Next-Gen Protocols for Cooperative Autonomous Driving: V2X Communication and Machine Learning Integration

Cooperative autonomous driving represents the frontier of intelligent transportation systems, where vehicles, infrastructure, and other road users share information to achieve collective goals. While traditional vehicle-to-everything (V2X) standards such as DSRC and basic cellular-V2X (C-V2X) have established initial frameworks for safety message exchange, the increasing density of connected and automated vehicles (CAVs) and the demand for high-level cooperation—including joint trajectory planning, energy-efficient platooning, and dynamic congestion mitigation—reveal critical limitations in current architectures, especially for the complex intelligent transportation environment. These include latency under high-density scenarios, inconsistent handling of communication uncertainties, and difficulties in fusing heterogeneous sensory and operational data from distributed sources.

A promising pathway lies in integrating sensing, communication, and computation (ISCC) into a unified cyber-physical framework. Augmented by edge computing, this enables real-time environmental awareness and adaptive resource allocation. Embedding Artificial Intelligence—via machine learning and probabilistic inference—optimizes this loop, dynamically managing resources for robust prediction and decision-making.

This research sits at the confluence of telecommunications, distributed computing, control theory, and artificial intelligence. The goal is to create a seamless, adaptive ecosystem where vehicles transition from isolated agents to synchronized components of an intelligent transportation network, through the tight fusion of communication, sensing, computation, and control.

The rapid evolution of autonomous driving demands a paradigm shift from isolated vehicle intelligence to cooperative systems. While V2X communication provides a foundation, existing protocols struggle with the dynamic complexity of real-world traffic, leading to challenges in reliability, latency, and scalability under diverse conditions. This Research Topic aims to address these limitations by fostering the development of next-generation, adaptive protocols. We seek to explore how hybrid communication strategies—integrating C-V2X and sidelink technologies—can be synergistically combined with advanced ISCC technologies, artificial intelligence and distributed control algorithms. Recent advances in edge computing, federated learning, and predictive modeling offer unprecedented opportunities to create intelligent, resilient systems that enhance safety and traffic efficiency. Our goal is to collate innovative solutions that enable CAVs to make robust, real-time decisions in cooperative maneuvers like platooning and intersection negotiation, ultimately supporting the vision of sustainable, fluid urban mobility.

We invite contributions that address the integrated design of communication, sensing, computation, and control for cooperative autonomous driving. Specific themes of interest include, but are not limited to:
• AI/ML-enhanced V2X protocols for reliable, low-latency message dissemination.
• Hybrid communication architectures (e.g., C-V2X/5G/6G integration) for mixed-traffic scenarios.
• Distributed machine learning (federated, reinforcement) for scalable and privacy-aware cooperative perception and control.
• Novel protocols for specific applications: platooning, collision avoidance, intersection management, and eco-driving.
• Security, privacy, and trust frameworks for AI-driven V2X systems.
• Simulation platforms and real-world testbed results validating integrated protocols.
• AI-driven cloud-edge/fog-end collaborative computing in ISCC V2X communication systems.
• AI-driven aerial platform trajectory planning and resource scheduling in ISCC V2X communication systems.
• AI-driven digital twin models for simulation and real-time management of ISCCV2X communication systems.

We welcome high-quality manuscript types including original research, reviews, and perspective articles. Submissions should present novel ideas, rigorous evaluations, and discuss implications for the future of intelligent transportation.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: cooperative driving; connected vehicle; V2X communication; machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.