---
# System prepended metadata

title: Self-Calibrated Multiple-Lane Detection System
tags: [Finish, IEEE, 論文]

---

Self-Calibrated Multiple-Lane Detection System
===
###### Yan Jiang, Feng Gao, Guoyan Xu School of Transportation Science and Engineering Beihang University Beijing, China

## Keyword
Multiple lane detection, inverse perspective mapping, lateral position determination, calibration of pitch angle

### 中英 

## Content
提出了一種多車道檢測系統，為了應對直彎道路，根據車輛的GPS位置和數位地圖數據識別道路類型，並指定不同的檢測方案。與將車道標記視為單個實體的已知系統不同，該系統聚焦於條紋的內邊緣以精確地將車輛定位在車道中。 攝像頭的安裝參數，尤其是俯仰角，是通過實時檢測結果估算出來的，以補償車輛姿態的變化。 使用估計檢測樣式，即使相鄰車道被其他車輛部分擋住，也可以檢測到相鄰車道的標記。

### 實驗方法 
##### 1. Determination of the Road Type Whether 
使用GPS數據判斷車輛所在道路是直的還是彎曲的

##### 2. Straight Lane Detection Algorithm For Inverse Perspective Mapping and Hough
先使用Hough Transform做邊緣偵測在使用IPM，之後使用車道之間的間隔作為車道檢測的判斷標準(簡單而言就是設定白色區塊的大小與長度的範圍值)
![](https://i.imgur.com/nvosf23.png)
IPM參數調整會以相機傾斜角度計算
$θ = atan((r_v - r_c) / f)$
![](https://i.imgur.com/WE0ifLt.png)

##### 3. Locating Vehicle in a Straight Lan
取畫面中心點作為車子位置，偏移依照左右像素差異，作為偏移的依據



#### 實驗環境
提出的車道檢測算法用於高速公路下測試（直線、曲線道路），且每輛車都需搭載GPS。

#### 實驗結果
![](https://i.imgur.com/ttbgPoH.png)
 
## Review
本篇論文以GPS為基底偵測目前適合用的模式，模式分為彎曲與直線
於直線多使用IPM轉換，彎道時則直接進行Hough轉換做邊緣偵測提取出道路線段，線段採用提取白色區塊的長度與寬度，作為車道線篩選依據

## Reference
[1] Y. Feng, R. Wang and R. Zhang, "Algorithm on lane changing and tracking control technology for Intelligent Vehicle," Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on, pp. 1888-1893, 2007.
[2] S. K. Kenue and S. Bajpayee, "LaneLok: Robust line and curve fitting of lane boundaries," in 1993, pp. 491-503.
[3] T.-Y. Sun, S.-J. Tsai and V. Chan, "HSI color model based lane-marking detection," Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE, pp. 1168-1172, 2006.
[4] R. Labayrade, "How Autonomous Mapping Can Help a Road Lane Detection System ?" Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on, pp. 1-6, 2006.
[5] B. Wu and C. Lin, "Robust lane detection and tracking for driving assistance systems," Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, pp. 3848-3853, 2007.
[6] C. Lipski, B. Scholz, K. Berger, C. Linz, T. Stich and M. Magnor, "A Fast and Robust Approach to Lane Marking Detection and Lane Tracking," Image Analysis and Interpretation, 2008. SSIAI 2008. IEEE Southwest Symposium on, pp. 57-60, 2008.
[7] M. Bertozzi and A. Broggi, "GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection," Image Processing, IEEE Transactions on, vol. 7, pp. 62-81, 1998.
[8] Z. Sun, G. Bebis, and R. Miller, “On-road vehicle detection: A review,” IEEE Transactions on Pattern Analysis and Machine Intelligence,, vol. 28, no. 5, pp. 694-711, May 2006.
[9] Parag H. Batavia. Driver-Adaptive Lane Departure Warning Systems. Doctoral dissertation, tech. report CMU-RI-TR-99-25. Robotics Institute, Carnegie Mellon University. pp. 33, 1999.


###### tags: `Finish` `論文` `IEEE`