Gabriel Rodrigues de Campos, Fabio Della Rossa and Alessandro Colombo
We design an optimal, driver-adaptive supervisor algorithm for collision avoidance at an intersection. Given the estimated drivers’s intent, the algorithm is able to identify optimal corrections to the human-decided inputs, to keep the system collision-free. To determine the set of safe control actions, we exploit the notion of maximal controlled invariant set. We leverage results from scheduling theory to verify the safety of a given control input, and propose an efficient optimization algorithm providing optimal solutions with respect to the drivers’ intent. We also present an approximate supervisor algorithm that can be solved in polynomial time and has guaranteed bounded errors. Finally, we validate the efficiency of our approach with simulation results, as well as on naturalistic data.