الفهرس 
Light Detection and Ranging: LIDAROnly 14 pages are availabe for public view
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Abstract
Intelligent Transportation Systems (ITS) refers to a variety of tools, software, hardware, and communication technologies that could be applied in an integrated fashion to improve the efficiency and safety of vehicular traffic system of interest. ITS provides support to enhance operation of transportation services, transit management and information to travelers. Research in ITS is targeting an obvious improvement in safety of future transportation systems through integrating safety enhancing functions within vehicles. Technologies such as Radar/ Lidar, loop detectors and traffic video analysis have been used to provide such safety features. ITS provide the opportunity to establish functions in the infrastructure and/or vehicle to mitigate these deficiencies. For example, sensors in the main line highway could provide an advance warning about an oncoming vehicle to side street traffic on a stop controlled intersection, to compensate for any sight distance deficiencies for the side street traffic. In-vehicle sensors could provide advance warning to inattentive or drowsy drivers before hitting another vehicle or object, or before running off the road. The possibilities to improve the safety of our transportation system are endless. Vehicle detection and tracking would be manipulated according to hardware radars and lidars or machine learning and computer vision methodology of interest. In this thesis, a novel applications based intelligent transportation systems have been forwarded in the state of the art of vehicle detection and tracking methodologies. In addition, a new transportation systems have been discussed and utilized which would act as a new evolution in the transportation field. The appropriate systems were as follows: estimating critical distance between adjacent vehicles, calculating vehicular speed and level of crowdedness, and enhancing vehicle detection and tracking in the presence of a challenging weather conditions (light, fog, and darkness), and proposing a new methodology for an adaptive traffic control system. All discussed systems have been implemented in the state of the art of computer vision.