自动驾驶车辆的运动规划：当模型预测控制遇上集合卡尔曼平滑器
Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing
AI总结：
本文将基于神经网络车辆模型的NMPC运动规划重构为贝叶斯估计问题，并用序贯集合卡尔曼平滑器求解，显著降低计算成本。
摘要：
安全高效的运动规划对于自动驾驶车辆至关重要。本文研究基于非线性模型预测控制（NMPC）并采用神经网络车辆模型的运动规划。我们旨在克服神经网络模型的NMPC因高度非线性、非凸优化而产生的高昂计算成本。与数值优化方案不同，我们将基于NMPC的运动规划问题重新表述为贝叶斯估计问题，即从规划目标中推断最优规划决策。随后，我们使用序贯集合卡尔曼平滑器完成该估计任务，利用其在复杂非线性系统上的高计算效率。仿真结果表明，计算速度提升了数个数量级，显示出所提方法在实际运动规划中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
