Model predictive control for passing assistance in autonomous vehicles

Autonomous or self-driving vehicles are designed to operate without the need for direct driver control over steering, acceleration, and braking. These vehicles are intended to function in self-driving mode without requiring the driver to continuously monitor the road. The aim of this thesis is to present a novel and robust methodology contributing to the improvement of the performance of autonomous vehicles in both simple and complex manoeuvres. In this context, the dual-controller approach is utilized for merging the benefits of a Model Predictive Controller with a Stanley controller into a hybrid system, namely the Model Predictive and Stanley-based Controller (MPS). Each of them is suffering from some shortcomings: MPC can efficiently perform the path prediction but may lag in responding properly under dynamic conditions, while Stanley controller-though widely used for lateral control-exhibits high lateral errors on tight curves and complex road structure. These are combined carefully in the MPS method so that their respective disadvantages counterbalance each other: the predictive capabilities of MPC are combined with the robustness of the Stanley controller to yield superior path-following and vehicle control capabilities. Extensive analysis of the MPS system will thereafter be implemented to evaluate its performance under a variety of road conditions, namely straight sections, tight corners, and road environments filled with obstacles. The controller that has been developed demonstrates a level of flexibility and attainable adaptability that is appropriate for a wide range of scenarios while retaining lane stability and trajectory accuracy throughout a variety of speeds and road categories. Accordingly, the several measures of performance for tracking accuracy, minimization of errors, and computational efficiency indicate that the MPS system surpasses the single controller approach system, especially in situations where other systems may face difficulties. The paper also performs a comparison analysis with traditional control systems, pointing out that the MPS controller gives better performance in minimizing errors and providing smoother and more reliable vehicle movement under simple and complex conditions. Conclusively, the research positions the MPS controller as a significant advancement in the field of autonomous vehicle control by proposing an innovative combination of advantages of the model predictive and Stanley controllers. The results obtained so far have shown that MPS can help achieve safer and more precise autonomous driving, thus making it a potential candidate for real-world applications where reliability and adaptability are considered of prime importance.