Evaluation of systems to monitor blind areas behind trucks used in road construction and maintenance : phase 1

"The majority of fatalities that occur in road construction work zones in the United States involve a worker being struck by a piece of construction equipment or other vehicle. The Spokane Research Laboratory of the National Institute for Occupational Safety and Health, in cooperation with the Washington State Department of Transportation, is evaluating methods to decrease these accidents. One such method uses devices that assist equipment operators in monitoring blind areas around the equipment to prevent collisions with workers on foot or other objects. Several cameras and sensor systems are available for this application, and a study was conducted to evaluate these systems on various trucks used in road construction and maintenance. Tests were conducted using sanding trucks during the winter months, which allowed researchers to investigate the effectiveness and limitations of various technologies under the most extreme conditions. Tests were also conducted using dump trucks during the warmer months to study the effectiveness of the systems in highway work zones. Results show that many difficulties arise when using camera and sensor systems in cold, snowy climates. And, while the operation of these systems is more reliable during the warmer months, challenges still exist in using them on equipment in crowded work areas." - NIOSHTIC-

Development Of Algorithms For Vehicle Classification And Speed Estimation From Dynamic Scenes By On-Board Camera Using Image Processing Techniques

Vehicle assistance system applications benefit the drivers and passengers to promote better and safer driving situations. In terms of usability of dash camera, most vehicle owners pre­ installed the camera as a personal safety purpose to record the path they went through. The wide availability of various models of the dash cameras on the market, however, lacks in intelligence to process the information that can be obtained from the camera technology system itself. Moreover, in most studies for Intelligence Transport System (ITS), the implementation of static camera, for example CCTV, is popular thus, it is an encouragement for improvement to develop a vehicle assistance system using dynamic camera scenes. The main purpose of this research was to develop a vehicle detection, vehicle classification, and vehicle speed estimation system in dynamic scenes fully by image processing technique. The scope of this research covered Malaysia highway in Skudai, Johor; Ayer Keroh, Melaka and Kajang, Selangor. Video database of these highway areas was recorded by the on-board camera unit placed on the front dashboard area of the host vehicle. Image dataset was collected with positive image sets containing four vehicle classes namely car, lorry, bus, and motorcycle. It was decided that the technique for vehicle detection were Haar-Like and Cascade Classifier while vehicle classification was based on the ratio characteristics of the vehicle detected. The use of ratio value was an added advantage for the classification process since the prepared image dataset were based on each vehicle class dimension and the ratio value are the uniqueness property for each vehicle class. Speed estimation of the vehicle started with host vehicle speed estimation by lane detection technique since the road lane was the most consistence moving object inside the video region. The Host vehicle distance measurement used the broken lane detection and for a scale factor calculation, the width of the highway lanes was calculated by measuring the lane width inside the image and calibrated with real value in meter of the lanes stated by (Jabatan Kerja Raya, 1997). Detected vehicle speed measurements were based on its centroid tracking measurements. Result analysis on accuracy measurement in vehicle detection system obtained 0.93 true positive rates from 300 vehicles presented in the video data. Further analysis in vehicle classification was proved to obtain true positive rate of 0.98 for car class, 0.89 for lorry class, 0.89 for bus class, and 0.75 for motorcycle class. For analysis of speed estimation achieved with the average percentage 6.42% for speed error of host vehicle tested on 10 different videos. In detected vehicle, it speed estimations were based on the host vehicle speed calculation by observation its position and motion behavior in comparison with the host vehicle speed value. Overall the e development indicated that image processing has the ability to visualize the surrounding area for drivers and passengers that was near to real human visions a contribution to human-machine interactions that can be beneficial

DEVELOPMENT OF A NOVEL VEHICLE GUIDANCE SYSTEM: VEHICLE RISK MITIGATION AND CONTROL

Over a half of fatal vehicular crashes occur due to vehicles leaving their designated travel lane and entering other lanes or leaving the roadway. Lane departure accidents also result in billions of dollars in cost to society. Recent vehicle technology research into driver assistance and vehicle autonomy has developed to assume various driving tasks. However, these systems are do not work for all roads and travel conditions. The purpose of this research study was to begin the development a novel vehicle guidance approach, specifically studying how the vehicle interacts with the system to detect departures and control the vehicle A literature review was conducted, covering topics such as vehicle sensors, control methods, environment recognition, driver assistance methods, vehicle autonomy methods, communication, positioning, and regulations. Researchers identified environment independence, recognition accuracy, computational load, and industry collaboration as areas of need in intelligent transportation. A novel method of vehicle guidance was conceptualized known as the MwRSF Smart Barrier. The vision of this method is to send verified road path data, based AASHTO design and vehicle dynamic aspects, to guide the vehicle. To further development research was done to determine various aspects of vehicle dynamics and trajectory trends can be used to predict departures and control the vehicle. Tire-to-road friction capacity and roll stability were identified as traits that can be prevented with future road path knowledge. Road departure characteristics were mathematically developed. It was shown that lateral departure, orientation error, and curvature error are parametrically linked, and discussion was given for these metrics as the basis for of departure prediction. A three parallel PID controller for modulating vehicle steering inputs to a virtual vehicle to remain on the path was developed. The controller was informed by a matrix of XY road coordinates, road curvature and future road curvature and was able to keep the simulated vehicle to within 1 in of the centerline target path. Recommendations were made for the creation of warning modules, threshold levels, improvements to be applied to vehicle controller, and ultimately full-scale testing. Advisor: Cody S. Stoll

CMOS Image Sensors in Surveillance System Applications

Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application.https://doi.org/10.3390/s2102048

Sensors for autonomous navigation and hazard avoidance on a planetary micro-rover

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1993.Includes bibliographical references (p. 263-264).by William N. Kaliardos.M.S

Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications

This study presents a comprehensive investigation of methods and technologies for developing a computer vision-based framework for Structural Health Monitoring (SHM) and Structural Identification (St-Id) for civil infrastructure systems, with particular emphasis on various types of bridges. SHM is implemented on various structures over the last two decades, yet, there are some issues such as considerable cost, field implementation time and excessive labor needs for the instrumentation of sensors, cable wiring work and possible interruptions during implementation. These issues make it only viable when major investments for SHM are warranted for decision making. For other cases, there needs to be a practical and effective solution, which computer-vision based framework can be a viable alternative. Computer vision based SHM has been explored over the last decade. Unlike most of the vision-based structural identification studies and practices, which focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation, the proposed framework combines the vision-based structural input and the structural output from non-contact sensors to overcome the limitations given above. First, this study develops a series of computer vision-based displacement measurement methods for structural response (structural output) monitoring which can be applied to different infrastructures such as grandstands, stadiums, towers, footbridges, small/medium span concrete bridges, railway bridges, and long span bridges, and under different loading cases such as human crowd, pedestrians, wind, vehicle, etc. Structural behavior, modal properties, load carrying capacities, structural serviceability and performance are investigated using vision-based methods and validated by comparing with conventional SHM approaches. In this study, some of the most famous landmark structures such as long span bridges are utilized as case studies. This study also investigated the serviceability status of structures by using computer vision-based methods. Subsequently, issues and considerations for computer vision-based measurement in field application are discussed and recommendations are provided for better results. This study also proposes a robust vision-based method for displacement measurement using spatio-temporal context learning and Taylor approximation to overcome the difficulties of vision-based monitoring under adverse environmental factors such as fog and illumination change. In addition, it is shown that the external load distribution on structures (structural input) can be estimated by using visual tracking, and afterward load rating of a bridge can be determined by using the load distribution factors extracted from computer vision-based methods. By combining the structural input and output results, the unit influence line (UIL) of structures are extracted during daily traffic just using cameras from which the external loads can be estimated by using just cameras and extracted UIL. Finally, the condition assessment at global structural level can be achieved using the structural input and output, both obtained from computer vision approaches, would give a normalized response irrespective of the type and/or load configurations of the vehicles or human loads