Generic Sensor Model for Object Detection Algorithms Validation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)


In the artificial intelligence era of perception algorithms being used in the state-of-the-art Advance Driver Assistance Systems, algorithm validation is not an easy task, mainly due to the amount of sensor data that must be processed at once. To provide the highest possible safety level of the solution, algorithm performance must be assessed in various difficult conditions. To address this problem, a novel Generic Sensor Model algorithm is presented, which can be directly incorporated in performance analysis of the perception algorithm in highly occluded driving scenarios. In this paper, the Generic Sensor Model algorithm is comprehensively described and its usefulness and robustness are proven with a set of experiments.


Sensors Mathematical modeling Simulations Automotive 


  1. 1.
    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference, AFIPS ’67 (Spring), pp. 483–485. Association for Computing Machinery, New York (1967).
  2. 2.
    Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. arXiv preprint arXiv:1903.11027 (2019)
  3. 3.
    Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving (2016)Google Scholar
  4. 4.
    Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator (2017)Google Scholar
  5. 5.
    Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  6. 6.
    Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Mining Knowl. Manage. Process 5, 1–11 (2015).
  7. 7.
    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 Practice 94, 182–193 (2016).
  8. 8.
    Ku, J., Mozifian, M., Lee, J., Harakeh, A., Waslander, S.: Joint 3d proposal generation and object detection from view aggregation. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1–8 (2018).
  9. 9.
    Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: fast encoders for object detection from point clouds (2018)Google Scholar
  10. 10.
    Roth, S.D.: Ray casting for modeling solids. Comput. Graph. Image Process. 18(2), 109–144 (1982).
  11. 11.
    Simon, M., Milz, S., Amende, K., Gross, H.M.: Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds: Munich, Germany, 8–14 September 2018, Proceedings, Part I, pp. 197–209. Springer, Cham (2019).
  12. 12.
    Zeeshan Zia, M., Stark, M., Schindler, K.: Explicit occlusion modeling for 3d object class representations. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Automatic Control and RoboticsAGH University of Science and TechnologyKrakówPoland
  2. 2.APTIV Services Poland S.A.KrakówPoland

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