This thesis considers the cooperation and coordination of multi vehicle systems cohesively in order to keep the formation geometry and provide the string stability. We first present the modeling of aerial and road vehicles representing different motion characteristics suitable for cooperative operations. Then, a set of three dimensional cohesive motion coordination and formation control schemes for teams of autonomous vehicles is proposed. The two main components of these schemes are i) platform free high level online trajectory generation algorithms and ii) individual trajectory tracking controllers. High level algorithms generate the desired trajectories for three dimensional leader-follower structured tight formations, and then distributed controllers provide the individual control of each agent for tracking the desired trajectories. The generic goal of the control scheme is to move the agents while maintaining the formation geometry. We propose a distributed control scheme to solve this problem utilizing the notions of graph rigidity and persistence as well as techniques of virtual target tracking and smooth switching. The distributed control scheme is developed by modeling the agent kinematics as a single-velocity integrator; nevertheless, extension to the cases with simplified kinematic and dynamic models of fixed-wing autonomous aerial vehicles and quadrotors is discussed. The cohesive cooperation in three dimensions is so beneficial for surveillance and reconnaissance activities with optimal geometries, operation security in military activities, more viable with autonomous flying, and future aeronautics aspects, such as fractionated spacecraft and tethered formation flying. We then focus on motion control task modeling for three dimensional agent kinematics and considering parametric uncertainties originated from inertial measurement noise. We design an adaptive controller to perform the three dimensional motion control task, paying attention to the parametric uncertainties, and employing a recently developed immersion and invariance based scheme. Next, the cooperative driving of road vehicles in a platoon and string stability concepts in one-dimensional traffic are discussed. Collaborative driving of commercial vehicles has significant advantages while platooning on highways, including increased road-capacity and reduced traffic congestion in daily traffic. Several companies in the automotive sector have started implementing driver assistance systems and adaptive cruise control (ACC) support, which enables implementation of high level cooperative algorithms with additional softwares and simple electronic modifications. In this context, the cooperative adaptive cruise control approach are discussed for specific urban and highway platooning missions. In addition, we provide details of vehicle parameters, mathematical models of control structures, and experimental tests for the validation of our models. Moreover, the impact of vehicle to vehicle communication in the existence of static road-side units are given. Finally, we propose a set of stability guaranteed controllers for highway platooning missions. Formal problem definition of highway platooning considering constant and velocity dependent spacing strategies, and formal string stability analysis are included. Additionally, we provide the design of novel intervehicle distance based priority coefficient of feed-forward filter for robust platooning. In conclusion, the importance of increasing level of autonomy of single agents and platoon topology is discussed in performing cohesive coordination and collaborative driving missions and in mitigating sensory errors. Simulation and experimental results demonstrate the performance of our cohesive motion and string stable controllers, in addition we discuss application in formation control of autonomous multi-agent systems.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

This thesis presents a novel approach to several problems in intelligent transportation systems using collaborative driving coordination. With inter-vehicle communication and intelligent vehicle cooperation, important tasks in transportation such as lane position determination, lane assignment and platoon formation can be solved. Several topics in regard to inter-vehicle communication, lane positioning, lane assignment and platoon formation are explored in this thesis: First, the design and experimental results of low-cost lane-level positioning system that can support a large number of transportation applications are discussed. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within the communication range of each other, the lane positions of vehicles can be determined. The robustness effectiveness of the system is shown in both simulations and real road tests. Second, a decentralized approach to lane scheduling for vehicles with an aim to increase traffic throughput while ensuring the vehicles exit successfully at their destinations is presented. Most of current traffic management systems do not consider lane organization of vehicles and only regulate traffic flows by controlling traffic signals or ramp meters. However, traffic throughput and efficient use of highways can be increased by coordinating driver behaviors intelligently. The lane optimization problem is formulated as a linear programming problem that can be solved using the Simplex method. Finally, a direction for cooperative vehicle platoon formation is proposed. To enhance traffic safety, increase lane capacities and reduce fuel consumption, vehicles can be organized into platoons with the objective of maximizing the travel distance that platoons stay intact. Toward this end, this work evaluates a proposed strategy which assigns vehicles to platoons by solving an optimization problem. A linear model for assigning vehicles to appropriate platoons when they enter the highway is formulated. Simulation results demonstrate that lane capacity can be increased effectively when platooning operation is used.

This thesis presents a novel approach to several problems in intelligent transportation systems using collaborative driving coordination. With inter-vehicle communication and intelligent vehicle cooperation, important tasks in transportation such as lane position determination, lane assignment and platoon formation can be solved. Several topics in regard to inter-vehicle communication, lane positioning, lane assignment and platoon formation are explored in this thesis: First, the design and experimental results of low-cost lane-level positioning system that can support a large number of transportation applications are discussed. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within the communication range of each other, the lane positions of vehicles can be determined. The robustness effectiveness of the system is shown in both simulations and real road tests. Second, a decentralized approach to lane scheduling for vehicles with an aim to increase traffic throughput while ensuring the vehicles exit successfully at their destinations is presented. Most of current traffic management systems do not consider lane organization of vehicles and only regulate traffic flows by controlling traffic signals or ramp meters. However, traffic throughput and efficient use of highways can be increased by coordinating driver behaviors intelligently. The lane optimization problem is formulated as a linear programming problem that can be solved using the Simplex method. Finally, a direction for cooperative vehicle platoon formation is proposed. To enhance traffic safety, increase lane capacities and reduce fuel consumption, vehicles can be organized into platoons with the objective of maximizing the travel distance that platoons stay intact. Toward this end, this work evaluates a proposed strategy which assigns vehicles to platoons by solving an optimization problem. A linear model for assigning vehicles to appropriate platoons when they enter the highway is formulated. Simulation results demonstrate that lane capacity can be increased effectively when platooning operation is used.