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 110 of 251 papers

TitleStatusHype
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning0
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation ModelsCode3
DLNet: Direction-Aware Feature Integration for Robust Lane Detection in Complex EnvironmentsCode0
TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous DrivingCode2
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection0
OpenLKA: An Open Dataset of Lane Keeping Assist from Recent Car Models under Real-world Driving ConditionsCode1
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates0
Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VPGNetF10.87Unverified
2Overfeat CNN detector + DBSCANF10.86Unverified