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Paper details
Number 1 - March 2025
Volume 35 - 2025
Probabilistic lane segmentation using a low-dimensional linear parametrization
Carlos Acuña, Gustavo Arechavaleta, Mario Castelán
Abstract
Lane detection is an important module for active safety systems since it increases safety and reduces traffic accidents caused
by driver inattention. Illumination changes or occlusions make lane detection a challenging task, especially if the detection
is performed from a single image. Consequently, this paper presents a probabilistic approach based on the Kalman filter,
which uses information from previous image frames to estimate the lane that could not be detected in the current image
frame, considering uncertainty in the prediction as well as in the detection. To this end, a principal component analysis of
the segmented curvature is introduced with the purpose of dimensionality reduction, moving from a large dimensional pixel
representation to a considerably reduced space representation. Furthermore, the proposed approach is compared with a fully
connected pretrained CNN model for lane detection, demonstrating that the proposed method has a lower computational
cost in addition to a smoother transition between lane estimates.
Keywords
lane detection, Kalman filter, dimensionality reduction