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The Auxiliary System of Cleaning Vehicle Based on Road Recognition Technology
Technical Paper
2021-01-0245
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
With the development of economy, the road cleaning faces great challenges because the road area keeps increasing and the road types tend to be diversified. Cleaning vehicle is widely used in road surface cleaning, but it is more and more difficult to meet the demand of road surface cleaning only through using a single road surface cleaning method. If the way of manual adjustment of cleaning parameters is adopted, the driver is required to have rich experience. At present, there is an urgent need for a cleaning vehicle that can autonomously adjust cleaning parameters according to the road surface. This study is based on road recognition technology. After the pavement category is reflected by the visual sensor feedback information and the pavement adhesion coefficient, the parameters of the cleaning vehicle are adjusted by the controller to adapt to different roads. Firstly, the vehicle dynamics model is established, and the road adhesion coefficient is estimated by the least square method to realize the accurate identification of the road adhesion coefficient. On this basis, according to the image features of the camera, the visual sensor is used to comprehensively judge the categories of the road surface, to realize the distinction and recognition of the road surface, and control the cleaning parameters of the cleaning vehicle to adjust to a reasonable range to ensure the cleaning effect. The results show that the proposed algorithm can accurately estimate the road adhesion coefficient. At the same time, it can accurately distinguish and recognize dry asphalt pavement, wet asphalt pavement and muddy asphalt pavement, and adjust the cleaning parameters of the cleaning vehicle. The overall cleaning efficiency of the cleaning vehicle has been improved by 7% compared with the cleaning vehicle without the installation of this system.
Authors
Citation
Feng, J., Zhao, F., Ye, M., and Sun, W., "The Auxiliary System of Cleaning Vehicle Based on Road Recognition Technology," SAE Technical Paper 2021-01-0245, 2021, https://doi.org/10.4271/2021-01-0245.Data Sets - Support Documents
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References
- Jiahui , L. Research on Energy-saving Strategy of Pavement Garbage Identification and Installation System of The Certain Sweeper Jilin University 2020
- Cai , C. Research on Pavement Cleaning Technology and Design of Key Components of Innovative Pavement Cleaning System Xi’an University of Technology 2017
- Wenjing , C. Analysis of Influencing Factors of Pavement Cleaning Technology Selection Green Building Materials 10 93 94 2017
- Environment, New Environment Research has been Reported by Researchers at Politehnica University of Bucharest (Finite Element Analysis of the Compaction Plate from a Garbage Truck) Ecology Environment & Conservation 2020
- Han , Y. Collaboration between European Fuel Cell Garbage Truck Projects Fuel Cells Bulletin 20 2 2020
- Shaoyan , L. Vehicle Dynamic State Parameters Based on Kalman Filter Beijing Institute of Technology 2017
- Vanegas Useche , L.V. , Wahab , M.M. , and Parker , G.A. Effectiveness of Shifting Street Waste Waste Management 30 2 174 2010
- Ubeda , I. , Saavedra , J.M. , Nicolas , S. , Petitjean , C. , and Heutte , L. Improving Pattern Spotting in Historical Documents Using Feature Pyramid Networks Pattern Recognition Letters 131 2020
- Chaochun , Y. , Longfei , Z. , and Long , C. Review and Prospect of Road Adhesion Coefficient Identification Methods Machinery Manufacturing and Automation 47 02 1 4+7 2008
- Shaoting , L. Estimation of Vehicle Dynamic State Parameters and Road Adhesion Coefficient Shandong University of Technology 2017
- Bing , Z. , Qi , P. , Jian , Z. , Jian , W. , and Weiwen , D. Early Warning Strategy of Vehicle Longitudinal Collision Based on Road Adhesion Coefficient Estimation Automotive Engineering 38 04 446 452 2016
- Zhao , J. , Zhang , J. , Zhu , B. , and Momoniat , E. Development and Verification of the Tire/Road Friction Estimation Algorithm for Antilock Braking System Mathematical Problems in Engineering 201 4 2014
- Lili , F. , Hongwei , Z. , Haoyu , Z. , Huangshui , H. , and Zhen , W. A Review of Research on Target Detection Based on Deep Convolutional Neural Networks Optical Precision Engineering 28 05 1152 1164 2020
- Shiwu , X. , Jue , Z. , Shihui , Z. , Changzheng , L. , and Tingshi , L. A Classification Method for Deep Convolutional Neural Network Land Use Scene Photographs Bulletin of Surveying and Mapping 2020 02 24 28+42 2020
- Tianshun , Z. , Pengfei , D. , and Xie , H. Research on Remote Sensing Image Classification Method Based on Improved AlexNet Network Model Beijing Surveying and Mapping 32 11 1263 1266 2012
- Copper , M. , Nianyuan , L. , and Jinping , P. Research on a Cleaning Robot Cleaning Efficiency Test Method Mechanical and Electrical Engineering Technology 49 04 109 111 2020