International Journal of Automotive Technology

, Volume 19, Issue 1, pp 191–197 | Cite as

Development of a self-driving car that can handle the adverse weather

  • Unghui Lee
  • Jiwon Jung
  • Seokwoo Jung
  • David Hyunchul ShimEmail author


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.


Autonomous driving Path planning Obstacle detection Lane detection Adverse weather 


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  1. 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
  2. Goldbeck, J., Hürtgen, B., Ernst, S. and Kelch, L. (2000). Lane following combining vision and DGPS. Image and Vision Computing 18, 5, 425–433.CrossRefGoogle Scholar
  3. Gopalan, R., Hong, T., Shneier, M. and Chellappa, R. (2012). A learning approach towards detection and tracking of lane markings. IEEE Trans. Intelligent Transportation Systems 13, 3, 1088–1098.CrossRefGoogle Scholar
  4. 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
  5. Hsu, J. Y., Jhang, T. K., Yeh, C. J. and Chang, P. (2016). Vehicle lane following achieved by two degree-offreedom steering control architecture. Intelligent Transportation Engineering (ICITE), IEEE Int. Conf., 181–185.CrossRefGoogle Scholar
  6. Jung, C. R. and Kelber, C. R. (2005). Lane following and lane departure using a linear-parabolic model. Image and Vision Computing 23, 13, 1192–1202.CrossRefGoogle Scholar
  7. 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
  8. Li, Q., Chen, L., Li, M., Shaw, S. L. and Nuchter, A. (2014). A sensor-fusion drivable-region and lanedetection system for autonomous vehicle navigation in challenging road scenarios. IEEE Trans. Vehicular Technology 63, 2, 540–555.CrossRefGoogle Scholar
  9. Rasmussen, C. (2004). Grouping dominant orientations for ill-structured road following. Computer Vision and Pattern Recognition, CVPR. Proc. IEEE Computer Society Conf. Google Scholar
  10. Son, J., Yoo, H., Kim, S. and Sohn, K. (2015). Real-time illumination invariant lane detection for lane departure warning system. Expert Systems with Applications 42, 4, 1816–1824.CrossRefGoogle Scholar
  11. Vahidi, A. and Eskandarian, A. (2003). Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans. Intelligent Transportation Systems 4, 3, 143–153.CrossRefGoogle Scholar
  12. Wang, Y., Teoh, E. K. and Shen, D. (2004). Lane detection and tracking using B-Snake. Image and Vision Computing 22, 4, 269–280.CrossRefGoogle Scholar
  13. Yang, M. Y. and Förstner, W. (2010). Plane detection in point cloud data. Proc. 2nd Int. Conf. Machine Control Guidance, 95–104.Google Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  • Unghui Lee
    • 1
  • Jiwon Jung
    • 1
  • Seokwoo Jung
    • 1
  • David Hyunchul Shim
    • 1
    Email author
  1. 1.School of Mechanical and Aerospace EngineeringKAISTDaejeonKorea

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