Skip to main content
Log in

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

  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript


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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  • 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 

  • Goldbeck, J., Hürtgen, B., Ernst, S. and Kelch, L. (2000). Lane following combining vision and DGPS. Image and Vision Computing 18, 5, 425–433.

    Article  Google Scholar 

  • 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.

    Article  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 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  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 

  • 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.

    Article  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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wang, Y., Teoh, E. K. and Shen, D. (2004). Lane detection and tracking using B-Snake. Image and Vision Computing 22, 4, 269–280.

    Article  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 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to David Hyunchul Shim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, U., Jung, J., Jung, S. et al. Development of a self-driving car that can handle the adverse weather. Int.J Automot. Technol. 19, 191–197 (2018).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: