Development of a self-driving car that can handle the adverse weather
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Lane and road recognition are essential for self-driving where GPS solution is inaccurate due to the signal block or multipath in an urban environment. Vision based lane or road recognition algorithms have been studied extensively, but they are not robust to changes in weather or illumination due to the characteristic of the sensor. Lidar is a sensor for measuring distance, but it also contains intensity information. The road mark on the road is made to look good with headlight at night by using a special paint with good reflection on the light. With this feature, road marking can be detected with lidar even in the case of changes in illumination due to the rain or shadow. In this paper, we propose equipping autonomous cars with sensor fusion algorithms intended to operate in a different weather conditions. The proposed algorithm was applied to the self-driving car EureCar (KAIST) in order to test its feasibility for real-time use.
KeywordsAutonomous driving Path planning Obstacle detection Lane detection Adverse weather
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- Alon, Y., Ferencz, A. and Shashua, A. (2006). Off-road path following using region classification and geometric projection constraints. Computer Vision and Pattern Recognition, IEEE Computer Society Conf., 689–696.Google Scholar
- Homm, F., Kaempchen, N. and Burschka, D. (2011). Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers. Intelligent Vehicles Symp. (IV), IEEE, 969–974.Google Scholar
- Lee, U., Yoon, S., Shim, H., Vasseur, P. and Demonceaux, C. (2014). Local path planning in a complex environment for self-driving car. Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE 4th Annual Int. Conf., 445–450.Google Scholar
- Rasmussen, C. (2004). Grouping dominant orientations for ill-structured road following. Computer Vision and Pattern Recognition, CVPR. Proc. IEEE Computer Society Conf. Google Scholar
- Yang, M. Y. and Förstner, W. (2010). Plane detection in point cloud data. Proc. 2nd Int. Conf. Machine Control Guidance, 95–104.Google Scholar