Approaches of Road Boundary and Obstacle Detection Using LIDAR O.Yalcin* A.Sayar** O.F.Arar*** S.Akpinar**** S.Kosunalp***** * Information Technology Institute, TUBITAK BILGEM, Kocaeli, Turkey, (Tel: +90-262-675-3302; e-mail: ozkan.yalcin@tubitak.gov.tr) ** Computer Engineering Department, Kocaeli University, Kocaeli, Turkey, (e-mail: ahmet.sayar@kocaeli.edu.tr) *** Information Technology Institute, TUBITAK BILGEM, Kocaeli, Turkey, (Tel: +90-262-675-3023; e-mail: omer.arar@tubitak.gov.tr) **** Information Technology Institute, TUBITAK BILGEM, Kocaeli, Turkey, (Tel: +90-262-675-3168; e-mail: samet.akpinar@tubitak.gov.tr) ***** Electronics Department, York University, United Kingdom, (Tel: +44-7411668128, (e-mail: sk772@york.ac.uk) Abstract: This paper introduces an overview of the studies of two problems; (1) road boundary detection and (2) obstacle detection, in order to allow the movement of autonomous vehicles. Light Detection and Ranging (LIDAR) is the most used technology for solving these two problems. It is thoroughly described with its operating mechanism and its use in other areas. We explore comprehensively the methods used in the literature to solve the detection of road boundaries and the obstacles as well as the recent trends in the relevant area. Furthermore, the solutions based on the previous works and suggested future works will be described. 1. INTRODUCTION Transportation has become increasingly fast and comfortable with the use of the vehicles since the beginning of the seventeenth century. During the twentieth century, there were many advances in vehicle comfort and technology with the result that human life has become vehicle-dependant. Therefore, the number of vehicles has significantly increased. However, widespread use of the vehicles has brought new problems. These problems are briefly: traffic jam, air pollution from exhaust emission, injuries and deaths from road accidents. The main reasons for these problems are the sheer vehicle density and the effect of people not complying with the rules. In the last decade, automotive companies added new functionalities which provide flexibility in the use of vehicles. Examples of these functionalities are self- parking, an alert when changing lane, and maintaining a constant distance from the vehicle in front. Some projects have been undertaken in both the academic arena and the automotive world to design vehicles which do not require a driver and these are still on-going. These vehicles can be named as ‘autonomous’ or ‘driverless’ vehicles. Prototypes have been designed in different countries. Some of the prototypes have participated in an Urban Challenge competition which was hosted by the Defense Advanced Research Projects Agency (DARPA) in 2003 in U.S.A. An example of autonomous vehicle, Boss, was the winner of the competition; it was designed by Carnegie Mellon University (Urmson et al., 2008). Considering that most accidents are human-induced, more intelligent vehicles can reduce the rate of accidents and improve energy efficiency by maintaining the optimum level of fuel consumption. There are a number of problems that need to be solved in autonomous vehicles. Some of these problems are: route planning, precise positioning, detection of traffic lights, sensing traffic signs, obstacle detection and identifying the boundaries of roads. The most important of the problems is the detection of obstacles and road boundaries as this plays a vital role in the safety of the drivers, animals and passengers in traffic. There are many methods and sensors in autonomous vehicles for the detection of the road boundaries and obstacles. Typical examples are cameras and image processing techniques, sensitive radars, ultrasonic sensors and LIDAR. Cameras and image processing techniques have long been used as a research topic because cameras are cheap devices and easy to supply compared with sensors (Choi et al., 2012). Cameras are a relatively old technology and carry high-resolution information which has resulted in a great deal of successful work (Wijesoma et al., 2004). Some examples of works are: likelihood of image shape (LOIS) (Kluge et al., 1995), generic obstacle and lane detection system (GOLD) (Bertozzi et al., 1998), autonomous land vehicle in a neural network (ALVINN) (Pomerleau et al., 1992), automated road curvature and direction estimation (ARCADE) (Kluge et al., 1994), rapidly adapting lateral position handler (RALPH) (Pomerlau et al., 1996). Image processing techniques are negatively affected by basic Preprints of the 1st IFAC Workshop on Advances in Control and Automation Theory for Transportation Applications September 16-17, 2013. Istanbul, Turkey Copyright © 2013 IFAC 211