Cooperative driving in a connected vehicle (CV) environment has received increasing attention over the years due to its ability to enhance driving safety and efficiency. Despite many efforts that have been made in this field, the role of human drivers and pedestrians is frequently omitted. It is important to consider them to develop cooperative driving algorithms that are intelligent and robust to incorporate any uncertainty brought by humans.
In this dissertation, a framework of a multi-driver in-the-loop driving simulator and a pedestrian in-the-loop digital twin system is introduced. Three important topics in cooperative driving were investigated using the developed framework: the effects of human-machine interface (HMI) design for cooperative driving, vehicle-pedestrian interaction under occlusion scenarios, and multi-vehicle decision-making at weaving segments.
In the first topic, three HMIs were designed for collaborative speed adaptation following the skills, rules, and knowledge (SRK) taxonomy. The HMI designs were tested using a multi-driver simulator, and the results showed that the graphic-based HMI improved cooperative driving performance and was preferred by the participants. In the second task, a Digital Twin framework for CV and pedestrian in-the-loop simulation was proposed based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The effects of Vehicle-Pedestrian (V2P) warning systems under occlusion scenarios were investigated for different connectivity and vehicle automation levels. In the third task, an edge-enhanced graph attention deep reinforcement learning algorithm was developed to aid autonomous vehicles in diverging at weaving segments. The results showed that the proposed algorithms outperformed existing models and performed well in real-world driving scenarios.
The dissertation provides insights into developing safe and efficient cooperative driving algorithms and applying advanced simulation technologies to human-in-the-loop cooperative driving testing.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1267 |
| Date | 01 January 2024 |
| Creators | Wang, Zijin |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
| Rights | In copyright |
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