Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision

By R. Danescu, S. Nedevschi. 2009

142 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

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

In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Stereo camera systems [204], as well as 3D lidars [201], have been used determine the 3D structure of roads directly.”
Towards End-to-End Lane Detection: an Instance Segmentation Approach (2018, background): “, which are possibly combined with a hough transform [23], [37] and particle or Kalman filters [18], [8], [34].”
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].”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “Stereo vision-based methods were applied to extract the scene structure [35].”
A new performance measure and evaluation benchmark for road detection algorithms (2013, methodology): “Only recently, methods for detecting lane markings and curbstones have been combined explicitly [13], [14], [15] or implicitly [16] to achieve city lane detection.”
Recent progress in road and lane detection: a survey (2014, methodology): “Most methods use either The Kalman Filter [4] [3] [10] [53] or Particle Filtering [8] [22] [53] [9] for this task.”
Road Detection Based on Illuminant Invariance (2011, background): “Some authors propose the use of depth as an additional cue for adding robustness, as in [9], in which depth comes from active sensors, or in [10], in which depth comes from a stereo rig.”
A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios (2014, methodology): “Stereo vision has also been applied frequently [26], [27], [32].”
Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks (2019, background): “In addition, the geometry modeling-based lane detection can also be implemented with stereo visions [29]–[31], in which the distance of the lane can be estimated.”
D Traffic Scene Understanding from Movable Platforms (2013, unknown): “net/projects/intersection [18], [21], [41], [49] and road detection [1], [2], [17] algorithms.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, methodology): “This information is usually combined with a Hough transform [91], [92] and particle or Kalman filters [89], [93], [94] to detect lane markings.”
3D Traffic Scene Understanding From Movable Platforms (2014, background): “While autonomous driving on highways has been successfully demonstrated for several decades [21], navigating urban environments remains an unsolved problem.”
Stereo-Camera-Based Urban Environment Perception Using Occupancy Grid and Object Tracking (2012, background): “Based on 3-D reconstruction from dense stereo, detection and tracking of vehicles, lanes [7], and pedestrians is presented.”