Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach for autonomous vehicle route planning that balances driving time and data transfer needs in heterogeneous urban traffic environments.

Contribution

It presents a novel RL-based method that prioritizes high bandwidth roads to meet data requirements while minimizing travel time, addressing a gap in current route planning strategies.

Findings

Outperforms traffic-unaware baselines in heterogeneous traffic scenarios

Effectively balances driving time and data transfer needs

Provides insights for future heuristic development

Abstract

With recent advancements in the field of communications and the Internet of Things, vehicles are becoming more aware of their environment and are evolving towards full autonomy. Vehicular communication opens up the possibility for vehicle-to-infrastructure interaction, where vehicles could share information with components such as cameras, traffic lights, and signage that support a countrys road system. As a result, vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data used to make driving safer and more convenient. With 5G cellular networks and beyond, there is going to be more data bandwidth available on our roads, but it may be heterogeneous because of limitations like line of sight, infrastructure, and heterogeneous traffic on the road. This paper addresses the problem of route planning for autonomous vehicles in urban areas accounting for both driving time and data transfer needs. We propose a novel reinforcement learning solution that prioritizes high bandwidth roads to meet a vehicles data transfer requirement, while also minimizing driving time. We compare this approach to traffic-unaware and bandwidth-unaware baselines to show how much better it performs under heterogeneous traffic. This solution could be used as a starting point to understand what good policies look like, which could potentially yield faster, more efficient heuristics in the future.