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

arXiv: https://arxiv.org/abs/2309.12534 · HTML: https://mlsanity.com/paper/2309.12534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- [Route Planning in Transportation Networks](https://mlsanity.com/paper/1504.05140/) (2015)
- [Autonomous Highway Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.00035/) (2019)
- [Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.10268/) (2020)
- [Exploring applications of deep reinforcement learning for real-world autonomous driving systems](https://mlsanity.com/paper/1901.01536/) (2019)
- [Implementing the Deep Q-Network](https://mlsanity.com/paper/1711.07478/) (2017)

## Abstract (arXiv)

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.