Research On Autonomous Lane Changing Decision-making Mechanism And Control Of Intelligent Vehicles Based On Vision And Radar

Intelligent vehicle is using advanced electronics and information technology to automatically complete the regular, durable and tiring driving operations, which relieves drivers from traditional "driver-vehicle-road" closed loop systems. Thus intelligent vehicle greatly enhances traffic efficiency and safety. Autonomous lane-changing technology can further achieve cooperative driving among multiple vehicles, improve the efficiency of military equipment, and boost the ability to adapt to the battlefield environment. Meanwhile, it uses accurate environmental perception, scientific and rational decision analysis, and robust control algorithm to reduce the traffic accidents caused by human factors more effectively than a driving experience based lane changing.This dissertation analyzes the driving behavior of the driver, the generation and gradation of lane-changing intention, and the factors influencing the lane-changing. Furthermore, the decision mechanism of lane-changing is investigated and the "driver-vehicle-road" system model is proposed. From the point of cybernetics, it presents the evaluation index of the control system among the "driver-vehicle-road" system. Experimental analysis implies that the slow vehicles ahead are a major cause of lane-changing, and the driver's pursuit of space and time is a main factor. The dissertation classifies the lane-changing into four stages:generation of lane-changing intention, decision of lane-changing time, planning of lane-changing trajectory, and tracking control of lane-changing. After creating the typical highway scene of the "driver-vehicle-road" system, the autonomous lane-changing can be described as the interaction process of environment perception, behavior decision and strategy control. The dissertation chooses lane markings, lane edges, and the surrounding vehicles as the feature vectors of lane-changing, develops the "vision-vehicle-road" system model, and gives a solution to the decision mechanism of the autonomous lane-changing.The right of way radar map (RWRM) is developed for information fusion, and the three stages, generation of lane-changing intention, decision of lane-changing time, planning of lane-changing trajectory are analyzed. The decision model of the autonomous lane-changing is developed as well as the model to avoid both static and moving vehicles. According to the attention allocation mechanism of human cognitive behavior and the right of way, the RWRM with variable particle size is developed to accomplish the simulation and calculation for human cognitive behavior with less time and memory. A minimum safe distance for intelligent vehicle is defined and the desired value of generation of lane-changing intention is also computed. Two experienced drivers are selected as well as typical highway route and experiment scheme. A total of311feature parameters are extracted. Among them,97feature parameters are influencing the lane-keeping, while214feature parameters are influencing the lane-changing. The V-support vector machine is employed to train the samples, and the condition (8, v)=(0.12,0.03) is chosen, which yields an accuracy of91.05%in the lane-changing. A comparison of the usual path planning for lane-changing is made considering the lane-changing time, vehicle acceleration, and road curvature mutation. A trapezoidal based lateral acceleration method is used as well as variable scale grid method in the environment modeling. When the vehicle is running at a high speed, the variable scale grid method reduces the CPU occupancy by34%than the ordinary grid method. The fusion between the variable scale grid method and the RWRM with variable particle size, can avoid obstacle vehicle in case of an emergency.A combined longitudinal and lateral coupling kinematic model for intelligent vehicle is developed. It takes longitudinal motion, lateral motion and yaw motion into consideration, which can accurately achieve tracking control for lane-changing and subsequent lane-keeping. After the analysis of both longitudinal and lateral coupling influence, a combined longitudinal and lateral coupling control system is provided, and an exponential type sliding mode control method (ESMC) is used to design the tracking controller, which can meet the expectations of the dynamic and static performance index in lane-changing process. Aiming at the subsequent lane-keeping after the lane-changing, a lateral deviation from the lane center line is developed. A terminal sliding mode control method (TSMC) is used to design the lane-keeping controller combining the lateral motion with yaw motion, which can not only regulate the lateral deviation fitting in with the road curvature, but also improve both longitudinal and lateral stability of the lane-keeping process. The effectiveness and stability of both lane-changing controller and lane-keep controller are verified in the MATLAB environment.According to the structural characteristics of one off-road vehicle, the mechanism reconstruction is carried out, and both hardware and software platform of the intelligent vehicle are set up. The reliability and stability of the intelligent vehicle are verified in highway vehicle experiments. The hardware composition includes electric motor steering, hydraulik blocks based mechanical braking, and drawing by electric power throttle and so on. The software is classified into four threads by multi-threading technology, such as main thread, control thread, road information acquisition thread, and serial communication thread. The autonomous driving official highway test has been achieved for the first time in China. It covers1500km, and completes a total of95autonomous lane-changing, which verifies the reliability and stability of the proposed method on the highway.