Probabilistic lane segmentation using a low-dimensional linear parametrization

• Autorzy: Acuña Carlos , Arechavaleta Gustavo , Castelán Mario

Identyfikatory

DOI

10.61822/amcs-2025-0013

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

lane detection; Kalman filter; dimensionality reduction

PL

wykrywanie pasa ruchu; filtr Kalmana; redukcja wymiarowości

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2025
• Tom: Vol. 35, no. 1
• Strony: 179--189
• Opis fizyczny: Bibliogr. 36 poz., rys., wykr.

Twórcy

autor

Acuña Carlos

• Robotics and Advanced Manufacturing Group, Center for Research and Advanced Studies of the National Polytechnic Institute, 1062 Industria Metalúrgica, Ramos Arizpe, 25900, Mexico
• School of Engineering and Sciences, Monterrey Institute of Technology, 2501 Eugenio Garza Sada Sur, Monterrey, 64700, Mexico

autor

Arechavaleta Gustavo

• Robotics and Advanced Manufacturing Group, Center for Research and Advanced Studies of the National Polytechnic Institute, 1062 Industria Metalúrgica, Ramos Arizpe, 25900, Mexico

autor

Castelán Mario

• Robotics and Advanced Manufacturing Group, Center for Research and Advanced Studies of the National Polytechnic Institute, 1062 Industria Metalúrgica, Ramos Arizpe, 25900, Mexico

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).