Development of rear-end collision avoidance system

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.

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.

<div class="htmlview paragraph">This paper discusses the definition and performance of the warning algorithm in a potential Rear-End Collision Avoidance System (RECAS). Given vehicle range, velocity, and leader deceleration data, the warning algorithm computes whether imminent braking is required to prevent a collision with a vehicle directly ahead, and determines whether a warning should be given to alert the driver. The result of the study is an estimate of the number of warnings per hour under normal driving conditions for various types of drivers.</div>

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

For the advanced safety vehicle (ASV) project proposed by the Japanese Ministry of Transport, following three themes were chosen according to the analyzed results of traffic accidents data and each theme was promoted with an individual ASV for efficiency of study: accident prediction and prevention technology with an intelligent navigation system; collision avoidance by radar technology to compensate for driver errors in perception and judgment; and pedestrian protection safety technology to prevent accidents and reduce injury. The technology should be comfortable for human beings to use. Some examples are studied.

Many threat assessment algorithms are based on a collection of threshold equations that predict when a collision is to occur. The fact that there are numerous algorithms suggests a need to understand the underlying principles behind the equation design and threshold settings. In this paper, we present a methodology to develop appropriate alerting thresholds based on performance metrics. This also allows us to compare different alerting algorithms. The method is a performance-based approach in state-space. and can thus be utilized in conjunction with any chosen alerting algorithm or sensor system. Using carefully prescribed trajectory models (which may include uncertainties), the performance tradeoff with and without an alert can be predicted for different states along the course of an encounter situation. This information can then be used to set appropriate threshold values for the desired alerting logic. The development of the threshold criteria for a rear-end collision warning system is given as an example. Though the approach given is presented as a threshold design tool, the methodology is self-contained as a threat assessment logic. The possibility exists to compute the performance measures on-the-fly from which alerting decisions can be made directly.

Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.

This paper presents a probabilistic decision-making framework for rear-end collision avoidance systems, focusing on modeling the impact of major collision-causing factors on the occurrence of accidents. Decisions on when and how to assist drivers are made using a Bayesian network approach according to collision risk evaluation results, given a prior probabilistic knowledge. The structure of the Bayesian network model is learnt using a K2 algorithm with a practical dataset. To provide adequate response time for drivers, we also predict collision probability in the next monitoring interval using a Kalman filter model. The prediction accuracy is evaluated with different use cases and compared to real scenarios with the obtained dataset. To make our framework more relevant to practical applications, we also discuss the corresponding safety control strategies by classifying collision risk into high and low levels. In addition, the proposed model is evaluated through experiments including simulations and road tests. In the simulations, the algorithms are tested in different scenarios with various configurations of weather conditions, driver response capability, and vehicular dynamics. In order to demonstrate collision avoidance performance, the proposed model is also compared to existing schemes. In the road tests, the algorithm is embedded into an unmanned vehicle with predefined parameters to evaluate the impact of computational complexity on rear-end collision avoidance. Numerical results show that the proposed model provides an accurate estimation for car-following collision risk with a relatively low complexity, taking into account the impacts of vehicle dynamics, driver reaction capacity, and external environment on rear-end collisions.

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

The present rear-end collision accident proportion on the road increases day after day, car collision avoidance system is more and more paid attention. Analysising the existing car collision avoidance system, we propose a car anti-collision algorithm based on safe distance model in this paper. This method takes the influence factors of safety distance for main parameters which fully considers the speed change and the acceleration change of the car 1 and the car 2. It may realize real-time information acquisition and warning judgment according to the state of car 2, the car could automatic braking if necessary. VB simulation shows that the algorithm can effectively avoid collision, also automatically maintain the distance between vehicles, and lay a foundation for further research on the unmanned car.

Interactions between gender and other risk factors are associated with persistent neck pain after motor vehicle collision

More than 23% of annual vehicle accidents are rear-end collisions, this gives an important test-case for enhanced collision avoidance approaches based on v2v wireless communications. V2V wireless network concern with the impact of a IEEE 802.11 based multi-hop MAC protocol that propagates an emergency warning message (EWM) down a platoon of cars on a highway. The design objective is to ensure reception of this message with stringent delay constraints so as to provide drivers with requisite available manoeuvre time (AMT) to avoid rear-end collision. This V2V wireless communication protocol provides a realistic simulation in Ns-2 for various topologies (lane-1& lane-3) and the performances were evaluated for different parameters such as EWM delay, delay due to overhead packets and probability of successful delivery of the emergency warning message (EWM).

Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.

Previous vehicle-to-pedestrian impact simulations and experiments using pedestrian dummies and cadavers have shown that factors such as vehicle shape, pedestrian anthropometry and pre-impact conditions influence pedestrian kinematics and injury mechanisms. Generic pedestrian bucks, which approximate the geometrical and stiffness properties of current vehicles, would be useful in studying the influence of vehicle front-end structures on pedestrian kinematics and loading. This study explores the design of pedestrian bucks, intended to represent the basic vehicle front-end structures, consisting of five components: lower stiffener, bumper, hood leading edge and grille, hood and windshield. The deformable parts of the bucks were designed using types of currently manufactured materials, which allow fabricating the bucks in the future. The geometry of pedestrian bucks was approximated according to the contour cross-sections of two sedan vehicles used in previous pedestrian dummy and cadaver impact tests. Other cross-sectional dimensions and the stiffness of the buck components were determined by parameter identification using finite-element (FE) simulations of each sedan model. In the absence of a validated FE model of human, the FE model of the POLAR II pedestrian dummy was used to validate a mid-size sedan (MS) pedestrian buck. A good correlation of the pedestrian dummy kinematics and contact forces obtained in dummy–MS pedestrian buck with the corresponding data from dummy–MS vehicle simulation was achieved. A parametric study using the POLAR II FE model and different buck models – an MS buck and a large-size sedan (LS) buck – were run to study the influence of an automatic braking system for reducing the pedestrian injuries. The vehicle braking conditions showed reductions in the relative velocity of the head to the vehicle and increases in the time of head impact and in the wrap-around-distances (WAD) to primary head contact. The head impact velocity showed a greater sensitivity to the different buck shapes (e.g. LS buck versus MS buck) than to the braking deceleration. The buck FE models developed in this study are expected to be used in sensitivity and optimisation studies for the development of new pedestrian protection systems.

The effect of Rear-End collisions on triaxial acceleration to occupant cervical and lumbar Spines: An analysis of IIHS data