Lane Detection
84 papers with code • 11 benchmarks • 15 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Lane Detection
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Libraries
Use these libraries to find Lane Detection models and implementations
nuScenes
BDD100K
CULane
TuSimple
OpenLane
CurveLanes
comma 2k19
LLAMAS
ELAS
Latest papers with no code
A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions
This survey explores the adaptation of visual transformer models in Autonomous Driving, a transition inspired by their success in Natural Language Processing.
LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves
from object detection and segmentation tasks, while these approaches require manual adjustments for curved objects, involve exhaustive searches on predefined anchors, require complex post-processing steps, and may lack flexibility when applied to real-world scenarios. In this paper, we propose a novel approach, LanePtrNet, which treats lane detection as a process of point voting and grouping on ordered sets: Our method takes backbone features as input and predicts a curve-aware centerness, which represents each lane as a point and assigns the most probable center point to it.
CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention
A curve cross-attention module is introduced in the Transformer decoder to calculate similarities between image features and curve queries of lanes.
Improved Generalizability of CNN Based Lane Detection in Challenging Weather Using Adaptive Preprocessing Parameter Tuning
Ensuring the robustness of lane detection systems is essential for the reliability of autonomous vehicles, particularly in the face of diverse weather conditions.
PLCNet: Patch-wise Lane Correction Network for Automatic Lane Correction in High-definition Maps
Vision lane detection with LiDAR position assignment is a prevalent method to acquire initial lanes for HD maps.
3D Lane Detection from Front or Surround-View using Joint-Modeling & Matching
Therefore, accurate lane modeling is essential to align prediction results closely with the environment.
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution
Finally, we discuss the limitation and the future directions of the deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation.
ElasticLaneNet: An Efficient Geometry-Flexible Approach for Lane Detection
The task of lane detection involves identifying the boundaries of driving areas in real-time.
Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity
Lane detection is a vital task for vehicles to navigate and localize their position on the road.
Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions
Autonomous vehicle refers to a vehicle capable of perceiving its surrounding environment and driving with little or no human driver input.
