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[해외논문] Information fusion for automotive applications - An overview

Information fusion, v.12 no.4, 2011년, pp.244 - 252  

Stiller, C. (Institut fur Mess- und Regelungstechnik, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 21, D-76131 Karlsruhe, Germany) ,  Puente Leon, F. ,  Kruse, M.

Abstract AI-Helper 아이콘AI-Helper

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fu...

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참고문헌 (62)

  1. Special Issue on the 2007 DARPA Urban Challenge, Part I-III, Journal of Field Robotics 25 (2007) 8-10. 

  2. IEEE Intelligent Transportation Systems Magazine Shladover 1 1 10 2009 10.1109/MITS.2009.932716 Cooperative (rather than autonomous) vehicle-highway automation systems 

  3. Grand Cooperative Driving Challenge, 2001. <http://www.gcdc.net/>. 

  4. Information Fusion Leung 12 1 1 2011 10.1016/j.inffus.2010.06.009 Special issue on intelligent transportation systems 

  5. Z. Zomotor, U. Franke, Sensor fusion for improved vision based lane recognition and object tracking with range-finders, in: IEEE Intelligent Transportation Systems Conference, 1997, pp. 595-600. 

  6. IEEE Transactions on Aerospace and Electronic Systems Dezert 29 4 1275 1993 10.1109/7.259531 Joint probabilistic data association for autonomous navigation 

  7. Bar-Shalom vol. 1 1996 

  8. Mahler 2007 Statistical Multisource-Multitarget Information Fusion 

  9. IEEE Intelligent Transportation Systems Magazine Munz 2 1 6 2010 10.1109/MITS.2010.937293 Generic centralized multi sensor data fusion based on probabilistic sensor and environment models for driver assistance systems 

  10. Proceedings of IEEE Dasarathy 85 24 1997 10.1109/5.554206 Sensor fusion potential exploitation - innovative architectures and illustrative applications 

  11. Technisches Messen Ruser 74 3 93 2007 10.1524/teme.2007.74.3.93 Informationsfusion - eine Ubersicht 

  12. IEEE Transactions on Intelligent Transportation Systems van Arem 7 4 429 2006 10.1109/TITS.2006.884615 The impact of cooperative adaptive cruise control on traffic-flow characteristics 

  13. 10.1109/IVS.2005.1505121 K. Tischler, B. Hummel, Enhanced environmental perception by inter-vehicle data exchange, in: Proceedings of the IEEE Intelligent Vehicles Symposium, 2005, pp. 313-318. 

  14. 10.1109/IVS.2007.4290117 C. Stiller, G. Farber, S. Kammel, Cooperative cognitive automobiles, in: Proceedings of the IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 2007, pp. 215-220. 

  15. IEEE Transactions on Intelligent Transportation Systems Mitropoulos 11 3 539 2010 10.1109/TITS.2009.2034839 Wireless local danger warning: cooperative foresighted driving using intervehicle communication 

  16. N. Kampchen, M. Clauss, Y. Guenter, R.M. Schreier, M. Stiegeler, K. Tischler, K. Dietmayer, H.P. Grossmann, H. Kabza, H. Neumann, A.L. Rothermel, C. Stiller, Vernetzte Fahrzeug-Umfelderfassung fur zukunftige Fahrerassistenzsysteme, in: M. Maurer, C. Stiller (Eds.), Proceedings of the Workshop Fahrerassistenzsysteme, Freundeskreis Mess- und Regelungstechnik Karlsruhe e.V., Walting, Altmuhltal, 2005, pp. 139-150. 

  17. 10.1109/ITSC.2008.4732652 G. Toulminet, J. Boussuge, C. Laurgeau, Comparative synthesis of the 3 main European projects dealing with cooperative systems (CVIS, SAFESPOT and COOPERS) and description of COOPERS demonstration site 4, in: 11th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2008, pp. 809-814, doi:10.1109/ITSC.2008.4732652. 

  18. 10.1109/ITSC.2010.5625007 P. Lytrivis, G. Thomaidis, I. Karaseitanidis, A. Amditis, Situation refinement for in-vehicle platforms in vehicular networks, in: 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010, pp. 204-209, doi:10.1109/ITSC.2010.5625007. 

  19. 10.1109/ITSC.2010.5625101 B. Roessler, K. Fuerstenberg, First European STREP on cooperative intersection safety, INTERSAFE-2, in: 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010, pp. 422-427, doi:10.1109/ITSC.2010.5625101. 

  20. Metrology & Measurement Systems Thuy XVII 3 311 2010 10.2478/v10178-010-0027-3 Lane detection and tracking based on lidar data 

  21. 10.1016/j.inffus.2010.06.007 C. Lundquist, T.B. Schon, Joint ego-motion and road geometry estimation, Information Fusion, 2011, pp. 253-263, doi:10.1016/j.inffus.2010.06.007. 

  22. 10.1016/j.inffus.2010.06.004 A. Sathyanarayana, P. Boyraz, J.H. Hansen, Information fusion for robust ‘context and driver aware’ active vehicle safety systems, Information Fusion, 2011, pp. 293-303, doi:10.1016/j.inffus.2010.06.004. 

  23. 10.1016/j.inffus.2010.06.006 A. Ndjeng Ndjeng, D. Gruyer, S. Glaser, A. Lambert, Low cost IMU-odometer-GPS ego localization for unusual maneuvers, Information Fusion, 2011, pp. 264-274, doi:10.1016/j.inffus.2010.06.006. 

  24. 10.1016/j.inffus.2010.06.002 A. Grassi, V. Frolov, F. Puente, Information fusion to detect and classify pedestrians using invariant features, Information Fusion, 2011, pp. 284-292, doi:10.1016/j.inffus.2010.06.002. 

  25. Doucet 2001 Sequential Monte Carlo Methods in Practice 

  26. Ristic 2004 Beyond the Kalman Filter: Particle Filters for Tracking Applications 

  27. 10.1109/ICIF.2010.5712086 H. Eberhardt, V. Klumpp, U.D. Hanebeck, Density trees for efficient nonlinear state estimation, in: Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 2010. 

  28. 10.1109/MFI.2008.4648009 V. Klumpp, U.D. Hanebeck, Dirac mixture trees for fast suboptimal multi-dimensional density approximation, in: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), Seoul, Republic of Korea, 2008, pp. 593-600, doi:10.1109/MFI.2008.4648009. 

  29. Transactions of the ASME Journal of Basic Engineering Kalman 82 35 1960 10.1115/1.3662552 A new approach to linear filtering and prediction problems 

  30. IEEE Transactions on Aerospace and Electronic Systems Lin 41 3 899 2005 10.1109/TAES.2005.1541438 Multisensor-multitarget bias estimation for general asynchronous sensors 

  31. 10.1109/IVS.2009.5164273 S. Wu, M. Bansal, J. Eledath, Pedestrian localization by appearance matching and multi-mode filtering, in: IEEE Intelligent Vehicles Symposium, 2009, pp. 172-178, doi:10.1109/IVS.2009.5164273. 

  32. IEEE Transactions on Aerospace and Electronic Systems Rong Li 41 4 1255 2005 10.1109/TAES.2005.1561886 Survey of maneuvering target tracking. Part V. Multiple-model methods 

  33. S.J. Davey, M.G. Rutten, B. Cheung, A comparison of detection performance for several track-before-detect algorithms, in: 11th International Conference on Information Fusion, 2008, pp. 1-8, <doi:10.1109/ICIF.2008.4632251>. 

  34. IEEE Transactions on Signal Processing Vo 58 10 5129 2010 10.1109/TSP.2010.2050482 Joint detection and estimation of multiple objects from image observations 

  35. Daley 2004 An Introduction to the Theory of Point Processes I: Elementary Theory and Methods 

  36. Daley 2008 An Introduction to the Theory of Point Processes II: General Theory and Structure 

  37. IEEE Transactions on Military Electronics Sittler 8 2 125 1964 10.1109/TME.1964.4323129 An optimal data association problem in surveillance theory 

  38. IEEE Transactions on Automatic Control Reid 24 6 843 1979 10.1109/TAC.1979.1102177 An algorithm for tracking multiple targets 

  39. Blackman 1986 Multiple-Target Tracking with Radar Applications 

  40. IEEE Transactions on Pattern Analysis and Machine Intelligence Cox 18 2 138 1996 10.1109/34.481539 An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking 

  41. Blackman 1999 Design and Analysis of Modern Tracking Systems 

  42. IEEE Transactrions on Aerospace and Electronic Systems Koch 36 1 2 2000 10.1109/7.826308 Fixed-interval retrodiction approach to Bayesian IMM-MHT for maneuvering multiple targets 

  43. IEEE Aerospace and Electronic Systems Magazine Blackman 19 1 5 2004 10.1109/MAES.2004.1263228 Multiple hypothesis tracking for multiple target tracking 

  44. IEEE Transactions on Aerospace and Electronic Systems Bar-Shalom 43 1 392 2007 10.1109/TAES.2007.357141 Dimensionless score function for multiple hypothesis tracking 

  45. Automatica Bar-Shalom 11 5 451 1975 10.1016/0005-1098(75)90021-7 Tracking in a cluttered environment with probabilistic data association 

  46. IEEE Journal of Oceanic Engineering Fortmann 8 3 173 1983 10.1109/JOE.1983.1145560 Sonar tracking of multiple targets using joint probabilistic data association 

  47. IEEE Transactions on Aerospace and Electronic Systems Musicki 40 3 1093 2004 10.1109/TAES.2004.1337482 Joint integrated probabilistic data association: JIPDA 

  48. Robotics and Autonomous Systems Hoffmann 57 3 268 2009 10.1016/j.robot.2008.10.009 Cheap joint probabilistic data association filters in an interacting multiple model design 

  49. 10.1109/ICIF.2002.1020953 S. Challa, B.-N. Vo, X. Wang, Bayesian approaches to track existence - IPDA and random sets, in: Proceedings of the Fifth International Conference on Information Fusion, vol. 2, Annapolis, MD, 2002, pp. 1228-1235, doi:10.1109/ICIF.2002.1020953. 

  50. 10.1109/ICIF.2003.177322 M.R. Morelande, S. Challa, A multi-target tracking algorithm based on random sets, in: Proceedings of the Sixth International Conference on Information Fusion, vol. 2, Cairns, Queensland, 2003, pp. 807-814, doi:10.1109/ICIF.2003.177322. 

  51. R. Chakravorty, S. Challa, Multitarget tracking algorithm - Joint IPDA and Gaussian mixture PHD filter, in: 12th International Conference on Information Fusion, Seattle, WA, 2009, pp. 316-323. 

  52. IEEE Transactions on Aerospace and Electronic Systems Panta 45 3 1003 2009 10.1109/TAES.2009.5259179 Data association and track management for the Gaussian mixture probability hypothesis density filter 

  53. IEEE Transactions on Aerospace and Electronic Systems Lin 42 3 778 2006 10.1109/TAES.2006.248213 Track labeling and PHD filter for multitarget tracking 

  54. K. Panta, Multi-target tracking using 1st moment of random finite sets, Ph.D. thesis, University of Melbourne, 2007. 

  55. IEEE Transactions on Aerospace and Electronic Systems Mahler 39 4 1152 2003 10.1109/TAES.2003.1261119 Multitarget Bayes filtering via first-order multitarget moments 

  56. 10.1117/12.488533 T. Zajic, R.P. Mahler, Particle-systems implementation of the PHD multitarget-tracking filter, in: Proceedings of SPIE, vol. 5096, 2003, p. 291, doi:10.1117/12.488533. 

  57. IEEE Transactions on Signal Processing Vo 54 11 4091 2006 10.1109/TSP.2006.881190 The Gaussian mixture probability hypothesis density filter 

  58. IEEE Transactions on Circuits and Systems for Video Technology Maggio 18 8 1016 2008 10.1109/TCSVT.2008.928221 Efficient multitarget visual tracking using random finite sets 

  59. 10.1109/ICIF.2003.177321 H. Sidenbladh, Multi-target particle filtering for the probability hypothesis density, in: Proceedings of the Sixth International Conference of Information Fusion, vol. 2, 2003, pp. 800-806, doi:10.1109/ICIF.2003.177321. 

  60. 10.1109/SSP.2005.1628561 B. Balakwmar, A. Sinha, T. Kirubarajan, J. Reilly, PHD filtering for tracking an unknown number of sources using an array of sensors, in: IEEE/SP 13th Workshop on Statistical Signal Processing, 2005, pp. 43-48, doi:10.1109/SSP.2005.1628561. 

  61. Technisches Messen Kruse 78 4 190 2011 10.1524/teme.2011.0103 Mehrobjekt-Verfolgung mit dem PHD-Filter 

  62. IEEE Transactions on Signal Processing Vo 57 2 409 2009 10.1109/TSP.2008.2007924 The cardinality balanced multi-target multi-Bernoulli filter and its implementations 

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