# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 1 Introduction  
There has been considerable progress in the field of vehicle safety systems in the last few decades--from safety belts in the 60s to electrical systems such as air bags, anti-lock brake systems (ABSs), and electric power steering (EPS) in the 90s; this progress has been aimed at increasing passenger safety. However, these systems offer only passive safety because they operate in response to the state of the in-vehicle system [1]. In line with the recent development of smart technologies, considerable research has been conducted on the implementation of intelligent and sophisticated safety systems [2]. Such a system is referred to as an advanced safety vehicle (ASV). An ASV is a safety system designed to alarm drivers in advance against accident-prone situations so that they can cope with such situations actively. ASV-enabled vehicles are cutting-edge, safe vehicles equipped with artificial intelligence involving various types of intelligent safety technologies and thus maximizing driving safety and comfort. ASV's representative core technologies are a vehicle collision alarm system and a lane recognition system. The former is a vehicle-to-vehicle distance detection sensor that alarms the driver when the distance from the vehicle in front or behind becomes alarmingly small. The latter, commonly called a lane departure warning system (LDWS), is a sensor detecting the vehicle leaving its lane; it checks the lane and informs the driver of an imminent lane departure with an audible signal. The sensors mainly used in ASV are an ultrasonic sensor to recognize obstacles in front, a vision-based system to recognize the lane and road situations such as traffic lights with radar, and a laser range finder (LRF). The information detected is used forrecognizing obstacles, map building, and driving, thereby ensuring location detection, collision avoidance, path tracking, lane recognition, etc. [3; 4; 5; 6]. Lane recognition technology, one of the core technologies of ASV, involves a vision-based system and a laser-sensor-based system, among others. The vision-based system has a large capacity of extracting road information with low-cost equipment, but its drawback is its difficult processing due to its oversensitivity to illumination conditions, which makes it easily saturated when there is a shadow or a change in luminosity. Furthermore, we tested the performance of a vision-based lane recognition system in a previous study by varying the techniques, such as Hough transform, template matching, splines, and polynomial estimation, depending on the road conditions, but it showed vulnerability to some curved road conditions [7; 8]. In order to overcome this drawback, research studies have been conducted using various types of road modeling, taking into account road properties such as horizontal and vertical clothoid curves [9], polynomial curves [10; 11], and splines [12], but these methods are sensitive to noise or false detection and require an elaborate control method using filters or the like to remove noises or reduce recognition errors [13]. Further, the number of frames should be maintained high in order to ensure a stable recognition. Such elaborate control and frame number maintenance are considerable cost factors, and because of this method's sensitivity to weather conditions, particularly under foggy conditions that reduce visibility, difficulties are encountered while judging road conditions and recognizing lanes. Unlike the vision-based system, an LRF-based system is not influenced by illumination changes. It can recognize lanes even in the dark because it measures distance using the returning emitted laser signal. Moreover, it responds only to specific frequencies such that steep upward driving does not affect it. It can easily recognize lanes even under foggy weather conditions. These advantages keep attracting research interests to LRF-based lane recognition [14; 15; 16].