Towards Intelligent Control Centers: Case-Based Reasoning for Waypoint Assistance

Autonomous-driving research has traditionally focused on refining on-board intelligence for individual vehicles. Recent studies highlight the potential of intelligent control centers-equipped with machine learning capabilities-to complement on-board systems by sharing insights across an entire fleet and assisting with corner cases in real time. In this paper, we advance the concept of intelligent control centers through a case-based reasoning approach for remote waypoint assistance. Our system captures and reuses operator interventions, converting human-generated solutions for unforeseen obstacles into transferable “cases”. The remote operator remains in the loop to validate every suggested waypoint, ensuring safety before execution. Preliminary field tests with two autonomous shuttles demonstrate the feasibility of retrieving previous waypoint interventions under realistic conditions. Our initial small-scale results indicate that (i) the prototype can achieve performance comparable to fully manual interventions and (ii) solutions devised for one vehicle can effectively be transferred to another. Together, these outcomes lay a solid foundation for more sophisticated, learning-based control-center architectures.