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Radar and Smart Camera Based Data Fusion for Multiple Vehicle Tracking System in Autonomous Driving
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 31, 2022 by SAE International in United States
Annotation ability available
In advanced driver assistance systems (ADAS) or autonomous driving Systems (ADS) the robust and reliable perception of the environment, especially for the detecting and tracking the surrounding vehicle is prerequisite for collision warning and collision avoidance. In this paper a post-fusion tracking approach is presented which combines the front view Radar observation and front smart camera information. The approach can improve the tracking accuracy of the tracking system to support ADAS or ADS function such as adaptive cruise control (ACC) or autonomous emergency braking (AEB). The paper describes the state estimation algorithm, data association in the fusion architecture. Furthermore, the fusion architecture is tested and validated in real highway driving scenario.
CitationLi, F., Wu, Z., Zhu, Y., and Lu, K., "Radar and Smart Camera Based Data Fusion for Multiple Vehicle Tracking System in Autonomous Driving," SAE Technical Paper 2022-01-7019, 2022, https://doi.org/10.4271/2022-01-7019.
- Rohling , H. , Heuel , S. , and Ritter , H. Pedestrian Detection Procedure Integrated Into an 24GHz Automotive Radar 2010 IEEE Radar Conference 1229 1232
- Kellner , D. , Barjenbruch , M. , Dietmayer , K. , Klappstein , J. et al. Tracking of Extend Objects with High Resolution Doppler Radar IEEE Transitions on Intelligent Transportation Systems 17 5 2016 1341 1353
- Macaveiu , A. and Campeanu , A. 11# International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS) Nis, Serbia 2 553 556 2013
- Bar-Shalom , Y. and Tse , E. Tracking in a cluttered environment with probabilistic data association Automotive 11 5 1975 451 460
- Elfring , J. and Appeldoorn , R. Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving Sensors 16 2016 1668
- Govaers , F. , Charlish , A. , and Koch , W. On the Distributed Kalman Filter under Measuement Origin Uncertainty Proceedings of the 15th International Conference on Information Fusion 2012
- Bar-Shalom , Y. and Li , X. Estimation with Applications to Tracking and Navigation Wiley-Inter Science 2011
- Lundquist , C. , Orguner , U. , and Gustafsson , F. Extended Target Tracking Using Polynomials with Applications to Road-Map Estimation IEEE Transactions on Signal Processing 59 1 2011 15 26
- Schubert , R. , Adam , C. , Richter , E. , Bauer , S. et al. Generalized Probabilistic Data Association for Vehicle Tracking under Clutter 2012 IEEE Intelligent Vehicle Symposium (IV) 2012 962 968
- Blackman , S.S. and Popoli , R.F. Design and Analysis of Modern Tracking Systems Norwood, MA Artech House 1999
- Maehlisch , M. , Ritter , W. , and Dietmayer , K. De-Cluttering with Integrated Probabilistic Data Association for Multi-Sensor Multi-Target ACC Vehicle Tracking 2007 IEEE, Intelligent Vehicles Symposium 2007 178 183
- Liu , C.Y. , Shui , P.L. , and Li , S. Unscented Extend Kalman Filter for Target Tracking Journal of Systems Engineering and Electronics 2011 188 192
- Xiang , Y. , Alahi , A. , and Savarese , S. Learning to Track: Online Multi-Object Tracking by Decision Making Proceedings of the IEEE International Conference on Computer Vision 2015 4705 4713
- Oh , S. , Russel , S. , and Sastry , S. Markov Chain Monte Carlo Data Association for Multi-Target Tracking IEEE Transaction on Automatic Control 54 3 2009 481 497
- Rangesh , A. , Yuen , K. , Satzoda , R.K. , Rajaram , R.N. et al. 2017
- Milan , A. , Schindler , K. , and Roth , S. Challenges of Ground Truth Evaluation of Multi-Target Tracking Proceedings of the IEEE Conference on Computer Vision and pattern Recognition Workshops 2013 735 737