mlsanityData to 14 Oct 2026

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

arXiv 2309.12534 ↗First posted 21 Sep 2023cs.LG cs.SY eess.SYcited by 0

On the map: End-to-end autonomous driving → Cooperative and V2X driving

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.

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.

Builds on

12 of 33 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
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  7. 2018Application of Machine Learning in Wireless Networks: Key Techniques and Open Issuescited by 30
  8. 2015Route Planning in Transportation Networkscited by 23
  9. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
  10. 2020Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learningcited by 21
  11. 2019Exploring applications of deep reinforcement learning for real-world autonomous driving systemscited by 18
  12. 2017Implementing the Deep Q-Networkcited by 10

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