Mixed platoon control of automated and human-driven vehicles at a signalized intersection: dynamical analysis and optimal control
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Abstract (arXiv)
The emergence of Connected and Automated Vehicles (CAVs) promises better traffic mobility for future transportation systems. Existing research mostly focused on fully-autonomous scenarios, while the potential of CAV control at a mixed traffic intersection where human-driven vehicles (HDVs) also exist has been less explored. This paper proposes a notion of "1+n" mixed platoon, consisting of one leading CAV and n following HDVs, and formulates a platoon-based optimal control framework for CAV control at a signalized intersection. Based on the linearized dynamics model of the "1+n" mixed platoon, fundamental properties including stability and controllability are under rigorous theoretical analysis. Then, a constrained optimal control framework is established, aiming at improving the global traffic efficiency and fuel consumption at the intersection via direct control of the CAV. A hierarchical event-triggered algorithm is also designed for practical implementation of the optimal control method between adjacent mixed platoons when approaching the intersection. Extensive numerical simulations at multiple traffic volumes and market penetration rates validate the greater benefits of the mixed platoon based method, compared with traditional trajectory optimization methods for one single CAV.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 4; 2025: 1; 2026: 1.
7 papers.
- 2024HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Drivingcited by 32
- 2024Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learningcited by 8
- 2024D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluationcited by 3
- 2022Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learningcited by 2
- 2025Optimizing Multi-Lane Intersection Performance in Mixed Autonomy Environmentscited by 1
- 2024Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehiclescited by 0
- 2026Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehiclescited by 0
- 2026Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehicles26 Mar 2026
- 2025Optimizing Multi-Lane Intersection Performance in Mixed Autonomy Environments4 Nov 2025
- 2024D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation12 Apr 2024
- 2024Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles10 Apr 2024
- 2024Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning20 Jan 2024
- 2024HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving6 Jan 2024
- 2022Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning9 Apr 2022