Artificial intelligence research is ushering in an era of sophisticated, mass-market transportation technology. While computers can fly a passenger jet better than a human pilot, people still face the dangerous yet tedious task of driving. Intelligent Transportation Systems (ITS) is the field focused on integrating information technology with vehicles and transportation infrastructure. Recent advances in ITS point to a future in which vehicles handle the vast majority of the driving task. Once autonomous vehicles become popular, interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination will be outdated. The bottleneck for efficiency will no longer be drivers, but the mechanism by which those drivers' actions are coordinated. Current methods for controlling traffic cannot exploit the superior capabilities of autonomous vehicles. This thesis describes a novel approach to managing autonomous vehicles at intersections that decreases the amount of time vehicles spend waiting. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this system, agents use a new approach built around a detailed communication protocol, which is also a contribution of the thesis. In simulation, I demonstrate that this mechanism can significantly outperform current intersection control technology-traffic signals and stop signs. This thesis makes several contributions beyond the mechanism and protocol. First, it contains a distributed, peer-to-peer version of the protocol for low-traffic intersections. Without any requirement of specialized infrastructure at the intersection, such a system would be inexpensive and easy to deploy at intersections which do not currently require a traffic signal. Second, it presents an analysis of the mechanism's safety, including ways to mitigate some failure modes. Third, it describes a custom simulator, written for this work, which will be made publicly available following the publication of the thesis. Fourth, it explains how the mechanism is "backward-compatible" so that human drivers can use it alongside autonomous vehicles. Fifth, it explores the implications of using the mechanism at multiple proximal intersections. The mechanism, along with all available modes of operation, is implemented and tested in simulation, and I present experimental results that strongly attest to the efficacy of this approach. / text

Lau Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 63-65). / Abstracts in English and Chinese. / abstract --- p.I / 摘要 --- p.II / acknowledgement --- p.III / table of contents --- p.IV / list of figures --- p.VI / Chapter CHAPTER 1 --- introduction --- p.1 / Chapter CHAPTER 2 --- literature review --- p.4 / Chapter 2.1 --- Traffic data collection methods --- p.4 / Chapter 2.2 --- Vision-based traffic monitoring techniques --- p.6 / Chapter 2.2.1 --- Vehicle tracking approaches --- p.7 / Chapter 2.2.2 --- Image processing techniques --- p.10 / Chapter CHAPTER 3 --- methodology --- p.15 / Chapter 3.1 --- Solution Concept --- p.16 / Chapter 3.2 --- System Framework --- p.18 / Chapter 3.2.1 --- Edge Detection Module --- p.20 / Chapter 3.2.2 --- Background Update Module --- p.22 / Chapter 3.2.3 --- Feature Extraction Modules --- p.25 / Chapter CHAPTER 4 --- experiments and evaluation --- p.41 / Chapter 4.1 --- Setup and Data Collection --- p.41 / Chapter 4.2 --- Evaluation Criteria --- p.42 / Chapter 4.3 --- Experimental Results --- p.44 / Chapter 4.3.1 --- Comparing overall accuracies --- p.44 / Chapter 4.3.2 --- Accuracies for different traffic conditions --- p.46 / Chapter 4.3.3 --- Comparing balanced sampling and random sampling --- p.48 / Chapter 4.3.4 --- Comparing day and night conditions --- p.50 / Chapter 4.3.5 --- Testing on time-series of images --- p.52 / Chapter CHAPTER 5 --- analysis --- p.54 / Chapter 5.1 --- Strengths and Weaknesses --- p.54 / Chapter 5.1.1 --- Sobel Edge Histogram --- p.54 / Chapter 5.1.2 --- Horizontal Line Detection --- p.55 / Chapter 5.1.3 --- Block Detection --- p.56 / Chapter 5.1.4 --- Combined Learning --- p.57 / Chapter 5.1.5 --- Overall Framework --- p.58 / Chapter 5.2 --- Future Research --- p.59 / Chapter 5.2.1 --- Static image based monitoring combined with other traffic monitoring approaches --- p.59 / Chapter 5.2.2 --- Horizontal Line Detection as tracked features of vehicles --- p.60 / Chapter 5.2.3 --- Application in aerial image-based system --- p.60 / Chapter CHAPTER 6 --- conclusion --- p.62 / bibliography --- p.63 / appendix a sobel edge detection --- p.66 / appendix b neural network setup --- p.67 / appendix c numerical results --- p.69

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Available via ProQuest Digital Dissertations. Title from first page of PDF file (viewed March 12, 2010). Includes bibliographical references (p. ). Also issued in print.

Urban freeway demand that frequently exceeds capacity has caused many agencies to consider many options to reduce congestion. A series of solutions that falls under the Active Traffic Management (ATM) banner have shown promising potential. Perhaps the most popular ATM strategy is ramp metering. Ramp metering involves limiting the access of vehicles to freeways at an entrance ramp. By doing this, freeway throughput, speeds, and travel time reliability can be increased, while the number of traffic incidents can be decreased. This study examines the application of an innovative ramp metering strategy, Intersection Ramp Metering (IRM), at a section of Loop 1 in Austin, TX. IRM implements the ramp metering function at the intersection immediately upstream of the entrance ramp, rather than on the ramp itself. A microsimulation analysis of this application is performed in VISSIM, and the results confirm that freeway throughput (+10%), and system average travel time (-14%), can be improved, as well as several other performance measures. / text

published_or_final_version / abstract / toc / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

As Intelligent Transportation Systems (ITS) become more prevalent, there is a need for a system capable of the rigorous evaluation of new ITS strategies for a wide variety of applications. Pre-deployment testing and fine-tuning of the system, performance evaluation, and alternatives analysis are all potential benefits that could be gained through the evaluation of ITS. Simulation, an increasingly popular tool for transportation analysis, would seem an ideal solution to this problem as it allows for the consideration of many scenarios that may be improbable or impossible to observe in the field. Also, simulation provides a framework that allows for the application of rigorous analysis techniques to the output data, providing an accurate and statistically significant conclusion. The difficulty is that many ITS strategies are difficult or impossible to implement in a simulated environment. The rapid nature of technology development and the complicated nature of many ITS solutions are difficult to emulate in simulation models. Furthermore, the emulation of a particular ITS solution is not guaranteed to provide the same result that the physical system would, were it subject to the same inputs. This study seeks to establish a framework for the analysis of advanced ITS applications through the use of Hardware-in-the-Loop Simulation (HILS), which provides a procedure for interfacing simulation models with real-world hardware to conduct analysis. This solution provides the benefits of both advanced ITS evaluation and simulation for powerful and accurate analysis. A framework is established that includes all the steps of the modeling process including construction, validation, calibration, and output analysis. This ensures that the process surrounding the HILS implementation is valid so that the results of the evaluation are accurate and defendable. Finally, a case study of the application of the developed framework to the evaluation, a real-world implementation of an advanced ITS application (SCATS in this case) is considered. The effectiveness of the framework in creating and evaluating a corridor using a simulation model wed to real-world hardware is shown. The results of the analysis show the power of this method when correctly applied and demonstrate where further analysis could expand upon the proposed procedure.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2005. / Leonard, John D., Committee Chair ; Goldsman, Dave, Committee Member ; Amekudzi, Adjo, Committee Member ; Hunter, Michael, Committee Member ; Dixon, Karen, Committee Member. Vita. Includes bibliographical references.

Intelligent transportation systems (ITS) have become a popular method to enhance the safety and efficiency of transportation. Pedestrians, as an essential participant of ITS, are very vulnerable in a traffic collision, compared with the passengers inside the vehicle. In order to protect the safety of all traffic participants and enhance transportation efficiency, the novel autonomous vehicles are required to detect pedestrians accurately and timely. In the area of pedestrian detection, deep learning-based pedestrian detection methods have gained significant development since the appearance of powerful GPUs. A large number of researchers are paying efforts to improve the accuracy of pedestrian detection by utilizing the Convolutional Neural Network (CNN)-based detectors. In this thesis, we propose a one-stage anchor-free pedestrian detector named Bi-Center Network (BCNet), which is aided by the semantic features of pedestrians' visible parts. The framework of our BCNet has two main modules: the feature extraction module produces the concatenated feature maps that extracted from different layers of ResNet, and the four parallel branches in the detection module produce the full body center keypoint heatmap, visible part center keypoint heatmap, heights, and offsets, respectively. The final bounding boxes are converted from the high response points on the fused center keypoint heatmap and corresponding predicted heights and offsets. The fused center keypoint heatmap contains the semantic feature fusion of the full body and the visible part of each pedestrian. Thus, we conduct ablation studies and discover the efficiency of feature fusion and how visibility features benefit the detector's performance by proposing two types of approaches: introducing two weighting hyper-parameters and applying three different attention mechanisms. Our BCNet gains 9.82% MR-2 (the lower the better) on the Reasonable setup of the CityPersons dataset, compared to baseline model which gains 12.14% MR-2 . The experimental results indicate that the performance of pedestrian detection could be significantly improved because the visibility semantic could prompt stronger responses on the heatmap. We compare our BCNet with state-of-the-art models on the CityPersons dataset and ETH dataset, which shows that our detector is effective and achieves a promising performance.

The emergence of Vehicular Ad-hoc Networks (VANETs) in the past decade has added a level of complexity to the modelling of Intelligent Transportation System (ITS) applications. In this paper, the Vehicular Network Integrated Simulator (VNetIntSim) is introduced as a new transportation network and VANET simulation tool by integrating transportation and VANET modelling. Specifically, it integrates the OPNET software, a communication network simulator, and the INTEGRATION software, a microscopic traffic simulation software. The INTEGRATION software simulates the movement of travellers and vehicles, while the OPNET software models the data exchange through the communication system. Information is exchanged between the two simulators as needed. The paper describes the implementation and the operation details of the VNetIntSim as well as the features it supports such as multiclass support and vehicle reuse. Subsequently, VNetIntSim is used to quantify the impact of mobility parameters (vehicular traffic stream speed and density) on the communication system performance considering Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) applications. Specifically, the routing performance (packet drops and route discovery time), IP processing delay in case of a file transfer protocol (FTP) application, and jitter in case of a Voice over Internet Protocol (VoIP) application and evaluated.