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Northland Advanced Transportation Systems Research Laboratories

Persistent link for this communityhttps://hdl.handle.net/11299/262420

The Northland Advanced Transportation Systems Research Laboratories (NATSRL) was established in March 2001 as an advanced research program to develop innovative technologies for safe, productive, and sustainable transportation systems in northern areas. It was a cooperative research and education initiative of the University of Minnesota Center for Transportation Studies and its Intelligent Transportation Systems Institute, the Minnesota Department of Transportation, and the UMD College of Science and Engineering.

James Riehl, Dean of the College of Science and Engineering, was the first director. Eil Kwon, Professor in the Department of Civil Engineering, served as director 2006-2013. NATSRL may have existed until at least 2017, though its end date is uncertain.

This collection contains newsletters from NATSRL and a few reports and presentations, though the majority of reports produced by NATSRL are available in the Center for Transportation Studies Research Reports collection.

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Browsing Northland Advanced Transportation Systems Research Laboratories by Author "Tang, Hua"

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    Feasibility Study on Development of a CMOS Vision Processor for Vehicle Tracking
    (2007-03) Tang, Hua
    Vehicle tracking is an important area of intelligent transportation systems (ITS) technology, which could be applied in a wide range of transportation applications. Tracking typically needs to monitor real-time vehicle movements, and thus real time tracking is highly desirable. However it is well known that vehicle tracking processes are computationally very intensive. In the past, regardless of different algorithms employed in vehicle tracking, they have been implemented using software approaches, e.g., FPGA (Field Programmable Gate Array), microcontroller or embedded micro-processor, and PCs. While software approaches have an advantage of flexibility in implementation and future modifications, its long computational time often prevents real-time vehicle tracking from high resolution spatial or temporal data. It is well known in the area of VLSI (Very Large Scale Integrated) circuit design that a customized and dedicated hardware implementation of any algorithm minimizes its computational time. This gives us the motivation for direct implementation of tracking algorithms in hardware (i.e., device level), whether it is a partial or full implementation, to enhance real-time operation. The goal of this seed project is to investigate the feasibility and related issues in developing a tracking system with a new tracking algorithm based on vehicle motion detection, which is implemented in hardware whenever possible so that the computational time for tracking is minimized. The proposed overall tracking system consists of two parts. One part is the hardware, more specifically, a CMOS (Complementary Metal Oxide Semiconductor) hardware processor which is mainly responsible for vehicle motion detection. The other part is the software, for example an FPGA or micro-controller which is responsible for analyzing the data transmitted from the hardware and properly associating vehicles for tracking. The main computational time saving for the tracking process comes from the hardware part since the core of the new tracking algorithm, motion detection, is run on a dedicated hardware for that particular purpose. The proposed tracking algorithm is simulated in MATLAB and tested on traffic images captured from an intersection. It is found that vehicle movements can be accurately identified in spite of some noisy motion. Also, in this project, we estimate the computational time for the tracking algorithm in hardware implementation and discuss high-level hardware designs for actual implementation of the tracking algorithm.