We propose a Stochastic MPC (SMPC) formulation for autonomous driving at traffic intersections which incorporates multi-modal predictions of surrounding vehicles for collision avoidance constraints. The multi-modal predictions are obtained with Gaussian Mixture Models (GMM) and constraints are formulated as chance-constraints. Our main theoretical contribution is a SMPC formulation that optimizes over a novel feedback policy class designed to exploit additional structure in the GMM predictions, and that is amenable to convex programming. The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios. We evaluate our algorithm along axes of mobility, comfort, conservatism and computational efficiency at a simulated intersection in CARLA. Our simulations use a kinematic bicycle model and multimodal predictions trained on a subset of the Lyft Level 5 prediction dataset. To demonstrate the impact of optimizing over feedback policies, we compare our algorithm with two SMPC baselines that handle multi-modal collision avoidance chance constraints by optimizing over open-loop sequences.

翻译：我们提出交通十字路口自动驾驶的托盘式MPC(SMPPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC))(SMPC)(SMPC)(SMPC)(SMPC)(SMPC))(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(SMPC)(S)(SMPC)(SMPC)(SM)(SMPC)(SMPC)(SMPC)(SMPC)((SMPC)(SMPC)(SMPC)(SMPC)((SMPC)((SMPC)(SMPC)((SMPC)((SMPC)((SMPC)) )(为交通十字路交叉限制的交通十字路交叉路交叉路交叉路交叉路交叉交通的自动驾驶自驾驶自驾驶自驾驶自驾驶的自动驾驶式)(M(M)(MPC)(MPC)(MPC)(MPC)(MPC)(SM)(MPC)(Stototototototototototototototototototototototototototototototots)(M)(M)(Stototototototototototototototototototototototototod)(S)(M)(S)的配制)的配制)(M)(M)(MC)(MC)(S(S(S(S(S) ) ) ) )(MC)(MPC)(MPC)(MPC)(MPC)(MPC)(MPC)(MPC)(MPC)(MPC)的配制)(MPC)(MPC)(SMPC)(SMC(SMC)(SMC(SMPC)(S)(SMC(S)(MPC)(MPC)