InfoFusion Controller

Abstract

In this paper, we propose the InfoFusion Controller, an advanced path planning algorithm that integrates both global and local planning strategies to enhance autonomous driving in complex urban environments. The global planner utilizes the informed Theta-Rapidly-exploring Random Tree Star (Informed- TRRT*) algorithm to generate an optimal reference path, while the local planner combines Model Predictive Control (MPC) and Pure Pursuit algorithms. Mutual Information (MI) is employed to fuse the outputs of the MPC and Pure Pursuit controllers, effectively balancing their strengths and compensating for their weaknesses.

The proposed method addresses the challenges of navigating in dynamic environments with unpredictable obstacles by reducing uncertainty in local path planning and improving dynamic obstacle avoidance capabilities. Experimental results demonstrate that the InfoFusion Controller outperforms traditional methods in terms of safety, stability, and efficiency across various scenarios, including complex maps generated using SLAM techniques.

Index Terms—Path Planning, MPC, Pure Pursuit, Mutual Information

InfoFusion Controller

Pipeline of InfoFusionController: (a) Global Planning (Informed TRRT*): The Informed TRRT* algorithm generates a global reference path ( T_ref) based on the observations and current state. (b) Local Planning (Pure Pursuit): The Pure Pursuit algorithm steers the vehicle toward a point on the reference path ( T_ref). (c) Local Planning (MPC): The MPC algorithm predicts optimal control inputs to follow the reference path over a dynamic horizon. (d) Synchronization (Mutual Information): Mutual Information (MI) combines state predictions from Pure Pursuit ( S_k^P ) and MPC ( S_k^M ) to select the best path.

Obstacle Avoidance

Pure Pursuit

Pure Pursuit avoids obstacles by adjusting the target point along the reference path, steering towards a new point to bypass the obstacle.

MPC

MPC predicts the vehicle's future states over a defined horizon, optimizing control inputs to avoid obstacles while adhering to dynamic constraints.

Experimental Setup

map_easy: A simple map designed to evaluate the performance of basic path planning algorithms, based on the Informed TRRT* paper. map_middle: A medium-difficulty map focused on testing the algorithm's ability to navigate tight corners and narrow passages, emphasizing speed and handling stability. map_hard: A complex map generated using SLAM technology to simulate real-world environments, assessing the algorithm's efficiency and stability in challenging scenarios.

Performance

Obstacle Avoidance

In the left trajectory plot, InfoFusion demonstrates stable obstacle avoidance compared to other algorithms. While adaptive MPC and MPC basic show slight path distortions, and Pure Pursuit makes abrupt turns when navigating around dynamic objects, InfoFusion navigates smoothly with minimal deviation from the optimal path. This indicates that the InfoFusion controller maintains both safety and stability even when dynamic obstacles are introduced.

Driving Safety

In order to compare the safety of the algorithms, we were conducted in map_medium with a lot of cornering. MPC frequently oscillates back and forth excessively. This pattern can lead to unnecessary speed changes, reducing stability during driving. On the other hand, pure_pursuit shows more stable acceleration without significant fluctuations, but it has a tendency to overly reduce speed before cornering. In contrast, the info_fusion algorithm demonstrates a more consistent pattern compared to MPC.

Demo

Pure Pursuit

Pure Pursuit has a limitation in stability, often leading to abrupt changes in speed and steering, especially during sharp turns.

MPC

MPC, while offering precise control, frequently exhibits oscillatory behavior, impacting overall stability.

InfoFusion

The InfoFusion Controller overcomes these challenges by fusing the strengths of both approaches, ensuring smoother speed transitions and consistent handling for enhanced stability.

BibTeX

@article{choi2025infofusioncontroller,
  author    = {Choi, Seongjun and Kim, Youngbum and Nam, Namwoo and Shin, Mansun and Chae, Byunggi and Lee, Sungjin},
  title     = {InfoFusionController: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning},
  journal   = {ICCE},
  year      = {2025},
}