Date of Award
5-2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical Engineering
Committee Chair/Advisor
Prof. Ardalan Vahidi
Committee Member
Prof. John Wagner
Committee Member
Prof. Javad Velni
Committee Member
Dr. Phanindra Tallapragada
Abstract
Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver models, probabilistic approaches, rule-base techniques, and machine learning approach have addressed this problem only partially as they do not focus on the behavioral features of the vehicles. The main goal of this research is to develop a fast algorithm that predicts the future states of the neighboring vehicles, runs a fast decision process, and learns the regretfulness and rewardfulness of the executed decisions. The presented algorithm is developed based on level-K game theory to model and predict the interaction between the vehicles. Using deep reinforcement learning, this algorithm encodes and memorizes the past experiences that are recurrently used to reduce the computations and speed up motion planning. Also, we use Monte Carlo Tree Search (MCTS) as an effective tool that is employed nowadays for fast planning in complex and dynamic game environments. This development leverages the computation power efficiently and showcases promising outcomes for maneuver planning and predicting the environment’s dynamics. In the absence of traffic connectivity that may be due to either passenger’s choice of privacy or the vehicle’s lack of technology, this development can be extended and employed in automated vehicles for real-world and practical applications.
Recommended Citation
Karimi, Shahab, "Deep Reinforcement Learning and Game Theoretic Monte Carlo Decision Process for Safe and Efficient Lane Change Maneuver and Speed Management" (2023). All Dissertations. 3310.
https://open.clemson.edu/all_dissertations/3310
Author ORCID Identifier
0000-0003-4369-6151
Included in
Computer Engineering Commons, Controls and Control Theory Commons, Mechanical Engineering Commons, Navigation, Guidance, Control, and Dynamics Commons