speeds of through vehicles for crossing maneuvers and time to collision (TTC) for rear-end collisions at five unsignalized intersections in urban mixed traffic conditions. Critical conflicts are calculated by calculating a speed variable known as the critical speed, which is based on the braking distance. A study found that the motorized two wheeler (MTW) categories involve the highest proportion of critical conflict with right-turning vehicles, followed by cars, autos, and light commercial vehicle (LCVs). Furthermore, crossing conflicts were modeled as a function using the generalized linear regression approach. The findings revealed that the most significant factors were traffic volume and vehicular composition in a conflicting stream. The unsupervised classification technique k-mean clustering was used to determine the defined severity level threshold for rear-end maneuvers. The result observed was that a TTC threshold of less than 1.15 s was identified as high-risk vehicular interaction.Additional investigation indicated that presence of certain moving vehicle categories, including MTWs and cars, led to a higher proportion of critical crossing conflicts. The conceptualized safety framework can be applied to evaluate safety at unsignalized intersections in the mixed traffic scenarios.

Understanding key factors in automated vehicle collisions: Automating data extraction and analyzing key insights using explainable AI

With the growing development and deployment of automated vehicles (AVs), it is crucial to understand the associated risks and factors contributing to collisions involving AVs. California possesses an immense amount of publicly available data from AV testing due to the requirement laid out by the California Department of Motor Vehicles (DMV), which requires all automated vehicle operators to report collisions for any level of severity. However, this information is reported in specific forms and requires a laborious task to aggregate data from these reports. This study creates an automated data extraction system for these reports and analyses collision characteristics using logistic regression models as well as XGBoost models with SHapley Additive exPlanation (SHAP) interpretation. Additionally, these characteristics are matched with the characteristics of non-automated vehicles (non-AVs) for the same region. The results of the study indicate that rear-end collisions are the most common type of collision observed in currently deployed AVs. The analysis further revealed an increased likelihood of injury-prone rear-end collisions in AVs at intersections compared to non-AVs. Transportation policymakers and researchers should take these safety concerns into account when addressing AV deployment and developing appropriate measures to mitigate collision risks in mixed fleet conditions.

Prioritizing rear-end crash explanatory factors for injury severity level using deep learning and global sensitivity analysis

Traffic accidents are usually unique events with unpredictable geographical and temporal dimensions; thus, accident injury severity level (INJ-SL) analysis presents formidable categorization and data stability problems. Classical statistical models are limited in their ability to correctly model INJ-SL, whilst sophisticated machine learning approaches do not appear to have any equations to prioritize/analyze multiple contributing factors to forecast accidents accompanying INJ-SLs. In addition, the intercorrelations between the input variables may render the conclusions of a formal sensitivity analysis incorrectly. Rear-end collisions are the most common form of traffic accidents; consequently, their linked INJ-SL requires more research. This paper provides a complex technique based on a deep learning paradigm paired with different indicators of Global Sensitivity Analysis to address all of these concerns. Unlike existing neural network designs, this technique presents a deep residual neural network structure that employs residual shortcuts (i.e., connections). The connections enable the DRNNs to sidestep a few levels of the deep network architecture, evading the regular training with high accuracy issues. Using the trained DRNNs model, a Latin Hypercube sampling simulation was undertaken to determine each explanatory component's influence on the resulting INJ-SL. The latest available data from 2011 to 2018 is used to assess all rear-end collisions in North Carolina. A comparison was made between the performance of two different schemes of data categorization using a set of global sensitivity metrics. It was determined that the devised technique overcame the data heterogeneity problems to achieve an accuracy of 87%. In addition, the proposed sensitivity analysis identified the most relevant factors associated with INJ-SL rear-end collisions.

Conventional or parking-protected bike lanes? A Full-Bayesian before-and-after assessment

Objective Biking infrastructure plays a crucial role in ensuring cyclists’ safety and encouraging more people to bike. Recently, many North American municipalities started to adopt a new bike lane design, namely the parking-protected bike lane (PPBL), in which the bike lane is placed between the sidewalk and the parking lane. This study aims to assess the safety impacts associated with converting conventional bike lanes (CBLs) to PPBLs. Methods To that end, collision and traffic data were collected at 19 street sections from three corridors in Vancouver and Ottawa before and after the conversion. Poisson-Lognormal Linear Intervention model was developed to undertake a Full Bayesian before-and-after analysis to evaluate the change in the frequency of bike-vehicle collisions and other collaterally affected collisions (i.e., total and rear-end collisions) after implementing PPBLs. Results Reductions of 31.2%, 16.5%, and 4.4% were observed for total, rear-end, and bike collisions, respectively, after implementing the PPBLs, but the results varied significantly depending on the corridor characteristics. Conclusion Overall, PPBLs demonstrated positive impacts on cyclist safety in some corridors, but their performance is highly sensitive to bike path opening density, intersection density, and intersection treatments.

A New Early Warning Method of Train Tracking Interval Based on CTC

For railway operations worldwide, train rear-end collision accidents occasionally occur, resulting in heavy casualties and property loss. To avoid the occurrence of such incidents, a novel early warning method of train tracking interval based on centralized traffic control (CTC), or simply CTC interval prewarning, is proposed. When combined with the existing automatic train protection (ATP), the monitoring of train tracking interval is doubled. The proposed method calculates the minimum safety interval in real time and compares it with the actual spatial distance. When the actual spatial distance is less than the minimum safety interval, particularly when the ATP is at fault, the proposed method is capable of raising the alert and taking control measures to effectively avoid rear-end collision accidents. At the same time, this method displaces the manual supervision of train operation, thereby relieving labor intensity, and improves the effectiveness of supervision. This paper analyzes the necessity and feasibility of the proposed CTC interval prewarning method and explains the basic architecture of the system, data acquisition, tracking interval calculation, prewarning rules, and implementation. The CTC tracking interval calculation model has been established, and the table of warning distance of the different train speeds has been calculated. The reliability model of the train control system (TCS) has been established based on the CTC interval prewarning method, which is used to analyze the reliability and safety of the TCS.

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles.

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

A method for estimating the vehicle impact speed in collision with stationary vehicle or barrier using camera video record

In Korea, most drivers equip their vehicles with a black box camera, used to estimate the speed of the vehicle in case of traffic accidents such as car-to-car collisions, single-car accidents, and car-to-pedestrian accidents. Traditionally, speed estimation using the camera video has been done by using the distance between the road lane, surrounding structures, etc.However, when the following vehicle collides with a stationary leading vehicle or barrier, and no road lanes or structures are visible around the driving path in the black box camera video of the following vehicle, the speed of the following vehicle could be just estimated according the deformations on the collided vehicles or objects.Therefore, in this study, when a vehicle collision occurs, a virtual parallel line is drawn between the leading vehicle and the following vehicle in the black box camera image of the following vehicle and analyzed geometrically to estimate the distance and the speed of the following vehicle.After the vehicle collision experiment, the speed was estimated using the method proposed in this study using the captured black box video. As a result, it was confirmed that the accuracy was high when the collision speed was low. So The method proposed in this study is expected to be used to estimate collision speed in the cases of low impact speed in the rear end collisions.

Examination of the Factors Influencing the Electric Vehicle Accident Size in Norway (2020–2021)

With the great increase of electric vehicles (EVs) in the past decade, EV-involved traffic accidents have also been increasing quickly in many countries, bringing many new traffic safety challenges. Norway has the largest EV penetration rate in the world. Using the EV accident data from Norway in 2020 and 2021, this study aims to investigate the features of EV safety comprehensively. Firstly, a descriptive analysis is conducted. It has been found that rear-end collisions are the major collision type of EVs, and EVs are very likely to collide with pedestrians/cyclists. In addition, in terms of roadway type, EV accidents mainly occur on medium- and low-speed roads; in terms of environment, they mainly occur in good visibility conditions and dry road surface conditions. Then, a regression analysis is conducted to identify the key factors affecting the accident size, which is the number of traffic units involved in an accident and taken as the accident severity surrogate here. Since EV accidents are divided into four categories in order of accident size, the ordered logit model is adopted. It divides a multi-categorical dependent variable into multiple binary data points in order and calculates the probability of the dependent variable falling into each category with the logit model, respectively. The estimation results indicate that time of day, speed limit, and presence of medians have statistically significant impacts on the EV accident size. Finally, some countermeasures to prevent EV accidents are proposed based on the research results.

Investigating the impact of influential factors on crash types for autonomous vehicles at intersections

Autonomous Vehicles (AVs) are being promoted as an emerging technology with the potential to improve traffic efficiency and safety. However, the scarcity of publicly available AV crash data contributes to a limited understanding of safety issues for deploying AVs. To gain a deeper understanding of the factors contributing to AV crashes, this study created a unique dataset by combining 445 intersection AV crashes from the California Department of Motor Vehicles with detailed roadway geometric and traffic characteristic data. To address the issue of imbalanced data, the Borderline synthetic minority oversampling technique (BL-SMOTE) was utilized in this study. A framework for analyzing AV crash types at intersections was also developed using the Categorical gradient boosting (Catboost) and Bayesian networks (BNs) models. The results show that inappropriate vehicle maneuvers, such as sudden lane changes or speed control, can increase the likelihood of an AV being involved in a sideswipe crash. Additionally, in scenarios where the AADT of the major road is lower, AVs are more prone to rear-end crashes when proceeding. These findings can provide valuable insights for creating safety management strategies in different situations and improve the development of innovative car warning technologies, such as rear-end collision warnings.

The Impact of Connected and Autonomous Vehicle Platoon’s Length on Expressway Traffic Flow Characteristics Based on Symmetry Lane Changing Rules

This study mainly investigates the maximum length of CAV (Connected and Autonomous Vehicle) platoons in a heterogeneous traffic flow environment. By employing MATLAB to simulate the heterogeneous traffic flow on expressways, this study focuses on the maximum platoon length of CAV platoons and explores their impact on the traffic flow characteristics on expressways. Firstly, based on four different car-following modes of heterogeneous traffic flow, F-STCA (Flexible–Symmetric Two-Lane Cellular Automata Model) and the symmetric lane-changing strategy, the study refines and improves the construction of the NaSch (Nagel and Schreckenberg) model introduced into the Gipps safety distance formula. The whole improvement process is based on the acceleration decay characteristics of vehicles on expressway. Secondly, the congestion situations under different maximum platoon lengths are compared using fundamental heat maps of the simulation data. The evolution of the fundamental diagram with changes in maximum platoon length is studied to investigate the impact and magnitude of maximum platoon lengths on the road capacity under different CAV permeabilities. Finally, the study explores the stability and safety of heterogeneous traffic flow involving CAV platoons using SD (Standard Deviation) and TERCRI (Time-Exposed Rear-End Collision Risk Index). The results show that when the CAV’s permeability does not reach a high level on heavily trafficked sections of the expressway, considering the overall average speed, the maximum platoon length should not be set too long and should be around five vehicles. This restriction does not apply when the CAV permeability approaches 100%.