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Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit

Authors:

Nicholas R. GansAuthors Info & Claims

ICMI '15: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

Pages 291 - 298

https://doi.org/10.1145/2818346.2820741

Published: 09 November 2015 Publication History

Abstract

Advanced driver assistance systems are the newest addition to vehicular technology. Such systems use a wide array of sensors to provide a superior driving experience. Vehicle safety and driver alert are important parts of these system. This paper proposes a driver alert system to prevent and mitigate adjacent vehicle collisions by proving warning information of on-road vehicles and possible collisions. A dynamic Bayesian network (DBN) is utilized to fuse multiple sensors to provide driver awareness. It detects oncoming adjacent vehicles and gathers ego vehicle motion characteristics using an on-board camera and inertial measurement unit (IMU). A histogram of oriented gradient feature based classifier is used to detect any adjacent vehicles. Vehicles front-rear end and side faces were considered in training the classifier. Ego vehicles heading, speed and acceleration are captured from the IMU and feed into the DBN. The network parameters were learned from data via expectation maximization(EM) algorithm. The DBN is designed to provide two type of warning to the driver, a cautionary warning and a brake alert for possible collision with other vehicles. Experiments were completed on multiple public databases, demonstrating successful warnings and brake alerts in most situations.

References

[1]

H. Cheng, Autonomous Intelligent Vehicles: Theory, Algorithms, and Implementation. Springer Science & Business Media, 2011.

[2]

U. C. Bureau, "Transportation: Motor vehicle accidents and fatalities."

[3]

S. Sivaraman and M. M. Trivedi, "A general active-learning framework for on-road vehicle recognition and tracking," Intelligent Transportation Systems, IEEE Transactions on, vol. 11, no. 2, pp. 267--276, 2010.

Digital Library

[4]

N. Li and C. Busso, "Predicting perceived visual and cognitive distractions of drivers with multimodal features," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 51--65, February 2015.

Digital Library

[5]

N. Li, J. Jain, and C. Busso, "Modeling of driver behavior in real world scenarios using multiple noninvasive sensors," IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1213--1225, August 2013.

Digital Library

[6]

A. Mukhtar, L. Xia, and T. Tang, "Vehicle detection techniques for collision avoidance systems: A review," Intelligent Transportation Systems, IEEE Transactions on, vol. PP, no. 99, pp. 1--21, 2015.

[7]

J. Sun, Y. Zhang, and K. He, "Providing context-awareness in the smart car environment," in Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on, June 2010, pp. 13--19.

Digital Library

[8]

A. Rakotonirainy, "Design of context-aware systems for vehicle using complex systems paradigms," 2005.

[9]

J.-D. Lee, J.-D. Li, L.-C. Liu, and C.-M. Chen, "A novel driving pattern recognition and status monitoring system," in Advances in Image and Video Technology. Springer, 2006, pp. 504--512.

Digital Library

[10]

T.-T. Lee, H.-H. Chiang, J.-W. Perng, J.-J. Jiang, and B.-F. Wu, "Multi-sensor information integration on dsp platform for vehicle navigation safety and driving aid," in Networking, Sensing and Control, 2009. ICNSC '09. International Conference on, March 2009, pp. 653--658.

[11]

M. Rockl, P. Robertson, K. Frank, and T. Strang, "An architecture for situation-aware driver assistance systems," in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, April 2007, pp. 2555--2559.

[12]

D. Mitrovic, "Reliable method for driving events recognition," Intelligent Transportation Systems, IEEE Transactions on, vol. 6, no. 2, pp. 198--205, June 2005.

Digital Library

[13]

H. Eren, S. Makinist, E. Akin, and A. Yilmaz, "Estimating driving behavior by a smartphone," in Intelligent Vehicles Symposium (IV), 2012 IEEE. IEEE, 2012, pp. 234--239.

[14]

S. Sivaraman and M. Trivedi, "Active learning for on-road vehicle detection: a comparative study," Machine Vision and Applications, vol. 25, no. 3, pp. 599--611, 2014. {Online}. Available: http://dx.doi.org/10.1007/s00138-011-0388-y

Digital Library

[15]

S. Sivaraman and M. M. Trivedi, "Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis," Intelligent Transportation Systems, IEEE Transactions on, vol. 14, no. 4, pp. 1773--1795, 2013.

Digital Library

[16]

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1. IEEE, 2005, pp. 886--893.

Digital Library

[17]

S. E. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of texture features based on gabor fllters," Image Processing, IEEE Transactions on, vol. 11, no. 10, pp. 1160--1167, 2002.

Digital Library

[18]

L. Shen and L. Bai, "Adaboost gabor feature selection for classification," in Proc. of Image and Vision Computing NewZealand. Citeseer, 2004, pp. 77--83.

[19]

P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1. IEEE, 2001, pp. I--511.

[20]

J. J. Gibson, "The perception of the visual world." 1950.

[21]

N. Li, A. Sathyanarayana, C. Busso, and J. H. Hansen, "Rear-end collision prevention using mobile devices."

[22]

M. van Leeuwen and F. Groen, "Vehicle detection with a mobile camera: spotting midrange, distant, and passing cars," Robotics Automation Magazine, IEEE, vol. 12, no. 1, pp. 37--43, March 2005.

[23]

F. Jensen and T. Nielsen, Bayesian networks and decision graphs. New York, NY, USA: Springer Verlag, November 2010.

Digital Library

[24]

S. Al-Sultan, A. H. Al-Bayatti, and H. Zedan, "Context-aware driver behavior detection system in intelligent transportation systems," Vehicular Technology, IEEE Transactions on, vol. 62, no. 9, pp. 4264--4275, 2013.

[25]

S. O. Madgwick, "An efficient orientation filter for inertial and inertial/magnetic sensor arrays," Report x-io and University of Bristol (UK), 2010.

[26]

B. Barshan and H. F. Durrant-Whyte, "Inertial navigation systems for mobile robots," Robotics and Automation, IEEE Transactions on, vol. 11, no. 3, pp. 328--342, 1995.

[27]

T. Imamura, H. Yamashita, Z. Zhang, M. Rizal bin Othman, and T. Miyake, "A study of classification for driver conditions using driving behaviors," in Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on. IEEE, 2008, pp. 1506--1511.

[28]

N. Oliver and A. P. Pentland, "Driver behavior recognition and prediction in a smartcar," pp. 280--290, 2000.

[29]

T. Bar, D. Nienhuser, R. Kohlhaas, and J. Zollner, "Probabilistic driving style determination by means of a situation based analysis of the vehicle data," in Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, Oct 2011, pp. 1698--1703.

[30]

K. P. Murphy, "The bayes net toolbox for matlab," Computing Science and Statistics, vol. 33, p. 2001, 2001.

[31]

C. Caraffi, T. Vojir, J. Trefny, J. Sochman, and J. Matas, "A system for real-time detection and tracking of vehicles from a single car-mounted camera," in ITS Conference, Sep. 2012, pp. 975--982.

[32]

A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for autonomous driving? the kitti vision benchmark suite," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012, pp. 3354--3361.

Digital Library

[33]

J.-L. Blanco-Claraco, F.-á. Moreno-Dueñas, and J. González-Jiménez, "The málaga urban dataset: High-rate stereo and lidar in a realistic urban scenario," The International Journal of Robotics Research, vol. 33, no. 2, pp. 207--214, 2014.

Digital Library

Cited By

Hina MOrtalda ASoukane ARamdane-Cherif A(2020)Machine Learning-Assisted Cognition of Driving Context and Avoidance of Road ObstaclesKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-49559-6_7(137-160)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-49559-6_7
Aung NZhang WDhelim SAi Y(2018)Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban EnvironmentsInformation10.3390/info91203119:12(311)Online publication date: 7-Dec-2018
https://doi.org/10.3390/info9120311
Boukerche ASiddiqui AMammeri A(2017)Automated Vehicle Detection and ClassificationACM Computing Surveys10.1145/310761450:5(1-39)Online publication date: 5-Oct-2017
https://dl.acm.org/doi/10.1145/3107614

Index Terms

Adjacent Vehicle Collision Warning System using Image Sensor and Inertial Measurement Unit
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
        Vision for robotics

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Published In

cover image ACM Conferences

ICMI '15: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

November 2015

678 pages

ISBN:9781450339124

DOI:10.1145/2818346

General Chairs:
Zhengyou Zhang
Microsoft Research, USA
,
Phil Cohen
VoiceBox Technologies, USA
,
Program Chairs:
Dan Bohus
Microsoft Research, USA
,
Radu Horaud
INRIA Grenoble Rhone-Alpes, France
,
Helen Meng
Chinese University of Hong Kong, China

Copyright © 2015 ACM.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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New York, NY, United States

Publication History

Published: 09 November 2015

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ICMI '15

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ICMI '15: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 9 - 13, 2015

Washington, Seattle, USA

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ICMI '15 Paper Acceptance Rate 52 of 127 submissions, 41%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Hina MOrtalda ASoukane ARamdane-Cherif A(2020)Machine Learning-Assisted Cognition of Driving Context and Avoidance of Road ObstaclesKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-030-49559-6_7(137-160)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-49559-6_7
Aung NZhang WDhelim SAi Y(2018)Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban EnvironmentsInformation10.3390/info91203119:12(311)Online publication date: 7-Dec-2018
https://doi.org/10.3390/info9120311
Boukerche ASiddiqui AMammeri A(2017)Automated Vehicle Detection and ClassificationACM Computing Surveys10.1145/310761450:5(1-39)Online publication date: 5-Oct-2017
https://dl.acm.org/doi/10.1145/3107614

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