Rear-end Collisions Research Articles

Monocular Distance Estimation-based Approach using Deep Artificial Neural Network

Those in authority are evaluating the test evaluation for threat assessments currently in place. Since people often depend on their feelings and moods, this may create inequality. Therefore, this study suggested applying deep learning for Autonomous Emergency Steering (AES) and Autonomous Emergency Braking (AEB) assessments in the safety rating protocol. The suggested method for the test in situation-based threat assessments is a monocular distance estimation-based approach. The camera's objective is to make it simple to conduct assessments using only an onboard dash camera. This study proposes a method based on a monocular distance estimation-based approach for test methodology in the situational-based threat assessments using deep learning for the AES system to complement the AEB system for active safety features. Then, the accuracy of the distance estimation models has validated with the ground truth distances from the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset. Thus, the output of this study can contribute to the methodological base for further understanding of drivers the following behaviour with a long-term goal of reducing rear-end collisions.

Neurofilament light protein as a cerebrospinal fluid marker after whiplash trauma

The purpose was to investigate if a whiplash trauma may cause an increased concentration of the Neurofilament Light (NFL) protein, and if so; is this related to the injury severity and the radiological findings? Adult car occupants, with neck problems after rear-end collisions, were investigated in a study on Whiplash-Associated Disorders (WAD) in 1997-2001. The study protocol included a neurological examination, plain radiography and MRT of the cervical spine, and a lumbar puncture for Cerebrospinal Fluid (CSF) within six weeks after the accident. Similar CSF samples were also taken three and twelve months later. All CSF samples were analyzed for NFL. Of 52 subjects who entered the study, 43 completed it. The WAD grade was I in two of the 43 cases, II in 13, and III in 28. No one had radiological signs, indicating injuries to the cervical spine or spinal cord. Six subjects showed an increased NFL concentration at the primary examination. This was judged to be caused by whiplash trauma in three of them (7%). There was no relation between an increased NFL concentration and the number of pathological changes on plain radiographs or MRT. Neither was there a relation between the NFL concentration and the WAD grade. An increased NFL concentration can be found in some WAD patients. It might be difficult to relate such an increase to clinical or radiological findings. Further studies should investigate NFL as a marker for injuries to the central nervous system in whiplash trauma, including minimal traumatic brain injuries.

Modeling the influence of safety aid market penetration on traffic safety: Case of collision warning system for powered two-wheelers

Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU “Vision Zero” goal: “reduce road deaths to almost zero by 2050”. MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive, Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision.

Analyzing the time-varying patterns of contributing factors in work zone-related crashes

Work zones are crucial for maintaining and enhancing road infrastructure, but they also pose a significant risk to traffic safety. Between 2016 and 2020, work zone-related crashes in the United States increased by 13%, highlighting the pressing need for effective safety measures. This study examines the factors that influence work zone crashes, including traffic control devices, geometric configurations, traffic operations, and human factors. The study also investigates how these factors vary by work zone type, day of the week, and time of day. To explore these temporal and spatial impacts, the study utilized five years of fatal crash data from the Fatality Analysis Reporting System (FARS) and applied association rules mining. The findings demonstrate that rear-end crashes and collisions with other vehicles are the primary contributing factors. Although some common patterns emerged in the association rules, the study revealed temporal instability, highlighting the importance of developing work zone-specific safety countermeasures. These findings will inform the development of targeted safety interventions and ultimately reduce the risk of work zone crashes.

Flow and mixing dynamics in face-to-face and rear-end collisions of pairs of equal-sized droplets

In this work, the behaviors of pairs of equal-sized droplets in rear-end and face-to-face collisions were simulated using the improved lattice Boltzmann method for incompressible two-phase flows. First, the time evolution of the droplet shape was investigated by tracing colored particles, and this was compared between the rear-end and face-to-face collisions. For collinear collisions, the droplet shapes in the rear-end collisions were found to be similar to those in the face-to-face collisions. However, the behaviors of the tracer particles were different: the droplets in the rear-end collisions mixed more easily than those in the face-to-face collisions. For offset collisions, it was found that the rolling motion of the coalesced droplet accelerates the mixing inside it in both face-to-face and rear-end collisions. A new index—the total mixing intensity—was introduced, and the droplet mixing can be quantitatively evaluated by calculating its value. The results indicate that the droplet mixing process of a collinear collision can be characterized by the velocity ratio, which is defined as the ratio of the center-of-mass velocity to the relative impact velocity.

When and How to Apply Automatic Emergency Brakes Based on Risk Perception and Professional Driver Emergency Braking Behavior

<div>The key issues of automatic emergency braking (AEB) control algorithm are when and how to brake. This article proposes an AEB control algorithm that integrates risk perception (RP) and emergency braking characteristics of professional drivers for rear-end collision avoidance. Using the formulated RP by time to collision (TTC) and time headway (THW), the brake trigger time can be determined. Based on the professional driver fitting (PDF) characteristic, the brake pattern can be developed. Through MATLAB/Simulink simulation platform, the European New Car Assessment Programme (Euro-NCAP) test scenarios are used to verify the proposed control algorithm. The simulation results show that compared with the TTC control algorithm, PDF control algorithm, and the integrated PDF and TTC control algorithm, the proposed integrated PDF and RP control algorithm has the best performance, which can not only ensure safety and brake comfort, but also improve the road resource utilization rate.</div>

Applying the Accumulator model to predict driver’s reaction time based on looming in approaching and braking conditions

Introduction: The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver’s continuous reaction. However, these assumptions are not valid as the driver’s reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver’s reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver’s start of reaction based on the accumulation of looming and thereby reflects the driver’s intermittent reaction. Method: Fifty drivers’ behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off). Results: The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on. Conclusions: This study demonstrates that the Accumulator model is a promising method of predicting the driver’s reaction time in car-following situations, which affects rear-end crash risk. Practical Applications: The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

An integrated intelligent intersection control system for preventing rear-end and angled crashes: System design and deployment results

In view of the dynamic all-red extension (DARE) system’s effectiveness in preventing angled crashes (Park et al., 2018), this study has further enhanced its function to contend with rear-end collisions with dynamic green extension (DGE). With such a function, the enhanced Integrated Intelligent Intersection control system (III-CS) is capable of dynamically terminating the green at the interval of the lowest rear-end collision risk, so as to prevent undesirable “max-out” under actuated signal control which often traps some vehicles in the dilemma zone during high-volume traffic conditions. To ensure its effectiveness in practice, the proposed III-CS has been designed with the following new features: (i) executing the DGE within a customized time window of the green phase to ensure the signal’s effective coordination with its neighboring intersections; (ii) adopting the comparison-based heuristic for the DGE’s real-time risk prediction so as to circumvent the computing and communications delays. The results of two after-deployment assessments show that the system’s DARE has perfectly detected all red-light runners; 66.7 percent of the decisions by the DGE module were observed to achieve the control objective during the first field assessment. The DGE’s performance in making optimal decisions has improved over time and reached the level of 81.3% in the second field evaluation. Other measures of effectiveness, such as the number of vehicles trapped in the dilemma zone and the average deceleration rate of the driving populations approaching the target intersection, have also evolved to the anticipated trend after the deployment.

Investigation of Following Vehicles’ Driving Patterns Using Spectral Analysis Techniques

Despite the potential benefits of autonomous vehicles (AVs) of reducing human driver errors and enhancing traffic safety, a comprehensive evaluation of recent AV collision data reveals a concerning trend of rear-end collisions caused by following vehicles. This study aimed to address this issue by developing a methodology that identifies the relationship between driving patterns and the risk of collision between leading and following vehicles using spectral analysis. Specifically, we propose a process for computing three indices: reaction time, stimulus compliance index, and collision-risk aversion index. These indices consistently produced reliable results under various traffic conditions. Our findings align with existing research on the driving patterns of following vehicles. Given the consistency and robustness of these indices, they can be effectively utilized in advanced driver assistance systems or incorporated into AVs to assess the likelihood of collision risk posed by following vehicles and develop safer driving strategies accordingly.

A Two-Stage Sequential Framework for Traffic Accident Post-Impact Prediction Utilizing Real-Time Traffic, Weather, and Accident Data

Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables. With the arrival of the responders at the accident scene, more information is gained; therefore, the second stage model is activated, which updates the remaining accident duration time. We used eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, and Shapley Additive exPlanations (SHAP) for making predictions and interpreting results, respectively. The results show that our framework predicts traffic levels with overall accuracies of around 80%, and duration models have high forecast accuracy with mean absolute percentage errors ranging between 7.26% and 21.59%. Overall, in the absence of accident information, SHAP values identified that weather factors, the traffic speed difference before and after an accident, traffic volume, and the percentage of heavy vehicles before the accident are the most important variables. However, accident variables, including the occurrence of injury or fatal accidents, rear-end collisions, and the number of involved vehicles, are among the most important variables in the second stage of the framework. The findings have practical implications for real-time traffic management of accident events. Road operators could manage post-accident traffic conditions more effectively, and road users could be alerted to take another route or manage their trip.

THE RELATIONSHIP BETWEEN SPEED DIFFERENTIAL AND TIME TO COLLISION OF MALAYSIAN EXPRESSWAYS

Lane change (LC) manoeuvre has been recognized as an important aspect of driving behavior that significantly impacts traffic operation and management. Incorrect LC execution, particularly incorrect gap distance selection before the LC movement, can lead to traffic accidents, most often in the form of a rear-end, sideswipe, or angled collision. This paper investigates the relationship between time to collision and the speed differential between the leading and following vehicles when lane change occurs. Using an instrumented vehicle method, data was gathered along a typical length of the Kuala Lumpur-Seremban expressway. A VBox (Video Velocity Box) is an on-board data collection device that is used to videotape traffic incidents on the road, was installed in a passenger car. In a three-day period, a total of 175 instances of lane changing were documented. Following gap distance was used to calibrate the VBox equipment as a measure of efficiency. A simple linear regression was conducted between time to collision (TTC) and speed differential. It was found that 60% and 75% of drivers have TTC fewer than 5 sec and 10 sec, separately, with 6.10 sec average TTC. The time to collision (TTC) has a negative linear relationship with speed differential (R2 = 84.47%). The finding shows that the higher the speed differential between vehicles, the lower the TTC value, which indicates a higher probability of collisions. It can be concluded that the speed differential between the test car and following vehicles is affecting the TTC, which is to be utilised as a risk indicator throughout lane-changing operation.

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers.

Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated driving

Objective To practically apply level 2 automated driving in complex traffic conditions, it is necessary to prompt driver behaviors to prevent potential accidents in areas where manual interventions are frequently required. Methods A driving simulator experiment with 20 participants was conducted to evaluate the impact of different human machine interfaces (HMIs) on drivers’ interventions in terms of braking to avoid rear-end collisions during level 2 automated driving when a motorcycle abruptly cut in near intersections. Two types of HMIs were tested: a static HMI that informed drivers about approaching intersections, and a sensor HMI that displayed real-time object recognition results. Each driver participated in five experimental conditions, which varied the presence or absence of the static and sensor HMIs during level 2 automated driving, with manual driving serving as the baseline condition. Results The maximum deceleration in terms of braking to avoid rear-end collisions was significantly larger when level 2 automated driving was used without any HMI, compared to that of manual driving. However, when the sensor HMI was applied together with the static HMI during level 2 automated driving, a comparable time to collision could be achieved with a significantly smaller deceleration, compared to that without any HMI. Drivers’ eye-gaze behaviors revealed that no significant difference existed in the percentages of gaze to the road center area, indicating that they were not distracted by the HMIs. Finally, drivers’ attention levels to surrounding traffic and feeling of safety were significantly higher when level 2 automated driving was used in combination with the static and sensor HMIs. Conclusions The results demonstrated that the combination of static and sensor HMIs successfully aided drivers in ensuring driving safety with a significantly smaller deceleration to avoid rear-end collisions during level 2 automated driving. Furthermore, drivers’ attention levels were maintained, and their feeling of safety was improved when both HMIs were used in combination.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles (CAVs) rely heavily on intelligent algorithms and remote sensors. If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication, it can cause significant damage to CAVs or passengers. The primary objective of this study is to model cyber-attacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment. Based on the analysis on environmental perception system and possible cyber-attacks on sensors, a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed. The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lane-changing process is then quantitatively analyzed. The evaluation indexes include spatio-temporal evolution of average speed, spatial distribution of selected lane-changing gaps, lane-changing rate distribution, lane-changing preparation search time, efficiency and safety. Finally, the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack. Also, when the traffic system is under cyber-attack, more unsafe back gaps are chosen for lane-changing, especially in the center lane. Therefore, more lane-changing maneuvers are concentrated on approaching the off-ramp, causing severe congestions and potential rear-end collisions. In addition, as the number of cyber-attacked vehicles and the severity of cyber-attacks increase, the road capacity and safety level will rapidly decrease. The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

Optimized Active Collision Avoidance Algorithm of Intelligent Vehicles

This research introduces an innovative strategy to impede and lessen lateral and rear-end vehicular collisions by consolidating braking systems with active emergency steering controls. This study puts forward a T-type active emergency steering method, designed to circumvent both lateral and rear-end collisions at vehicular intersections. To secure vehicular stability and condense the time required for steering during the T-type active emergency process, this research formulates a nonlinear dynamic model for the vehicle, in addition to a nonlinear tire model. This study also engages in a thorough analysis of the constraints linked to the initial and terminal states of the steering process. The issue at hand is articulated as an optimization control problem with boundary value restrictions, which is subsequently resolved using the Radau pseudospectral method. Simulation results corroborate that the prompt commencement of the anti-collision strategy can effectively deter potential collisions. This pioneering approach shows considerable promise in augmenting the active safety of intelligent vehicles and bears meaningful implications for high-precision automotive collision evasion systems.

Long Term, Pre, and Post Impacts of SARS-CoV-2 Pandemic on Road Traffic Crashes in the Case of Budapest, Hungary

SARS-CoV-2 is a pandemic that affects road traffic flaw and crashes globally. This study attempted to compare the situation of road traffic crashes in the city of Budapest before and after the SARS-CoV-2 pandemic to better understand its long-term percussive effects. The study considers 12208 road traffic crashes that registered between 20 May 2018 – 31 December 2021. The rate and severity of road traffic crashes during the SARS-CoV-2 pandemic examined by using a percentage frequency distribution and a severity index. This study depicted that most crashes reported during the normal daytime between15:01-18:00 (peak hour). The study indicated that during the SARS-CoV-2 pandemic the road traffic crashes were reduced by 20.15%. A rear-end collision was one of the most common type of catastrophes highly registered. Road users, particularly drivers, heavily endorsed crashes. Even though the proportion of road traffic crashes caused by alcohol consumption was modest (6%), the rate of alcohol consumption and its concentration increased slightly during the SARS-CoV-2 pandemic. At the same time the number of crashes caused by high-speed traffic maneuvers reduced. Improper interpretation of road traffic signs, road pavement condition and failure to respect proper sight distance were influential reasons of road traffic crashes among the top. Meanwhile, the distributional impact of careless driving in the aftermath of the SARS-CoV-2 pandemic causes a shift in rank. Therefore, this study proved that during SARS-CoV-2 pandemics road traffic crashes reduced, the rate and concentration of alcohol consumption increased, and careless driving was encouraged.

The evaluation of decorated camera housings – A driving simulator study

Traffic enforcement cameras can lead to unwanted side-effects. For instance, red-light cameras at intersections can increase the chance at rear-end collisions, probably caused by slowing down abruptly. Additionally, on the highways, speed cameras could lead to kangaroo-jumps in driving speed. Warning drivers about cameras is a possible solution. Cameras that are uncommonly decorated (e.g. colored in pink or in yellow) may provide better conspicuity compared to those in standard colors like gray. However, besides warning drivers about their presence, more conspicuity of cameras could also distract drivers from the primary driving task. Therefore, the impact of such decoration on safety is unclear. This study evaluates different decorated designs of the camera housing and how such decoration may influence camera's conspicuity and induce distraction while driving.Three camera colored-designs were evaluated (gray, pink, and a mixture of yellow and black) using a driving simulator. Fifty-four participants drove nine different conditions, i.e. three camera-designs in three roadway-settings (basic highway segments, intersections operating during the amber-phase, and intersections with a bicycle-lane). Subjective and objective measures allowed to assess if: 1) decorated camera housings increased conspicuity, 2) signs of distraction could be found, and 3) driving was influenced. In addition to the detection success rate, other eye-tracking parameters were: time-to-first-fixation, fixation count, and average and total fixation duration. Driving measures, i.e. speed, acceleration or deceleration rate, lane-keeping behavior, and minimum time-to-collision, were included to assess if the tested camera-designs impacted driving behavior. Moreover, a post-drive questionnaire assessed the participants' opinions. Using camera colored-designs improved the camera's conspicuity. However, driving measures remained comparable across the three camera-designs. Although the subjective data indicated some distraction from the decorated cameras, clear indications for an effect on traffic safety were lacking. Meanwhile, a little over half of the respondents supported camera decoration. The current study does not allow firm conclusions, and additional research is recommended.

Model Predictive Control Based Multifunctional Advanced Driver-Assistance System Specialized for Rear-End Collision Avoidance

Model Predictive Control Based Multifunctional Advanced Driver-Assistance System Specialized for Rear-End Collision Avoidance

Performance and cost-effectiveness of air disc brakes and air drum brakes for truck semi-trailers in different road and speed conditions

This study uses TruckSimTM to model disc brakes and drum brakes on a fully loaded truck semi-trailer to study the performance of each brake type as downgrades and speeds vary. The brake performance is measured based on braking distance. A simplified economic comparison based on life cycle cost analysis to determine which road and vehicle conditions give rise to the cost-effectiveness of disc brakes is performed. The studies suggest that disc brakes shorten braking distances by 10-20%. They also suggest that the percentage reduction in braking distance as speed increases and downgrade gets steeper is approximately 12-19%. Evidence is provided that trucking companies operating their vehicles in steep terrain and at high speeds with disc brakes could benefit from 12-80% in long-term cost savings. Finally, at the societal level, by preventing crashes arising from rear-end collisions and runaway truck incidents, disc brakes save at least $649 million annually.

Automatic Emergency Collision Avoidance of Straight-Crossing Intelligent Vehicle at a 90-Degree Intersection Based on Vehicle-to-Everything Technology

<div>In order to reduce collision at a 90-degree intersection, an automatic emergency collision avoidance control method for intelligent vehicles based on vehicle-to-everything (V2X) technology is proposed. Most of the existing automatic emergency braking (AEB) control algorithms are designed for a single high-friction road with reference to the European New Car Assessment Programme (Euro NCAP) evaluation procedures, and they do not consider changes in road friction. Thus, it may be difficult to avoid collision successfully on a low-friction road. Although some studies have considered the variation of road friction, they are only applicable to straight-line rear-end collisions and cannot be directly applied to intersections. In addition, most studies regard the vehicle only as a particle, ignoring the actual dynamic characteristics of the vehicle. The main contribution of this article is to present an AEB control strategy by V2X technology, which can make the intelligent vehicle avoid collisions at a 90-degree intersection effectively. The proposed time-to-collision (TTC) adaptive algorithm has considered various road surfaces, and its effectiveness is verified by the co-simulation of Matlab/Simulink, CarSim, and Prescan on a typical urban intersection road.</div>