Rear-end Collision Warning System Research Articles

Rear-end collision warning of connected automated vehicles based on a novel stochastic local multivehicle optimal velocity model

Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system.

DSRC-based rear-end collision warning system – An error-component safety distance model and field test

Dedicated short-range communication (DSRC) technology can provide drivers with information about other vehicles that are beyond the normal range of vision and enables the development of driving support systems such as the rear-end collision warning system (ReCWS). However, technology constraints such as communication delays and GPS error affect the accuracy of a DSRC-based ReCWS. This paper proposes a ReCWS design that explicitly represents functional specifications of DSRC technology, including transmission delay specifications that describe the information transmission process and an error-component safety distance specification used to represent the effect of GPS error and the information propagation delay. We propose three collision warning strategies each with different deceleration requirements. The system is assembled with off-the-shelf DSRC and mobile technology that can be readily installed into test vehicles. To test the effectiveness of the proposed ReCWS, we ran a variety of controlled scenarios on a test track. The results show a high degree of warning accuracy. These field test results also provide calibrated system parameter values for future studies and designs of DSRC-based ReCWSs.

Event-Related Potential Responses of Motorcyclists Towards Rear End Collision Warning System

There are many types of collision warning systems to increase the safety of motorcyclists. These systems use different types of collision detection techniques with each one having some limitations, restricting the performance of the system. To find the effectiveness of collision warning system notifications, it is important to study the physiological response of drivers toward these systems. Existing studies are limited to the physiological response of car drivers and use only buzzer warnings for these systems. Unfortunately, no such work in that particular domain has been reported for motorcyclists. Since motorcycles have different maneuverability as compared with cars and other vehicles, it is important to investigate the response of motorcyclists toward these collision warning systems. Also, it is believed that providing verbal information about any potential hazard will further assist the motorcyclist to avoid it. The aim of this paper is to investigate the physiological responses of motorcyclists to the rear end collision warning system when auditory verbal warnings are utilized. To study the response of the motorcyclists, the N100, N200, P300, and N400 event-related potential components have been extracted from the recorded Electroencephalography data. It has been found that the rear end collision warning system with auditory verbal warnings significantly increases the alertness of the motorcyclist and can be helpful to avoid the possible rear-end collision scenarios. This system has shown positive effects at neural levels on motorcyclists and reduces their reaction time and attentional resources required for processing the target correctly.

Real-Time Associative Memory–Based Rear-End Collision Warning System

In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network–based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.

Effective Method for Rear-end Collision Warning System

Abstract The rear-end collision warning system is prone to give false positive warnings, which can interfere with the driver's concentration while driving, and very often, the high pitched warning sound used in these systems is found to be annoying, and drivers tend to turn the system function off. As a result, this research mainly focused on showing how human beings learn and behave unconsciously, and how a warning sound can be effective without being high pitched. This paper validates the effect of an inconspicuous alarm or warning sound, by testing on a driving simulator. The test subjects learned the false positive warning generation pattern also unconsciously, and they applied the brake quickly only in case of imminent danger.

Key Parameters Estimation and Adaptive Warning Strategy for Rear-End Collision of Vehicle

The rear-end collision warning system requires reliable warning decision mechanism to adapt the actual driving situation. To overcome the shortcomings of existing warning methods, an adaptive strategy is proposed to address the practical aspects of the collision warning problem. The proposed strategy is based on the parameter-adaptive and variable-threshold approaches. First, several key parameter estimation algorithms are developed to provide more accurate and reliable information for subsequent warning method. They include a two-stage algorithm which contains a Kalman filter and a Luenberger observer for relative acceleration estimation, a Bayesian theory-based algorithm of estimating the road friction coefficient, and an artificial neural network for estimating the driver’s reaction time. Further, the variable-threshold warning method is designed to achieve the global warning decision. In the method, the safety distance is employed to judge the dangerous state. The calculation method of the safety distance in this paper can be adaptively adjusted according to the different driving conditions of the leading vehicle. Due to the real-time estimation of the key parameters and the adaptive calculation of the warning threshold, the strategy can adapt to various road and driving conditions. Finally, the proposed strategy is evaluated through simulation and field tests. The experimental results validate the feasibility and effectiveness of the proposed strategy.

Research on Information Fusion Method to Improve the Measurement Precision of High Speed Vehicles Anti-Collision Distance

With the continuous development of the Internet of things technology, the high-speed vehicles to prevent rear-end collision warning system which is from the traditional passive to the current vehicle-to-vehicle communication active warning also continues to develop. In this paper, a vehicle ranging system is composed of Zigbee wireless communication and GPS positioning system. Through setting maneuvering model, establishing ranging system state equation and observation equation, error model, integrating the two vehicles’ distance, relative velocity, relative acceleration and the distance error, we can get the vehicle distance estimation by using of centralized Kalman filter techniques. The simulation results show that the method can provide vehicle-to-vehicle communication ranging system with the high precision data of anti-collision distance. The system has good tracking performance.

Research on a DSRC-Based Rear-End Collision Warning Model

Dedicated short-range communication (DSRC) is an emerging technology that allows vehicles to communicate with each other. The rear-end collision warning system based on DSRC has its unique advantages. However, there are problems (e.g., high rates of false alarms and missing alarms in emergency warnings) in the system due to uncertain measurement errors. In this paper, we propose to address the problems by establishing a robust rear-end collision warning model without using expensive high-end devices. Simulations have shown that high rates (up to 56%) of missing alarms occur in the vehicle kinematics (VK) model, as well as false alarms (most of which exceed 70%) in the VK model with maximum compensation (VK-MC). Pertaining to these rates, a novel model based on the neural network (NN) approach is implemented. Through training and validation, the NN model is able to provide emergency warnings with an improved performance of false alarm probability under 20% and the missing alarm probability under 10% for all test cases.

Anti-Rear-End Collision Warning System Based-on Zigbee for Highway Vehicle

The majority of traffic accidents on the highway is rear-end collision. According to statistics about ninety percent rear-end collision can be avoided if there is 1 second pretreatment time for drivers. So it is necessary to develop automotive rear-end collision warning system to prevent the occurrence of accidents. The anti-rear-end collision warning system based on Zigbee technology is designded in this paper. Vehicle speed, acceleration and other traffic information among the nodes can be exchanged on real-time by using self-organizing wireless sensor networks consisted by Zigbee network nodes. A vehicle safe distance model is established after considered the effects of other traffic safety factors, and then, the actual distance measured by radar is compared with the safe distance so as to provide early warning, alarm and other driving information to drivers. The experiment results show that the system can effectively provide early warning, avoid rear-end collision, and improve active safety of vehicles.

運転状況を推定し学習データを選択する適応的ブレーキ警報システム

The paper proposes a rear-end collision warning system for drivers, where the collision risk is adaptively set from driving signals. The system employs the inverse of the time-to-collision with a constant relative acceleration as the risk and the one-class support vector machine as the anomaly detector. The system also utilizes brake sequences for outliers detection. When a brake sequence has a low likelihood with respect to trained hidden Markov models, the driving data during the sequence are removed from the training dataset. This data selection is confirmed to increase the robustness of the system by computer simulations.

Rear-End Collision–Warning System: Design and Evaluation via Simulation

The design of an innovative rear-end collision-warning system was evaluated for effectiveness. The crash scenario involves a lead vehicle not moving (LVNM) in one lane of a straight, dry, paved arterial road and a following vehicle approaching in the same lane. The LVNM has a rearfacing sensor and is equipped with the rear crash-warning system, which allows the LVNM to flash its brake lights or its center high-mounted stop lamp, warning the following vehicle that it is approaching too rapidly. Because this problem is complex, the scope was narrowed, and it was assumed that the driver of the following vehicle always detected the warning after a response time lag and then applied hard braking. The warning algorithm (i.e., selecting the most appropriate warning distance for each approaching vehicle speed) was designed based on trade-offs to maximize the capability of preventing crashes, reduce the frequency of nuisance alarms, and minimize the severity of crashes. The overall measures of effectiveness of the warning design were then evaluated for a vehicle speed distribution that represented a suburban arterial road. The findings suggest that the warning system should be very effective in preventing crashes. The expected number of nuisance alarms was small, and for the very small percentage of vehicles that would crash, the expected crash severity was negligible. Experimental studies and field operational tests would be required to obtain more accurate numerical values for the design parameters.

Performance and functions of a rear-end collision warning system equipped with a small-sized laser radar

(1) To detect a stopped vehicle from a 120 meter distance and give an early warning from a 100 meter distance. (2) To improve practicality of the system under conditions of low transmission factor such as rain and fog. (3) To develop a compact laser radar of 600 cc capacity to achieve the above performance, capable of easy mounting on a vehicle. (4) To reduce the number of frequent unnecessary warnings which occurred when driving around a curve by use of the long distance detective radar. (5) To develop an air-blow type laser radar which can be used even in the snow. (6) To provide the system additionally with red, yellow, and blue warning lamps to alert a vehicle ahead to an approaching vehicle.