Deep Learning Algorithms for Autonomous Vehicle Communications: Technical Insights and Open Challenges

ABSTRACT Autonomous vehicles (AVs) are one of the building blocks of modern intelligent transportation systems and have the potential to change some aspects related to mobility, safety, and operational efficiency. In this paper, we analyze recent progress in AV algorithms and simulation frameworks, emphasizing their roles in decision‐making processes, trajectory planning, object detection, and traffic optimization strategies. This paper provides technical discussions on the three core research questions: (RQ1) Major methodologies used for decision‐making, trajectory planning, and traffic optimization in AV communications (RQ2) Effectiveness of simulation platforms at closing the gap between algorithm testing and real‐world performance (RQ3) Challenges for the scalability and deployment of AV technologies. This paper collates the results from important individual research articles from scientific databases that present different methodologies including deep learning, reinforcement learning, and rule‐based approaches. The major conclusions pointed out in this regard include increased reliance on deep learning for complex task handling, its good effectiveness in hybrid learning paradigms, and, most importantly, the central role that simulations can play in assessing scalability and safety over a large range of conditions. Still, several challenges do remain, including high computational demands for real‐ time decision‐making, integration of V2X communication, and the gap between simulated and real‐world performance. This paper identifies the emerging trends, highlights the technical limitations, and provides a roadmap for AV development using robust algorithms with realistic simulations.