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...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 18; S. 5139
Hauptverfasser:	Lin, Huei-Yung, Dai, Jyun-Min, Wu, Lu-Ting, Chen, Li-Qi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 09.09.2020 MDPI
Schlagworte:	advanced driver assistance system Algorithms Cameras forward collision warning Identification Investigations lane change detection Neural networks overtaking vehicle identification Parameter estimation Roads & highways Sensors Traffic accidents & safety Vehicles
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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.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. 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.
Author	Chen, Li-Qi Lin, Huei-Yung Wu, Lu-Ting Dai, Jyun-Min
AuthorAffiliation	2 Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan; jyunmin@godel.ee.ccu.edu.tw (J.-M.D.); luting@godel.ee.ccu.edu.tw (L.-T.W.); liqi@godel.ee.ccu.edu.tw (L.-Q.C.) 1 Department of Electrical Engineering, Advanced Institute of Manufacturing with High-Tech Innovation, National Chung Cheng University, Chiayu 621, Taiwan
AuthorAffiliation_xml	– name: 1 Department of Electrical Engineering, Advanced Institute of Manufacturing with High-Tech Innovation, National Chung Cheng University, Chiayu 621, Taiwan – name: 2 Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan; jyunmin@godel.ee.ccu.edu.tw (J.-M.D.); luting@godel.ee.ccu.edu.tw (L.-T.W.); liqi@godel.ee.ccu.edu.tw (L.-Q.C.)
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This paper is an extended version of Jyun-Min Dai; Lu-Ting Wu; Huei-Yung Lin and Wen-Lung Tai, A driving assistance system with vision based vehicle detection techniques. In Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea, 13–16 December 2016.
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SubjectTerms	advanced driver assistance system Algorithms Cameras forward collision warning Identification Investigations lane change detection Neural networks overtaking vehicle identification Parameter estimation Roads & highways Sensors Traffic accidents & safety Vehicles
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