Machine Learning in Autonomous Transportation: Optimizing Route Planning

Abstract

Autonomous transportation is transforming modern mobility by leveraging advanced machine learning (ML) techniques to optimize various systems within transportation networks. One of the most crucial aspects is route planning, where ML models help in determining the most efficient routes for autonomous vehicles (AVs), accounting for traffic patterns, road conditions, weather, and other dynamic factors. This paper explores the role of ML in enhancing route planning algorithms, focusing on the use of reinforcement learning (RL), deep learning (DL), and neural networks for real-time decision-making. The integration of these algorithms into autonomous vehicle systems holds significant promise in improving traffic flow, reducing energy consumption, and enhancing safety.