# Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner
## 3 Results  
In order to evaluate the results of the developed algorithm, road tests have been conducted in highway and urban areas. In them, a 16-layer Velodyne VLP-16 laser scanner was installed on top of a passenger car. Additionally, the trajectory was recorded by means of a Trimble R4 DGPS receiver, and the scenario was captured by a camera. Firstly, the accuracy and robustness of the specific functions is assessed without the information from previous scans and information from digital map. Afterwards, the complete algorithm is considered to analyze whether misdetections of isolated functions could be solved in the proposed iterative loops. In the first place, the differences of both methods for roadway delimitation are analyzed. Figure 7 shows an example of the use of the second method. Table 1 shows the comparison of the detection of curbs. Detections at distances less or greater than 10 m are distinguished. This fact is relevant since the first layers acquired with the laser scanner provide a higher resolution and the first method presented above is especially sensitive to this fact. In this case, only in 28% of the frames is it possible to detect the curb more than 10 m in advance. In 67%, the detection is performed correctly, but at distances of less than 10 m, which greatly limits its applicability since the anticipation for any action or automatic control is very small. On the contrary, method II improves these results in a significant way, reaching a reliability index in the detection with a range greater than 10 m of 94% of the frames. It should be noted that the first method is based on information from each laser layer, so the results are quite sensitive to any deviations that could appear in one layer. Other obstacles near the boundaries could lead to incorrect detections because they could be considered as the road boundaries following the method criteria. The second method relies on the information of two consecutive layers, so the impact of obstacles is reduced if it is only detected by one of the layers. These results are obtained in real scenarios with traffic around the vehicle which causes occlusions of the curbs. Despite this, the degree of robustness detected for Method II is very high, and there are not a significant number of consecutive frames with erroneous detection, which would be quite limiting for autonomous driving applications. Furthermore, results take into account neither the information from previous scans nor a digital map. Additionally, it should be noted that the roadway detection function (Method II) has also been tested in off-road areas in order to differentiate passable areas from those that may not be. With a small computational cost, it is observed that satisfactory results are obtained, as can be seen in Figure 8, where the two possible paths are seen as passable, and they differ from the rest of the paths defined as non-passable. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Method I** & **Method II** \\ \hline Correct detection with a range greater than 10 m & 28\% & 94\% \\ Correct detection with a range lower than 10 m & 67\% & 2\% \\ Incorrect detection & 5\% & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of roadway detection methods (analysis over 875 frames). Figure 8: Detection of passable areas in an off-road environment. (**a**) Scenario; (**b**) Three-dimensional detection of the laser scanner; (**c**) Function results (points belonging to the passable path inside the region of interest marked in green). Figure 7: Roadway detection using Method II. (**a**) Scenario; (**b**) Tridimensional detection of the laser scanner; (**c**) Function results (points belonging to the passable path inside the region of interest marked in green). Subsequently, the reliability of the function for determining the number of lanes has been checked.