# Multi-Modal Model Predictive Path Integral Control for Collision Avoidance

## TL;DR

This paper introduces a multi-modal Model Predictive Path Integral control algorithm for automated vehicle collision avoidance, effectively exploring diverse trajectories and ensuring vehicle stability in complex scenarios.

## Contribution

It presents a novel multi-modal control method that incorporates Sobol sampling and analytical collision avoidance solutions for improved motion planning.

## Key findings

- Successfully avoids obstacles in high-fidelity simulations
- Maintains vehicle stability during evasive maneuvers
- Outperforms standard MPPI in complex scenarios

## 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.