Abstract: A vehicle controlled for traveling on a road having a geometric design defined by one or a combination of an alignment, a profile, and a cross-section of the road, such that different values of parameters of the geometric design of the road, traffic on the road, traffic rules for the flow of the traffic on the road define different traffic scenarios. The vehicle is controlled by transforming the mixed-integer non-convex constrained optimization problem for the current real-world traffic scenario into a mixed-integer convex optimization problem for an approximate representation of the real-world traffic scenario by relaxing the configuration parameters of the real-world scenarios and tightening corresponding limitation parameters. The transformed mixed-integer convex optimization problem for the approximate representation of the real-world traffic scenario is solved to produce a current control command for controlling one or multiple actuators of the vehicle.

Abstract: The present disclosure provides a system and a method for controlling a motion of a device from an initial state to a target state in an environment having obstacles that form constraints on the motion of the device. The method includes executing a learned function trained with machine learning to generate a feasible or infeasible trajectory connecting the initial state of the device with the target state of the device while penalizing an extent of violation of at least some of the constraints to produce an initial trajectory. The method further includes solving a convex optimization problem subject to the constraints to produce an optimal trajectory that minimizes deviation from the initial trajectory and controlling the motion of the device according to the optimal trajectory.