9,667 research outputs found

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

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    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

    Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System

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    Az http://intechweb.org/ alatti "Books" fül alatt kell rákeresni a "Stereo Vision" címre és az 1. fejezetre

    Vehicle Lane Departure Prediction Based On Support Vector Machines

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    Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system will assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g., lane departure) and alert the driver to take corrective action. In this dissertation, we explored utilizing the nonlinear binary support vector machine (SVM) technique and the time series of vehicle variables to predict unintentional lane departure, which is innovative as no machine learning technique has previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM\u27s prediction performance. Our SVMs were trained and tested using the experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company. The data represented 16 drowsy drivers (about three-hour driving time per subject) and six control drivers (approximately 20 minutes driving per subject), all of which drove a simulated 2000 Volvo S80. More than 100 vehicle variables were sampled at 50 Hz. There were a total of 3,508 unintentional lane departure occurrences for the 16 drowsy drivers and 23 for four of the six control drivers (two had none). We optimized the performances of the SVMs by experimentally finding their best kernel functions and parameter values as well as the most appropriate vehicle variables as their input variables. Our experiment results involving the 22 drivers with a total of over 6.84 million prediction decisions demonstrate that: (1) the two-stage training scheme significantly outperformed the commonly used (one-stage) training scheme, (2) excellent SVM performances, as measured by numbers of false positives and false negatives, were achieved when the prediction horizon was set at 0.6 s or shorter, (3) lateral position and lateral velocity served as the best input variables among the nine variable sets that we explored, and (4) the radical basis function was the best kernel function (the other two kernel functions that we tested were the linear function and the second-order polynomial). We conclude that the two-stage-training SVM approach deserves further exploration because to the best of our knowledge, it has demonstrated the best unintentional lane departure prediction performance relative to the literature

    Tracking the use of onboard safety technologies across the truck fleet

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    Special ReportThe Transportation Safety Analysis and the Automotive Analysis Divisons at the University of Michigan Transportation Research Institute (UMTRI) initiated the Onboard Safety Technologies project in 2007, supported by FMCSA, to collect detailed information about the penetration of onboard safety technologies in the trucking fleet and future use of these technologies. The five technologies examined included: lane departure warning (LDWS), electronic stability control (ESC), forward and side collision warning (FCWS/SCWS), and vehicle tracking systems (TRACKING). Previous work in estimating the penetration of onboard safety technologies never approached the question of technology penetration by sampling the popluation of trucking companies. This project uses that approach through the use of a random sample survey of the entire fleet of trucking companies to measure current penetration, future use, and the advantages available to companies employing these technologies. The source for the sample was the 2007 Motor Carrier Management Information System (MCMIS) file. Interviews were also conducted with companies with high penetration of the technologies as well as system suppliers of the technologies, in order to gather more detailed information about usage and future technology direction. The results of the survey show the expected low levels of usage of LDWS, FCWS, and SCWS, slightly higher levels of usage of ESC, and much higher usage of TRACKING. Analysis shows higher usage related to larger company size. Company usage of these technologies is expected to double over the next five years. The main factors noted by participants for using the technologies that vary little among the technologies include: proven safety benefits of the technologies, positive feedback by drivers, driver improvement, improved safety culture, reduced cost of accidents, and insurance benefits. The interviews yielded important views about the cost advantages of usage, the difficulty of justifying the purchase of the technologies, alternatives to safety technologies, and the future of technology integration.Federal Motor Carrier Safety Administration, Washington, D.Chttp://deepblue.lib.umich.edu/bitstream/2027.42/91262/1/102868.pd

    A NON OVERLAPPING CAMERA NETWORK: CALIBRATION AND APPLICATION TOWARDS LANE DEPARTURE WARNING

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    ABSTRACT In this paper, we present a new multi camera approach to Lane Departure Warning (LDW). First, a perspective removal transformation is applied to the camera captured images to convert them into bird's-eye view images. Then, the position of the two cameras relative to a reference point is accurately determined using a new calibration technique. Lane detection is performed on the front and rear camera images who results are combined using data fusion. Finally, LDW is implemented by determining the distance between the vehicle and adjacent lane boundaries. The proposed system was tested on real world driving videos and shows good results when compared to ground truth
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