Advertisement

Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection

Conference paper
  • 819 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)

Abstract

We present a generalized and scalable method, called Gen-LaneNet, to detect 3D lanes from a single image. The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network. However, we propose unique designs for Gen-LaneNet in two folds. First, we introduce a new geometry-guided lane anchor representation in a new coordinate frame and apply a specific geometric transformation to directly calculate real 3D lane points from the network output. We demonstrate that aligning the lane points with the underlying top-view features in the new coordinate frame is critical towards a generalized method in handling unfamiliar scenes. Second, we present a scalable two-stage framework that decouples the learning of image segmentation subnetwork and geometry encoding subnetwork. Compared to 3D-LaneNet, the proposed Gen-LaneNet drastically reduces the amount of 3D lane labels required to achieve a robust solution in real-world applications. Moreover, we release a new synthetic dataset and its construction strategy to encourage the development and evaluation of 3D lane detection methods. In experiments, we conduct extensive ablation study to substantiate the proposed Gen-LaneNet significantly outperforms 3D-LaneNet in average precision (AP) and F-measure.

Keywords

3D lane detection Geometry-guided anchor Two-stage framework Monocular camera Unified network 

Notes

Acknowledgements

This work was supported by Apollo autonomous driving solution, Baidu USA.

Supplementary material

504479_1_En_40_MOESM1_ESM.pdf (22.2 mb)
Supplementary material 1 (pdf 22709 KB)

References

  1. 1.
  2. 2.
    Bai, M., Máttyus, G., Homayounfar, N., Wang, S., Lakshmikanth, S.K., Urtasun, R.: Deep multi-sensor lane detection. CoRR (2019)Google Scholar
  3. 3.
    Brabandere, B.D., Gansbeke, W.V., Neven, D., Proesmans, M., Gool, L.V.: End-to-end lane detection through differentiable least-squares fitting. CoRR abs/1902.00293 (2019)Google Scholar
  4. 4.
    Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213–3223, June 2016Google Scholar
  5. 5.
    Coulombeau, P., Laurgeau, C.: Vehicle yaw, pitch, roll and 3D lane shape recovery by vision. In: Intelligent Vehicle Symposium, 2002. IEEE (2002)Google Scholar
  6. 6.
    Garnett, N., Cohen, R., Pe’er, T., Lahav, R., Levi, D.: 3D-lanenet: end-to-end 3D multiple lane detection. In: IEEE International Conference on Computer Vision, ICCV (2019)Google Scholar
  7. 7.
    He, B., Ai, R., Yan, Y., Lang, X.: Accurate and robust lane detection based on dual-view convolutional neutral network. In: Intelligent Vehicles Symposium (2016)Google Scholar
  8. 8.
    Homayounfar, N., Ma, W., Lakshmikanth, S.K., Urtasun, R.: Hierarchical recurrent attention networks for structured online maps. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June 2018, pp. 3417–3426 (2018)Google Scholar
  9. 9.
    Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNs by self attention distillation. In: IEEE International Conference on Computer Vision, ICCV (2019)Google Scholar
  10. 10.
    Huval, B., et al.: An empirical evaluation of deep learning on highway driving. CoRR (2015)Google Scholar
  11. 11.
    Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, Quebec, Canada, 7–12 December 2015, pp. 2017–2025 (2015)Google Scholar
  12. 12.
    Kheyrollahi, A., Breckon, T.P.: Automatic real-time road marking recognition using a feature driven approach. Mach. Vis. Appl. 23, 123–133 (2012).  https://doi.org/10.1007/s00138-010-0289-5CrossRefGoogle Scholar
  13. 13.
    Kim, J., Lee, M.: Robust lane detection based on convolutional neural network and random sample consensus. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8834, pp. 454–461. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-12637-1_57CrossRefGoogle Scholar
  14. 14.
    Kogan, V., Shimshoni, I., Levi, D.: Lane-level positioning with sparse visual cues. In: 2016 IEEE Intelligent Vehicles Symposium, IV 2016, Gotenburg, Sweden, 19–22 June 2016, pp. 889–895 (2016)Google Scholar
  15. 15.
    Lee, S., et al.: Vpgnet: vanishing point guided network for lane and road marking detection and recognition. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22–29 October 2017, pp. 1965–1973 (2017)Google Scholar
  16. 16.
    Li, J., Mei, X., Prokhorov, D.: Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans. Neural Netw. Learn. Syst. 28, 690–703 (2016)CrossRefGoogle Scholar
  17. 17.
    Meyer, A., Salscheider, N., Orzechowski, P., Stiller, C.: Deep semantic lane segmentation for mapless driving. In: IROS (2018).  https://doi.org/10.1109/IROS.2018.8594450
  18. 18.
    Mǎriut, F., Foşalǎu, C., Petrisor, D.: Lane mark detection using Hough transform. In: 2012 International Conference and Exposition on Electrical and Power Engineering, pp. 871–875 (2012)Google Scholar
  19. 19.
    Nedevschi, S., et al.: 3D lane detection system based on stereovision. In: Proceedings the 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749), pp. 161–166 (2004)Google Scholar
  20. 20.
    Neven, D., Brabandere, B.D., Georgoulis, S., Proesmans, M., Gool, L.V.: Towards end-to-end lane detection: an instance segmentation approach. In: 2018 IEEE Intelligent Vehicles Symposium, IV 2018, Changshu, Suzhou, China, 26–30 June 2018, pp. 286–291 (2018)Google Scholar
  21. 21.
    Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: Spatial CNN for traffic scene understanding. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI 2018), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 7276–7283 (2018)Google Scholar
  22. 22.
    Philion, J.: Fastdraw: addressing the long tail of lane detection by adapting a sequential prediction network. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16–20 June 2019, pp. 11582–11591 (2019)Google Scholar
  23. 23.
    Poudel, R.P.K., Bonde, U., Liwicki, S., Zach, C.: Contextnet: exploring context and detail for semantic segmentation in real-time. BMVC (2018)Google Scholar
  24. 24.
    Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: Erfnet: efficient residual factorized convnet for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 19, 263–272 (2018)CrossRefGoogle Scholar
  25. 25.
    Yuan, C., Chen, H., Liu, J., Zhu, D., Xu, Y.: Robust lane detection for complicated road environment based on normal map. IEEE Access 6, 49679–49689 (2018)CrossRefGoogle Scholar
  26. 26.
    Zhang, W., Mahale, T.: End to end video segmentation for driving: lane detection for autonomous car. CoRR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Baidu ApolloSunnyvaleUSA

Personalised recommendations