ITSC 2025 Paper Abstract

Paper TH-LM-T25.3

ZHANG, CHEN (Shanghai Jiao Tong University), Xu, Yunwen (Shanghai Jiao Tong University), Li, Dewei (Department of Automation, Shanghai Jiao Tong University), Chen, Youren (Shanghai Jiao Tong University)

CAV Trio: A Structurally Mobile Regulator for Lane-Wise Mixed Traffic with Hierarchical Deep Learning Control Strategy

Scheduled for presentation during the Regular Session "S25a-Cooperative and Connected Autonomous Systems" (TH-LM-T25), Thursday, November 20, 2025, 11:10−11:30, Cooleangata 4

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Cooperative Driving Systems and Vehicle Coordination in Multi-vehicle Scenarios, Multi-vehicle Coordination for Autonomous Fleets in Urban Environments, Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks

Abstract

This paper introduces a novel "CAV Trio" structure, where three connected autonomous vehicles (CAVs) are strategically scattered across adjacent lanes, serving as a key regulatory means for multi-lane mixed traffic flow. Based on the CAV Trio structure, an end-to-end hierarchical deep learning control framework is proposed to dynamically adjust the formation and speed of CAVs in the CAV Trio, further guiding surrounding vehicles and regulating lane-wise macro traffic flow. The control framework features a spatio-temporal perception module, constructed using a Gated Recurrent Unit (GRU) and Graph Attention Network (GAT), which accurately extracts traffic features. A centralized Proximal Policy Optimization (PPO) agent generates macro-level decisions, while a Model Predictive Control (MPC) layer filters actions to ensure safety and practicality. Additionally, a decision filtering mechanism and a reward coupling term are integrated to prevent the formation of traffic barriers and enhance policy tracking.Simulation results demonstrate that compared with baseline methods, the proposed approach can effectively achieve multilane macroscopic traffic regulation under different targets, and balance throughput enhancement and speed preservation, highlighting the structure’s effectiveness and the framework’s superiority in traffic regulation.