Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this paper, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the vehicle dynamics. As the neural state-space model will lead to highly complex, nonlinear and nonconvex optimization landscapes, mainstream gradient-based MPC methods will be computationally too heavy to be a viable solution. In a departure, we propose the idea of model predictive inferential control (MPIC), which seeks to infer the best control decisions from the control objectives and constraints. Following the idea, we convert the MPC problem for motion planning into a Bayesian state estimation problem. Then, we develop a new particle filtering/smoothing approach to perform the estimation. This approach is implemented as banks of unscented Kalman filters/smoothers and offers high sampling efficiency, fast computation, and estimation accuracy. We evaluate the MPIC approach through a simulation study of autonomous driving in different scenarios, along with an exhaustive comparison with gradient-based MPC. The results show that the MPIC approach has considerable computational efficiency, regardless of complex neural network architectures, and shows the capability to solve large-scale MPC problems for neural state-space models.

翻译：模型预测控制（MPC）在确保自动驾驶车辆安全与最优运动规划方面已展现出实用性。本文研究了当采用神经网络状态空间模型表征车辆动力学时，如何实现基于MPC的运动规划。由于神经网络状态空间模型将导致高度复杂、非线性和非凸的优化问题，主流基于梯度的MPC方法计算负担过重而难以实用。为此，我们创新性地提出模型预测推断控制（MPIC）概念，旨在通过控制目标与约束条件推断出最优控制决策。基于这一思路，我们将运动规划的MPC问题转化为贝叶斯状态估计问题，进而开发出新型粒子滤波/平滑方法进行求解。该方法采用无迹卡尔曼滤波器/平滑器组实现，具有高采样效率、快速计算和估计精度优势。通过不同场景的自动驾驶仿真研究及与梯度MPC的详尽对比，评估了MPIC方法。结果表明，无论神经网络架构多复杂，MPIC方法均展现出显著的计算效率，并具备求解神经网络状态空间模型的大规模MPC问题的能力。