# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 1 Introduction  
The transition of autonomous vehicles from theoretical concepts to everyday reality marks a significant milestone in urban development, promising to transform transportation systems and urban infrastructures alike. This is not merely an advancement in vehicle control and navigation; it is a shift in how vehicles interact with their environment, passengers, and each other. Each autonomous vehicle, essentially a supercomputer on wheels, is estimated to generate up to 4 terabytes of data per day [1]. As these data-intensive vehicles become increasingly interconnected, they face a dual challenge, navigating not just through physical traffic but through an invisible sea of information. Autonomous vehicles are equipped with a suite of advanced features that promise to redefine the driving experience. These vehicles can detect and navigate around obstacles, adjust speed in response to traffic conditions, and plan routes in real-time to avoid congestion [2]. Moreover, autonomous vehicles have the potential to significantly improve road safety by reducing human error, which is the leading cause of traffic accidents [3]. These capabilities are made possible by a combination of hardware and software technologies, including LiDAR sensors, radar sensors, cameras, and sophisticated machine learningalgorithms. This technological synergy allows vehicles to perceive their surroundings, make decisions, and learn from experience with a high degree of precision and consistency. Furthermore, with the advancement of vehicle-to-vehicle and vehicle-to-infrastructure communications, vehicles can now share information with components such as cameras, traffic lights, and even other vehicles [4]. This opens up new possibilities for managing traffic flow and reducing congestion, which can lead to a more efficient use of road networks and lower travel times. It can also allow vehicles to interact with roadside units with dedicated computing resources to offload computing processes from vehicles and to perform real-time calibration functions while on the road [5]. As a result, vehicles are evolving beyond their traditional role as means of transportation; they generate, store, process, and transmit large amounts of data used to improve driver convenience and safety [6]. Building on these transformative advancements in vehicular technology and the emerging capabilities of autonomous vehicles, a significant challenge has emerged in data management and communication. The integration of these technologies not only enhances the vehicle's ability to navigate complex urban environments but also forms part of a larger, interconnected intelligent transportation system. This integration necessitates robust data management and transmission capabilities to fully utilize its potential. Furthermore, the introduction of 5G cellular networks, which aims to increase bandwidth for road data transmission, introduces additional challenges. The variability of these networks--affected by line of sight, infrastructure differences, and traffic density--can lead to fluctuations in data transmission rates and reliability. This paper addresses the problem of path planning for autonomous vehicles within urban environments, where the need for timely data transfer is as important as minimizing driving time. We formulate the problem of path planning considering both traffic and bandwidth heterogeneity on the road as a reinforcement learning problem. To practically assess this formulation, we implemented an environment representing a graphical road network using GraphML [7]. This framework enables the incorporation of real map data and vehicle mobility patterns to create a realistic and robust testing ground to evaluate the reinforcement learning approach to path planning. Using real-world data, our model can simulate the dynamics of urban traffic, providing a comprehensive understanding of the challenges and opportunities in optimizing path planning for autonomous vehicles.