Computer vision-based multiple-lane detection on straight road and in a curve

By Yan Jiang, Feng Gao, Guoyan Xu. 2010

83 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Jacob Lambert
2. Alexander Carballo
3. Ekim Yurtsever
4. Kazuya Takeda
5. Xiaohao Chen
6. Aharon Bar-Hillel
7. Dan Levi
8. Ping Tan

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Alexander Carballo
3. Jacob Lambert
4. Xiaohao Chen
5. Aharon Bar-Hillel
6. Dan Levi
7. Ping Tan
8. Kazuya Takeda

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Even more challenging is determining other lanes and their direction [199], and finally understanding complex semantics, as in their current and future direction, or merging and turning lanes [43].”
• Recent progress in road and lane detection: a survey (2014, methodology): “in [19] digital map+GPS is used to distinguish curved vs.”
• CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution (2021, methodology): “Traditional lane detection methods usually rely on handcrafted operators to extract features [24, 43, 13, 17, 15, 1, 16, 33], and then fit the line shape through post-processing such as Hough transform [24, 43] and Random Sampling Consensus (RANSAC) [17, 15].”