Autonomous Vehicle Tracking and Collision Avoidance Using Adaptive Control Algorithms

With the rapid economic growth, global car ownership increasing leads more researchers to focus their work on making cars more intelligent and safer. Autonomous vehicle control (AVC) problems have drawn increasing research in the last decades because of their potential to reduce vehicle accidents and commuting time. Vehicle traffic control at street intersections is one of the most important problems for its special and complex situations in AVC. Adaptive control, which is known for its power to deal with system uncertainties, is a useful method to solve AVC problems without the knowledge of the system parameters.

This thesis studies the control problems of a vehicle passing an intersection: the designed controller should make the controlled vehicle pass the intersection quickly and avoid any collision. In other words, the control design needs to create a proper reference trajectory satisfying certain traffic requirements. Then, it requires to make the vehicle's trajectory track arbitrary vehicle reference trajectory as quickly as possible. In this research, the state-space model of the vehicle dynamics, containing several uncertain parameters, is established. The adaptive control method is adopted to deal with the systems parameter uncertainties in such vehicle control problems. For this study, two adaptive control designs are developed to solve the problem: a baseline adaptive control design and an enhanced adaptive control design. Unlike the classic PI controller which can only make the vehicle track constant velocity trajectories, both two adaptive control designs can achieve asymptotic tracking of arbitrary vehicle velocity trajectories. The enhanced adaptive design can even further improve the system tracking performance. Analysis and simulation results have demonstrated the effectiveness of the proposed adaptive control systems.