SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map
Generation and Prediction
- URL: http://arxiv.org/abs/2211.15656v1
- Date: Mon, 28 Nov 2022 18:59:02 GMT
- Title: SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map
Generation and Prediction
- Authors: Hao Dong, Xianjing Zhang, Xuan Jiang, Jun Zhang, Jintao Xu, Rui Ai,
Weihao Gu, Huimin Lu, Juho Kannala and Xieyuanli Chen
- Abstract summary: We propose a novel network named SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels.
The results show that by using the long-range HD maps predicted by our method, we can make better path planning for autonomous vehicles.
- Score: 21.061273391348376
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-definition (HD) semantic map generation of the environment is an
essential component of autonomous driving. Existing methods have achieved good
performance in this task by fusing different sensor modalities, such as LiDAR
and camera. However, current works are based on raw data or network
feature-level fusion and only consider short-range HD map generation, limiting
their deployment to realistic autonomous driving applications. In this paper,
we focus on the task of building the HD maps in both short ranges, i.e., within
30 m, and also predicting long-range HD maps up to 90 m, which is required by
downstream path planning and control tasks to improve the smoothness and safety
of autonomous driving. To this end, we propose a novel network named
SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels.
We benchmark our SuperFusion on the nuScenes dataset and a self-recorded
dataset and show that it outperforms the state-of-the-art baseline methods with
large margins. Furthermore, we propose a new metric to evaluate the long-range
HD map prediction and apply the generated HD map to a downstream path planning
task. The results show that by using the long-range HD maps predicted by our
method, we can make better path planning for autonomous vehicles. The code will
be available at https://github.com/haomo-ai/SuperFusion.
Related papers
- Augmenting Lane Perception and Topology Understanding with Standard
Definition Navigation Maps [51.24861159115138]
Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative.
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Representations from transFormers.
This enhancement consistently and significantly boosts (by up to 60%) lane detection and topology prediction on current state-of-the-art online map prediction methods.
arXiv Detail & Related papers (2023-11-07T15:42:22Z) - 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) - InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning [6.062751776009753]
We propose online HD map learning framework that detects HD map elements from onboard sensor observations.
InstaGraM, instance-level graph modeling of HD map brings accurate and fast end-to-end vectorized HD map learning.
Our proposed network outperforms previous models by up to 13.7 mAP with up to 33.8X faster time.
arXiv Detail & Related papers (2023-01-10T08:15:35Z) - High-Definition Map Generation Technologies For Autonomous Driving: A
Review [0.0]
High-definition (HD) maps have drawn lots of attention in recent years.
This paper reviews recent HD map generation technologies that leverage both 2D and 3D map generation.
arXiv Detail & Related papers (2022-06-11T02:32:11Z) - HDMapNet: An Online HD Map Construction and Evaluation Framework [23.19001503634617]
HD map construction is a crucial problem for autonomous driving.
Traditional HD maps are coupled with centimeter-level accurate localization which is unreliable in many scenarios.
Online map learning is a more scalable way to provide semantic and geometry priors to self-driving vehicles.
arXiv Detail & Related papers (2021-07-13T18:06:46Z) - HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps [81.86923212296863]
HD maps are maps with precise definitions of road lanes with rich semantics of the traffic rules.
There are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack.
We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps.
arXiv Detail & Related papers (2021-06-28T17:59:30Z) - MP3: A Unified Model to Map, Perceive, Predict and Plan [84.07678019017644]
MP3 is an end-to-end approach to mapless driving where the input is raw sensor data and a high-level command.
We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations.
arXiv Detail & Related papers (2021-01-18T00:09:30Z) - DAGMapper: Learning to Map by Discovering Lane Topology [84.12949740822117]
We focus on drawing the lane boundaries of complex highways with many lanes that contain topology changes due to forks and merges.
We formulate the problem as inference in a directed acyclic graphical model (DAG), where the nodes of the graph encode geometric and topological properties of the local regions of the lane boundaries.
We show the effectiveness of our approach on two major North American Highways in two different states and show high precision and recall as well as 89% correct topology.
arXiv Detail & Related papers (2020-12-22T21:58:57Z) - HDNET: Exploiting HD Maps for 3D Object Detection [99.49035895393934]
We show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors.
We design a single stage detector that extracts geometric and semantic features from the HD maps.
As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data.
arXiv Detail & Related papers (2020-12-21T21:59:54Z)
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.