Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
Source abstract
Abstract
A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Advanced Vision and Imaging · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
School of Automotive Engineering, Chongqing University, Chongqing 400044, China
Funding
the Fundamental Research Funds for the Central Universities · the National Natural Science Foundation of China
References (32)
Show source-provided references
1. Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision10.1109/JSEN.2018.2832291 ↗
2. Lane detection algorithm based on geometric moment sampling10.1360/N112016-00174 ↗
3. A review of recent advances in lane detection and departure warning system10.1016/j.patcog.2017.08.014 ↗
4. Li, C., Nie, Y., Dai, B., and Wu, T. (2014, January 24). Multi-lane detection based on multiple vanishing points detection. Proceedings of the Sixth International Conference on Graphic and Image Processing (ICGIP 2014), Beijing, China.10.1117/12.2178744 ↗
5. Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation10.1109/TITS.2016.2586187 ↗
6. Lane line detection method based on orientation variance Haar feature and hyperbolic model
7. Robust Lane Detection and Tracking for Real-Time Applications10.1109/TITS.2018.2791572 ↗
8. Li, S., Xu, J., Wei, W., and Qi, H. (2017, January 28–30). Curve lane detection based on the binary particle swarm optimization. Proceedings of the 29th Chinese Control and Decision Conference (CCDC), Chongqing, China.10.1109/CCDC.2017.7978069 ↗
9. Lane Departure Identification for Advanced Driver Assistance
10. Yuhao, H., Shitao, C., Yu, C., Zhiqiang, J., and Nanning, Z. (2018, January 25–27). Spatial-temproal based lane detection using deep learning. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Rhodes, Greece.
11. Wang, L. (2014). Design and Implementation of a Track Detection Algorithm Based on Hyperbolic Model. [Master’s Thesis, Jilin University].
12. Kim, J., and Park, C. (2017, January 21–26). End-To-End ego lane estimation based on sequential transfer learning for self-driving cars. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.10.1109/CVPRW.2017.158 ↗
13. Longjard, C., Kumsawat, P., Attakitmongkol, K., and Srikaew, A. (2007, January 15–17). Automatic Lane Detection and Navigation using Pattern Matching Mode. Proceedings of the International Conference on Signal, Speech and Image Processing, Beijing, China.
14. Wang, J., Gu, F., Zhang, C., and Zhang, G. (2010, January 20–23). Lane boundary detection based on parabola model. Proceedings of the International Conference on Information and Automation, Harbin, China.10.1109/ICINFA.2010.5512219 ↗
15. Zhao, K., Meuter, M., Nunn, C., Muller, D., Muller-Schneiders, S., and Pauli, J. (2012, January 3–7). A novel multi-lane detection and tracking system. Proceedings of the 2012 Intelligent Vehicles Symposium, Alcala de Henares, Spain.10.1109/IVS.2012.6232168 ↗
16. Jung, C.R., and Kelber, C.R. (2005, January 9–12). An Improved Linear-Parabolic Model for Lane Following and Curve Detection. Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI’05), Natal, Rio Grande do Norte, Brazil.10.1109/SIBGRAPI.2005.10 ↗
17. A method of selective image graying method
18. Modified two-dimensional Otsu image segmentation algorithm and fast realisation10.1049/iet-ipr.2010.0078 ↗
19. Matching method for oblique aerial images based on plane perspective projection
20. A Robust Lane Detection Method Based on Vanishing Point Estimation Using the Relevance of Line Segments10.1109/TITS.2017.2679222 ↗
21. Baozheng, F. (2018). Research on Monocular Vision Detection Method of Structured Road Lane. [Master’s Thesis, Hunan University].
22. The relation between braking distance and speed
23. Research on Image Segmentation Based on Global Optimization Search Algorithm
24. Image Interpolation with Predicted Gradients
25. On gray prediction model based on an improved FCM algorithm
26. Lane mark recognition based on improved least squares lane mark model
27. Sun, P., and Chen, H. (2014, January 13). Lane detection and tracking based on improved Hough transform and least-squares method. Proceedings of the International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, Beijing, China.10.1117/12.2072393 ↗
28. Lane line recognition using region division on structured roads
29. He, B., Ai, R., Yan, Y., and Lang, X. (2016, January 19–22). Accurate and robust lane detection based on Dual-View Convolutional Neutral Network. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.
30. Aly, M. (2008, January 4–6). Real time detection of lane markers in urban streets. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, Netherlands.10.1109/IVS.2008.4621152 ↗
31. He, J., Rong, H., Gong, J., and Huang, W. (2010, January 11–12). A Lane Detection Method for Lane Departure Warning System. Proceedings of the 2010 International Conference on Optoelectronics and Image Processing, Haikou, China.10.1109/ICOIP.2010.307 ↗
32. Seo, Y., and Rajkumar, R.R. (2014, January 8–11). Utilizing instantaneous driving direction for enhancing lane-marking detection. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.10.1109/IVS.2014.6856467 ↗