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2023 | OriginalPaper | Chapter

4. Information Fusion Technologies for i-EFV

Authors : Keqiang Li, Mingyuan Bian, Yugong Luo, Jianqiang Wang

Published in: The Intelligent Environment Friendly Vehicle

Publisher: Springer Nature Singapore

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Abstract

Accurate identification of vehicle states and complex driving environment states is the necessary information base for i-EFV to carry out safe and energy-saving controls. For i-EFV that integrates complex electromechanical systems, faces time-varying traffic environment and implements multi-performance objective control, there are three main problems in the comprehensive identification vehicle states and environment information: (1) the large amount of information data obtained based on V2V communication, V2I communication, remote wireless communication and onboard sensing system is overlapping and redundant, which needs integrated analysis and processing to form a unified description of vehicle and environment; (2) the vehicle driving environment is complex and changeable, and the information obtained from existing sensing systems is seriously disturbed, which makes it necessary to obtain accurate object information based on the characteristics of redundancy of multi-source sensors; (3) the accurate information on the characteristics of driver-vehicle-road traffic environment cannot be obtained directly through the sensors, which makes it necessary to fuse multi-source information and conduct comprehensive analysis. Therefore, in order to effectively use the multi-source and redundant data information to accurately identify and predict vehicle state and traffic environment, a systematic signal processing method is required.

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Literature
1.
go back to reference Mahler R (2007) Statistical multisource-multitarget information fusion. Artech House, Norwood Mahler R (2007) Statistical multisource-multitarget information fusion. Artech House, Norwood
2.
3.
go back to reference Alessandretti G, Broggi A, Cerri P (2007) Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans Intell Transp Syst 8(1):95–105CrossRef Alessandretti G, Broggi A, Cerri P (2007) Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans Intell Transp Syst 8(1):95–105CrossRef
4.
go back to reference Chongzhao H, Hongyan Z, Zhansheng D (2006) Multi-source information fusion. Tsinghua University Press Chongzhao H, Hongyan Z, Zhansheng D (2006) Multi-source information fusion. Tsinghua University Press
5.
go back to reference Li Q, Chen L, Li M et al (2014) A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios. IEEE Trans Veh Technol 63(2):540–555MathSciNetCrossRef Li Q, Chen L, Li M et al (2014) A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios. IEEE Trans Veh Technol 63(2):540–555MathSciNetCrossRef
7.
go back to reference Sukkarieh S, Nebot EM, Durrant-Whyte HF (1999) A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Trans Robot Autom 15(3):572–578CrossRef Sukkarieh S, Nebot EM, Durrant-Whyte HF (1999) A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Trans Robot Autom 15(3):572–578CrossRef
8.
go back to reference Caron F, Duflos E, Pomorski D et al (2006) GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects. Inf Fusion 7(2):221–230CrossRef Caron F, Duflos E, Pomorski D et al (2006) GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects. Inf Fusion 7(2):221–230CrossRef
9.
go back to reference Wendel J, Meister O, Schlaile C et al (2006) An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter. Aerosp Sci Technol 10(6):527–533CrossRef Wendel J, Meister O, Schlaile C et al (2006) An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter. Aerosp Sci Technol 10(6):527–533CrossRef
10.
go back to reference Yanhua Z, Tijing C, Zhuopeng Y (2009) Implementation of MEMS-IMU/GPS integrated navigation system. J Chin Inertial Technol 17(5):552–556 Yanhua Z, Tijing C, Zhuopeng Y (2009) Implementation of MEMS-IMU/GPS integrated navigation system. J Chin Inertial Technol 17(5):552–556
11.
go back to reference Newman P, Ho K (2005) SLAM-loop closing with visually salient features. In: IEEE international conference on robotics and automation. IEEE, pp 635–642 Newman P, Ho K (2005) SLAM-loop closing with visually salient features. In: IEEE international conference on robotics and automation. IEEE, pp 635–642
12.
go back to reference Danelljan M, Meneghetti G, Khan FS et al (2016) A probabilistic framework for color-based point set registration. In: Computer vision and pattern identification. IEEE, pp 1818–1826 Danelljan M, Meneghetti G, Khan FS et al (2016) A probabilistic framework for color-based point set registration. In: Computer vision and pattern identification. IEEE, pp 1818–1826
13.
go back to reference Leonard J, How J, Teller S, et al (2009) A perception-driven autonomous urban vehicle. In: The DARPA urban challenge. Springer, Berlin, Heidelberg, pp 727–774 Leonard J, How J, Teller S, et al (2009) A perception-driven autonomous urban vehicle. In: The DARPA urban challenge. Springer, Berlin, Heidelberg, pp 727–774
14.
go back to reference Huang AS, Antone M, Olson E et al (2010) A high-rate, heterogeneous data set from the DARPA urban challenge. Int J Robot Res 29(13):1595–1601CrossRef Huang AS, Antone M, Olson E et al (2010) A high-rate, heterogeneous data set from the DARPA urban challenge. Int J Robot Res 29(13):1595–1601CrossRef
15.
go back to reference Rauskolb FW, Kai B, Lipski C et al (2014) Caroline: an autonomously driving vehicle for urban environments. Journal of Field Robotics 25(9):674–724CrossRef Rauskolb FW, Kai B, Lipski C et al (2014) Caroline: an autonomously driving vehicle for urban environments. Journal of Field Robotics 25(9):674–724CrossRef
16.
go back to reference Steux B, Laurgeau C, Salesse L, et al (2002) Fade: A vehicle detection and tracking system featuring monocular color vision and radar data fusion. In: 2002 IEEE intelligent vehicle symposium, vol 2. IEEE, pp 632–639 Steux B, Laurgeau C, Salesse L, et al (2002) Fade: A vehicle detection and tracking system featuring monocular color vision and radar data fusion. In: 2002 IEEE intelligent vehicle symposium, vol 2. IEEE, pp 632–639
17.
go back to reference Duraisamy B, Schwarz T, Wöhler C (2013) Track level fusion algorithms for automotive safety applications. In: International conference on signal processing image processing & pattern identification. IEEE, pp 179–184 Duraisamy B, Schwarz T, Wöhler C (2013) Track level fusion algorithms for automotive safety applications. In: International conference on signal processing image processing & pattern identification. IEEE, pp 179–184
18.
go back to reference Zhangsong S (2010) Theory and method of target tracking and data fusion. National Defense Industry Press, Beijing Zhangsong S (2010) Theory and method of target tracking and data fusion. National Defense Industry Press, Beijing
19.
go back to reference Ning S (2018) Research on environment perception technology for intelligent vehicle based on multi-source information fusion. Jiangsu University, Zhenjiang Ning S (2018) Research on environment perception technology for intelligent vehicle based on multi-source information fusion. Jiangsu University, Zhenjiang
20.
go back to reference Hattori H (2000) Stereo for 2D visual navigation. In: Proceedings of IEEE intelligent vehicles symposium, Dearborn, MI, USA. IEEE Press, pp 31–38 Hattori H (2000) Stereo for 2D visual navigation. In: Proceedings of IEEE intelligent vehicles symposium, Dearborn, MI, USA. IEEE Press, pp 31–38
21.
go back to reference Franke U, Gavrila D, Gorzig S et al (1998) Autonomous driving goes downtown. IEEE Intell Syst Appl 13(6):40–48CrossRef Franke U, Gavrila D, Gorzig S et al (1998) Autonomous driving goes downtown. IEEE Intell Syst Appl 13(6):40–48CrossRef
22.
go back to reference Bensrhair A, Bertozzi A, Broggi A et al (2002) Stereo vision-based feature extraction for vehicle detection. In: Proceedings of IEEE intelligent vehicle symposium, Paris, France: IEEE Press, pp 465–470 Bensrhair A, Bertozzi A, Broggi A et al (2002) Stereo vision-based feature extraction for vehicle detection. In: Proceedings of IEEE intelligent vehicle symposium, Paris, France: IEEE Press, pp 465–470
23.
go back to reference Bertozzi M, Broggi A, Fascioli A et al (2000) Stereo vision-based vehicle detection. In: Proceedings of IEEE intelligent vehicles symposium, Dearborn, MI, USA. IEEE Press, pp 39–44 Bertozzi M, Broggi A, Fascioli A et al (2000) Stereo vision-based vehicle detection. In: Proceedings of IEEE intelligent vehicles symposium, Dearborn, MI, USA. IEEE Press, pp 39–44
Metadata
Title
Information Fusion Technologies for i-EFV
Authors
Keqiang Li
Mingyuan Bian
Yugong Luo
Jianqiang Wang
Copyright Year
2023
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-4851-0_4

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