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

2024-07-10

American Control Conference

AI Summary

Nonlinear model predictive control (NMPC) for motion planning using neural-network-based vehicle models suffers from high computational cost and poor real-time performance due to the inherent nonconvexity of the underlying optimization problem. Method: This paper reformulates NMPC as a Bayesian estimation problem—its first such formulation—thereby circumventing traditional numerical optimization bottlenecks. We propose an efficient solution framework based on the ensemble Kalman smoother (EnKS), integrating nonlinear dynamical modeling with sequential data assimilation principles. The approach requires neither gradient evaluation nor iterative optimization, drastically reducing computational complexity. Contribution/Results: Simulation results demonstrate a 100×–1000× improvement in planning speed while preserving trajectory accuracy and closed-loop stability. This work establishes a novel paradigm for real-time NMPC leveraging learned vehicle models, enabling practical deployment in safety-critical autonomous driving applications.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Safe and efficient motion planning is of fundamen-tal 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 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.

Problem

• Overcoming high computational costs in NMPC for neural network vehicle models
• Reformulating NMPC-based motion planning as a Bayesian estimation problem
• Improving computational speed for practical autonomous vehicle motion planning

Innovation

• Uses nonlinear model predictive control (NMPC)
• Reformulates NMPC as Bayesian estimation problem
• Applies ensemble Kalman smoother for efficiency