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Found 10 papers, 6 papers with code
LaneCorrect: Self-supervised Lane Detection
Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments.
Translating Images to Road Network:A Non-Autoregressive Sequence-to-Sequence Approach
Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement
Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG.
Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving
Large vision-language models (VLMs) have garnered increasing interest in autonomous driving areas, due to their advanced capabilities in complex reasoning tasks essential for highly autonomous vehicle behavior.
Translating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections.
RCLane: Relay Chain Prediction for Lane Detection
This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution.
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
Laneformer: Object-aware Row-Column Transformers for Lane Detection
We present Laneformer, a conceptually simple yet powerful transformer-based architecture tailored for lane detection that is a long-standing research topic for visual perception in autonomous driving.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CurveLanes
