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In this paper, an early prediction of vehicle trajectories and turning movements are investigated using traffic cameras. A vision-based tracking system is developed to monitor intersection videos and collect vehicle trajectories with their labels known as turning movements. Firstly, two intersection videos are monitored for 2 h, and collected trajectories with their labels are used to train deep neural networks and obtain the turning models for the prediction task. Deep neural networks are further investigated on a third intersection with different video settings. The future 2 s evaluation of trajectories shows the success of long short-term memory networks to early predict the turning movements with more than 92% accuracy.

Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand.

Real-time monitoring on displacement and acceleration of a structure provides vital information for people in different applications such as active control and damage warning systems. Recent developments of the Internet of Things (IoT) and client-side web technologies enable a wireless microcontroller board with sensors to process structural-related data in real-time and to interact with servers so that end-users can view the final processed results of the servers through a browser in a computer or a mobile phone. Unlike traditional structural health monitoring (SHM) systems that deliver warnings based on peak acceleration of earthquake, we built a real-time SHM system that converts raw sensor results into movements and rotations on the monitored structure’s three-dimensional (3D) model. This unique approach displays the overall structural dynamic movements directly from measured displacement data, rather than using force analysis, such as finite element analysis, to predict the displacement statically. As an application to our research outcomes, patterns of movements related to its structure type can be collected for further cross-validating the results derived from the traditional stress-strain analysis. In this work, we overcome several challenges that exist in displaying the 3D effects in real-time. From our proposed algorithm that converts the global displacements into element’s local movements, our system can calculate each element’s (e.g., column’s, beam’s, and floor’s) rotation and displacement at its local coordinate while the sensor’s monitoring result only provides displacements at the global coordinate. While we consider minimizing the overall sensor usage costs and displaying the essential 3D movements at the same time, a sensor deployment method is suggested. To achieve the need of processing the enormous amount of sensor data in real-time, we designed a novel structure for saving sensor data, where relationships among multiple sensor devices and sensor’s spatial and unique identifier can be presented. Moreover, we built a sensor device that can send the monitoring data via wireless network to the local server or cloud so that the SHM web can integrate what we develop altogether to show the real-time 3D movements. In this paper, a 3D model is created according to a two-story structure to demonstrate the SHM system functionality and validate our proposed algorithm.

Scrum is a widely-used framework in industry, so many schools apply it to their software engineering courses, particularly capstone courses. Due to the differences between students and industrial professionals, changing Scrum is necessary to fit capstone projects. In this paper, we suggest a decision-making process to assist instructors in developing a strategy to adapt Scrum for their course. This framework considers critical differences, such as student’s workloads and course schedules, and keeps the Agile principles and Scrum events. To evaluate the adapted Scrum, we investigated student’s learning experiences, satisfaction, and performance by quantitatively analyzing user story points and source codes and qualitatively studying instructor’s evaluations, student’s feedback, and Sprint Retrospective notes. Our two case studies about adapted Scrum showed that having daily stand-up meetings in every class was not helpful, student’s satisfaction positively correlated to the difficulty of the task they tackled, and the project provided good learning experiences.

This paper presents intersection analysis using computer vision techniques with Simulation of Urban MObility (SUMO). First, an efficient deep-visual tracking pipeline is proposed by using the off-the-shelf YOLO object detection architecture and cascading it with a discriminative correlation filter to produce reliable trajectories for traffic analysis of vehicles and pedestrians. While a variety of traffic measurements can be directly estimated from the extracted trajectories (e.g., speed, turning movement count), a method of incorporating turning movement count (TMC) within SUMO is proposed in order to mimic a realistic traffic flow for an observed intersection and its comprehensive analysis. Experimental evaluations on the developed tracking system implies that the YOLOv5 variant is the best for traffic cameras and, after appropriate fine-tuning using the University of Nevada, Las Vegas pedestrian data set, the YOLOv5 performance manifested a significant improvement with a recall value of 0.62. The tracking system is further employed for monitoring three other intersections in the downtown area of Las Vegas and turning movement counts were estimated for peak hours in the morning and evening of one day (7:00–9:00 and 16:00–18:00) at 15-min intervals. Finally, the intersection design, including traffic signals with estimated TMC, is used to calibrate SUMO to provide critical parameters (e.g., lane density, travel time, occupancy) for traffic signal performance evaluation and comprehensive intersection analysis. The signal design treatment demonstrates a significant improvement in travel times and simulation results indicate that the turning-left ratio is a crucial factor affecting the travel time of vehicles on each intersection leg.

The main objective of my dissertation is to provide a vision-based system to automatically understands traffic patterns and analyze intersections. The system leverages the existing traffic cameras to provide safety and behavior analysis of intersection participants including behavior and safety. The first step is to provide a robust detection and tracking system for vehicles and pedestrians of intersection videos. The appearance and motion based detectors are evaluated on test videos and public available datasets are prepared and evaluated. The contextual fusion method is proposed for detecting pedestrians and motion-based technique is proposed for vehicles based on evaluation results. The detections are feed to the tracking system which uses the mutual cooperation of bipartite graph and enhance optical flow. The enhanced optical flow tracker handles the partial occlusion problem, and it cooperates with the detection module to provide long-term tracks of vehicles and pedestrians. The system evaluation shows 13% and 43% improvement in tracking of vehicles and pedestrians respectively when both participants are addressed by the proposed framework. Finally, trajectories are assessed to provide a comprehensive analysis of safety and behavior of intersection participants including vehicles and pedestrians. Different important applications are addressed such as turning movement count, pedestrians crossing count, turning speed, waiting time, queue length, and surrogate safety measurements. The contribution of the proposed methods are shown through the comparison with ground truths for each mentioned application, and finally heat-maps show benefits of using the proposed system through the visual depiction of intersection usage.

Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment.

In this chapter, intersection analysis including capacity, delay, and safety is presented using computer vision techniques. An intersection appropriate vision‐based tracking system is presented, which aims to provide long‐term tracks of road users provide classification (i.e., vehicle and pedestrian) and handle the partial occlusion problem. Road trajectories are further investigated and modeled to provide road user count, vehicle queue length, and safety analysis including accidents and conflicts. Since accidents are infrequent events, surrogate safety measurements were leveraged to provide conflict severity measures at intersection facilities. Finally, technology‐enhanced safety for all participants, including vehicles, drivers, and pedestrians, through communication and sharing of dynamic profiles between infrastructure and cooperative vehicles is highlighted.