Abstract
In recent years, shared spaces are gaining popularity in urban planning and transportation engineering, which is encouraging the reduction in motor vehicle dependency and promoting shared street design. This approach increases the interaction between motorized and non-motorized road users, which mandates the understanding of their interactions to design safe, sustainable, and equitable shared spaces. To facilitate the understanding of these interactions between road users, it is important to have a well-developed trajectory prediction and generation model. Considering this motivation, this thesis aims to develop a trajectory generation model using deep reinforcement learning that considers social context as observation. The first part of this thesis focuses on pedestrian trajectory generation using social context in a vehicle crowd interaction space, while the second part extends the study to encompass both pedestrian and bicycle trajectories. This method explores different social observation and action generation methodologies for different road users. The developed model is validated using two benchmark datasets DUT and SDD dataset. Average Displacement Error and Final Displacement Error are used to assess the performance of these models. In the final part of the thesis, an early conflict detection system is developed with the application of trajectory generation. The developed pedestrian trajectory generation model can be effectively used in simulation environments. The contribution of this thesis is using social context to generate observation and using action clusters to discretize action space for reinforcement learning agents. It also combines trajectory generation, deep learning, and early conflict detection to assist urban planners, transportation engineers, and shared space designers. The outcome of this research provides valuable insights to make shared spaces safer, more efficient, and more sustainable.
Notes
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Graduation Date
2023
Semester
Summer
Advisor
Zaki Hussein, Mohamed
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Department
Civil, Environmental, and Construction Engineering
Degree Program
Civil Engineering; Smart Cities Track
Identifier
CFE0009686; DP0027793
URL
https://purls.library.ucf.edu/go/DP0027793
Language
English
Release Date
August 2024
Length of Campus-only Access
1 year
Access Status
Masters Thesis (Campus-only Access)
STARS Citation
Ali, Syed Mostaquim, "Modeling Active Road-User Interaction: Deep Reinforcement Learning-Based Approach for Trajectory Generation with Social Context and Trajectory Prediction" (2023). Electronic Theses and Dissertations, 2020-. 1868.
https://stars.library.ucf.edu/etd2020/1868
Restricted to the UCF community until August 2024; it will then be open access.