Using Uncertainty Data in Chance-Constrained Trajectory Planning
For autonomous systems operating in dynamic environments, this provides a theoretically grounded method for chance-constrained planning with learned uncertainties.
The paper tackles trajectory planning with obstacles of uncertain location, where uncertainty distributions are learned online. It derives tight concentration bounds for moment estimation and a tractable mixed-integer convex reformulation, achieving high-confidence feasibility.
We consider the problem of trajectory planning in an environment comprised of a set of obstacles with uncertain locations. While previous approaches model the uncertainties with a prescribed Gaussian distribution, we consider the realistic case in which the distribution's moments are unknown and are learned online. We derive tight concentration bounds on the error of the estimated moments. These bounds are then used to derive a tractable and tight mixed-integer convex reformulation of the trajectory planning problem, assuming linear dynamics and polyhedral constraints. The solution of the resulting optimization program is a feasible solution for the original problem with high confidence. We illustrate the approach with a case study from autonomous driving.