InfoFusion Controller: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning Seongjun Choi Kyung-Hee University Autonomous Driving Lab, MODULABS, Republic of Korea Y oungbum Kim Korea Aviation University Autonomous Driving Lab, MODULABS, Republic of Korea Nam Woo Kim Unity T echnologies Autonomous Driving Lab, MODULABS, Republic of Korea Mansun Shin SP ACEEDUING Co., Ltd. Autonomous Driving Lab, MODULABS, Republic of Korea Byunggi Chae Auroka Pankyo Autonomous Driving Lab, MODULABS, Republic of Korea Sungjin Lee Dong Seoul University, Autonomous Driving Lab, MODULABS, Republic of Korea 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.

Many of the top players in technology and automobiles are fervently working towards a world in which autonomous driving vehicles are commonplace. Some current vehicles, like the Tesla Model S, already offer self-driving, auto-pilot capabilities, but these are a precursor to fully autonomous vehicles that operate with virtually no human intervention. As you'd probably expect, Intel is actively working in the area as well. Massive amounts of processing power and storage are needed to churn through and store the deep learning models that will disseminate data for autonomous vehicles. Today the company posted a short video that gives a glimpse into its Autonomous Driving Lab in Chandler, Arizona.