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）在实现安全最优的自主车辆运动规划中已被证明有效。本文研究了当使用神经状态空间模型表示车辆动力学时，如何实现基于MPC的运动规划。由于神经状态空间模型将导致高度复杂、非线性和非凸的优化场景，主流的基于梯度的MPC方法在计算上过于繁重而无法成为可行方案。为此，我们提出模型预测推断控制（MPIC）的概念，旨在从控制目标和约束条件中推断最优控制决策。基于这一思想，我们将运动规划的MPC问题转化为贝叶斯状态估计问题，并提出一种新型粒子滤波/平滑方法进行估计。该方法通过无迹卡尔曼滤波/平滑器组实现，具有高采样效率、快速计算和估计精度等优势。我们通过不同场景下的自主驾驶仿真实验对MPIC方法进行评估，并与基于梯度的MPC方法进行了全面比较。结果表明，无论神经网络架构的复杂性如何，MPIC方法均具有显著的计算效率，并展现出解决神经状态空间模型大规模MPC问题的能力。