DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks

@article{Gurghian2016DeepLanesEL,
  title={DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks},
  author={Alexandru Gurghian and Tejaswi Koduri and Smita V. Bailur and Kyle J. Carey and Vidya N. Murali},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2016},
  pages={38-45}
}
Camera-based lane detection algorithms are one of the key enablers for many semi-autonomous and fullyautonomous systems, ranging from lane keep assist to level-5 automated vehicles. Positioning a vehicle between lane boundaries is the core navigational aspect of a self-driving car. Even though this should be trivial, given the clarity of lane markings on most standard roadway systems, the process is typically mired with tedious pre-processing and computational effort. We present an approach to… CONTINUE READING
Highly Cited
This paper has 30 citations. REVIEW CITATIONS
22 Citations
29 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 22 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 29 references

A Deformable - Template Approach to Lane Detection Deep Neural Network for Structural prediction and Lane Detection in Traffic Scene

  • C. Cañero, F. Lumbreras
  • 2016

Generation and Usage of Virtual Data for the Development of Perception Algorithms Using Vision

  • V. N. Murali, A. Micks, M. J. Goh, D. Liu
  • Technical report, SAE Technical Paper,
  • 2016

Similar Papers

Loading similar papers…