Collision Mitigation System Research Articles

Cognitive mechatronic smart system with obstacle detection using sensor-based system to avoid car accidents

Nowadays the maximum number of cars have a high level of automatic safety system which help driver to maintain control of car and their safety system warn the driver of any potential danger. Car safety is increased by these types of distinctive attributes, but they are expensive and can be found only in expensive cars. Our research paper focuses on making a forward collision mitigation system that can work in all Kinds of vehicles without making any kind of differences between them because of Age, make, model or type and can be affordable at minimum price. If two vehicles are Moving on the same road and in the same direction, the system will analyse the distance Between two vehicles, then warn the driver about any potential danger in which she or He can be in by using ultrasonic sensors and a microcontroller. To alert the driver and Passengers effectively, the distance between two vehicles is calculated by an audio-Visual alarm and an ultrasonic.

Implementation Study Of Field Programmable Gate Array (FPGA) And Complex Programmable Logic Device (CPLD) In Collision Avoidance System Using Vhsic Hardware Description Language (VHDL)

A collision avoidance system, also known as a pre-crash system, forward collision warning system, or collision mitigation system, is a sophisticated driver-assistance system that aims to avoid or mitigate the severity of a collision. For this research, collision avoidance system will be fabricating to show that this system can detect avoidance range before apply the braking action to prevent collision. The ultrasonic sensor will be used in this system to detect the avoidance range. In this collision avoidance system, there will be uses of Field Programmable Gate Array (FPGA) and Complex Programmable Logic Device (CPLD). This research will observe how to implement FPGA and CPLD in the collision avoidance system using VHSIC Hardware Description Language (VHDL). The VHDL will be done in Quartus II 15.0 Software. In this research, Terasic DE-10 Standard board has been used. It contains FPGA microcontroller model Cyclone V SoC 5CSXFC6D6F31C6N. Max II board is used because it contains CPLD microcontroller model EPM240T100C5.

A Predictive Frontal and Oblique Collision Mitigation System for Autonomous Vehicles

Abstract This paper presents a collision mitigation system for an unavoidable collision with an arbitrary oncoming obstacle vehicle. A set of candidate trajectories are generated by a model-based method and a hierarchical efficient collision-checking method is applied to check the potential collision between the predicted trajectory of the obstacle vehicle and the candidate trajectories of the ego vehicle. A novel method based on the vehicle polygon is applied to identify the specific impact location of the vehicle body. The predicted Delta-V and the identified impact location are combined to evaluate the outcome severity of the upcoming accident for each candidate trajectory. Based on the evaluated results, a path with the least damage would be selected and executed to mitigate the collision. Simulation and analysis are performed to investigate the performance of the presented system in a high-speed scenario of a detailed vehicle model.

ARDUINO based accident prevention and auto intimation system

<p>Often modern cars have a collision avoidance system built into them known as Pre-Crash System, or Collision Mitigation System in order to reduce the collision. But majority of vehicles on the road, especially heavy motor vehicles lack in such a system. In this paper, the implementation of the Collision Avoidance System is to reduce the risks of collisions at the hairpin bend on a Hilly track, Ghats, or other Zero visibility turns. The proposed system contains a set of IR sensors, LEDs, etc. It uses four IR sensors, which are placed on either side of the hairpin bend. The sensors are mutually exclusive and are connected to LEDs through wires. Based on the output of sensors, the LEDs will glow and start alerting the other vehicle approaching from the other end, Hence the drivers will decrease their speeds which would help in preventing collision. The LEDs will help the drivers in detecting the position of the vehicles on either side of the bend. During climatic conditions like fog, snow, etc, the visibility of the drivers would decrease due to which they will not be able to see the LEDs, Hence, a collision may take place. To bring help as soon as possible to the injured, we have also made a proposed system which would alert the nearby hospitals that an accident has taken place. We have used Arduino UNO, GSM sim module and these will be kept inside a black box which will be inside the, car safe from breakage during the accident.<strong></strong></p>

Evaluation of a momentum based impact model in frontal car collisions for the prospective assessment of ADAS

MotivationThe advent of active safety systems calls for the development of appropriate testing methods that are able to assess their capabilities to avoid accidents or lower impact speeds and thus, to mitigate the injury severity. Up to now the assessment is mostly based on the decrease of the collision speed due to CMS (collision mitigation systems). In order to assess the effects on injury severity developing methods, that are able to predict collision parameters correlating with the risk of getting injured, such as delta-v, for different impact situations is a mandatory task.ObjectiveIn this study a momentum based impact model is assessed in terms of reliability to solve the collision mechanics and therefore to predict delta-v for frontal car collisions.MethodReal accidents were re-simulated using pre-defined input parameters for the impact model (virtual forward simulation – VFS). Subsequently the impact model was analyzed for its sensitivity to specific input parameters.ConclusionIt was shown that VFS works for full impacts while improvements and optimizations are required for impacts that include a sliding movement in the contact zone of the vehicles.

Auto safety features raise repair costs: Cars with advanced technologies cost thousands more to fix - [News

There is little debate over whether advanced driver-assistance systems (ADAS) could reduce both the number and severity of vehicle crashes. But the additional cost of these systems has slowed their adoption. A collision-mitigation system alone can increase the price of a new vehicle by as much as US $1,500. And in late October, the American Automobile Association (AAA) released new research showing significantly higher repair costs for advanced systems compared with the cost of standard repairs made after even a minor accident.

Evaluating variability in foot to pedal movements using functional principal components analysis

There are reasons why the driver’s foot may not be applied to the correct pedal while driving and this can lead to unintended consequences. In this study, we seek to capture common and unique patterns of variations in drivers’ foot movements using functional principal components analysis (FPCA). This analysis technique was used to analyze three categories of pedal response types (direct hits, corrected trajectories, and pedal errors) based on the various foot to pedal trajectories. Data from a driving simulator study with video data of foot movements for 45 drivers was used for analyses. Most foot movements show common patterns associated with direct hits and corrected trajectories with some level of variation. However, those foot movements associated with unique patterns might be early indicators of pedal errors. The findings of this study can be used with collision mitigation systems to provide early detection of foot trajectories that are more likely to result in a pedal error.

Concept of an enhanced V2X pedestrian collision avoidance system with a cost function–based pedestrian model

ABSTRACTObjective: State-of-the-art collision avoidance and collision mitigation systems predict the behavior of pedestrians based on trivial models that assume a constant acceleration or velocity. New sources of sensor information—for example, smart devices such as smartphones, tablets, smartwatches, etc.—can support enhanced pedestrian behavior models. The objective of this article is the development and implementation of a V2Xpedestrian collision avoidance system that uses new information sources.Methods: A literature review of existing state-of-the-art pedestrian collision avoidance systems, pedestrian behavior models in advanced driver assistance systems (ADAS), and traffic simulations is conducted together with an analysis of existing studies on typical pedestrian patterns in traffic. Based on this analysis, possible parameters for predicting pedestrian behavior were investigated. The results led to new requirements from which a concept was developed and implemented.Results: The analysis of typical pedestrian behavior patterns in traffic situations showed the complexity of predicting pedestrian behavior. Requirements for an improved behavior prediction were derived. A concept for a V2X collision avoidance system, based on a cost function that predicts pedestrian near future presence, and its implementation is presented. The concept presented considers several challenges such as information privacy, inaccuracies of the localization, and inaccuracies of the prediction.Conclusion: A concept for an enhanced V2X pedestrian collision avoidance system was developed and introduced. The concept uses new information sources such as smart devices to improve the prediction of the pedestrian's presence in the near future and considers challenges that come along with the usage of these information sources.

Association of Impact Velocity with Risks of Serious Injuries and Fatalities to Pedestrians in Commercial Truck-Pedestrian Accidents.

This study aimed to clarify the relationship between truck-pedestrian crash impact velocity and the risks of serious injury and fatality to pedestrians. We used micro and macro truck-pedestrian accident data from the Japanese Institute for Traffic Accident Research and Data Analysis (ITARDA) database. We classified vehicle type into five categories: heavy-duty trucks (gross vehicle weight [GVW] ≥11 × 103 kg [11 tons (t)], medium-duty trucks (5 × 103 kg [5 t] ≤ GVW < 11 × 103 kg [11 t]), light-duty trucks (GVW <5 × 103 kg [5 t]), box vans, and sedans. The fatality risk was ≤5% for light-duty trucks, box vans, and sedans at impact velocities ≤ 30 km/h and for medium-duty trucks at impact velocities ≤20 km/h. The fatality risk was ≤10% for heavy-duty trucks at impact velocities ≤10 km/h. Thus, fatality risk appears strongly associated with vehicle class. The results also revealed that a 10 km/h reduction in impact velocities could mitigate the severity of pedestrian injuries at impact velocities ≥30 km/h for all five analyzed vehicle types. Therefore, serious injuries and fatalities to pedestrians could be decreased by the development and deployment of collision mitigation systems (CMSs) to all vehicles, including to commercial trucks, because CMSs can detect pedestrians in even severe conditions, such as when the drive's view is obstructed, and can reduce the impact velocity. The present results indicate that CMS design specifications should differ between vehicle types because of the strong dependence of seriousinjury and fatality risks on vehicle type.

Traffic Accidents Involving Cyclists Identifying Causal Factors Using Questionnaire Survey, Traffic Accident Data, and Real-World Observation.

The purpose of this study is to clarify the mechanism of traffic accidents involving cyclists. The focus is on the characteristics of cyclist accidents and scenarios, because the number of traffic accidents involving cyclists in Tokyo is the highest in Japan. First, dangerous situations in traffic incidents were investigated by collecting data from 304 cyclists in one city in Tokyo using a questionnaire survey. The survey indicated that cyclists used their bicycles generally while commuting to work or school in the morning. Second, the study investigated the characteristics of 250 accident situations involving cyclists that happened in the city using real-world bicycle accident data. The results revealed that the traffic accidents occurred at intersections of local streets, where cyclists collided most often with vehicles during commute time in the morning. Third, cyclists' behavior was observed at a local street intersection in the morning in the city using video pictures. In one hour during the morning commute period, 250 bicycles passed through the intersection. The results indicated that one of the reasons for traffic accidents involving cyclists might be the combined effect of low visibility, caused by the presence of box-like building structures close to the intersections, and the cyclists' behavior in terms of their velocity and no confirming safety. It was observed that, on average, bicycle velocity was 3.1 m/s at the initial line of an intersection. The findings from this study could be useful in developing new technologies to improve cyclist safety, such as alert devices for cyclists and vehicle drivers, wireless communication systems between cyclists and vehicle drivers, or advanced vehicles with bicycle detection and collision mitigation systems.

Animal-Vehicle Collision Mitigation System for Automated Vehicles

Detecting large animals on roadways using automated systems such as robots or vehicles is a vital task. This can be achieved using conventional tools such as ultrasonic sensors, or with innovative technology based on smart cameras. In this paper, we investigate a vision-based solution. We begin the paper by performing a comparative study between three detectors: 1) Haar-AdaBoost; 2) histogram of oriented gradient (HOG)-AdaBoost; and 3) local binary pattern (LBP)-AdaBoost, which were initially developed to detect humans and their faces. These detectors are implemented, evaluated, and compared to each other in terms of accuracy and processing time. Based on our evaluation and comparison results, we design a two-stage architecture which outperforms the aforementioned detectors. The proposed architecture detects candidate regions of interest using LBP-AdaBoost in the first stage, which offers robustness to false positives in real-time conditions. The second stage is based on support vector machine classifiers that were trained using HOG features. The training data are generated from our novel dataset called large animal dataset, which contains common and thermographic images of large road-animals. We emphasize that no such public dataset currently exists.

Prospective Evaluation of the Collision Severity L7e Vehicles Considering a Collision Mitigation System

SEV (small electric vehicle) is one of the future solutions for urban mobility, since these cars show a small environmental footprint due to their lightweight design for optimizing the range. Due to the low number of SEVs in real accidents it is difficult to judge the collision severity of these vehicles, especially if such vehicles are equipped with ADAS (Advanced Driver Assistance Systems) in order to avoid accidents or mitigate injury severity.The objective of this study is to analyze the collision severity of SEVs in real accidents to assess the injury severity of the occupants. Further the effectiveness of a collision mitigation system (CMS) that aims to reduce impact speeds or, if possible, avoid collisions of passenger cars is evaluated.The method used in this study refers to the virtual pre-crash simulation. In a first simulation (called the baseline simulation) the original vehicles were successively replaced by an L7e so that collisions between original cars and L7e cars are considered. In a second simulation (system simulation) it was assumed that the L7e vehicles are virtually equipped with a CMS. Certain characteristics of the CMS such as sensor range or opening angle and various response strategies were varied. The response strategies under investigation are:a)warning to the driver at 2.6 s TTC (time to collision) and fully braking after 0.8 s reaction time,b)braking with 50% brake force at 1.6 s TTC and fully braking at 0.8 s TTC,c)braking with 50% brake force at 1.6 s TTC and finallyd)fully braking at 0.8 s TTC.Occupants of L7e vehicles generally have a higher risk to suffer MAIS3+ injury than in M1 vehicles. The analysis showed a reduction of the risk to suffer a MAIS3+ injury up to 65% when involved in an accident situation in an L7e vehicle for CMS with sensor system 1 and up to 87% with sensor system 2.

Driver behavior following an automatic steering intervention

The study investigated driver behavior toward an automatic steering intervention of a collision mitigation system. Forty participants were tested in a driving simulator and confronted with an inevitable collision. They performed a naïve drive and afterwards a repeated exposure in which they were told to hold the steering wheel loosely. In a third drive they experienced a false alarm situation. Data on driving behavior, i.e. steering and braking behavior as well as subjective data was assessed in the scenarios. Results showed that most participants held on to the steering wheel strongly or counter-steered during the system intervention during the first encounter. Moreover, subjective data collected after the first drive showed that the majority of drivers was not aware of the system intervention. Data from the repeated drive in which participants were instructed to hold the steering wheel loosely, led to significantly more participants holding the steering wheel loosely and thus complying with the instruction. This study seems to imply that without knowledge and information of the system about an upcoming intervention, the most prevalent driving behavior is a strong reaction with the steering wheel similar to an automatic steering reflex which decreases the system's effectiveness. Results of the second drive show some potential for countermeasures, such as informing drivers shortly before a system intervention in order to prevent inhibiting reactions.

Forward vehicle collision mitigation by braking system based on artificial bee colony algorithm

Decision-making is a crucial step in vehicles' safety systems, which determines the right time for the system to intervene and to take the required action, depending on the current situation of the vehicle. The warning/intervention system becomes active when the driving situation considered unpleasant while still giving the control to the driver. Taking the action of warning, braking and determining the best time for this intervention are important decisions in collision mitigation systems. In this work, a novel system is designed based on artificial bee colony algorithm to enhance the decision-making in such systems. This work c oncentrates on collision mitigation by braking systems only, for front-to-rear accidents. This paper studies cases when the hosting vehicle approaches moving and stationary objects. This work is simulated, and the results are obtained and analyzed which proved the contribution to this work in reducing the collision speed and the stopping distance, in comparison with previous related works.

Target modeling and deduction of automotive radar resolution requirements for pedestrian classification

Pedestrian Collision Mitigation Systems (PCMS) are already in the market for some years. Due to continuously evolving EuroNCAP regulations their presence will increase. Visual sensors, already capable of pedestrian classification, provide functional benefits, because the reaction behavior can be optimized when the imminent collision object is recognized as pedestrian or cyclist. Nevertheless their performance will suffer under adverse environmental conditions like darkness, fog, rain or backlight. Even in such unfavorable situations the performance of radar sensors is not significantly deteriorated. Enabling classification capability to automotive radar will further improve road safety and will lower PCMS's overall costs. In this paper, a multi-reflection-point pedestrian target model based on motion analysis is presented. Together with an appropriate sensor model, pedestrian radar signal responses can be provided for a wide range of accident scenarios. Additionally velocity separation requirements that are needed for classification of pedestrians are derived from the simulations. Besides determination of classification features, the model discloses the limits of classical radar signal processing and further offers the opportunity to evaluate parametric spectral analysis. Based on simulated and measured baseband radar signals of pedestrians one of these techniques is deeper analyzed and its enhancement especially on the velocity separation capability is evaluated.

Evaluation of Forward Vehicle Collision Mitigation Systems

Evaluation of Forward Vehicle Collision Mitigation Systems

ADVANCED HIGH FREQUENCY LTCC MATERIALS FOR APPLICATIONS BEYOND 60 GHZ

In this paper we will present an application of advanced Low Temperature Cofired Ceramic (LTCC) technology beyond 60 GHz. Therefore a RF frontend for 76–81 GHz radar frequency was built. LTCC is a well established technology for applications for consumer handheld units &amp;lt;5 GHz but will provide solutions for applications for high frequencies in the range of 60 GHz and beyond. Radar sensors operating in the 76-81 GHz range are considered key for Advanced Driver Assistance Systems (ADAS) like Adaptive Cruise Control (ACC), Collision Mitigation and Avoidance Systems (CMS) or Lane Change Assist (LCA). These applications are the next wave in automotive safety systems and have thus generated increased interest in lower-cost solutions especially for the mm-wave frontend section.

레이져 스캐너를 이용한 전방 충돌 예측 알고리즘 개발

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

Collision detection system design using a multi-layer laser scanner for collision mitigation

Collision detection plays a key role in collision mitigation systems. The malfunction of a collision mitigation system can result in a dangerous or unexpected situation for the driver and passengers. In order to prevent this situation, reliable collision detection algorithms are essential in terms of the collision time, the overlap and the collision decision. This study focuses on the reliable determination of the time to collision and the frontal overlap between the ego vehicle and the objects. The path prediction method using information on the ego vehicle is proposed to improve the accuracies of the time to collision and the frontal overlap calculations. The proposed algorithm is developed on the basis of a multi-layer laser scanner. The procedure of collision detection includes three steps. The first step is to select a high-risk object among multiple objects, the second step is to judge the time to collision and the frontal overlap to the high-risk object and, finally, the third step is to decide whether a crash is imminent or not. The performance of the proposed detection algorithm is validated in simulations and experiments.

Evaluation of Forward Vehicle Collision Mitigation Systems

Evaluation of Forward Vehicle Collision Mitigation Systems