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.

中文翻译：

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具有交通路口多模态预测的随机 MPC

我们提出了一种用于交通路口自动驾驶的随机 MPC (SMPC) 公式，该公式结合了周围车辆的多模态预测来避免碰撞约束。多模态预测是通过高斯混合模型 (GMM) 获得的，并且约束被表述为机会约束。我们的主要理论贡献是 SMPC 公式，它优化了一个新颖的反馈策略类，旨在利用 GMM 预测中的附加结构，并且适合凸编程。使用反馈策略进行预测的动机是在处理周围车辆的多模态预测时需要减少保守性，尤其是在交通交叉口场景中普遍存在。我们沿着移动性、舒适性、CARLA 模拟交叉路口的保守性和计算效率。我们的模拟使用运动学自行车模型和在 Lyft 5 级预测数据集子集上训练的多模态预测。为了证明优化反馈策略的影响，我们将我们的算法与通过优化开环序列来处理多模态碰撞避免机会约束的两个 SMPC 基线进行比较。