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.

Multi-agent robot systems, especially for mobile robots in dynamic environments interacting with humans, have seen an increased interest over the past years. Many vehicle manufactures (e.g.Volvo GTO) have been following the trend and has started investigating a possible implementation of an autonomous-transport robot system for material delivery in production environments.First implementations of a system have been built using ROS2 and initialising static amounts of participating robots.Throughout this thesis, scalability is emphasised to enhance and add new use cases to the system.This thesis investigates possible improvements for the system by adding a dynamic connection handling, which allows robots to connect and disconnect under the system's run time.Furthermore, the performance of the connection handling in the system is evaluated in simulation for increasing system complexity in terms of the amount of connected robots.The first part of the thesis presents an approach for the dynamic connection and disconnection of robots to the network using service client communication approaches.An implementation is tested in a simulation based on an excerpt from the legacy system.Furthermore, two methods are proposed for detecting possible communication losses. The thesis work simulates the increase of the number of robots in the system at different publishing rates. It compares a many to one communication approach, where multiple robots communicate to a central node over one topic, to the one to one communication approach, where multiple robots communicate over particular topics to a central node.The simulations have shown that with an increase of nodes, the average data age and the data miss ratio in the one to one approach were significantly lower than in the multi to one approach.

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<p dir="ltr">Autonomous robots can perform dangerous and tedious tasks, eliminating the need for human involvement. To deploy an autonomous robot in the field, a typical planning and control hierarchy is used, consisting of a high-level planner, a mid-level motion planner, and a low-level tracking controller. In applications such as simultaneous localization and mapping, package delivery, logistics, and surveillance, a group of autonomous robots can be more efficient and resilient than a single robot. However, deploying a multi-robot team by directly aggregating each robot's planning hierarchy into a larger, centralized hierarchy faces challenges related to scalability, resilience, and real-time computation. Distributed algorithms offer a promising solution for introducing effective coordination within a network of robots, addressing these issues. This thesis explores the application of distributed algorithms in multi-robot systems, focusing on several essential components required to enable distributed multi-robot coordination, both in general terms and for specific applications.</p>