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Research On The Key Theory And Technology Of Car Rear-End Collision Alarm System Based On Multi-Agent And Driving Behavior

Posted on:2016-05-10Degree:DoctorType:Dissertation
Country:ChinaCandidate:J LiangFull Text:PDF
GTID:1222330482459873Subject:Carrier Engineering
Abstract/Summary:PDF Full Text Request
Car Rear-End Collision Alarm System(CRCAS), as an important guarantee for the driving safety, has been playing an indispensable role in the advanced driver assistance systems. CRCAS has now become the focus of the vehicle active safety research, and is coming into a hotspot and nodus of the cross-domain of the traffic safety and vehicle engineering. Researchers at home and abroad have carried out a lot of research work on the theory and technology of CRCAS, but there is still much room for improvements in many respects.This dissertation is focused on CRCAS and has done a thorough and comprehensive research on the key technologies, including driving behavior study, driving environment perception, car-following model, system architecture and early alarm algorithm. The research is supported by National Natural Science Committee funded project "CRCAS based on Multi-agent and Driving behavior" and Jiangsu Province Natural Science Committee funded project "Car rear end collision alarm model research based on Multi-agent theory". The paper is organized as follows.Firstly, current research achievements and remaining problems at home and abroad concerning the driving behavior model and CRCAS was introduced. Following the fundamental idea of Multi-agent system and hierarchical centralized control theory, we proposed a CACAS based on the multi-agent and the driving behavior and focused on improving intelligent data processing and modeling of CRCAS.The concept of cognitive driving behavior was proposed based on the cognitive theory. Taking the rear-end accident for example, we described and analyzed the driving behavior and its control strategy under the urgent condition so, which has laid a solid theoretical basis for driving behavior study and car-following model development. Specifically, we focused on the ANNI driving behavior rule extraction model and integrated model (ANNIREA), based on which the data of the driving behavior principles was obtained. The results have provided important insights into the design of vehicle model as well as vehicle collision alarm strategy design and algorithm.Facing the problem of uncertain and incomplete information from the traffic image on the road, we put forward a MM-OLP algorithm based on constraint and the edge merging, and a CM-LPR algorithm based on convolutional neural network and new template matching, so as to overcome the deficiencies of the traditional methods, such as low accuracy in locating license plate for shaded,, defaced license plate recognition, low recognition accuracy to the traffic signs and the rear lights, and high ration of error detection. Meanwhile, the driving environment perception fusion method based on MFA-DMFS and the intelligent fusion algorithm based on Multi-agent were studied, to ensure the reliability, efficiency, timeliness and resource adaptability of CRCAS, and to realize a more widely integration of road traffic driving environment information.Targeting at the problem that the reduction rate of the three common car-following models do not consider the reduction of the front vehicle braking caused by the calculation of the safety distance deviation, the vehicle model based on the acceleration of the vehicle is built. This model can calculate the safe distance precisely. A set of system method was also proposed, which was used to calculate the minimum distance and the neural network was applied to the servo control model. The dynamic control of the system is well-linear, and the PID was used to improve the acceleration. The simulation results show that the model works fine regardless of operating conditions. Considering the driving behavior and dynamic characteristics of the rear vehicle, the dynamic equilibrium model of the vehicle was discussed.MAAM based on MAS was proposed, whose major characteristics is that the agent is adopted to design in varied aspects, meanwhile introducing the agent of distributed computing, and the agent was adopted to solve the agent and the message storage between the centralized processing layer and the layer. On this basis, the systematic structure of CRCAS based on MAAM was established and the main functions of Agent were designed.The intelligent forecasting method (IPMM) was proposed based on Multi-Agent. IPMM was revealed to be suitable for the distributed intelligent forecasting environment based on the current road traffic data with multiple sources, multiple, being incomplete, inconsistent and inaccurate. On this basis, the stability, correlation and chaos of the rear end collision alarm data was described to analyze other characteristics of the road traffic data. Its feasibility of the method of CRCAS was verified from the perspective of mathematics. Focusing on the rear end accident prediction agent structure and function, this study proposed Bayesian graph model of the rear-end-accident prediction based on Agent as well as car-rear-collision alarm algorithm based on genetic neural network. The simulation results verified the feasibility of the two methods.Finally, by employing softwares PreScan, and Logitech G27, as well as the existing simulation computing platform, a controllable test simulation platform was invented. This platform has the characteristics of high-speed computing power and low risk. On this basis, the rear end collision alarm prototype system was developed, which makes the system parameter adjustment convenient and manipulation simple, data recording fast and reliable, so as to greatly shorten the test cycle, reduce the cost of test and verify the full system design and development. The results show that compared with traditional method, the method proposed in this study issues early warning and response time of two seconds in advance and can effectively avoid the occurrence of rear end accident, and the brake pedal opening change also has the human driving behavior characteristics, and two early warning algorithms are timely and effective. The results verified the feasibility and efficiency of the theory and method proposed in the paper.
Keywords/Search Tags:Car rear-end collision warning, Driving behavior learning, Car following model, Multi-agent theory, Hierarchical centralized control, Artificial neural network ensemble, Bayes graph model, Prediction algorithm
PDF Full Text Request
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