Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles
This paper focuses on a method of rearview camera-based blind-spot detection and a lane change assistance system for autonomous vehicles, utilizing a convolutional neural network and lane detection. In this study, we propose a method for providing real-time warnings to autonomous vehicles and driver...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/419 |
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| author | Yunhee Lee Manbok Park |
| author_facet | Yunhee Lee Manbok Park |
| author_sort | Yunhee Lee |
| collection | DOAJ |
| description | This paper focuses on a method of rearview camera-based blind-spot detection and a lane change assistance system for autonomous vehicles, utilizing a convolutional neural network and lane detection. In this study, we propose a method for providing real-time warnings to autonomous vehicles and drivers regarding collision risks during lane-changing maneuvers. We propose a method for lane detection to delineate the area for blind-spot detection and for measuring time to collision—both utilized to ascertain the vehicle’s location and compensate for vertical vibrations caused by vehicle movement. The lane detection method uses edge detection on an input image to extract lane markings by employing edge pairs consisting of positive and negative edges. Lanes were extracted through third-polynomial fitting of the extracted lane markings, with each lane marking being tracked using the results from the previous frame detections. Using the vanishing point where the two lanes converge, the camera calibration information is updated to compensate for the vertical vibrations caused by vehicle movement. Additionally, the proposed method utilized YOLOv9 for object detection, leveraging lane information to define the region of interest (ROI) and detect small-sized objects. The object detection achieved a precision of 90.2% and a recall of 82.8%. The detected object information was subsequently used to calculate the collision risk. A collision risk assessment was performed for various objects using a three-level collision warning system that adapts to the relative speed of obstacles. The proposed method demonstrated a performance of 11.64 fps with an execution time of 85.87 ms. It provides real-time warnings to both drivers and autonomous vehicles regarding potential collisions with detected objects. |
| format | Article |
| id | doaj-art-2073425a1ac54a19b44d30cbe8d4c9f1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2073425a1ac54a19b44d30cbe8d4c9f12025-01-10T13:15:29ZengMDPI AGApplied Sciences2076-34172025-01-0115141910.3390/app15010419Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous VehiclesYunhee Lee0Manbok Park1IVIS Corp., Suwon-si 16229, Republic of KoreaDepartment of Electronic Engineering, College of Convergence Technology, Korea National University of Transportation, Chungju-si 27469, Republic of KoreaThis paper focuses on a method of rearview camera-based blind-spot detection and a lane change assistance system for autonomous vehicles, utilizing a convolutional neural network and lane detection. In this study, we propose a method for providing real-time warnings to autonomous vehicles and drivers regarding collision risks during lane-changing maneuvers. We propose a method for lane detection to delineate the area for blind-spot detection and for measuring time to collision—both utilized to ascertain the vehicle’s location and compensate for vertical vibrations caused by vehicle movement. The lane detection method uses edge detection on an input image to extract lane markings by employing edge pairs consisting of positive and negative edges. Lanes were extracted through third-polynomial fitting of the extracted lane markings, with each lane marking being tracked using the results from the previous frame detections. Using the vanishing point where the two lanes converge, the camera calibration information is updated to compensate for the vertical vibrations caused by vehicle movement. Additionally, the proposed method utilized YOLOv9 for object detection, leveraging lane information to define the region of interest (ROI) and detect small-sized objects. The object detection achieved a precision of 90.2% and a recall of 82.8%. The detected object information was subsequently used to calculate the collision risk. A collision risk assessment was performed for various objects using a three-level collision warning system that adapts to the relative speed of obstacles. The proposed method demonstrated a performance of 11.64 fps with an execution time of 85.87 ms. It provides real-time warnings to both drivers and autonomous vehicles regarding potential collisions with detected objects.https://www.mdpi.com/2076-3417/15/1/419rearview camerablind-spot detectionlane change assistance systemconvolutional neural networklane detection |
| spellingShingle | Yunhee Lee Manbok Park Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles Applied Sciences rearview camera blind-spot detection lane change assistance system convolutional neural network lane detection |
| title | Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles |
| title_full | Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles |
| title_fullStr | Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles |
| title_full_unstemmed | Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles |
| title_short | Rearview Camera-Based Blind-Spot Detection and Lane Change Assistance System for Autonomous Vehicles |
| title_sort | rearview camera based blind spot detection and lane change assistance system for autonomous vehicles |
| topic | rearview camera blind-spot detection lane change assistance system convolutional neural network lane detection |
| url | https://www.mdpi.com/2076-3417/15/1/419 |
| work_keys_str_mv | AT yunheelee rearviewcamerabasedblindspotdetectionandlanechangeassistancesystemforautonomousvehicles AT manbokpark rearviewcamerabasedblindspotdetectionandlanechangeassistancesystemforautonomousvehicles |