A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic

Tong Liu, Leilei Cui, Bo Pang, Zhong Ping Jiang

Research output: Contribution to journalArticlepeer-review


This article presents a unified approach to the problem of learning-based optimal control of connected human-driven and autonomous vehicles in mixed-traffic environments including both the freeway and ring road settings. The stabilizability of a string of connected vehicles including multiple autonomous vehicles (AVs) and heterogeneous human-driven vehicles (HDVs) is studied by a model reduction technique and the Popov-Belevitch-Hautus (PBH) test. For this problem setup, a linear quadratic regulator (LQR) problem is formulated and a solution based on adaptive dynamic programming (ADP) techniques is proposed without a priori knowledge on model parameters. To start the learning process, an initial stabilizing control law is obtained using the small-gain theorem for the ring road case. It is shown that the obtained stabilizing control law can achieve general Lp string stability under appropriate conditions. Besides, to minimize the impact of external disturbance, a linear quadratic zero-sum game is introduced and solved by an iterative learning-based algorithm. Finally, the simulation results verify the theoretical analysis and the proposed methods achieve desirable performance for control of a mixed-vehicular network.

Original languageEnglish (US)
Article number3287131
Pages (from-to)4131-4145
Number of pages15
JournalIEEE Transactions on Intelligent Vehicles
Issue number8
StatePublished - Aug 1 2023


  • Connected and autonomous vehicles (CAVs)
  • adaptive dynamic programming
  • disturbance attenuation
  • optimal control
  • stabilizability

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Artificial Intelligence


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