Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A
  Benchmarking Study
Fouzi Boukhalfa Reda Alami Mastane Achab Eric Moulines Mehdi Bennis
In today's era, autonomous vehicles demand a safety level on par with
aircraft. Taking a cue from the aerospace industry, which relies on redundancy
to achieve high reliability, the automotive sector can also leverage this
concept by building redundancy in V2X (Vehicle-to-Everything) technologies.
Given the current lack of reliable V2X technologies, this idea is particularly
promising. By deploying multiple RATs (Radio Access Technologies) in parallel,
the ongoing debate over the standard technology for future vehicles can be put
to rest. However, coordinating multiple communication technologies is a complex
task due to dynamic, time-varying channels and varying traffic conditions. This
paper addresses the vertical handover problem in V2X using Deep Reinforcement
Learning (DRL) algorithms. The goal is to assist vehicles in selecting the most
appropriate V2X technology (DSRC/V-VLC) in a serpentine environment. The
results show that the benchmarked algorithms outperform the current
state-of-the-art approaches in terms of redundancy and usage rate of V-VLC
headlights. This result is a significant reduction in communication costs while
maintaining a high level of reliability. These results provide strong evidence
for integrating advanced DRL decision mechanisms into the architecture as a
promising approach to solving the vertical handover problem in V2X.