Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs
  Using Reinforcement Learning
Yousef AlSaqabi Bhaskar Krishnamachari
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.