Road-condition recognition using 24-GHz automotive radar
IEEE Transactions on Intelligent Transportation Systems
General road detection from a single image
IEEE Transactions on Image Processing
Joint ego-motion and road geometry estimation
Information Fusion
Approximate vehicle waiting time estimation using adaptive video-based vehicle tracking
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Recent progress in road and lane detection: a survey
Machine Vision and Applications
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Treats a problem arising in the design of intelligent vehicles: automated detection of lane and pavement boundaries using forward-looking optical and radar imaging sensors mounted on an automobile. In previous work, lane and pavement boundaries have always been located separately. This separate detection strategy is problematic in situations when either the optical or the radar image is too noisy. We propose a Bayesian multisensor image fusion method to solve our boundary detection problem. This method makes use of a deformable template model to globally describe the boundaries of interest. The optical and radar imaging processes are described with random field likelihoods. The multisensor fusion boundary detection problem is reformulated as a joint MAP estimation problem. However, the joint MAP estimate is intractable, as it involves the computation of a notoriously difficult normalization constant, also known as the partition function. Therefore, we settle for the so-called empirical MAP estimate, as an approximation to the true MAP estimate. Several experimental results are provided to demonstrate the efficacy of the empirical MAP estimation method in simultaneously detecting lane and pavement boundaries. Fusion of multi-modal images is not only of interest to the intelligent vehicles community, but to others as well, such as biomedicine, remote sensing, target recognition. The method presented in the paper is also applicable to image fusion problems in these other areas