# Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner
## 4 Conclusions  
In this paper, an algorithm has been presented that is able to characterize the transversal road section using 3D laser scanner, so that the boundaries of the road and the lines of separation of the lanes are identified, positioning the vehicle laterally in it, performing these functions in a coordinated manner to increase the reliability of the results. This positioning improves the accuracy that could be obtained with conventional means of GNSS or inertial systems when the loss or deterioration of satellite positioning extends over long stretches.  
The tests carried out in real scenarios show the reliability of the detection of the road boundaries and the difficulties in estimating the number of lanes, especially in sections of the road where singularities, such as merging lanes, exits, crossings, etc. occur. In such situations, the reliability of the function falls notably, although it is estimated that final rates of correct detection could increase significantly with knowledge of the environment, for example, through the information of a digital map; therefore, the algorithms should contrast the information captured by the laser scanner with known information, instead of estimating information without any reference. However, results only show the operating mode without that external help of a digital map.  
Additionally, the detected errors--although they are abundant in complex scenarios--do not appear in a continuous way, so reliable detections are mixed with other erroneous ones, but the first ones allow establishing a reference in positioning.  
It should be noted that the proper functioning of the boundaries detection function in off road scenarios shows its robustness and versatility, which behaves satisfactorily in an environment for which it was initially not foreseen, since its calibration is oriented towards the transition between road and sidewalk. This function also correctly identifies the boundaries of the roadway with or without curbs, so it is not limited to the case of urban areas with these elements.  
Figure 9: Examples of situations of erroneous or uncertain detections. (**a**) Road shoulder detection; (**b**) Occlusions; (**c**) Loss of information of any of the lane lines; (**d**) Merging lane detection.  
Despite the accurate results, the proposed approach could only be used for positioning in a practical way (from an economic point of view, and considering current state-of-the-art technology and costs) if the sensor is used for other purposes. This fact is not really a limitation when dealing with autonomous driving applications, because of the common current use of LiDAR technology, but this fact is not strictly the same when dealing with only assistance systems.  
Finally, integration of computer vision could be used to enhance the extracted information, because of the capacity of managing other kinds of image properties (such as colors, contrast, etc.), and limitations of this technology could be mitigated by sensor fusion with the current laser scanners.  
[PERSON] conceived the complete algorithm, the applications for autonomous driving and supervised the results. [PERSON] performed the tests and helped in the algorithm approach. [PERSON] implemented and tested the lane lines detection function. [PERSON] implemented and tested the roadway boundaries detection functions.  
This work has received the support of the Spanish Ministerio de Economia y Competitividad MINECO (CAV project TRA2016-78886-C3-3-R), the Spanish Ministrio de Defensa (REMOTE DRIVE project 2015/SP03390102/0000035-10032/15/0029/00) and Madrid Region Excellence Network SEGVAUTO-TRIES.  
The authors declare no conflict of interest.