Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing

TL;DR

This paper introduces a novel approach to autonomous vehicle motion planning by reformulating NMPC as a Bayesian estimation problem and applying an ensemble Kalman smoother, significantly improving computational efficiency.

Contribution

It presents a new method combining NMPC with ensemble Kalman smoothing to reduce computational costs in nonlinear, nonconvex motion planning problems.

Findings

Order of magnitude faster computation compared to traditional methods

Effective handling of highly nonlinear vehicle models

Potential for real-time autonomous vehicle motion planning

Abstract

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.