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A Data-Driven Radar Object Detection and Clustering Method Aided by Camera
ISSN: 0148-7191, e-ISSN: 2688-3627
Published February 24, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The majority of road accidents are caused by human oversight. Advanced Driving Assistance System (ADAS) has the potential to reduce human error and improve road safety. With the rising demand for safety and comfortable driving experience, ADAS functions have become an important feature when car manufacturers developing new models. ADAS requires high accuracy and robustness in the perception system. Camera and radar are often combined to create a fusion result because the sensors have their own advantages and drawbacks. Cameras are susceptible to bad weather and poor lighting condition and radar has low resolution and can be affected by metal debris on the road.
Clustering radar targets into objects and determine whether radar targets are valid objects are challenging tasks. In the literature, rule-based and thresholding methods have been proposed to filter out stationary objects and objects with low reflection power. However, static vehicles could be missed and thus result in low detection accuracy. To overcome these drawbacks, a data-driven method has been proposed, which uses a variety of features and thus is more suitable for complex real-world scenarios.
Data-driven methods require a large amount of labeled data. In this paper, we propose a data-driven radar object detection and clustering method aid by camera data. As cameras have high accuracy in object detection, it is used to train a classifier to determine whether radar object is valid. The algorithm is validated with real-world driving data and has shown good performance in object detection.
- Liu Ruoyu - Laval University
- Zhang Darui - Dongfeng Motor Corporation Technial Center, China
- Yang Hang - Dongfeng Motor Corporation Technial Center, China
- Wang Daihan - Dongfeng Motor Corporation Technial Center, China
- Bian Ning - Dongfeng Motor Corporation Technial Center, China
- Zhou Jianguang - Dongfeng Motor Corporation Technial Center, China
CitationRuoyu, L., Darui, Z., Hang, Y., Daihan, W. et al., "A Data-Driven Radar Object Detection and Clustering Method Aided by Camera," SAE Technical Paper 2020-01-5035, 2020, https://doi.org/10.4271/2020-01-5035.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Stanton , N.A. and Salmon , P.M. Human Error Taxonomies Applied to Driving: A Generic Driver Error Taxonomy and Its Implications for Intelligent Transport Systems Safety Science 47 2 227 237 2009
- SAE On-Road Automated Vehicle Standards Committee Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems SAE Standard J 3016 1 16 2014
- Cho , H. , et al. A Multi-Sensor Fusion System for Moving Object Detection and Tracking in Urban Driving Environments 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE 2014
- Darms , M.S. , Rybski , P.E. , Baker , C. , and Urmson , C. Obstacle Detection and Tracking for the Urban Challenge IEEE Transactions on Intelligent Transportation Systems 10 3 475 485 2009
- Zhou , Y. , et al. Image-Based Vehicle Analysis Using Deep Neural Network: A Systematic Study 2016 IEEE International Conference on Digital Signal Processing (DSP), IEEE 2016
- Chavez-Garcia , Omar , R. , Trung-Dung , V. , and Aycard , O. Fusion at Detection Level for Frontal Object Perception 2014 IEEE Intelligent Vehicles Symposium Proceedings, IEEE 2014
- Ekström , L. , and Risberg , J.
- Chavez-Garcia , Omar , R. 2014
- Serfling , M. , et al. Camera and Imaging Radar Feature Level Sensorfusion for Night Vision Pedestrian Recognition 2009 IEEE Intelligent Vehicles Symposium, IEEE 2009
- Ji , Z. and Prokhorov , D. Radar-Vision Fusion for Object Classification 2008 11th International Conference on Information Fusion, IEEE 2008
- Kämpchen , N. 2007
- Winner , H. , Hakuli , S. , Lotz , F. , and Singer , C. Handbook of Driver Assistance Systems 2016
- Lee , J.-E. et al. Harmonic Clutter Recognition and Suppression for Automotive Radar Sensors International Journal of Distributed Sensor Networks 13 9) 2017
- Nordenmark , V. , and Forsgren , A. 2015
- Zhong , Z. , Liu , S. , Mathew , M. , and Dubey , A. Camera Radar Fusion for Increased Reliability in ADAS Applications Electronic Imaging 17 2018 258 251 2018
- Ester , M. et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise Kdd. 96 34 1996
- De , M. , Roy , D.J.-R. , and Massart , D.L. The Mahalanobis Distance Chemometrics and Intelligent Laboratory Systems 50 1 1 18 2000
- Burkard , R.E. , and Cela , E. Linear Assignment Problems and Extensions Handbook of Combinatorial Optimization Boston, MA Springer 1999 75 149
- Altendorfer , R. Observable Dynamics and Coordinate Systems for Automotive Target Tracking 2009 IEEE Intelligent Vehicles Symposium, IEEE 2009
- Lee , S.-I. , Lee , H. , Abbeel , P. , and Andrew , Y.N. Efficient L~ 1 Regularized Logistic Regression AAAI 6 401 408 2006
- Srivastava , D.K. and Bhambhu , L. Data Classification Using Support Vector Machine Journal of Theoretical and Applied Information Technology 2009
- Liaw , A. and Wiener , M. Classification and Regression by Random Forest R News 2 3 18 22 2002