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In this paper, we present a platform for aiding in the development and evaluation of novel ITS passive safety applications. Such applications work by having vehicles detect certain events that may be dangerous to other vehicles and disseminating reports about these events using wireless communication. A vehicle receiving the report about the event can then be warned. However, a large number of false...
A method of estimating a vehicle's attitude in relation to the road surface using only light detection and ranging (lidar) measurements is presented. Gaussian processes, a machine learning technique, is used to relate the measurements of the road surface to the pitch and roll of the vehicle. Testing was performed under normal driving conditions on a test track as well as under high dynamic maneuvers...
In this paper, we consider the problem of tracking fine-grained speeds variations of vehicles using signal strength traces from GSM enabled phones. Existing speed estimation techniques using mobile phone signals can provide longer-term speed averages but cannot track short-term speed variations. Understanding short-term speed variations, however, is important in a variety of traffic engineering applications-for...
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 automatic target recognition (ATR), correlation filters are widely used to detect target signature variations. In this paper we concentrate on a particular case: target pose angle. For the traditional maximum average correlation height (MACH) filter method, only a few special angles can be used due to the limitation of the training data and the requirement on efficiency for real-time applications...
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