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Each individual vehicle detection system provides information about the number of detected vehicles and both their relative position and speed.
Here, a GIS road vector map is used to constrain a vehicle detection system to road networks.
To decrease vehicle crash, a new rear view vehicle detection system based on monocular vision is designed.
In particular, Microwave Vehicle Detection System (MVDS) deployed on an expressway network in Orlando was utilized to achieve the objectives.
In 2016, a man in Florida was killed while at the wheel of a Tesla while using its Autopilot feature, which uses a computer vision-based vehicle detection system that differs from the technology used by Uber.
A field prototype of a laser-based non-intrusive vehicle detection system has been developed for the measurement of delineations of moving vehicles on the highway.
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The outputs of the side, forward, and rear vehicle detection systems at frame are the number of detected vehicles and their corresponding distances to the host vehicle (note that is used here as a distance/range measurement).
Various sensors can also be used for vehicle detection systems.
Forward- and rear-looking vehicle detection systems share the same algorithmic core.
Recently, the part-based model approach proposed by Felzenszwalb et al. [5] was applied to vehicle detection systems [6 8].
Vehicle detection systems were trained by three different approaches: the conventional cascade learning approach, the asymmetric boosting approach, and the proposed system.
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