Tutorial Topic

Reinforcement Learning-Based Cooperative Control for Connected and Autonomous Vehicle

Brief description

With the rapid advancement of intelligent vehicle technologies, decision-making and control methods based on single-agent systems face significant challenges in managing increasingly complex traffic environments. This tutorial presents cutting-edge methodologies for heterogeneous multi-agent collaborative learning, integrating reinforcement learning, distributed optimization, and system control theories. In multimodal and multi-source data environments, this tutorial discusses robust strategies for achieving efficient communication, real-time data sharing, and collaborative control among various vehicles and infrastructures. By referencing widely recognized research findings and incorporating our latest work in optimal vehicle control, this tutorial further elucidates the application of mature heterogeneous multi-agent reinforcement learning methods to enhance cooperative driving processes. The proposed approach leverages the synergistic effects among heterogeneous agents to achieve superior overall performance, resilience, and scalability in complex traffic scenarios. Additionally, this tutorial provides an extended discussion on architectural design, algorithm selection, and practical deployment, emphasizing reliability under uncertain operational conditions. Based on our recent research outcomes, it highlights key advancements and conducts an in-depth evaluation of future research directions, including distributed parameter tuning, hierarchical control, and cloud-based data fusion, to shape sustainable development pathways. Ultimately, this tutorial aims to establish advanced heterogeneous multi-agent collaborative control strategies, fostering the development of an efficient, robust, and scalable ecosystem for heterogeneous intelligent vehicle collaboration.

Turtorial objectives

This tutorial outlines four core learning objectives centered on the theory and application of heterogeneous multi-agent systems in connected and autonomous vehicles. First, it clarifies the fundamental principles of heterogeneity, highlighting the impact of diverse vehicle types and mixed traffic flows on collaborative control processes. By emphasizing the unpredictability of real-world driving scenarios, it underscores the importance of adaptive control strategies and efficient collaboration among connected and autonomous vehicles as well as human-driven vehicles in varying operational contexts. Second, it introduces an interactive "vehicle-road" modeling and learning framework tailored to multi-agent heterogeneity, emphasizing the integration of multi-modal and multi-source data through spatiotemporal heterogeneous graph-based techniques. This approach enhances understanding of vehicle energy consumption behaviors across different categories, enabling the development of methods that optimize both action execution and state representation. Third, it presents cooperative decision-making and control techniques for heterogeneous multi-agent systems, addressing key limitations of multi-agent reinforcement learning in large-scale and complex traffic scenarios. Drawing from potential game-theoretic insights, it proposes advanced collaborative control strategies that consider the interplay between connected and autonomous vehicles and the surrounding transportation infrastructure. These strategies aim to improve system efficiency, enhance resilience, and ensure robust adaptability, achieving globally optimal control effects such as energy savings. Real-time data exchange and distributed optimization are identified as critical enablers, supporting coordinated maneuvers that enhance traffic throughput and vehicle-level energy management. Finally, the tutorial explores emerging research trends by addressing persistent challenges in developing scalable, reliable, and secure multi-agent solutions for next-generation mobility. Through investigations into rapid sensing, high-fidelity modeling, and spatiotemporal data assimilation, it identifies pathways to bridge the gap between theoretical advancements and practical applications. Special attention is given to key enablers, including self-evolving network architectures, algorithmic flexibility in multi-agent coordination, and the integration of deep learning with distributed control algorithms. These components are vital for managing uncertainties in traffic flow, driver behaviors, and environmental conditions. By systematically addressing these objectives, the tutorial provides a comprehensive roadmap for researchers and practitioners to develop advanced solutions that accommodate the complexities of heterogeneous multi-agent systems. It demonstrates how real-time communication and collaborative planning enable heterogeneous agents to adapt efficiently to complex road conditions while prioritizing energy efficiency and operational stability. This emphasis on energy-saving performance not only fosters environmental sustainability but also reduces operational costs associated with expanding vehicle fleets. Furthermore, the proposed learning-based control architecture extends to broader intelligent transportation contexts, including shared mobility services and on-demand logistics, offering flexible, scalable, and future-proof traffic management solutions. Ultimately, the tutorial aims to inspire interdisciplinary discussions and stimulate innovation toward an intelligent transportation ecosystem leveraging heterogeneous multi-agent cooperation for sustainable, efficient, and secure automated driving solutions.

Innovation&Novelty

Based on the state-of-the-art review and our research accumulation, this tutorial introduces three key innovations in the field of heterogeneous multi-agent intelligent vehicle control to address critical challenges. First, considering the technical bottlenecks in mixed traffic environments, such as the diversity of vehicle types, the complexity of infrastructure, and the unpredictability of human factors, this tutorial comprehensively presents a modeling approach for multimodal and multi-source data fusion. By leveraging the representation of spatiotemporal heterogeneous graphs, real-time data from various sources, including onboard sensors, roadside units, and cloud platforms, are integrated. This approach not only significantly improves prediction accuracy in complex traffic scenarios but also maintains high robustness and adaptability in the presence of partial data loss or noise interference. 

Second, this tutorial introduces energy-efficient optimization control strategies for heterogeneous intelligent connected vehicle fleets. Unlike traditional methods that focus solely on homogeneous vehicles or neglect traffic signals and network constraints, this approach incorporates vehicles with different powertrains, dynamic performance, and connectivity levels into a unified framework, while fully considering road conditions and traffic management factors. By balancing overall energy efficiency and average vehicle speed, this method effectively reduces the total energy consumption of the fleet while enhancing traffic flow stability.

 Lastly, this tutorial, based on potential game theory, introduces a heterogeneous multi-agent reinforcement learning framework for intelligent traffic management. This theoretical innovation enables each agent to consider the global system objectives while making local decisions. By modeling multi-agent interactions as a potential game, this framework systematically addresses communication overhead, conflict resolution, and incentive compatibility among heterogeneous agents, thereby improving decision-making robustness and algorithm convergence speed. Furthermore, combining the potential game perspective with reinforcement learning not only deepens the theoretical understanding of cooperative behaviors but also provides novel algorithmic ideas and practical pathways for large-scale traffic environments. Overall, the tutorial opens broader prospects for future research and practical deployment.