This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Weather Classification for Lidar based on Deep Learning
Technical Paper
2022-01-7073
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Lidar is the most important sensor for roadside perception in autonomous driving and the Connected Automated Vehicle Highway(CAVH). Generally, perception algorithms based on point cloud only detect dynamic and static traffic participants, which lacks an analysis of the impact of abnormal weather types on point cloud detection. In practical applications, the CAVH system needs to determine whether it works within its operating design range according to different weather types, and adjusts accordingly. The main work of this paper is as follows: firstly, a large amout of various weather conditions data is collected as the basis for in-depth analysis of point cloud under changing environmental conditions. Secondly, the performance of roadside Lidar perception algorithm in different weather types is analyzed. Different from the traditional way of signal processing, this paper introduces deep neural network and realizes the classification of different weather types. Finally, in view of the efficiency problem of the classification network, an optimized structure is designed to realize the accurate identification of different weather types. The recognition accuracy rate increased to 96.86%, and the FPS increased to 30.
Authors
- Jinying Wu - VanJee Suzhou Internet of Vehicles Technoligy Co., Ltd., Chi
- Bing Ma - VanJee Suzhou Internet of Vehicles Technoligy Co., Ltd., Chi
- Dengjiang Wang - VanJee Suzhou Internet of Vehicles Technoligy Co., Ltd., Chi
- Qijun Zhang - VanJee Suzhou Internet of Vehicles Technoligy Co., Ltd., Chi
- Jianchao Liu - VanJee Suzhou Internet of Vehicles Technoligy Co., Ltd., Chi
- Yajun Wang - VanJee Suzhou Internet of Vehicles Technoligy Co., Ltd., Chi
- Gang Ma - Beijing VanJee Technology Co., Ltd., China
Topic
Citation
Wu, J., Ma, B., Wang, D., Zhang, Q. et al., "Weather Classification for Lidar based on Deep Learning," SAE Technical Paper 2022-01-7073, 2022, https://doi.org/10.4271/2022-01-7073.Also In
References
- Rasshofer , R.H. , Spies , M. , and Spies , H. Influences of Weather Phenomena on Automotive Laser Radar Systems Advances in Radio Science 9 2011
- Reif , K. Fahrstabilisierungssysteme und Fahrerassistenzsysteme Springer Automotive Media 2010
- Heinzler , R. et al. Weather Influence and Classification with Automotive Lidar Sensors 2019 IEEE Intelligent Vehicles Symposium (IV) IEEE 2019
- Shimano , M. et al. Wetness and Color from a Single Multispectral Image 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE 2017
- Massey , L.K. The Effect of UV Light and Weather on Plastics and Elastomers 2013 iii
- Mamouri , R.E. and Ansmann , A. Fine and Coarse Dust Separation with Polarization Lidar Atmospheric Measurement Techniques 7 11 2014 3717 3735
- Cheng , X. et al. Influence and Analysis of Atmospheric Attenuation on the Performance of Virtual Lidar Journal of Physics: Conference Series 1971 1 2021 012034
- Wang , E. et al. Influence Analysis of Atmosphere on Heterodyne Detection Lidar Infrared and Laser Engineering 40 10 2011 1896 1899
- Filgueira , A. , González-Jorge , H. , Lagüela , S. , Díaz-Vilariño , L. et al. Quantifying the Influence of Rain in Lidar Performance Meas. 95 2017 143 148 10.1016/j.measurement.(2016).10.009
- Peynot , T. , Underwood , J. , and Scheding , S. Towards Reliable Perception for Unmanned Ground Vehicles in Challenging Conditions IEEE/RSJ International Conference on Intelligent Robots and Systems 1170 1176 2009
- Hasirlioglu , S. , Kamann , A. , Doric , I. , and Brandmeier , T. Test Methodology for Rain Inflfluence on Automotive Surround Sensors IEEE International Conference on Intelligent Transportation Systems 2242 2247 2016
- Bijelic , M. , Gruber , T. , and Ritter , W. A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down? IEEE Intelligent Vehicle Symposium 760 767 2018
- Ryde , J. and Hillier , N. Performance of Laser and Radar Ranging Devices in Adverse Environmental Conditions Journal of Field Robotics 26 9 2009 712 727
- Phillips , T.G. , Guenther , N. , and McAree , P.R. When the Dust Settles: The Four Behaviors of Lidar in the Presence of Fifine Airborne Particulates Journal of Fifield Robotics 34 5 2017 985 1009
- Kutila , M. , Pyykonen , P. , Ritter , W. , Sawade , O. et al. Automotive Lidar Sensor Development Scenarios for Harsh Weather Conditions IEEE International Conference on Intelligent Transportation Systems 265 270 2016
- Papagiannopoulos , N. et al. An Automatic Aerosol Classification for Earlinet: Application and Results EPJ Web of Conferences 176 2018
- Shamsudin , A.U. et al. Fog Removal Using Laser Beam Penetration, Laser Intensity, and Geometrical Features for 3D Measurements in Fog-Filled Room Advanced Robotics: The International Journal of the Robotics Society of Japan 2016
- Golyanik , V. and Stricker , D. Classification of Lidar Sensor Contaminations with Deep Neural Networks Proceedings of the Computer Science in Cars Symposium (CSCS) Munich, Germany 8 2018
- Zhang , S.H. et al. Weather Classification of Sunny or Cloudy Day Based on an Outdoor Color Image Acta Metrologica Sinica 2019
- Al-Haija , Q.A. and Smadi , M.A. Multi-Class Weather Classification Using ResNet-18 CNN for Autonomous IoT and CPS Applications IEEE 7th Annual Conf. on Computational Science & Computational Intelligence (CSCI’20) 2020
- Sharma , A. and Ismail , Z.S. 2022
- Dhananjaya , M.M. , Kumar , V.R. , and Yogamani , S. Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021
- Zhang , Y. et al. Multi-Weather Classification Using Evolutionary Algorithm on EfficientNet 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) 2021
- Wang , Y. and Li , Y.X. Research on Multi-class Weather Classification Algorithm Based on Multi-model Fusion 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) 2020
- Li , Z. et al. Multi-Class Weather Classification Based on Multi-Feature Weighted Fusion Method IOP Conference Series Earth and Environmental Science 558 2020 042038
- Yan , Y. , Mao , Y. , and Li , B. Second: Sparsely Embedded Convolutional Detection Sensors 18 10 2018
- Lin , T.Y. et al. Focal Loss for Dense Object Detection IEEE Transactions on Pattern Analysis & Machine Intelligence 2999 3007 2017
- Qi , C.R. et al. 2017
- Guo , M.H. et al. 2020
- Zhou , Y. et al. 2019
- Smith , L.N. and Topin , N. 2017