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We propose a Model Predictive Control (MPC) for collision avoidance betweenan autonomous agent and dynamic obstacles with uncertain predictions. Thecollision avoidance constraints are imposed by enforcing positive distancebetween convex sets representing the agent and the obstacles, and tractablyreformulating them using Lagrange duality. This approach allows for smoothcollision avoidance constraints even for polytopes, which otherwise requiremixed-integer or non-smooth constraints. We consider three widely useddescriptions of the uncertain obstacle position: 1) Arbitrary distribution withpolytopic support, 2) Gaussian distributions and 3) Arbitrary distribution withfirst two moments known. For each case we obtain deterministic reformulationsof the collision avoidance constraints. The proposed MPC formulation optimizesover feedback policies to reduce conservatism in satisfying the collisionavoidance constraints. The proposed approach is validated using simulations oftraffic intersections in CARLA.

Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using Model Predictive Control