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Driving Data Collection Framework Using Low Cost Hardware

  • Johnny JacobEmail author
  • Pankaj RabhaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11133)

Abstract

Autonomous driving is driven by data. The availability of large and diverse data set from different geographies can help in maturing Autonomous driving technology faster. It is challenging to build a system to collect driving data which is cost intensive especially in emerging economies. Paradoxically these economies have chaotic driving conditions leading to a valuable data set. To address the issue of cost and scale, we have developed a data collection framework. In this paper, we’ll discuss our motive for the framework, performance bottlenecks, a two stage pipeline design and insights on how to tune the system to get maximum throughput.

Keywords

Autonomous driving Data collection ROS Sensing Perception Dataset 

References

  1. 1.
    Caraffi, C., Vojíř, T., Trefný, J., Šochman, J., Matas, J.: A system for real-time detection and tracking of vehicles from a single car-mounted camera. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 975–982, September 2012.  https://doi.org/10.1109/ITSC.2012.6338748
  2. 2.
    Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. CoRR abs/1604.01685 (2016). http://arxiv.org/abs/1604.01685
  3. 3.
    Dhall, A., Chelani, K., Radhakrishnan, V., Krishna, K.M.: LiDAR-camera calibration using 3D-3D point correspondences. ArXiv e-prints, May 2017Google Scholar
  4. 4.
    Dueholm, J.V., Kristoffersen, M.S., Satzoda, R.K., Ohn-Bar, E., Moeslund, T.B., Trivedi, M.M.: Multi-perspective vehicle detection and tracking: challenges, dataset, and metrics. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 959–964, November 2016.  https://doi.org/10.1109/ITSC.2016.7795671
  5. 5.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361, June 2012.  https://doi.org/10.1109/CVPR.2012.6248074
  6. 6.
    Kumar, A., Kambaluru, S., Vanguri, V.R.P., Jacob, J., Rabha, P.: Driving data collection reference kit opensource repository (2018). https://github.com/intel/driving-data-collection-reference-kit. Accessed 17 July 2018
  7. 7.
    Olson, E.: AprilTag: a robust and flexible multi-purpose fiducial system. Technical report, University of Michigan APRIL Laboratory, May 2010Google Scholar
  8. 8.
    Pagel, F.: Calibration of non-overlapping cameras in vehicles. In: 2010 IEEE Intelligent Vehicles Symposium, pp. 1178–1183, June 2010.  https://doi.org/10.1109/IVS.2010.5547991
  9. 9.
    Pandey, G., McBride, J.R., Eustice, R.M.: Ford campus vision and lidar data set. Int. J. Robot. Res. 30(13), 1543–1552 (2011).  https://doi.org/10.1177/0278364911400640CrossRefGoogle Scholar
  10. 10.
    Rangesh, A., Yuen, K., Satzoda, R.K., Rajaram, R.N., Gunaratne, P., Trivedi, M.M.: A multimodal, full-surround vehicular testbed for naturalistic studies and benchmarking: design, calibration and deployment. CoRR abs/1709.07502 (2017). http://arxiv.org/abs/1709.07502
  11. 11.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000).  https://doi.org/10.1109/34.888718CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Intel CorporationBengaluruIndia

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