用贝叶斯估计加速自动驾驶规划，速度提升数个数量级。
Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing
- 将非线性模型预测控制转化为贝叶斯估计问题，避免复杂优化。
- 基于集合卡尔曼平滑器实现高速求解，在仿真中提速数个数量级。
- 适合需要实时运动规划的自动驾驶系统，尤其适用于高复杂度车辆模型。
安全高效的运动规划对自动驾驶车辆至关重要。本文研究基于神经网络车辆模型的非线性模型预测控制（NMPC）运动规划。针对神经网络模型下NMPC因高度非线性和非凸优化带来的高计算成本问题，本文摒弃传统数值优化方法，将NMPC规划问题重新建模为贝叶斯估计问题，旨在从规划目标中推断最优决策。随后，采用顺序集合卡尔曼平滑器完成估计任务，利用其在复杂非线性系统中的高效计算能力。仿真结果表明，计算速度提升了数个数量级，展示了该方法在实际运动规划中的潜力。
原文摘要 · Abstract (English)
Safe and efficient motion planning is of fundamental importance for autonomous vehicles. This paper investigates motion planning based on nonlinear model predictive control (NMPC) over a neural network vehicle model. We aim to overcome the high computational costs that arise in NMPC of the neural network model due to the highly nonlinear and nonconvex optimization. In a departure from numerical optimization solutions, we reformulate the problem of NMPC-based motion planning as a Bayesian estimation problem, which seeks to infer optimal planning decisions from planning objectives. Then, we use a sequential ensemble Kalman smoother to accomplish the estimation task, exploiting its high computational efficiency for complex nonlinear systems. The simulation results show an improvement in computational speed by orders of magnitude, indicating the potential of the proposed approach for practical motion planning.
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