A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection
One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are ad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/18/5139 |
| _version_ | 1797554112105021440 |
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| author | Huei-Yung Lin Jyun-Min Dai Lu-Ting Wu Li-Qi Chen |
| author_facet | Huei-Yung Lin Jyun-Min Dai Lu-Ting Wu Li-Qi Chen |
| author_sort | Huei-Yung Lin |
| collection | DOAJ |
| description | One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques. |
| first_indexed | 2024-03-10T16:27:06Z |
| format | Article |
| id | doaj.art-19e5ab3482ec4071afbf3f284d7d7b69 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:27:06Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-19e5ab3482ec4071afbf3f284d7d7b692023-11-20T13:07:05ZengMDPI AGSensors1424-82202020-09-012018513910.3390/s20185139A Vision-Based Driver Assistance System with Forward Collision and Overtaking DetectionHuei-Yung Lin0Jyun-Min Dai1Lu-Ting Wu2Li-Qi Chen3Department of Electrical Engineering, Advanced Institute of Manufacturing with High-Tech Innovation, National Chung Cheng University, Chiayu 621, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi 621, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi 621, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi 621, TaiwanOne major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.https://www.mdpi.com/1424-8220/20/18/5139advanced driver assistance systemforward collision warningovertaking vehicle identificationlane change detection |
| spellingShingle | Huei-Yung Lin Jyun-Min Dai Lu-Ting Wu Li-Qi Chen A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection Sensors advanced driver assistance system forward collision warning overtaking vehicle identification lane change detection |
| title | A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection |
| title_full | A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection |
| title_fullStr | A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection |
| title_full_unstemmed | A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection |
| title_short | A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection |
| title_sort | vision based driver assistance system with forward collision and overtaking detection |
| topic | advanced driver assistance system forward collision warning overtaking vehicle identification lane change detection |
| url | https://www.mdpi.com/1424-8220/20/18/5139 |
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