This paper presents a framework for learning of system parameters for vision-based lane detection systems. Learning is achieved by ground-truth data based optimization of a performance measure evaluated on video sequences. Different options for evaluating the performance of lane detection systems are discussed, and in order to allow for a linear combination, we show how these performance measures can be normalized. The approach presented is applied to the optimization of the state noise variances of a Kalman filter. The surroundings around the located solutions are examined by 2D-grid analysis. It turns out that this approach leads to the same regions for robust parametrizations independent on the starting conditions for the optimization, and thereby a well generalizing parameter set can be obtained.
Learning of Kalman Filter Parameters for Lane Detection
2006 IEEE Intelligent Vehicles Symposium ; 552-557
2006-01-01
4457006 byte
Conference paper
Electronic Resource
English
Learning of Kalman Filter Parameters for Lane Detection
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