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In today's era, autonomous vehicles demand a safety level on par withaircraft. Taking a cue from the aerospace industry, which relies on redundancyto achieve high reliability, the automotive sector can also leverage thisconcept by building redundancy in V2X (Vehicle-to-Everything) technologies.Given the current lack of reliable V2X technologies, this idea is particularlypromising. By deploying multiple RATs (Radio Access Technologies) in parallel,the ongoing debate over the standard technology for future vehicles can be putto rest. However, coordinating multiple communication technologies is a complextask due to dynamic, time-varying channels and varying traffic conditions. Thispaper addresses the vertical handover problem in V2X using Deep ReinforcementLearning (DRL) algorithms. The goal is to assist vehicles in selecting the mostappropriate V2X technology (DSRC/V-VLC) in a serpentine environment. Theresults show that the benchmarked algorithms outperform the currentstate-of-the-art approaches in terms of redundancy and usage rate of V-VLCheadlights. This result is a significant reduction in communication costs whilemaintaining a high level of reliability. These results provide strong evidencefor integrating advanced DRL decision mechanisms into the architecture as apromising approach to solving the vertical handover problem in V2X.

Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study