# Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner
## 1 Introduction  
New driver assistance systems (ADAS) require a more precise and detailed knowledge of the vehicle environment and its positioning [1]. These requirements are even greater when autonomous driving functions are introduced where, in many cases, knowledge of \"where in the lane\" is required [2]. However, conventional GNSS (Global Navigation Satellite System)-based positioning systems are not able to achieve these levels of accuracy under real traffic conditions, even when receivers that accept DGPS (Differential Global Positioning Systems) are available, since centimeter accuracy levels cannot be guaranteed along a whole route [3], much less so, in urban areas [4]. A solution that has been used for many years is the fusion of GNSS information with inertial systems (e.g., [5; 6]). These inertial sensors have the disadvantage of drift along time, that causes a cumulative error that makes its use unviable over very large distances unless corrections are introduced by means of another positioning source. However, in areas where GNSS signal loss or deterioration is short, this fusion provides adequate results. However, problems reappear in situations when the GNSS signal is zero, or bad over long distances, such as in tunnels or in urban areas of narrow streets. In such cases, even navigation systems that do not require high levels of precision do not work reliably [7]. Particularly critical examples are deviations inside tunnels. Another solution for positioning that has been explored is the use of smartphones [8; 9; 10]. However, accuracy is far below what is required for many assistance systems, and autonomous driving and coverage problems could be common in certain scenarios. The incorporation of sensors in vehicles for the perception of the environment for safety functions and autonomous driving (such as LiDAR--e.g., [11; 12; 13], computer vision--e.g., [14; 15], radar--e.g., [16], or sensor fusion of some of them--e.g., [17; 18]) has opened another field to improve positioning. Thus, different methods of visual odometry and SLAM (Simultaneous Localization and Mapping), based on computer vision and laser scanners, have emerged in recent years (e.g., [19; 20; 21; 22]). In this way, the sensors used for obstacle detection are also used as primary or secondary sensors for positioning and road mapping [23; 24]. Thus, lane keeping systems are based, generally, on computer vision systems, and base their correct operation on the perception of the lines that delimit the lanes. However, the detection of lane lines presents deficiencies in complex scenarios with dense traffic, where other vehicles cover these marks, or due to the limitations of the sensors themselves, such as the malfunctioning under lighting changes in the case of computer vision [25], or for errors in the detection of badly maintained lines, areas with complex patterns on the road (merging lanes, exits, intersections, change in the number of lanes, or confusion with areas on the roadway due to maintenance operations). In addition, visual odometry methods also introduce cumulative errors in the calculation of trajectories, and SLAM algorithms require significant computational calculations when high precisions are required [26]. In this paper, a 3D laser scanner is used for transversal road characteristics detection, taking advantage of the use of this equipment for other purposes such as safety critical assistance systems or for autonomous driving. In this sense, there are solutions that seek to partially solve this problem. Such is the case of [27; 28], that proposes a method for the detection of the curvature of the road with a 3D laser scanner, and introduces the use of the robust regression method--named least trimmed squares--to deal with occluding scenes. The curb detector is also used as an input to a Monte Carlo localization algorithm, which is capable of estimating the position of the vehicle without an accurate GPS sensor. Also, in [29] an approach for laser-based curb detection is presented.