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Open AccessArticle
An Obstacle Detection Method Based on Longitudinal Active Vision
by
Shuyue Shi
Shuyue Shi
Juan Ni
Juan Ni
Juan Ni received her B.S. degree in Traffic and Transportation Engineering from Shandong University [...]
Juan Ni received her B.S. degree in Traffic and Transportation Engineering from Shandong University of Technology, Zibo, China, in 2022. She is currently a master's student in Traffic and Transportation Engineering at Shandong University of Technology. Her research interests include vehicle environment perception.
Xiangcun Kong
Xiangcun Kong
Xiangcun Kong received his B.S. degree in Traffic and Transportation Engineering from Shandong of in [...]
Xiangcun Kong received his B.S. degree in Traffic and Transportation Engineering from Shandong University of Technology, Zibo, China, in 2022. He is currently a master's student in Traffic and Transportation Engineering at Shandong University of Technology. His research interests include active binocular environment perception and target tracking control methods.
Huajian Zhu
Huajian Zhu
Huajian Zhu received his B.S. degree in Traffic and Transportation Engineering from Shandong of in a [...]
Huajian Zhu received his B.S. degree in Traffic and Transportation Engineering from Shandong University of Technology, Zibo, China, in 2023. He is currently a master's student in Traffic and Transportation Engineering at Shandong University of Technology. His research interests include vehicle environment perception.
Jiaze Zhan
Jiaze Zhan
Jiaze Zhan received his B.S. degree in Traffic and Transportation Engineering from Shandong of Zibo, [...]
Jiaze Zhan received his B.S. degree in Traffic and Transportation Engineering from Shandong University of Technology, Zibo, China, in 2023. He is currently a master's student in Traffic and Transportation Engineering at Shandong University of Technology. His research interests include vehicle environment perception and path planning.
Qintao Sun
Qintao Sun
Qintao Sun received his B.S. degree in Traffic and Transportation Engineering from Shandong of Zibo, [...]
Qintao Sun received his B.S. degree in Traffic and Transportation Engineering from Shandong University of Technology, Zibo, China, in 2023. He is currently a master's student in Traffic and Transportation Engineering at Shandong University of Technology. His research interests include sensor fusion for vehicle environment perception.
Yi Xu
Yi Xu
Yi Xu received his B.S. degree in Transportation from Ludong University, Yantai, China, in 2011, and [...]
Yi Xu received his B.S. degree in Transportation from Ludong University, Yantai, China, in 2011, and his Ph.D. degree in Vehicle Operation Engineering from Jilin University, Changchun, China, in 2016. He is currently an Associate Professor with the School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China. He has authored over 10 academic papers in journals. His research interests include vehicle environment perception, dynamic decision-making and planning.
1
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
2
Qingte Group Co., Ltd., Qingdao 266106, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(13), 4407; https://doi.org/10.3390/s24134407 (registering DOI)
Submission received: 19 April 2024
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Revised: 24 June 2024
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Accepted: 3 July 2024
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Published: 7 July 2024
Abstract
The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist.
Share and Cite
MDPI and ACS Style
Shi, S.; Ni, J.; Kong, X.; Zhu, H.; Zhan, J.; Sun, Q.; Xu, Y.
An Obstacle Detection Method Based on Longitudinal Active Vision. Sensors 2024, 24, 4407.
https://doi.org/10.3390/s24134407
AMA Style
Shi S, Ni J, Kong X, Zhu H, Zhan J, Sun Q, Xu Y.
An Obstacle Detection Method Based on Longitudinal Active Vision. Sensors. 2024; 24(13):4407.
https://doi.org/10.3390/s24134407
Chicago/Turabian Style
Shi, Shuyue, Juan Ni, Xiangcun Kong, Huajian Zhu, Jiaze Zhan, Qintao Sun, and Yi Xu.
2024. "An Obstacle Detection Method Based on Longitudinal Active Vision" Sensors 24, no. 13: 4407.
https://doi.org/10.3390/s24134407
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