# Highly Curved Lane Detection Algorithms Based on Kalman Filter
## 5 Conclusions  
In this paper, a curve line detection algorithm using the Kalman filter is presented. The algorithm is split into two sections:  
Figure 27: The result of curve lane detection based on circle model and Kalman filter (Yellow); (**a**) Input Image for Circle Detection, (**b**) Output Result.  
Figure 26: Comparison between measured value (blue), KF estimation (Orange), and true value (Red) of circle detection.  
1. Image pre-processing. It contains [PERSON]'s threshold method. Top view image transforms to create a top-view image of the road. A Hough transform to track a straight lane in the near-field of view of the camera sensor.
2. Curve lane detection. The Kalman filter provides the detection result for curve lanes in the far-field of view. This section consists of two different methods, the first method is based on the parabola model, and a second method is based on the circle model.  
The experimental results show that the curve lane detection method can be effectively detected even under a very noisy environment and with parabola and circle model. Also, we have deployed the algorithm in the gazebo simulation environment to verify the performance. One advantage of the proposed algorithm is its robustness against noise, as our algorithms are based on the Kalman filter. The viability of the proposed curve lane detection strategy can be applied to the self-driving car systems as well as to the advanced driver assistant systems. Based on our curve lane detection results, we can predict road turning, and also estimate suitable velocity and angular velocity for the self-driving car. Also, our proposed algorithm provides close-loop lane keeping control to stay in lane. The experimental result shows the proposed algorithm achieves an average of 10 fps. Even though the algorithm has an auto threshold method to adjust with different light conditions such as low-light, further study is needed to detect lane in conditions like light reflection, shadows, worn-lane, etc. Moreover, the proposed algorithm does not require high GPU processing unit to perform other CNN-based algorithms. The performance is satisfactory in the CPU-based system according to the fps. However, CNN-based study in the pre-processing step can provide a more efficient result for edge detection.  
Conceptualization, B.D. and D.-J.L.; methodology, B.D. and S.H.; software, S.H. and B.D.; validation, S.H., and B.D.; formal analysis, B.D.; investigation, B.D. and S.H.; resources, D.-J.L.; data curation, D.-J.L.; writing--original draft preparation, B.D. and S.H.; writing--review and editing, S.H.; visualization, S.H. and B.D.; supervision, D.-J.L.; project administration, D.-J.L.; funding acquisition, D.-J.L.. All authors have read and agreed to the published version of the manuscript.  
This research was funded and conducted under the Competency Development Program for Industry Specialists of Korean Ministry of Trade, Industry and Energy (MOTIE), operated by Korea Institute for Advancement of Technology (KIAT). (No. N0002428, HRD program for Future Car). This research was supported by the Development Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NO.2019 RIFI1A1049711).  
I ([PERSON]) would like to express my thanks and appreciation to my supervisor Professor [PERSON] for his direction and support throughout this paper. I would like to thank [PERSON] for his cooperation in this paper. Additionally, I would also like to pay my profound feeling of appreciation to all CAIAS (Center for Artificial Intelligence and Autonomous System) lab members for their support and CAIAS lab for providing me all the facilities that were required from the lab.  
The authors declare no conflict of interest.