# Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Pavement Extraction

#### 2.2. Road Marking Extraction

#### 2.2.1. Rasterization of Pavement Point Clouds

#### 2.2.2. Adaptive Threshold Segmentation

#### 2.2.3. Euclidean Clustering of Road Marking Point Clouds

- (1)
- Starting from any point ${P}_{i}$ in point set $P$, add ${P}_{i}$ to the point set $C$.
- (2)
- (3)
- Repeat step (2) with each newly added point in point set $C$ as the center, until no new points are added to point set $C$, as shown in Figure 7b,c.
- (4)
- Start a new clustering by repeating steps (1)–(3) from the remaining points in P until all points are handled, as shown in Figure 7d.

#### 2.3. Road Markings Vectorization

- Determination of the growth point. Suppose the total number of road marking clusters is n. Starting from the arbitrary cluster cluster_i(i∈[1,n]). If the distance between the MBR center of cluster_j(j∈[1,n], j≠i) and the MBR center of cluster_i is less than r (r = 20 m), cluster_j will be put into set clusterSet_r, as shown in Figure 8.
- Determination of the direction of growth. The direction of growth is determined by the direction of the vehicle trajectory, as shown in Figure 9.
- Firstly, calculate the coordinate azimuth ${A}_{i\_j}$ between the MBR center of cluster_i and the MBR center of the clusters concluded in the set clusterSet_r. Secondly, calculate the difference $angl{e}_{i\_j}$ (see Figure 9) between the azimuth ${A}_{i\_j}$ and the azimuth of cluster_i. The clusters that satisfy Equation (22) will be put into set clusterSet_A. The parameter ${A}_{search}$ in this paper is set as 5°.$$angl{e}_{i\_j}<{A}_{search}$$$$angl{e}_{i\_j}{}^{\prime}<A$$
- Calculate the similarity between cluster_i and the cluster concluded in the set clusterSet_A’ according to Equation (21). The clusters that satisfy Equation (24) are named as clusterSet_S
_{i}. The parameter S_{i}in this paper is set as 0.58.$$Similarity>{S}_{i}$$ - If the set clusterSet_S
_{i}does not exist, the line segment growth will be over. Set the cluster_i to be any unconnected cluster and start a new line segment growth. Repeat steps $i\u2013\mathrm{iii}$ until all of the road marking clusters are connected. - In set clusterSet_S
_{i}, find out the cluster nearest to cluster_i, and mark it as cluster_j. cluster_j is the next cluster to be connected. - Update the growth point to the MBR center of cluster_j and repeat the above steps until all of the clusters have been processed.

## 3. Experimental Data and Scheme

#### 3.1. Experimental Data

#### 3.2. Experimental Scheme

## 4. Experimental Results

#### 4.1. Pavement Extraction

#### 4.2. Rasterization of Pavement Point Clouds

#### 4.3. Binarization of Pavement Image

#### 4.4. Euclidean Clustering

#### 4.5. Recognition of Road Markings

#### 4.6. Vectorization of Road Markings

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Scan line; (

**b**) Distance between scanning points; (

**c**) Variation of distance between scanning points.

**Figure 7.**Diagrammatic sketch of Euclidean clustering. (

**a**–

**c**) The points before Euclidean clustering is complete; (

**d**) The points after Euclidean clustering is complete. 2.3. Road Markings Recognition.

**Figure 14.**The raster image of the point cloud. (

**a**) Raster image converted from area A2; (

**b**) Raster image converted from area A3; (

**c**) Raster image converted from area A4; (

**d**) Raster image converted from area A1.

Parameter Type | Value |
---|---|

Scanner model | RIEGL VUX-1 |

Scanning mode | Single line cross section scanning |

Vehicle speed | 35~40 km/h |

Scanning frequency | 200 hz |

Scanning speed | 500,000 points/s |

Vertical field of view | 330° |

Parameter | Value | Parameter | Value |
---|---|---|---|

${L}_{d}$ | 5 m | $mS$ | 25° |

${M}_{l}$ | 7 m | $fH$ | 0.05 m |

${M}_{n}$ | 650 | $minN$ | 350 |

$\mathrm{c}H$ | 0.08 m |

Category | Total | TP | FN | FP | Precision | Recall | F-Score |
---|---|---|---|---|---|---|---|

Broken line | 325 | 310 | 16 | 3 | 0.95 | 0.99 | 0.97 |

Guideline | 4 | 4 | 0 | 4 | 1.00 | 0.5 | 0.66 |

Straight arrow | 38 | 37 | 1 | 13 | 0.97 | 0.74 | 0.84 |

Left turn arrow | 1 | 0 | 1 | 0 | 0 | - | - |

Right turn arrow | 3 | 3 | 0 | 0 | 1 | 1 | 1 |

Test Data | $(\mathit{s}\mathit{,}\mathit{t})$ | Completeness | Correctness | F-Score |
---|---|---|---|---|

a | (7, 1.28) | 0.94 | 0.73 | 0.83 |

b | (14, 1.38) | 0.86 | 0.79 | 0.82 |

c | (13, 1.43) | 0.82 | 0.83 | 0.83 |

d | (13, 1.36) | 0.90 | 0.78 | 0.84 |

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**MDPI and ACS Style**

Yao, L.; Qin, C.; Chen, Q.; Wu, H.
Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data. *Remote Sens.* **2021**, *13*, 2612.
https://doi.org/10.3390/rs13132612

**AMA Style**

Yao L, Qin C, Chen Q, Wu H.
Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data. *Remote Sensing*. 2021; 13(13):2612.
https://doi.org/10.3390/rs13132612

**Chicago/Turabian Style**

Yao, Lianbi, Changcai Qin, Qichao Chen, and Hangbin Wu.
2021. "Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data" *Remote Sensing* 13, no. 13: 2612.
https://doi.org/10.3390/rs13132612