A Multiple Model Estimation Approach to Robust Lane Detection via Computer Vision Based Models

During recent years, different lane detection algorithms have been presented based on computer vision and deep learning. Each model may have a better performance in some situations and fail in others. For example, deep learning-based methods are vulnerable to new samples. In this research, multiple...

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Vydáno v:Proceedings of the IEEE International Symposium on Industrial Electronics (Online) s. 576 - 581
Hlavní autoři: Fakhari, Iman, Anwar, Sohel
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2022
Témata:
Adaptation models
Atmospheric modeling
Computational modeling
Computer vision
Estimation
Lane detection
Multiple Model Estimation
Uncertainty
ISSN:2163-5145
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Popis
Shrnutí:During recent years, different lane detection algorithms have been presented based on computer vision and deep learning. Each model may have a better performance in some situations and fail in others. For example, deep learning-based methods are vulnerable to new samples. In this research, multiple models of lane detection are evaluated and used together to implement a robust lane detection algorithm. Here an estimator-based Multiple Model Adaptive Estimation (MMAE) algorithm for the lane-keeping system is developed to improve the robustness of the lane detection system. To verify the performance of the implemented algorithm, the AirSim simulation environment was used. The test simulation vehicle was equipped with one front camera and one rear camera to implement the proposed algorithm. The front camera images are used for detecting the lane and the offset of the vehicle and center point of the lane. The rear camera, which offered better performance in lane detection, was used as an estimator for calculating the uncertainty of each model. The simulation results showed that combining two implemented models with MMAE performed robustly even in those case studies where one of the models failed. The proposed algorithm was able to detect the failures of either of the models and then switch to another good working model to improve the robustness of the lane detection system.
ISSN:2163-5145
DOI:10.1109/ISIE51582.2022.9831692