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Towards of a modular framework for semi-autonomous driving assistance systems

  • Luis A. Curiel-Ramirez
  • Ricardo A. Ramirez-MendozaEmail author
  • Gerardo Carrera
  • Javier Izquierdo-Reyes
  • M. Rogelio Bustamante-Bello
Original Paper

Abstract

Road traffic accidents are a leading cause of deaths globally and represent the main cause of death among 15–29 years olds (World Health Organization in Global status report on road safety 2015, World Health Organization, Geneva, 2015). Efforts have been made to develop and improve advanced driver assistance systems as to provide better quality of driving and reduce the number of traffic accidents. However, most of the current solutions focus on developing systems for fully autonomous cars. This study investigates a modular architecture that could potentially assist drivers in situations such as traffic jams, autonomous parking, and detection of obstacles such as pedestrians. The aim is to develop a low-cost driver assistance system that could be acquired and easily incorporated by non-technical users. It is becoming increasingly feasible to build a powerful and low-cost system because of the low cost of big-sized cameras, electronics, and processing cards. As a proof of the concept, a hardware and software solution had been developed for autonomous steering-wheel actuation that only utilizes a stereoscopic camera sensor.

Graphical Abstract

Keywords

Semi-autonomous driving Steering prediction Deep learning Interactive engineering Modular framework Assistance systems 

References

  1. 1.
    World Health Organization: Global status report on road safety 2015. World Health Organization, Geneva (2015)Google Scholar
  2. 2.
    Rodolfo, S., Thomas, B.: Enhancement of active and passive safety by future pre-safe systems. In: Proceedings of the 19th ESV conference and Washington and DC and USA (2005)Google Scholar
  3. 3.
    Mei, C., Sibley, G., Cummins, M., Newman, P., Reid, I: A constant-time efficient stereo slam system. In: BMVC, pp. 1–11 (2009)Google Scholar
  4. 4.
    Sagar, B.: Architecting Autonomous Automotive Systems. https://www.diva-portal.org/smash/get/diva2:615888/FULLTEXT02.pdf (2013). Accessed 28 May 2017
  5. 5.
    Bruno, F.D.F.A.S.H.S.F.: A modular architecture for a driving simulator based on the fdmu approach. Int. J. Interact. Des. Manuf. 8, 139–150 (2013).  https://doi.org/10.1007/s12008-013-0182-3 Google Scholar
  6. 6.
    Nass, J.K.J.K.W.J.M.S.L.L.C.: Why did my car just do that? explaining semi-autonomous driving actions to improve driver understandingand trustand and performance. Int. J. Interact. Des. Manuf. 9, 269–275 (2014).  https://doi.org/10.1007/s12008-014-0227-2 Google Scholar
  7. 7.
    González, J.P.N.: Vehicle fault detection and diagnosis combining an AANN and multiclass SVM. Int. J. Interact. Des. Manuf. (2017).  https://doi.org/10.1007/s12008-017-0378-z Google Scholar
  8. 8.
  9. 9.
    Shaoshan, L., Jie, T., Zhe, Z., Jean-luc, G.: CAAD: Computer Architecture for Autonomous Driving. arXiv:1702.01894 (2017)
  10. 10.
    Behere, S., Torngren, M.: A functional reference architecture for autonomous driving. Inf. Softw. Technol. 73, 136–150 (2016).  https://doi.org/10.1016/j.infsof.2015.12.008 CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Kichun, J., Junsoo, K., Student, M., Dongchul, K.: Development of autonomous car part I: distributed system architecture and development process. IEEE Trans. Ind. Electron. 61(12), 7131–7140 (2014)CrossRefGoogle Scholar
  13. 13.
    Nvidia: Nvidia drivepx. http://www.nvidia.com/object/drive-px.html (2017). Accessed 31 June 2017
  14. 14.
    Ritter, S., Cory, T., Eiswierth, B., Gaus, J., Pregmon, A.: Side shots: sideview cameras, Delphi automotive. http://php.scripts.psu.edu/users/j/p/jpg5390/DelphiProjectReport.pdf (2014). Accessed 27 June 2017
  15. 15.
    Masataka, M., Chiyomi, M., Pongtep, A., Takatsugu, H., Yiyang, L., Norihide, K., Kazuya, T.: Measuring driver awareness based on correlation between gaze behavior and risks of surrounding vehicles. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 644–647. IEEE (2012)Google Scholar
  16. 16.
    Fabien, M., Bogdan, S., Amaury, B.: Real-time visual detection of vehicles and pedestrians with new efficient adaboost features. In: 2nd Workshop on Planning and Perception and Navigation for Intelligent Vehicles (PPNIV)and at 2008 IEEE International Conference on Intelligent RObots Systems (IROS 2008) (2008)Google Scholar
  17. 17.
    Martin, A., Ashish, A., Paul, B., Eugene, B., Zhifeng, C., Craig, C., S, C.G., Andy, D., Jeffrey, D., Matthieu, D., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)
  18. 18.
    Yangqing, J., Evan, S., Jeff, D., Sergey, K., Jonathan, L., Ross, G., Sergio, G., Trevor, D.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  19. 19.
    Jaesik, C.: Realtime on-road vehicle detection with optical flows and haar-like feature detectors. Technical Report (2012)Google Scholar
  20. 20.
    Massimo, B., Alberto, B., Stefano, C.: A real-time oriented system for vehicle detection. J. Syst. Arch. 43(1–5), 317–325 (1997)Google Scholar
  21. 21.
    Nd, M., Pe, A., C, D., CJ, H.: Vehicle detection and recognition in greyscale imagery. Control Eng. Pract. 4(4), 473–479 (1996)CrossRefGoogle Scholar
  22. 22.
    Narayan, S.: Vision-based vehicle detection and tracking method for forward collision warning in automobiles. In: Intelligent Vehicle Symposium and 2002. IEEE, vol. 2, pp. 626–631. IEEE (2002)Google Scholar
  23. 23.
    Franke, U., Kutzbach, I.: Fast stereo based object detection for stop & go traffic. In: Intelligent Vehicles Symposium and 1996 and Proceedings of the 1996 IEEE, pp. 339–344. IEEE (1996)Google Scholar
  24. 24.
    Andrea, G., Marco, C., Vincent, T.: The use of optical flow for road navigation. IEEE Trans. Robot. Autom. 14(1), 34–48 (1998)CrossRefGoogle Scholar
  25. 25.
    Brody, H., Tao, W., Sameep, T., Jeff, K., Will, S., Joel, P., Mykhaylo, A., Pranav, R., Toki, M., Royce, C.Y., et al.: An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716 (2015)
  26. 26.
    Clemens-Alexander, B., Sven, S., Marcel, S., Erik, R., Joachim, D.: Convolutional patch networks with spatial prior for road detection and urban scene understanding. arXiv preprint arXiv:1502.06344 (2015)
  27. 27.
    Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, D.L., Monfort, M., Muller, U., Zhang, J., Xin, Z., Zhao, J., Zieba, K.: End to end learning for self-driving cars, pp. 1–9. arXiv:1604.07316 (2016)
  28. 28.
    Sully, C.: Autopilot-tensorflow. https://github.com/SullyChen/Autopilot-TensorFlo (2016). Accessed 27 June 2017

Copyright information

© Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Escuela de Ingenieria y CienciasTecnologico de MonterreyMexico CityMexico

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