Studies On Algorithm Of Vehicle Adaptive Cruise Control System

Adaptive Cruise Control (ACC) system is a new generation Driver Assistant System (DAS), which develops on the traditional Cruise Control System (CCS). ACC can release human drivers'burden, reduce car accidents due to bad driving skills, enhance driving comfort, increase traffic capacity, decrease the fuel consumption and exhaust emission. Therefore, the research on ACC system is an important issue in terms of human, traffic and environment.Spacing policy and the control algorithm are the most important parts of the ACC control module, and test procedure is performed to verify the selected spacing policies and designed control algorithms. This paper takes ACC system as the research object; investigate the spacing policy, the control algorithm considering optimal switching between throttle and brake, and the simulation lab platform using a smart car, respectively. The main contributions of this paper were concluded as follows:(1) As the existing spacing policy can't adapt the complex traffic scenarios, a variable time headway policy which considers the acceleration of preceding vehicle is proposed, and the corresponding stability of spacing error and relative velocity is analyzed. This spacing policy not only considers the influence of the relative velocity, but also introduces the acceleration disturbance of preceding vehicle to improve the ability regarding forward-looking and anti-disturbance of spacing control. Moreover, the introduction of saturation function makes time headway more reasonable. Compared to the traditional spacing policies, the proposed one can improve the dynamical performance of spacing control, and ensure the safety and car-following under the complex traffic scenarios.(2) As the existing two-level control of ACC system can't optimize the switching performance between throttle and brake, based on Model Predictive Control (MPC), a control algorithm with optimal switching between throttle and brake is proposed. This control algorithm is designed to satisfy not only safety, car-following, driving comfort and fuel efficiency, but also the optimal switching performance between throttle and brake, by introducing the switching times minimizing as a control object. Compared to the tradition threshold switching, the proposed one can effectively reduce the switching between throttle and brake, avoid the mechanism abrasion caused by frequent switching between different actuators, and improve the driving comfort and fuel efficiency further up.(3) As the above controller design is transformed to be an online Mixed Integer Nonlinear Programming (MINLP) at last, a nested two-loop method based on Particle Swarm Optimization (PSO) is proposed. Under this method, the primal problem is decomposed into an integer programming and a quadratic programming. In the outer loop, PSO method is used to find optimal integer. When integer variables are fixed, it is referred to as a standard quadratic programming in the inner loop, which can be solved by Matlab Optimization Toolbox. Then the inner solution is returned to the outer loop to generate a new solution for the next iteration. This procedure continues until the termination criterion is satisfied. This method can degrade the complex degree of MINLP effectively, successfully obtain the control sequences and control input of throttle and brake.(4) As computer simulation test can't reproduce the actual traffic scenes perfectly and is easy to ignore the problems caused by practical application of ACC system. A simulation lab platform based on an ASURO smart car is proposed to further observe the control effect of ACC control algorithm. This platform is easy to build and code. Finally, because of the experimental conditions and equipment limitations, only the incremental PID algorithm on cruise model is simulating by this lab platform, preliminary proves the effectiveness and necessity of the lab platform test.