SOTAVerified

Lane Detection

Lane Detection is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts.

Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.

( Image credit: End-to-end Lane Detection )

Papers

Showing 51–100 of 251 papers

TitleStatusHype
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World DomainsCode1
Towards Driving-Oriented Metric for Lane Detection ModelsCode1
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse LanesCode1
Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous DrivingCode1
K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and HighwaysCode1
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard ImagesCode1
YOLOP: You Only Look Once for Panoptic Driving PerceptionCode1
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane DetectionCode1
Structure Guided Lane DetectionCode1
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional ConvolutionCode1
Lane Graph Estimation for Scene Understanding in Urban DrivingCode1
LaneAF: Robust Multi-Lane Detection with Affinity FieldsCode1
Robust Lane Detection via Expanded Self AttentionCode1
End-to-end Lane Shape Prediction with TransformersCode1
Keep your Eyes on the Lane: Real-time Attention-guided Lane DetectionCode1
RONELD: Robust Neural Network Output Enhancement for Active Lane DetectionCode1
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision SensorCode1
RESA: Recurrent Feature-Shift Aggregator for Lane DetectionCode1
Towards Lightweight Lane Detection by Optimizing Spatial EmbeddingCode1
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point BlendingCode1
End-to-End Lane Marker Detection via Row-wise ClassificationCode1
PolyLaneNet: Lane Estimation via Deep Polynomial RegressionCode1
Inter-Region Affinity Distillation for Road Marking SegmentationCode1
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane DetectionCode1
Key Points Estimation and Point Instance Segmentation Approach for Lane DetectionCode1
Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style TransferCode1
3D-LaneNet: End-to-End 3D Multiple Lane DetectionCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning—0
DLNet: Direction-Aware Feature Integration for Robust Lane Detection in Complex EnvironmentsCode0
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving—0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection—0
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates—0
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review—0
A Robust Real-Time Lane Detection Method with Fog-Enhanced Feature Fusion for Foggy Conditions—0
GLane3D : Detecting Lanes with Graph of 3D Keypoints—0
Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling—0
CLRNetV2: A Faster and Stronger Lane Detector—0
Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection—0
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection—0
GLane3D: Detecting Lanes with Graph of 3D Keypoints—0
TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding—0
RowDetr: End-to-End Row Detection Using Polynomials—0
End-to-End Steering for Autonomous Vehicles via Conditional Imitation Co-Learning—0
Implementation of Real-Time Lane Detection on Autonomous Mobile Robot—0
Attention-based U-Net Method for Autonomous Lane Detection—0
Inadequate contrast ratio of road markings as an indicator for ADAS failure—0
Technical Report of 1:10 Scale Autonomous Vehicle Robot—0
QuadBEV: An Efficient Quadruple-Task Perception Framework via Bird's-Eye-View Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DLNetF1 score81.23—Unverified
2CLRerNet-DLA34F1 score81.12—Unverified
3CLRerNet-Res101F1 score80.91—Unverified
4CondLSTR(ResNet-101)F1 score80.77—Unverified
5CLRerNet-Res34F1 score80.76—Unverified
6CLRKDNet (DLA-34)F1 score80.68—Unverified
7CLRNetV2 (DLA34)F1 score80.68—Unverified
8CondLSTR(ResNet-34)F1 score80.55—Unverified
9CLRNet(DLA-34)F1 score80.47—Unverified
10CLRNetV2 (ResNet101)F1 score80.43—Unverified
#ModelMetricClaimedVerifiedStatus
1SCNN_UNet_Attention_PL*Accuracy98.38—Unverified
2GANet(ResNet-34)F1 score97.71—Unverified
3GANet(ResNet-18)F1 score97.68—Unverified
4CLRNet(ResNet-101)F1 score97.62—Unverified
5GANet(ResNet-101)F1 score97.45—Unverified
6CondLaneNet(ResNet-34)F1 score97.01—Unverified
7CLRNetV2 (ResNet18)Accuracy96.99—Unverified
8PE-RESAAccuracy96.93—Unverified
9FOLOLane(ERFNet)Accuracy96.92—Unverified
10CLRNet(ResNet-34)Accuracy96.9—Unverified
#ModelMetricClaimedVerifiedStatus
1CondLSTR (ResNet-101)F1 score88.47—Unverified
2CondLSTR (ResNet-34)F1 score88.23—Unverified
3CondLSTR (ResNet-18)F1 score87.99—Unverified
4CANet-LF1 score87.87—Unverified
5CLRNetV2 (ResNet101)F1 score87.81—Unverified
6CANet-MF1 score87.19—Unverified
7CANet-SF1 score86.57—Unverified
8CLRerNet-DLA34F1 score86.47—Unverified
9CondLaneNet-L(ResNet-101)F1 score86.1—Unverified
10CLRNet-DLA34F1 score86.1—Unverified
#ModelMetricClaimedVerifiedStatus
1TwinLiteNetPlus-LargeIoU (%)34.2—Unverified
2TwinLiteNetPlus-MediumIoU (%)32.3—Unverified
3HybridNetsIoU (%)31.6—Unverified
4TwinLiteNetIoU (%)31.08—Unverified
5TriLiteNet-baseIoU (%)29.8—Unverified
6TwinLiteNetPlus-SmallIoU (%)29.3—Unverified
7A-YOLOM(s)IoU (%)28.8—Unverified
8YOLOPv2IoU (%)27.25—Unverified
9YOLOPIoU (%)26.2—Unverified
10TwinLiteNetPlus-NanoIoU (%)23.3—Unverified
#ModelMetricClaimedVerifiedStatus
1FENetV2mF171.85—Unverified
2CLRNet (DLA-34)F10.96—Unverified
3BézierLaneNet (ResNet-34)F10.96—Unverified
4LaneAFF10.96—Unverified
5CLRNet (ResNet-18)F10.96—Unverified
6BézierLaneNet (ResNet-18)F10.96—Unverified
7LaneATT (ResNet-34)F10.94—Unverified
8LaneATT (ResNet-122)F10.94—Unverified
9LaneATT (ResNet-18)F10.93—Unverified
10PolyLaneNetF10.88—Unverified
#ModelMetricClaimedVerifiedStatus
1DSLPIoU0.45—Unverified
2LaneGraphNetIoU0.42—Unverified
3STSUIoU0.39—Unverified
#ModelMetricClaimedVerifiedStatus
1CondLSTR (ResNet-101)F1 score63.4—Unverified
2CondLSTR (ResNet-34)F1 score62—Unverified
3CondLSTR (ResNet-18)F1 score60.1—Unverified
#ModelMetricClaimedVerifiedStatus
1VPGNetF10.88—Unverified
2Overfeat CNN detector + DBSCANF10.87—Unverified
#ModelMetricClaimedVerifiedStatus
1VPGNetF10.87—Unverified
2Overfeat CNN detector + DBSCANF10.86—Unverified
#ModelMetricClaimedVerifiedStatus
1LDNetAverage IOU62.79—Unverified
#ModelMetricClaimedVerifiedStatus
1LLDN-GFCF182.12—Unverified
#ModelMetricClaimedVerifiedStatus
1TopoLogicmAP33.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SCNN_UNet_Attention_PL*F10.92—Unverified