ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings
Source abstract
Abstract
One of the main actions of the driver is to keep the vehicle in a road lane within its markings, which could be aided with modern driver-assistance systems. Forward digital cameras in vehicles allow deploying computer vision strategies to extract the road recognition characteristics in real-time to support several features, such as lane departure warning, lane-keeping assist, and traffic recognition signals. Therefore, the road lane marking needs to be recognized through computer vision strategies providing the functionalities to decide on the vehicle’s drivability. This investigation presents a modular architecture to support algorithms and strategies for lane recognition, with three principal layers defined as pre-processing, processing, and post-processing. The lane-marking recognition is performed through statistical methods, such as buffering and RANSAC (RANdom SAmple Consensus), which selects only objects of interest to detect and recognize the lane markings. This methodology could be extended and deployed to detect and recognize any other road objects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Image and Object Detection Techniques · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electronics, Federal University of Technology, Ponta Grossa 84017-220, PR, Brazil
Graduate Program in Industrial Engineering, Federal University of Technology, Ponta Grossa 84017-220, PR, Brazil
Escola Politécnica, Universidade de São Paulo, Butantã 05508-010, SP, Brazil
Funding
Federal University of Technology - Paraná
References (36)
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