Robust lane detection and tracking with ransac and Kalman filter

By Amol Borkar, M. Hayes, Mark T. Smith. 2009

209 citations recorded by the source corpus.

How researchers see this paper

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

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Geometric fitting through parametric models such as lines [206] and splines”
Recent progress in road and lane detection: a survey (2014, methodology): “In [10] the last three images were averaged and adaptive thresholding mechanism was applied.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “In [29], a Kalman filter-based RANSAC method was found to lane detection.”
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].”
Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks (2019, methodology): “For lane tracking in continue frames, Kalman ﬁlter is widely used [33]–[35].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “For example, the Kalman filter can be found in [69,97,107], and particle filter is applied in [80,85,92,104].”