Enhancing Logistics Efficiency with Autonomous Vehicles: Leveraging Reinforcement Learning, Sensor Fusion, and Path Planning Algorithms

Abstract

This research explores the transformative potential of incorporating autonomous vehicles (AVs) into logistics operations, with an emphasis on enhancing efficiency through advanced computational methods. The study integrates reinforcement learning, sensor fusion, and path planning algorithms to optimize vehicle operations in complex, dynamic environments. Reinforcement learning is employed to enable AVs to learn optimal strategies for navigation and task execution through interaction with their environment. Sensor fusion techniques are utilized to amalgamate data from multiple sensors, improving the reliability and accuracy of real-time environmental perception. Path planning algorithms are designed to compute optimal routes under varying constraints, such as traffic conditions, road closures, and delivery priorities. A comprehensive simulation framework is developed to test the integration of these technologies, revealing significant improvements in delivery speed, fuel efficiency, and safety. Results indicate that the synergy between reinforcement learning and sensor fusion significantly enhances decision-making capabilities, while advanced path planning algorithms ensure timely and cost-effective logistics operations. The findings suggest that such integrated systems could lead to substantial operational gains, positioning autonomous vehicles as a cornerstone of future logistics strategies. This paper contributes to the body of knowledge by demonstrating a scalable, adaptable model for AV deployment in logistics, offering insights for researchers and industry stakeholders aiming to leverage artificial intelligence in transportation networks.