首页|Lane Detection for Autonomous Driving: Comprehensive Reviews, Current Challenges, and Future Predictions
Lane Detection for Autonomous Driving: Comprehensive Reviews, Current Challenges, and Future Predictions
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NETL
NSTL
IEEE
Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.
Lane detectionAutonomous vehiclesSensorsRoadsThree-dimensional displaysFeature extractionVehiclesDeep learningReviewsCameras
NETL
NSTL
IEEE
