Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations
This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track de...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7802 |
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| author | Donghyun Kim Yonghwan Jeong |
| author_facet | Donghyun Kim Yonghwan Jeong |
| author_sort | Donghyun Kim |
| collection | DOAJ |
| description | This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads. Video frames are annotated with the Computer Vision Annotation Tool (CVAT) to identify pixels containing snow tire tracks. To mitigate overfitting, the detector is trained on a combined dataset that incorporates both snow tire track images and road scenes from the Udacity dataset. The lane center estimator uses the detected tire tracks to estimate a reference line for lane keeping. Detected tracks are binarized and transformed into a bird’s-eye view image. Then, skeletonization and Hough transformation techniques are applied to extract tire track lines from the classified pixels. Finally, the Kalman filter estimates the lane center based on tire track lines. Evaluations conducted on unseen images demonstrate that the proposed algorithm provides a reliable lane reference, even under heavy snowfall conditions. |
| format | Article |
| id | doaj-art-33723a7e718e434d9787cda16d70f608 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-33723a7e718e434d9787cda16d70f6082024-12-13T16:32:49ZengMDPI AGSensors1424-82202024-12-012423780210.3390/s24237802Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall SituationsDonghyun Kim0Yonghwan Jeong1Department of Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaDepartment of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaThis study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads. Video frames are annotated with the Computer Vision Annotation Tool (CVAT) to identify pixels containing snow tire tracks. To mitigate overfitting, the detector is trained on a combined dataset that incorporates both snow tire track images and road scenes from the Udacity dataset. The lane center estimator uses the detected tire tracks to estimate a reference line for lane keeping. Detected tracks are binarized and transformed into a bird’s-eye view image. Then, skeletonization and Hough transformation techniques are applied to extract tire track lines from the classified pixels. Finally, the Kalman filter estimates the lane center based on tire track lines. Evaluations conducted on unseen images demonstrate that the proposed algorithm provides a reliable lane reference, even under heavy snowfall conditions.https://www.mdpi.com/1424-8220/24/23/7802autonomous drivingsnowy roadslane keepingtire track detectiondeep learning |
| spellingShingle | Donghyun Kim Yonghwan Jeong Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations Sensors autonomous driving snowy roads lane keeping tire track detection deep learning |
| title | Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations |
| title_full | Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations |
| title_fullStr | Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations |
| title_full_unstemmed | Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations |
| title_short | Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations |
| title_sort | development of robust lane keeping algorithm using snow tire track recognition in snowfall situations |
| topic | autonomous driving snowy roads lane keeping tire track detection deep learning |
| url | https://www.mdpi.com/1424-8220/24/23/7802 |
| work_keys_str_mv | AT donghyunkim developmentofrobustlanekeepingalgorithmusingsnowtiretrackrecognitioninsnowfallsituations AT yonghwanjeong developmentofrobustlanekeepingalgorithmusingsnowtiretrackrecognitioninsnowfallsituations |