Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation

By Joel C. McCall, Mohan Manubhai Trivedi. 2006

1,097 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Environmental Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “In this section, we provide an overview of current methods used for road and lane detection, and refer the reader to in-depth surveys of traditional methods [194] and the state-of-the-art methods [195], [196].”
Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines (2007, background): “The authors would like to thank the graduate students in the Cognitive Systems Laboratory for offering valuable comments, as well as the assistance of T. Lopes at The University of Iowa Public Policy Center in preparing this manuscript.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “Some lane detection systems [11]: (a) Lane departure warning (b) Adaptive cruise control (c) Lane keeping or centering (d) Lane change assist.”
Visual Tracking: an Experimental Survey (2013, neutral): “The survey of [18] is on tracking lanes for driver assistance.”
D Traffic Scene Understanding from Movable Platforms (2013, neutral): “net/projects/intersection [18], [21], [41], [49] and road detection [1], [2], [17] algorithms.”
Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene (2017, methodology): “The simplest detector checks the output of applying an intensity bump ﬁlter to the image patch [37], [38], [48].”
Recent progress in road and lane detection: a survey (2014, methodology): “Like the flow diagram presented in [3], we use this generic system as a skeleton enabling comparison between different algorithms according to their functional parts.”
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].”
Driving style recognition using a smartphone as a sensor platform (2011, background): “As research advances in the area of holistic sensing for driver safety and assistance systems, we will continue to see a growing fusion of technologies including omnidirectional video processing [25], [26], speech recognition [13], driver behavior analysis [10], [11], [12], and object detection [27]”
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.”
Real time detection of lane markers in urban streets (2008, background): “One useful technology is lane detectio n which has received considerable attention since the mid 1980s [15], [8], [13], [10], [2].”
Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy (2016, methodology): “Traditional methods of camera-based drivable path estimation for road vehicles involve preprocessing steps to remove shadow and exposure artefacts [7], [8], extraction of lowlevel road and lane features [9], [10], fitting road and lane models to feature detections [11], [12], and temporal fusion of ”
A Hybrid Vision-Map Method for Urban Road Detection (2017, neutral): “Thus, parabolic curves [22], clothoids [23], B-splines [24], or active contours (snakes) [25] are more complex models adapted to curved road.”
PolyLaneNet: Lane Estimation via Deep Polynomial Regression (2020, background): “The traditional approach for the lane estimation (or detection) task consists in the extraction of hand-crafted features [5], [6] followed by a curve-fitting process.”
A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios (2014, methodology): “More recently, some researchers have focused on lane detection on highways and other structured roads [9]–[12].”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “Lane change detection [2, 6] and pedestrian detection [7] have been studied in computer vision and are recently being added as safety features in recent personal vehicles.”