Rear-end Collision Avoidance System Research Articles

REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles

Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.

Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.

IoT based rear-end collision avoidance system in highways

IoT based rear-end collision avoidance system in highways

Behavioral Analysis of Drivers Following Winter Maintenance Trucks Enabled with Collision Avoidance System

Winter maintenance trucks (WMTs) often operate at lower speeds during inclement weather and roadway conditions, creating potential safety issues for motorists following close behind. In this study, a new prototype radar-based rear-end collision avoidance and mitigation system (CAMS) was tested to assess its impact on the behavior of drivers following WMTs. The system is designed to flash an auxiliary rear-facing warning light upon detection of a vehicle encroaching within an unsafe relative headway with the rear of the WMT. A series of field evaluations was performed during actual winter maintenance operations to assess the effectiveness of the system compared with normal operating conditions (i.e., without the CAMS warning light) toward improving driver behavior related to rear-end crash risk. Specifically, two measures were assessed: (a) rate of vehicles encroaching beyond a safe time headway threshold to the rear of the WMT, and (b) the reaction–response time of drivers. Classification and regression tree models were created for identifying the relevant factors influential in determining the change in driver response. The results indicate that this warning light was effective in reducing the likelihood of the subject drivers crossing beyond a relative headway of 4.5 s. It was also effective in reducing the reaction and response times of the drivers by 0.83 and 0.55 s (36% and 20% reduction), respectively. Although the results were encouraging, additional field testing is recommended before conclusions are drawn regarding the traffic safety impacts of the system.

Effective optimisation of Honda algorithm for rear end collision avoidance system with genetic algorithm

Honda is one of the most popular algorithm for rear end collision avoidance system, but does not intend to avoid all the accidents, this due to that it gives a smaller range of warning which cannot avoid the accidents at high speed, to overcome this issue we proposing the optimisation of warning range with a genetic algorithm to minimise the probability of collision. The results show that proposed algorithm avoids 7% more accident than the standard Honda algorithm. We presented the MATLAB implementation of proposed work with examples to show the efficiency of our work.

Effective Optimization of Honda Algorithm for Rear End Collision Avoidance System with Genetic Algorithm

Honda is one of the most popular algorithm for rear end collision avoidance system, but does not intend to avoid all the accidents, this due to that it gives a smaller range of warning which cannot avoid the accidents at high speed, to overcome this issue we proposing the optimisation of warning range with a genetic algorithm to minimise the probability of collision. The results show that proposed algorithm avoids 7% more accident than the standard Honda algorithm. We presented the MATLAB implementation of proposed work with examples to show the efficiency of our work.

Hardware Simulation of Advanced Driver Assistance Systems Based on Fuzzy Logic

Advanced Driver Assistance Systems (ADAS) is a system that has the function of helping the driver to be safe the trip without reducing the convenience. The subsystems developed in ADAS among other Automatic Braking System (ABS) which functions to make braking decisions when there is an object in front of it, Rear-end Collision Avoidance System (RCAS) that serves to avoid rear-end collision, Active Lane Keeping Assist (ALKA) which serves to keep the vehicle from getting off lane from its proper lane, and Adaptive Cruise Control (ACC) which serves to adjust the distance of the car with another car in front. This paper discusses the development of fuzzy logic based algorithms for those subsystems. The developed algorithm test was conducted using experimental car. The test results on the three variations of speed: slow-speed, medium-speed, and high-speed shows that the algorithm developed of ABS, RCAS, ALKA, and ACC can work according to design.

Heavy vehicle collision avoidance control in heterogeneous traffic using varying time headway

A rear-end collision avoidance system (RECAS) could reduce accidents by assisting drivers during emergencies through autonomous braking. If the decision to intervene is made too early, the intervention can become a disturbance to the driver and if the decision is made too late, the safety benefits of the system will be reduced. Hence, proper threat assessment is important to make a decision for intervention in a RECAS. In this study, a new threat assessment measure using a varying time headway has been proposed to develop a rear-end collision avoidance algorithm (RECAA) for heavy commercial road vehicles in heterogeneous traffic. The proposed varying time headway depends on the type of lead vehicle, host vehicle load conditions, road conditions and relative longitudinal speed. The developed RECAA was implemented in a hardware-in-loop experimental setup that is equipped with the vehicle dynamic simulation software, IPG/TruckMaker® and tested for vehicle following scenarios using different types of lead vehicles and host vehicle loading conditions. The results showed that the proposed varying time headway improved the RECAA by activating the host vehicle's brake system at different time instants based on the type of lead vehicle and thereby preventing the unintended early brake intervention of RECAS.

A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles

Recently, the deep learning schemes have been well investigated for improving the driving safety and efficiency in the transportation systems. In this paper, a probabilistic model named as CPGN (Collision Prediction model based on GA-optimized Neural Network) for decision-making in the rear-end collision avoidance system is proposed, targeting modeling the impact of important influential factors of collisions on the occurring probability of possible accidents in the Internet of Vehicles (IoV). The decision on how to serve the chauffeur is determined by a typical deep learning model, i.e., the BP neural network through evaluating the possible collision risk with V2I (Vehicle-to-Infrastructure) communication, V2V (Vehicle-to-Vehicle) communication and GPS infrastructure supporting. The proper structure of our BP neural network model is deeply learned with training data generated from VISSIM with multiple influential factors considered. In addition, since the selection of the connection coefficient array and thresholds of the neural network has great randomness, a local optimization issue is readily occurring during the modeling procedure. To overcome this problem and consider the ability to efficiently find out a global optimization, this paper chooses the genetic algorithm to optimize the coefficient array and thresholds of proposed neural network. For the purpose of enhancing the convergence speed of the proposed model, we further adjust the studying rate according to the relationship between the actual and predicated values of two adjacent iterations. Simulation results demonstrate that the proposed collision risk evaluation framework could offer rationale estimations to the possible collision risk in car-following scenarios for the next discrete monitoring interval.

Autonomous rear-end collision avoidance using an electric power steering system

A step towards fully autonomous vehicles can be achieved by autonomous collision avoidance. This paper presents the development and implementation of a rear-end collision avoidance system which consists of novel linear threat assessment, projected escape path planning with non-zero initial condition, reference generation and linear path control by an electric steering actuator using dynamic inversion. The linear threat assessment continuously calculates threat metrics associated with collision avoidance by either steering or braking. The escape path planner continuously estimates a path with constraints on the peak lateral acceleration and the required lateral deviation. The reference generator then estimates the states for avoidance according to the available friction. Linear state feedback control is applied for path following to avoid a collision in both straight-road scenarios and curved-road scenarios. The proposed approach is evaluated in simulations, and in practice on a C-segment Ford vehicle. A user-related assessment about the usefulness and satisfaction is included to conclude the paper.

Research on Safety Distance Mathematical Model of Pro-Active Head Restraint in Rear-end Collision Avoidance System

Pro-active head restraint is a new automotive safety device with both active safety and passive safety characteristics. It can make a pre-estimation of the occurrence possibility of rear-end collision. Thus, the whiplash injury can be effectively reduced or even prevented. One of the key elements in rear-end collision avoidance system is to establish an effective safety distance mathematical model. Based on the running state of front car, the related calculation models of safety distance are established by means of dynamical and kinematical analysis of vehicle braking process and following process. Taken randomness and dynamic characteristics of parameters into consideration, the fuzzy relations between these parameters should be validated by means of fuzzy theory. Thus, it may accord with the actual driving conditions. By using the MATLAB software, the study shows that safety distance model and the methods used to determine parameters are reasonable, and the false alarm can be effectively minimized.

Simulink implementation of IEEE 802.11p for rear end collision avoidance system

Road safety can be enhanced by the deployment of wireless communication systems for vehicular networks, which enable new services such as collision detection and collision avoidance, traffic management, adaptive traffic control and other communication facilities between vehicles on road. The 802.11p protocol modifies the 802.11 standard for wireless local area networks (WLANs) in a vehicular environment. The main application of 802.11p is vehicle-to-vehicle (V2V) communication. This paper presents the MATLAB/SIMULINK Implementation of IEEE 802.11p for vehicular area network and its application to rear end collision avoidance system with Honda algorithm. To show the efficiency of the work we also presented the case studies with different vehicle speeds.

Safety Distance Mathematical Model of Pro-Active Head Restraint Based on Fuzzy Theory

Pro-active head restraint is a new automotive safety device. It can make a pre-estimation of the occurrence possibility of rear-end collision. Thus, the whiplash injury can be effectively reduced or even prevented. One of the key elements in rear-end collision avoidance system is to establish an effective safety distance mathematical model. Based on the running state of front car, the related calculation models of safety distance are established by means of dynamical and kinematical analysis of vehicle braking process and following process. Taken randomness of parameters into consideration, the fuzzy relations between these parameters should be validated by means of fuzzy theory. By using the MATLAB software, the study shows that safety distance model and the methods used to determine parameters are reasonable, and the false alarm can be effectively minimized.

Study on Modeling and Simulation of Vehicle Safety Distance in Low Speed for Rear-end Collision Avoidance

Rear-end collisions of low speed vehicles on the urban road are common accident scenarios. A judgment of safety distance is a crucial part of a rear-end collision avoidance system which relies on safety distance model. In order to solve the problems of traditional safety distance models, this paper proposes a new model to avoid rear-end collisions. The new model considers the braking performance of the host vehicle and the motion state of the heading vehicle. Then a rear-end collision avoidance system including the new model is established using a combination of CarSim with Simulink software. Three typical working conditions through the co-simulation is simulated for verifying the model. Simulation results show that the new model ensures rear-end collision can be avoided, meanwhile keeping a short relative distance that improves road efficiency.

An Approach to the Vehicle Tracking Method using Intensity Projection of the Edge Image

This paper describes the preceding vehicle recognition and road-lane recognition methods for the rear-end collision avoidance system (RCAS) which we are developing. These methods are using an edge histogram method based on modelbased vision. The edge histogram method can detect line elements of the objects stably with low calculation cost. When the region of interests for the preceding vehicle and road lanes in the image are established and their projected edge histograms are observed in time series order, we can recognize the positions of the vehicle and the lane. Furthermore, we apply Kalman Filter to their motions and predict their locations for time series detection. Using this stable recognition, we derive a collision time to control the on-board brake system. We show the performance of these methods by experimental results

Development of rear-end collision avoidance system

RCAS (rear-end collision avoidance system) has been developed as a system of ASV (advanced safety vehicle). It is a driver assist system to avoid the rear-end collision by informing the distance headway. It also has automatic braking function in case of emergency and distance warning function to the trailing vehicle. This paper describes the outline of RCAS, techniques about the preceding vehicle recognition and collision potential hazard evaluation. And experimental results about distance headway warning and collision avoidance by automatic braking are reported.

Development of rear-end collision avoidance system ? Final: The basic technology for ASV (Advanced Safety Vehicle) Hideo Araki, Kenichi Yamada, Yasuhisa Hiroshima, Toshio Ito and Kunio Nishioka (Daihatsu Motor Co., Ltd.)

Development of rear-end collision avoidance system ? Final: The basic technology for ASV (Advanced Safety Vehicle) Hideo Araki, Kenichi Yamada, Yasuhisa Hiroshima, Toshio Ito and Kunio Nishioka (Daihatsu Motor Co., Ltd.)

Evaluation of rear-end collision avoidance system

Evaluation of rear-end collision avoidance system

Development of a rear-end collision avoidance system with automatic brake control

We have studied active safety technologies from the standpoint of “collision avoidance”. This paper describes a rear-end collision avoidance system with automatic brake control, which avoids a collision to the vehicle in front caused by inadvertent human errors using automatic emergency braking. The system is comprised of four key technological elements, headway distance measurement using scanning laser radar, path estimation algorithm with vehicle dynamics, collision prediction to the vehicle in front by a safe/danger decision algorithm, and longitudinal automatic brake control.