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Corner Test Cases for ADAS and HAVs: A Computational Study on the Influence of Road Irregularities on Vehicle Vision Systems

  • Yannik WeberEmail author
  • Stratis KanarachosEmail author
Conference paper
  • 5 Downloads
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Automated Vehicles and next generation ADAS hold the promise of disrupting mobility. However, public field trials have recently highlighted road anomalies, such as potholes and bumps, as a source of autopilot disengagements. In this paper, we research the influence of road anomalies on the performance of Artificial Intelligence-based vision systems. To this end, we conducted controlled real-world experiments and developed a validated vehicle system computational model using IPG Carmaker. The vehicle detection, tracking and distance estimation performance have been investigated by undertaking a thorough sensitivity analysis. The results indicate the system limitations in performing adequately for a range of bump sizes and vehicle speeds. With our findings we put emphasis on the importance of vehicle dynamics in the development of automated driving systems.

Keywords

Highly Automated Vehicles Road bumps Autopilot disengagements 

References

  1. 1.
    Ahmed, S., Huda, M.N., Rajbhandari, S., Saha, C., Elshaw, M., Kanarachos, S.: Pedestrian and cyclist detection and intent estimation for autonomous vehicles: a survey. Appl. Sci. 9(2335), 1–38 (2019).  https://doi.org/10.3390/app9112335CrossRefGoogle Scholar
  2. 2.
    Awasthi, A., Singh, J.K., Roh, S.H.: Monocular vision based distance estimation algorithm for pedestrian collision avoidance systems. In: Proceedings of the 5th International Conference on Confluence 2014: The Next Generation Information Technology Summit, pp. 646–650 (2014).  https://doi.org/10.1109/CONFLUENCE.2014.6949272
  3. 3.
    Christiansen, R.H., Hsu, J., Gonzalez, M., Wood, S.L.: Monocular vehicle distance sensor using HOG and Kalman tracking. In: Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, pp. 178–182 (2018).  https://doi.org/10.1109/ACSSC.2017.8335162
  4. 4.
    Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1–14 (2014).  https://doi.org/10.1109/TPAMI.2014.2300479. https://es.mathworks.com/help/vision/ref/detectpeopleacf.html%5Cngithub.com/pdollar/toolboxCrossRefGoogle Scholar
  5. 5.
    García, F., Prioletti, A., Cerri, P., Broggi, A.: PHD filter for vehicle tracking based on a monocular camera. Expert Syst. Appl. 91, 472–479 (2018).  https://doi.org/10.1016/j.eswa.2017.09.018CrossRefGoogle Scholar
  6. 6.
    Gat, I., Benady, M., Shashua, A.: A monocular vision advance warning system for the automotive aftermarket. SAE Technical Paper Series 1(October 2004) (2010).  https://doi.org/10.4271/2005-01-1470
  7. 7.
    Grimble, M.J.: Robust Industrial Control Systems. Wiley, Hoboken (2014).  https://doi.org/10.1002/9780470020753CrossRefGoogle Scholar
  8. 8.
    Hu, H.N., Cai, Q.Z., Wang, D., Lin, J., Sun, M., Krähenbühl, P., Darrell, T., Yu, F.: Joint monocular 3D vehicle detection and tracking. Technical Report National Tsing Hua University, Nanjing University, UC Berkeley, MIT, UT Austin (2018). http://arxiv.org/abs/1811.10742
  9. 9.
    Kalra, N., Paddock, S.M.: Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability? Transp. Res. Part A Policy Pract. 94, 182–193 (2016).  https://doi.org/10.1016/j.tra.2016.09.010CrossRefGoogle Scholar
  10. 10.
    Kanarachos, S., Dizqah, A.M., Chrysakis, G., Fitzpatrick, M.E.: Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation. Appl. Soft Comput. J. 62, 463–477 (2018).  https://doi.org/10.1016/j.asoc.2017.11.005CrossRefGoogle Scholar
  11. 11.
    Konstantinova, P., Udvarev, A., Semerdjiev, T.: A study of a target tracking method using Global Nearest Neighbor algorithm. In: International Conference on Computer Systems and Technologies-CompSysTech’2003, pp. 290–295 (2013).  https://doi.org/10.5937/vojtehg0602160r
  12. 12.
    Lambert, F.: Tesla Autopilot will be able to avoid potholes on the road, says Elon Musk (2019). https://electrek.co/2019/04/07/tesla-autopilot-avoid-potholes-elon-musk/
  13. 13.
    Lessmann, S., Meuter, M., Muller, D., Pauli, J.: Probabilistic distance estimation for vehicle tracking application in monocular vision. In: IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, pp. 1199–1204 (2016).  https://doi.org/10.1109/IVS.2016.7535542
  14. 14.
    Lv, C., Cao, D., Zhao, Y., Auger, D.J., Wang, H., Dutka, L.M., Skrypchuk, L., Mouzakitis, A.: Analysis of autopilot disengagements occurring during autonomous vehicle testing. IEEE/CAA J. Autom. Sin. 5(1), 58–68 (2018)CrossRefGoogle Scholar
  15. 15.
    Nakamura, K., Ishigaki, K., Ogata, T., Muramatsu, S.: Real-time monocular ranging by Bayesian triangulation. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 1368–1373. IEEE (2013).  https://doi.org/10.1109/IVS.2013.6629657
  16. 16.
    Park, K.Y., Hwang, S.Y.: Robust range estimation with a monocular camera for vision-based forward collision warning system. Sci. World J. 2014, 1–9 (2014).  https://doi.org/10.1155/2014/923632CrossRefGoogle Scholar
  17. 17.
    Salter, S., Diels, C., Herriotts, P., Kanarachos, S., Thake, D.: Motion sickness in automated vehicles with forward and rearward facing seating orientations. Appl. Ergon. 78, 54–61 (2019).  https://doi.org/10.1016/j.apergo.2019.02.001CrossRefGoogle Scholar
  18. 18.
    Singh, S.: Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Technical Report National Highway Traffic Safety Administration, Washington, DC (2015). https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115
  19. 19.
    Wotawa, F., Peischl, B., Klück, F., Nica, M.: Quality assurance methodologies for automated driving. Elektrotech. Informationstechnik 135(4–5), 322–327 (2018).  https://doi.org/10.1007/s00502-018-0630-7CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Coventry UniversityCoventryUK

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