⾞道線檢測在無⼈⾞駕駛的系統中扮演著相當重要的⾓⾊。在本⽂中，
我們發現當⾞⼦進⼊⾼速公路閘道出⼊⼝時，過去的⽅法常受槽化線區域
影響，無法完整的描述槽化線左右兩側的⾞道線。因此本⽂針對⾞道線檢
測中，容易被當成錯誤或未被檢測的區域，槽化線，提出⼀套解決⽅案。
我們不僅針對⾼速公路上閘道出⼊⼝固定出現的雪弗⿓型槽化線區，亦
能處理在都市中的斜型槽化線區。本⽂的最後，提出了有別於過往的資料
集，富含槽化線區域的實驗資料及實驗結果，平均準確率達91.11%。

In this thesis we consider one of challenge cases in a lane detection system, which is channelizing line, and propose an efficient method to not only keep the pixels belonging to the lane located on the channelizing line, but also detect and model the lanes which were filtered from the observation. Channelizing lines are used to guide drivers where travel in the same direction for permitting on both sides, such as entrance and exit ramps. Channelizing line usually has same intensity with the lane, but has pavement markings, solid white lines with wide diagonal lines or chevrons within two channelizing lines, which are usually influence the detection result in several previous work. The experiments show that the proposal has a good performance even passing through several channelizing lines. While passing through single channelizing line zone, the average accuracy can reach to 91.11%. Experiments show that our approach reaches competitive performances on channelizing lines.

1 Introduction 1
2 Related Work 4
3 Approach 8
3.1 Lane Marking Segmentation . . . . . . . . . . . . . . . . . . . 10
3.1.1 Inverse Perspective Mapping . . . . . . . . . . . . . . . 11
3.1.2 Separable Gaussian Filter . . . . . . . . . . . . . . . . 12
3.1.3 Histogram Equalization & Intensity-based Segmentation 13
3.2 Lane Marking Detection . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 Lane Marking Extraction . . . . . . . . . . . . . . . . 17
3.2.2 Lane Marking Clustering . . . . . . . . . . . . . . . . . 18
3.2.3 Lane Modeling . . . . . . . . . . . . . . . . . . . . . . 22
4 Experiment 24
4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.1.1 NCTU Dataset . . . . . . . . . . . . . . . . . . . . . . 26
4.1.2 TuSimple Dataset . . . . . . . . . . . . . . . . . . . . 27
4.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3 Failure cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5 Conclusion 58

[1] D. Neven, B. De Brabandere, S. Georgoulis, M. Proesmans, and L. Van
Gool, “Towards End-to-End Lane Detection: an Instance Segmentation
Approach,” arXiv e-prints, arXiv:1802.05591, arXiv:1802.05591, Feb.
2018. arXiv: 1802.05591 [cs.CV].
[2] X. Pan, J. Shi, P. Luo, X. Wang, and X. Tang, “Spatial As Deep: Spatial
CNN for Traffic Scene Understanding,” arXiv e-prints, arXiv:1712.06080,
arXiv:1712.06080, Dec. 2017. arXiv: 1712.06080 [cs.CV].
[3] J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel,
J. Z. Kolter, D. Langer, O. Pink, V. Pratt, M. Sokolsky, G. Stanek, D.
Stavens, A. Teichman, M. Werling, and S. Thrun, “Towards fully autonomous
driving: Systems and algorithms,” in 2011 IEEE Intelligent
Vehicles Symposium (IV), Jun. 2011, pp. 163–168.
[4] S. Kammel and B. Pitzer, “Lidar-based lane marker detection and
mapping,” in 2008 IEEE Intelligent Vehicles Symposium, Jun. 2008,
pp. 1137–1142.
[5] M. Bertozzi and A. Broggi, “Gold: A parallel real-time stereo vision
system for generic obstacle and lane detection,” IEEE Transactions on
Image Processing, vol. 7, no. 1, pp. 62–81, Jan. 1998.
[6] M. Aly, “Real time detection of lane markers in urban streets,” in 2008
IEEE Intelligent Vehicles Symposium, Jun. 2008, pp. 7–12.
[7] J. Son, H. Yoo, S. Kim, and K. Sohn, “Real-time illumination invariant
lane detection for lane departure warning system,” Expert Systems with
Applications, vol. 42, no. 4, pp. 1816–1824, 2015.
[8] Y. Xing, C. Lv, L. Chen, H. Wang, H. Wang, D. Cao, E. Velenis,
and F. Wang, “Advances in vision-based lane detection: Algorithms,
integration, assessment, and perspectives on acp-based parallel vision,”
IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 3, pp. 645–661,
May 2018.
[9] A. Bar Hillel, R. Lerner, D. Levi, and G. Raz, “Recent progress in
road and lane detection: A survey,” Machine Vision and Applications,
vol. 25, no. 3, pp. 727–745, Apr. 2014.
[10] S. P. Narote, P. N. Bhujbal, A. S. Narote, and D. M. Dhane, “A review
of recent advances in lane detection and departure warning system,”
Pattern Recognition, vol. 73, pp. 216–234, 2018.
[11] A. S. Huang, D. Moore, M. Antone, E. Olson, and S. Teller, “Finding
multiple lanes in urban road networks with vision and lidar,” Autonomous
Robots, vol. 26, no. 2, pp. 103–122, Apr. 2009.
[12] Q. Li, N. Zheng, and H. Cheng, “An adaptive approach to lane markings
detection,” in Proceedings of the 2003 IEEE International Conference
on Intelligent Transportation Systems, vol. 1, Oct. 2003, 510–514
vol.1.
[13] A. López, J. Serrat, C. Cañero, F. Lumbreras, and T. Graf, “Robust
lane markings detection and road geometry computation,” International
Journal of Automotive Technology, vol. 11, no. 3, pp. 395–407,
Jun. 2010.
[14] S. Wu, H. Chiang, J. Perng, C. Chen, B. Wu, and T. Lee, “The heterogeneous
systems integration design and implementation for lane keeping
on a vehicle,” IEEE Transactions on Intelligent Transportation Systems,
vol. 9, no. 2, pp. 246–263, Jun. 2008.
[15] H. Deusch, J. Wiest, S. Reuter, M. Szczot, M. Konrad, and K. Dietmayer,
“A random finite set approach to multiple lane detection,” in
2012 15th International IEEE Conference on Intelligent Transportation
Systems, Sep. 2012, pp. 270–275.
[16] J. Wang, Y. Wu, Z. Liang, and Y. Xi, “Lane detection based on random
hough transform on region of interesting,” in The 2010 IEEE
International Conference on Information and Automation, Jun. 2010,
pp. 1735–1740.
[17] S. Zhou, Y. Jiang, J. Xi, J. Gong, G. Xiong, and H. Chen, “A novel
lane detection based on geometrical model and gabor filter,” in 2010
IEEE Intelligent Vehicles Symposium, Jun. 2010, pp. 59–64.
[18] E. Romera, L. M. Bergasa, and R. Arroyo, “Can we unify monocular
detectors for autonomous driving by using the pixel-wise semantic segmentation
of CNNs?” arXiv e-prints, arXiv:1607.00971, arXiv:1607.00971,
Jul. 2016. arXiv: 1607.00971 [cs.CV].
[19] J. Kim and M. Lee, “Robust lane detection based on convolutional
neural network and random sample consensus,” in Neural Information
Processing, C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, and K. Huang,
Eds., Cham: Springer International Publishing, 2014, pp. 454–461.
[20] S. Lee, J. Kim, J. S. Yoon, S. Shin, O. Bailo, N. Kim, T.-H. Lee,
H. S. Hong, S.-H. Han, and I. S. Kweon, “VPGNet: Vanishing Point
Guided Network for Lane and Road Marking Detection and Recognition,”
arXiv e-prints, arXiv:1710.06288, arXiv:1710.06288, Oct. 2017.
arXiv: 1710.06288 [cs.CV].
[21] J. Hur, S. Kang, and S. Seo, “Multi-lane detection in urban driving environments
using conditional random fields,” in 2013 IEEE Intelligent
Vehicles Symposium (IV), Jun. 2013, pp. 1297–1302.
[22] A. B. Lopez, C. Cañero, J. Serrat, J. Saludes, F. Lumbreras, and T.
Graf, “Detection of lane markings based on ridgeness and ransac,” Proceedings.
2005 IEEE Intelligent Transportation Systems, 2005., pp. 254–
259, 2005.
[23] T. Veit, J. Tarel, P. Nicolle, and P. Charbonnier, “Evaluation of road
marking feature extraction,” in 2008 11th International IEEE Conference
on Intelligent Transportation Systems, Oct. 2008, pp. 174–181.
[24] S. W. Smith, The Scientist and Engineer’s Guide to Digital Signal Processing.
San Diego, CA, USA: California Technical Publishing, 1997,
isbn: 0-9660176-3-3.
[25] Ren and Malik, “Learning a classification model for segmentation,” in
Proceedings Ninth IEEE International Conference on Computer Vision,
Oct. 2003, 10–17 vol.1.
[26] C. Huang, B. Wu, and R. Nevatia, “Robust object tracking by hierarchical
association of detection responses,” in ECCV , 2008.
27] M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm
for model fitting with applications to image analysis and automated
cartography,” Commun. ACM, vol. 24, no. 6, pp. 381–395, Jun. 1981.
[28] TuSimple Inc., “TuSimple Benchmark Platform,” 2017. [Online]. Available:
http://benchmark.tusimple.ai/.
[29] H. Lin, L. Chen, Y. Lin, and M. Yu, “Lane departure and front collision
warning using a single camera,” in 2012 International Symposium on
Intelligent Signal Processing and Communications Systems, Nov. 2012,
pp. 64–69.