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We develop an algorithm aimed at estimating travel time on segments of a road network using a convex optimization framework. Sampled travel time from probe vehicles are assumed to be known and serve as a training set for a machine learning algorithm to provide an optimal estimate of the travel time for all vehicles. A kernel method is introduced to allow for a non-linear relation between the known...
In this paper, the framework is presented for using active learning to train a robust monocular on-road vehicle detector for active safety, based on Adaboost classification and Haar-like rectangular image features. An initial vehicle detector was trained using Adaboost and Haar-like rectangular image features and was very susceptible to false positives. This detector was run on an independent highway...
Urban traffic surveillance, which is designed to improve traffic management, is an important part of intelligent traffic system (ITS). In particular, airborne moving vehicle detection has become a new but hot research area since its wide view and low cost. However, airborne urban traffic surveillance is impacted by many difficulties such as camera vibration, vehicle congestion, background variance,...
The target of this research is to adapt a Neural Network Approach that could be used in determining proper vehicle speed upon relevant conditions data. These include weather condition such as: Precipitation intensity, speed of wind, and degree of visibility, and road surface condition. Data are input to neural network. Output driven is a decision about allowed (advised) speed of a vehicle along highway...
Automobiles have deeply impacted the way in which people travel but they have also caused a number of deaths and injury due to crashes. Driver inattention, inexperience, poor judgment and/or fatigue etc., are the major contributing factors in crashes. In order to address these complex issues, scientists have developed a number of technological systems ranging from anti-lock braking system (ABS), early...
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