Date of Award
Computer Science and Engineering
Duan Zhang, Ph.D.
Mohammad H. Mahoor, Ph.D.
Kyoung-Dae Kim, Ph.D.
Jun Jason Zhang, Ph.D.
Kimon P. Valavanis, Ph.D.
Amin Khodaei, Ph.D.
David Gao, Ph.D.
Algorithm, Autonomous vehicles, Intersection control, Optimization
In this dissertation, we address a problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose several algorithms to handle different traffic environments. First, an algorithm that is called the Discrete-time occupancies trajectory (DTOT) based Intersection traffic Coordination Algorithm (DICA) is proposed. All vehicles in the system are Connected and Autonomous Vehicles (CAVs) and capable of wireless Vehicle-to-Intersection communication. The main advantage of DICA is that it enables us to utilize the intersection space more efficiently resulting in less delay for vehicles to cross the intersection. In the proposed framework, an intersection coordinates the motions of CAVs based on their proposed DTOTs to let them cross the intersection efficiently while avoiding collisions. In case when there is a potential collision between vehicles' DTOTs, the intersection modifies conflicting DTOTs to avoid the collision and requests CAVs to approach and cross the intersection according to the modified DTOTs. We also prove that the basic DICA is deadlock free and starvation free. We show that the basic DICA has a computational complexity of O(n2 L3m) where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational techniques. The enhanced algorithm has a reduced computational complexity of O(n2 Lm log2 Lm).
The problem of evacuating emergency vehicles as quickly as possible through autonomous and connected intersection traffic is also addressed in this dissertation. The proposed Reactive DICA aims to determine an efficient vehicle-passing sequence which allows the emergency vehicle to cross an intersection as soon as possible while the travel times of other normal vehicles are minimally affected. When there are no emergency vehicles within the intersection area, the vehicles are controlled by DICA. When there are emergency vehicles entering communication range, we prioritize emergency vehicles through the optimal ordering of vehicles. Since the number of possible vehicle-passing sequences increases rapidly with the number of vehicles, finding an efficient sequence of vehicles in a short time is the main challenge of the study. A genetic algorithm is proposed to solve the optimization problem which finds the optimal vehicle sequence in real time that gives the emergency vehicles the highest priority.
We then address an optimization problem of autonomous intersection control which provides the optimal trajectory for every entering vehicle. Based on the algorithm DICA, we improve the conservative way of trajectory generation which is the key part of DICA to be an optimization approach using mixed integer programming. The new algorithm is named Mixed integer programming based Intersection Coordination Algorithm (MICA) with the objective of maximizing the final position of a new head vehicle over a fixed time interval. Constraints from space conflicting vehicles are modeled using binary variables to represent the vehicle's future crossing behavior. The influence of immediate front vehicles of the vehicle of interest is also modeled as constraints in the problem formulation to obtain a feasible optimal trajectory while potential collisions are safely avoided. Finally, based on MICA, we propose a novel vehicle-intersection interaction mechanism MICACO which is designed to handle imperfect communication, i.e., message delay and loss. To ensure the successful delivery of messages, we add two more message types and corresponding simple rules. State machines of intersection and vehicles are designed properly to ensure the safety of every vehicle.
We verify the efficiency of the proposed algorithms through simulations using SUMO. The simulation results show that DICA performs better than another existing intersection management scheme: Concurrent Algorithm in . The overall throughput, as well as the computational efficiency of the computationally enhanced DICA, are also compared with those of an optimized traffic light control. The efficiency of the proposed Reactive DICA is validated through comparisons with DICA and a reactive traffic light algorithm. The results show that Reactive DICA is able to decrease the travel times of emergency vehicles significantly in light and medium traffic volumes without causing any noticeable performance degradation of normal vehicles. The simulation results show that MICA is able to reduce congestions of an intersection significantly compared with DICA. We also show MICACO's performance through comparisons with MICA and an optimized traffic light.
Copyright is held by the author. User is responsible for all copyright compliance.
Lu, Qiang, "Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles" (2019). Electronic Theses and Dissertations. 1541.
Received from ProQuest