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


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

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.


  1. 1.

    World Health Organization: Global status report on road safety 2015. World Health Organization, Geneva (2015)

  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)

  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)

  4. 4.

    Sagar, B.: Architecting Autonomous Automotive Systems. (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).

    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).

    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).

    Google Scholar 

  8. 8.

    Meiyuan, Z.: Advanced Driver Assistant System. (2016). Accessed 8 June 2017

  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).

    Article  Google Scholar 

  11. 11.

    Fröstl, M.: 12 years of AUTOSAR, enabling innovation with model-based design. Automotive Conference. (2015). Accessed 12 June 2017

  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)

    Article  Google Scholar 

  13. 13.

    Nvidia: Nvidia drivepx. (2017). Accessed 31 June 2017

  14. 14.

    Ritter, S., Cory, T., Eiswierth, B., Gaus, J., Pregmon, A.: Side shots: sideview cameras, Delphi automotive. (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)

  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)

  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)

  19. 19.

    Jaesik, C.: Realtime on-road vehicle detection with optical flows and haar-like feature detectors. Technical Report (2012)

  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)

    Article  Google 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)

  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)

  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)

    Article  Google 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. (2016). Accessed 27 June 2017

Download references

Author information



Corresponding author

Correspondence to Ricardo A. Ramirez-Mendoza.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Curiel-Ramirez, L.A., Ramirez-Mendoza, R.A., Carrera, G. et al. Towards of a modular framework for semi-autonomous driving assistance systems. Int J Interact Des Manuf 13, 111–120 (2019).

Download citation


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