Chen, Mingyang;
(2025)
Intelligent Control of Connected Autonomous Vehicles in Mixed Autonomy Environments.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
In recent years, the advent of connected and autonomous vehicles (CAVs) that feature advanced sensing, communication, and control capabilities has gained increasing attention and market interest. However, mixed traffic from human-driven vehicles (HDV) and CAVs will dominate road traffic in the foreseeable future, while complicated interactions between different types of vehicles with various driving behaviors can affect traffic efficiency and safety. To address such concerns, this thesis focuses on the control of CAVs in mixed-traffic environments. The first part of this thesis is dedicated to investigating the trade-off between intersection throughput and the energy efficiency of all vehicles on the road by co-optimizing signal phase and time (SPAT) and CAV trajectories. Instead of using further alternative signal phases to promote CAV-led platoons, the platoon formation is enforced by trajectory optimization and lane-changing is not allowed in this part. However, lane-changing maneuvers are inevitable and thus the interactions between CAVs and HDVs need further study. In the second part of this thesis, a game-based optimal lane change control framework for the CAV is proposed. The lane-changing involves one CAV and one HDV is comprehensively studied where their interaction is considered by a Stackelberg game, which yields an unconstrained optimal control solution by the Hamilton–Jacobi equation (HJE). Moreover, a theoretical proof is provided to show that the unconstrained optimal strategy is an asymptotically stable equilibrium, and with a suitable design of the weight matrices, safety can be guaranteed. The final part of this thesis extends to multi-vehicle lane-changing scenarios. A game-based coalition structure is proposed to group all the vehicles based on their position and lane-changing intention. To enhance robustness against human uncertainty, data-enabled predictive control is applied to the CAVs. Human-in-the-loop (HIL) experiments are conducted to show the real-time performance of the proposed method.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Intelligent Control of Connected Autonomous Vehicles in Mixed Autonomy Environments |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209488 |
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