Online Lane Graph Extraction from Onboard Video
- URL: http://arxiv.org/abs/2304.00930v1
- Date: Mon, 3 Apr 2023 12:36:39 GMT
- Title: Online Lane Graph Extraction from Onboard Video
- Authors: Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc Van Gool
- Abstract summary: We use the video stream from an onboard camera for online extraction of the surrounding's lane graph.
Using video, instead of a single image, as input poses both benefits and challenges in terms of combining the information from different timesteps.
A single model of this proposed simple, yet effective, method can process any number of images, including one, to produce accurate lane graphs.
- Score: 133.68032636906133
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous driving requires a structured understanding of the surrounding
road network to navigate. One of the most common and useful representation of
such an understanding is done in the form of BEV lane graphs. In this work, we
use the video stream from an onboard camera for online extraction of the
surrounding's lane graph. Using video, instead of a single image, as input
poses both benefits and challenges in terms of combining the information from
different timesteps. We study the emerged challenges using three different
approaches. The first approach is a post-processing step that is capable of
merging single frame lane graph estimates into a unified lane graph. The second
approach uses the spatialtemporal embeddings in the transformer to enable the
network to discover the best temporal aggregation strategy. Finally, the third,
and the proposed method, is an early temporal aggregation through explicit BEV
projection and alignment of framewise features. A single model of this proposed
simple, yet effective, method can process any number of images, including one,
to produce accurate lane graphs. The experiments on the Nuscenes and Argoverse
datasets show the validity of all the approaches while highlighting the
superiority of the proposed method. The code will be made public.
Related papers
- SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior [53.52396082006044]
Current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints.
This issue stems from the sparse training views captured by a fixed camera on a moving vehicle.
We propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model.
arXiv Detail & Related papers (2024-03-29T09:20:29Z) - Neuromorphic Synergy for Video Binarization [54.195375576583864]
Bimodal objects serve as a visual form to embed information that can be easily recognized by vision systems.
Neuromorphic cameras offer new capabilities for alleviating motion blur, but it is non-trivial to first de-blur and then binarize the images in a real-time manner.
We propose an event-based binary reconstruction method that leverages the prior knowledge of the bimodal target's properties to perform inference independently in both event space and image space.
We also develop an efficient integration method to propagate this binary image to high frame rate binary video.
arXiv Detail & Related papers (2024-02-20T01:43:51Z) - Prior Based Online Lane Graph Extraction from Single Onboard Camera
Image [133.68032636906133]
We tackle online estimation of the lane graph from a single onboard camera image.
The prior is extracted from the dataset through a transformer based Wasserstein Autoencoder.
The autoencoder is then used to enhance the initial lane graph estimates.
arXiv Detail & Related papers (2023-07-25T08:58:26Z) - An Efficient Transformer for Simultaneous Learning of BEV and Lane
Representations in 3D Lane Detection [55.281369497158515]
We propose an efficient transformer for 3D lane detection.
Different from the vanilla transformer, our model contains a cross-attention mechanism to simultaneously learn lane and BEV representations.
Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively.
arXiv Detail & Related papers (2023-06-08T04:18:31Z) - Graph-based Topology Reasoning for Driving Scenes [102.35885039110057]
We present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks.
We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2.
arXiv Detail & Related papers (2023-04-11T15:23:29Z) - Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction [43.682460811194694]
Lane graph construction is a promising but challenging task in autonomous driving.
Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection.
We present a path-based online lane graph construction method, termed LaneGAP, which preserves the continuity of the lane and encodes traffic information for planning.
arXiv Detail & Related papers (2023-03-15T17:59:13Z) - Learning and Aggregating Lane Graphs for Urban Automated Driving [26.34702432184092]
Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning.
We propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph.
We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de.
arXiv Detail & Related papers (2023-02-13T08:23:35Z) - RNGDet: Road Network Graph Detection by Transformer in Aerial Images [19.141279413414082]
Road network graphs provide critical information for autonomous vehicle applications.
manually annotating road network graphs is inefficient and labor-intensive.
We propose a novel approach based on transformer and imitation learning named RNGDet.
arXiv Detail & Related papers (2022-02-16T01:59:41Z) - Road Extraction from Overhead Images with Graph Neural Networks [18.649284163019516]
We propose a method that directly infers the final road graph in a single pass.
The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest and a Graph Neural Network which predicts links between these points.
We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results.
arXiv Detail & Related papers (2021-12-09T21:10:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.