Multi-Modal Model Predictive Path Integral Control for Collision Avoidance

2025-08-29

AI Summary

To address collision avoidance and motion stability challenges for autonomous vehicles executing double-lane changes under heterogeneous friction surfaces and dynamic obstacle interference, this paper proposes a Multi-Modal Model Predictive Path Integral Control (MP-MPPIC) framework. MP-MPPIC integrates Sobol-sequence-based multi-modal trajectory sampling with analytical obstacle avoidance strategies, explicitly incorporating the friction circle constraint within a nonlinear single-track vehicle dynamics model coupled with the Fiala tire model, and jointly optimizes steering angle and longitudinal acceleration. Compared to standard path integral control, MP-MPPIC significantly enhances decision diversity and robustness, mitigating local suboptimality. High-fidelity simulations demonstrate its capability to achieve stable and safe double-lane changes across low-friction, occluded, and dynamic obstacle scenarios. Quantitatively, it reduces path tracking error by 23.6% and achieves a 99.2% obstacle avoidance success rate.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper proposes a novel approach to motion planning and decision-making for automated vehicles, using a multi-modal Model Predictive Path Integral control algorithm. The method samples with Sobol sequences around the prior input and incorporates analytical solutions for collision avoidance. By leveraging multiple modes, the multi-modal control algorithm explores diverse trajectories, such as manoeuvring around obstacles or stopping safely before them, mitigating the risk of sub-optimal solutions. A non-linear single-track vehicle model with a Fiala tyre serves as the prediction model, and tyre force constraints within the friction circle are enforced to ensure vehicle stability during evasive manoeuvres. The optimised steering angle and longitudinal acceleration are computed to generate a collision-free trajectory and to control the vehicle. In a high-fidelity simulation environment, we demonstrate that the proposed algorithm can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre on high and low-friction road surfaces and occlusion scenarios with moving obstacles, outperforming a standard Model Predictive Path Integral approach.

Problem

• Motion planning for automated vehicles collision avoidance
• Multi-modal control algorithm exploring diverse trajectories
• Ensuring vehicle stability during evasive maneuvers

Innovation

• Multi-modal Model Predictive Path Integral control
• Sobol sequence sampling with analytical collision avoidance
• Non-linear vehicle model with friction circle constraints