Improving Sustainable Safe Transport via Automated Vehicle Control with Closed-Loop Matching

The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration learning-based approaches and methods control theory. Through integration, high amount information can be incorporated. Thus, operation, i.e., energy-efficient safe motion with automated vehicles, achieved. Despite advantages approaches, enhanced poses crucial safety challenges. In this paper, novel closed-loop matching method for control-oriented purposes context systems presented. goal match nonlinear dynamics linear system predefined structure; thus, model obtained. achieved by additional input from neural network, which designed based on input–output signals system. process matching, dataset generation, training, evaluation proposed. network through data-driven reachability analysis statistical performance carried out. proposed applied path following functionality, nonlinearities lateral are handled. effectiveness functionality fidelity CarMaker simulations illustrated.