A Portable Driver Assistance System Headset Using Augmented Reality

  • Authors

    • Shantanu Misra
    • Vedika Parvez
    • Tarush Singh
    • E Chitra
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.6.15071
  • Augmented reality, driver assistance system.

  • Vehicle collision leading to life threatening accidents is a common problem which is incrementing noticeably. This necessitated the need for Driver Assistance Systems (DAS) which helps drivers sense nearby obstacles and drive safely. However, it’s inefficiency in unfavorable weather conditions, overcrowded roads, and low signal penetration rates in India posed many challenges during it’s implementation. In this paper, we present a portable Driver Assistance System that uses augmented reality for it’s working. The headset model comprises of five systems working in conjugation in order to assist the driver. The pedestrian detection module, along with the driver alert system serves to assist the driver in focusing his attention to obstacles in his line of sight. Whereas, the speech recognition, gesture recognition and GPS navigation modules together prevent the driver from getting distracted while driving. In the process of serving these two root causes of accidents, a cost effective, portable and holistic driver assistance system has been developed.

     

     

  • References

    1. [1] Mohan D, Road accidents in Indiaâ€, IATSS research, Vol.33, No.1, (2009), pp.75-79.

      [2] Dabbour E & Said E, “Proposed collision warning system for right-turning vehicles at two-way stop-controlled rural intersectionsâ€, Transportation research part C: emerging technologies, Vol.42, (2014), pp.121-131.

      [3] Ujjainiya L & Kalyan Chakravarthi M, “Raspberry Pi Based Cost Effective Vehicle Collision Avoidance System Using Image Processingâ€, ARPN J. Eng. Appl. Sci., Vol.10, (2015), pp.3001-3005.

      [4] Karoui I, Isabelle Q & Michel L, “Automatic sea-surface obstacle detection and tracking in forward-looking sonar image sequencesâ€, IEEE Transactions on Geoscience and Remote Sensing, Vol.53, No.8, (2015), pp.4661-4669.

      [5] Datcu D, Marina C, Stephan L, David MO & Mikael W, “Virtual co-location to support remote assistance for inflight maintenance in ground training for space missionsâ€, 15th International Conference on Computer Systems and Technologies, (2014), pp.134-141.

      [6] Lukosch S, Heide L, DragoÅŸ D & Marina C, “Providing information on the spot: Using augmented reality for situational awareness in the security domainâ€, Computer Supported Cooperative Work (CSCW), Vol.24, No.6, (2015), pp.613-664.

      [7] Azuma RT, “A survey of augmented realityâ€, Presence: Teleoperators & Virtual Environments, Vol.6, No.4 (1997), pp.355-385.

      [8] Back A & Deady C, “Introducing the Pi NoIR Cameraâ€, The MagPi, Vol.18, (2013), pp.16-18.

      [9] Dalal N & Bill T, “Histograms of oriented gradients for human detectionâ€, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1, (2005), pp.886-893.

      [10] Zhu Q, Mei CY, Kwang TC & Shai A, “Fast human detection using a cascade of histograms of oriented gradientsâ€, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2, (2006), pp.1491-1498.

      [11] Yadav RP, Vinuchackravarthy S, Krishnan K & Sunita PU, “Implementation of robust HOG-SVM based pedestrian classificationâ€, International Journal of Computer Applications, Vol.114, No.19, (2015).

      [12] Bristow H & Simon L, “Why do linear SVMs trained on HOG features perform so well?â€, arXiv preprint arXiv:1406.2419, (2014).

      [13] Chen Z, Kai C & James C, “Vehicle and Pedestrian Detection Using Support Vector Machine and Histogram of Oriented Gradients Featuresâ€, International Conference on Computer Sciences and Applications (CSA), (2013), pp.365-368.

      [14] Muin FA, Teddy SG, Mira K & Elsheikh MAE, “A review of lossless audio compression standards and algorithmsâ€, AIP Conference Proceedings, Vol.1883, No.1, (2017).

      [15] BRivero C & Prabhat M, “Lossles audio compression: A case studyâ€, Technical Report 08-415, Department of computer and information Science and Engineering, University of Florida, Gainesville, FL32611, USA, (2008).

      [16] Këpuska V & Gamal B, “Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx)â€, Int. J. Eng. Res. Appl., Vol.7, (2017), pp.20-24.

      [17] Kulkarni DH, “Review on Recent Speech Recognition Techniquesâ€, International Journal of Scientific and Research Publications, Vol.3, No.7, (2013).

      [18] Sak H, Andrew S & Françoise B, “Long short-term memory recurrent neural network architectures for large scale acoustic modelingâ€, Fifteenth annual conference of the international speech communication association, (2014).

      [19] Graves A, Abdel RM & Geoffrey H, “Speech recognition with deep recurrent neural networksâ€, IEEE international conference on Acoustics, speech and signal processing (icassp), (2013), pp.6645-6649.

      [20] Hochreiter S & Jürgen S, “Long short-term memoryâ€, Neural computation, Vol.9, No.8, (1997), pp.1735-1780.

      [21] Gallet A, Spigai M & Hamidi M, “Use of vehicle navigation in driver assistance systemsâ€, Intelligent Vehicles Symposium, (2000), pp. 492-497.

      [22] Iizuka H & Kunihiko K, “Automatic braking system with proximity detection to a preceding vehicleâ€, U.S. Patent 5,278,764, (1994).

      [23] Benbasat AY & Joseph AP, “An inertial measurement framework for gesture recognition and applicationsâ€, International Gesture Workshop, Springer, Berlin, Heidelberg, (2001), pp.9-20.

      [24] Mantyla VM, Mantyjarvi J, Seppanen T & Esa TH, “and gesture recognition of a mobile device userâ€, IEEE International Conference on Multimedia and Expo, Vol.1, (2000). pp.281-284.

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  • How to Cite

    Misra, S., Parvez, V., Singh, T., & Chitra, E. (2018). A Portable Driver Assistance System Headset Using Augmented Reality. International Journal of Engineering & Technology, 7(3.6), 294-297. https://doi.org/10.14419/ijet.v7i3.6.15071