This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
AUREATE: An Augmented Reality Test Environment for Realistic Simulations
Technical Paper
2018-01-1080
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Automated driving is currently one of the most active areas of research worldwide. While the general progress in developing specific algorithms for perception, planning and control tasks is very advanced, testing and validation of the resulting functions is still challenging due to the large number of possible scenarios and generation of ground-truth. Currently, real world testing and simulations are used in combination to overcome some of these challenges. While real world testing does not suffer from imperfect sensor models and environments, it is expensive, slow and not accurately repeatable and therefore unable to capture all possible scenarios. However, simulation models are not sophisticated enough to fully replace real world testing. In this paper, we propose a workflow that is capable of augmenting real sensor-level data with simulated sensor data. With this approach we are able to generate scenarios which are as realistic as possible while also being flexible with the ability to insert arbitrary objects. This sensor-level based approach enables testing of the whole algorithm chain for automated driving, including perception, object-detection, scene understanding, path planning, decision making, and control.
Recommended Content
Authors
Topic
Citation
Koduri, T., Bogdoll, D., Paudel, S., and Sholingar, G., "AUREATE: An Augmented Reality Test Environment for Realistic Simulations," SAE Technical Paper 2018-01-1080, 2018, https://doi.org/10.4271/2018-01-1080.Also In
References
- Kalra , N. and Paddock , S.M. Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? RAND Corporation 2016 1 16 10.1016/j.tra.2016.09.010
- Sutherland , I.E. A head-mounted three dimensional display AFIPS Conference Proceedings 33 1 757 764 1968 10.1145/1476589.1476686
- Matsumoto , T. , Watanabe , R. , Eguchi , S. , Aritake , H. et al. 1993
- Pokam , R. , Chauvin , C. , Debernard , S. , and Langlois , S. 2015
- Huang , W. , Wang , K. , Yisheng , L. , and Zhu , F. 2016 10.1109/ITSC.2016.7795548
- Gerkey , B. P. , Vaughan , R. T. , and Howard , A. The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems Proceedings of the 11th International Conference on Advanced Robotics 317 323 2003
- Carpin , S. , Lewis , M. , Wang , J. , Balakirsky , S. , and Scrapper C. 2007 10.1109/ROBOT.2007.363180
- Durst , P. J. , Goodin , C. , Cummins , C. , Gates , B. et al. 2012 10.1109/ICIC.2012.5
- Brand , J.G. Graphics for a 3D Driving Simulator 2008
- Lee , S. , Cho , J. , and Kim , S. 2013 10.1109/ICCVE.2013.6799900
- Zhang , C. , Liu , Y. , Zhao , D. , and Su , Y. 2014 10.1109/ITSC.2014.6957844
- Jayaraman , A. , Micks , A. , and Gross , E. Creating 3D Virtual Driving Environments for Simulation-Aided Development of Autonomous Driving and Active Safety SAE Technical Paper 2017-01-0107 2017 10.4271/2017-01-0107
- Mcity Test Facility https://mcity.umich.edu/our-work/mcity-test-facility/ 2017
- Zhao , D. , and Pend , H. 2017
- Inside Waymo’s Secret World for Training Self-Driving Cars https://www.theatlantic.com/technology/archive/2017/08/inside-waymos-secret-testing-and-simulation-facilities/537648/ 2017
- MCity uses AR to help test automated vehicles https://michigan.it.umich.edu/news/2017/06/27/mcity-uses-ar/ 2017
- Gechter , F. , Dafflon , B. , Gruer , P. , and Koukam , A. 2014
- Geiger , A. , Lenz , P. , Stiller , C. , and Urtasun , R. Vision meets Robotics: The KITTI Dataset International Journal of Robotics Research 32 11 1231 1237 2013 10.1177/0278364913491297
- PolySync Dataset Available http://selfracingcars.com/blog/2016/7/26/polysync
- Fischler , M.A. and Bolles , R.C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography Communications of the ACM 24 6 381 395 1981 10.1145/358669.358692
- Geschwandtner , M. , Kwitt , R. , Uhl , A. , and Pree , W. Proceedings of the 7th ISVC Part II 199 208 2011 10.1007/978-3-642-24031-7_20
- Majek , K. and Bedkowski , J. Range Sensors Simulation Using GPU Ray Tracing Proceedings of the 9th International Conference on Computer Recognition Systems CORES: 831-840 2015 10.1007/978-3-319-26227-7_78
- Lai , K. and Fox , D. Object Recognition in 3D Point Clouds Using Web Data and Domain Adaption SAGE Journals 29 8 1019 1037 2010 10.1177/0278364910369190
- Woods , J.O. and Christian , J.A. GLiDAR: An OpenGL-based, Real-Time, and Open Source 3D Sensor Simulator for Testing Computer Vision Algorithms Journal of Imaging 2 1 2016 10.3390/jimaging2010005
- Peinecke , N. , Lueken , T. and Korn , B. R. 2008 10.1109/DASC.2008.470283
- Wang , S. , Heinrich , S. , Wang , M. , and Rojas , R. 2012 10.1109/DASC.2008.4702838
- Sluys , M.v.d. , Kan , P.v. , and Sonneveld , P. CPV in the Built Environment AIP Conference Proceedings 1679 1 2015 10.1063/1.4931544
- Phong , B.T. Illumination for Computer Generated Pictures Communications of the ACM 18 6 311 317 1975 10.1145/360825.360839
- https://www.openstreetmap.org/ 2017
- https://www.google.com/maps/ 2017
- Ester , M. , Kriegerl , H.-P. , Sander , J. , and Xu , X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise Data Mining and Knowledge Discovery 2 2 226 231 1998
- Redmon , J. , and Farhadi , A. 2017