# Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner
## 3 Results
In this sense, it is necessary to distinguish the cases in which there are no singularities, such as merging lanes, exits or changes in the number of lanes, with scenarios in which one of those situations does occur. Table 2 shows the reliability of the function described in Section 3 in both scenarios. As can be seen, in areas without singularities, robustness is quite high even when the information from previous scan is not used (78% correct), while it is reduced in the case of road sections with singularities. Note that 47% accuracy is guaranteed, although this value is considered a lower threshold since it does not include those special cases where, for example, an additional lane appears as a merging or exit lane. In these cases, in the absence of a reference of a digital map, the function is not able to differentiate them from the lanes of the main roadway, so a conservative approach has been chosen not to include them in the correct detections, since they require additional information. In this case, as in the case of the road boundaries detection, there are not many consecutive frames with incorrect detection, and these are mainly due to the occlusion of the lane lines by other vehicles. Furthermore, it should be noted that the reason for many incorrect detections or non-performed calculations is the presence of obstacles. But, as these perturbations do not remain for long periods of time, estimations of road boundaries and lane lines could be done using previous information. The study of the most frequent situations of erroneous or unrealized calculations shows the following conclusions:  
* The shoulder can be identified as an additional lane, but has no influence on the detection of the other lanes after detection of the roadway boundaries in the first phase of the function (Figure 9a)
* Due to the presence of obstacles on the road (mainly, other vehicles), occlusions of the lane lines occur and, in extreme cases, complete information is lost relative to any of the lines (Figure 9b,c). * In this case, the loss of information in the lane through which the vehicle moves is more critical, since the initial estimates of the function are based on it. * The loss of information from the farthest lanes is less relevant, and causes errors in lane counting but not in the position of the vehicle on the road. * The loss of information from an intermediate lane is usually solved approximately by the function when estimating standard lane widths in the absence of information. * The presence of additional lanes can be reliably detected, but, in the absence of other information, they are not distinguished with respect to the main lanes of the roadway, as indicated above (Figure 9d)  
In the two final cases, it should be noted that the identification of the boundaries of the roadway allows us to obtain approximate estimates of the information of the undetected lanes. Finally, it should be noted that previous results from both functions show only their performance in an isolated mode; interactions between them or use of previous scans as Figure 1 states have not been considered in results of Tables 1 and 2. Even in such circumstances, the road characteristics extraction partial functions provide quite accurate results in many cases. When using the information of previous scans, transversal road characteristics estimation is possible throughout the test. Furthermore, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Scenario without Singularities** & **Scenario with Singularities (700** \\  & **(200 Frames)** & **Frames)** \\ \hline Correct detection & 78\% & \textgreater{}47\% * \\ Calculation not performed & 8\% & 18\% \\ Incorrect detection & 14\% & \textless{}35\% * \\ \hline \hline \end{tabular}  
* Every case that involves a merging or exit lane detection is not considered as correct, because it is not possible to determine its nature, and it could be confused as an additional lane.  
\end{table} 
Table 2: Reliability in lanes detection.