Enhancement of High-definition Map Update Service Through Coverage-aware
and Reinforcement Learning
- URL: http://arxiv.org/abs/2402.14582v1
- Date: Thu, 8 Feb 2024 13:51:13 GMT
- Title: Enhancement of High-definition Map Update Service Through Coverage-aware
and Reinforcement Learning
- Authors: Jeffrey Redondo, Zhenhui Yuan, Nauman Aslam
- Abstract summary: HD Map systems will play a pivotal role in advancing autonomous driving to a higher level.
Creating an HD Map requires a huge amount of on-road and off-road data.
There are challenges in transmitting the raw data over vehicular wireless channels due to the dynamic topology.
A Q-learning coverage-time-awareness algorithm is presented to optimize the quality of service for vehicular networks and HD map updates.
- Score: 2.8781600029638876
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-definition (HD) Map systems will play a pivotal role in advancing
autonomous driving to a higher level, thanks to the significant improvement
over traditional two-dimensional (2D) maps. Creating an HD Map requires a huge
amount of on-road and off-road data. Typically, these raw datasets are
collected and uploaded to cloud-based HD map service providers through
vehicular networks. Nevertheless, there are challenges in transmitting the raw
data over vehicular wireless channels due to the dynamic topology. As the
number of vehicles increases, there is a detrimental impact on service quality,
which acts as a barrier to a real-time HD Map system for collaborative driving
in Autonomous Vehicles (AV). In this paper, to overcome network congestion, a
Q-learning coverage-time-awareness algorithm is presented to optimize the
quality of service for vehicular networks and HD map updates. The algorithm is
evaluated in an environment that imitates a dynamic scenario where vehicles
enter and leave. Results showed an improvement in latency for HD map data of
$75\%$, $73\%$, and $10\%$ compared with IEEE802.11p without Quality of Service
(QoS), IEEE802.11 with QoS, and IEEE802.11p with new access category (AC) for
HD map, respectively.
Related papers
- Driving by Hybrid Navigation: An Online HD-SD Map Association Framework and Benchmark for Autonomous Vehicles [6.52409981498299]
We introduce textbfOnline textbfMap textbfAssociation, the first benchmark for the association of hybrid navigation-oriented online maps.<n>Based on existing datasets, the OMA contains 480k of roads and 260k of lane paths.
arXiv Detail & Related papers (2025-07-10T07:16:00Z) - A Benchmark for Vision-Centric HD Mapping by V2I Systems [20.00132555655793]
We release a real-world dataset which contains collaborative camera frames from both vehicles and roadside infrastructures.
We present an end-to-end neural framework (i.e., V2I-HD) leveraging vision-centric V2I systems to construct vectorized maps.
arXiv Detail & Related papers (2025-03-31T11:24:53Z) - TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior [70.84644266024571]
We propose to train a perception model to "see" standard definition maps (SDMaps)
We encode SDMap elements into neural spatial map representations and instance tokens, and then incorporate such complementary features as prior information.
Based on the lane segment representation framework, the model simultaneously predicts lanes, centrelines and their topology.
arXiv Detail & Related papers (2024-11-22T06:13:42Z) - DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles [0.0]
We introduce a semi-automatic method for creating HD maps from high-resolution aerial imagery.
Our method involves training neural networks to semantically segment aerial images into classes relevant to HD maps.
Exporting the map to the Lanelet2 format allows easy extension for different use cases.
arXiv Detail & Related papers (2024-10-01T15:05:05Z) - Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping [18.97422977086127]
High-Definition Maps (HD maps) are essential for the precise navigation and decision-making of autonomous vehicles.
The online construction of HD maps using on-board sensors has emerged as a promising solution.
This paper proposes the PriorDrive framework to address these limitations by harnessing the power of prior maps.
arXiv Detail & Related papers (2024-09-09T06:17:46Z) - MapNeXt: Revisiting Training and Scaling Practices for Online Vectorized
HD Map Construction [0.0]
We present a full-scale upgrade of MapTR and propose MapNeXt, the next generation of HD map learning architecture.
MapNeXt-Huge achieves state-of-the-art performance on the challenging nuScenes benchmark.
arXiv Detail & Related papers (2024-01-14T16:14:36Z) - 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) - THMA: Tencent HD Map AI System for Creating HD Map Annotations [12.554528330142732]
We introduce the Tencent HD Map AI (THMA) system, an end-to-end, AI-based, active learning HD map labeling system.
In THMA, we train AI models directly from massive HD map datasets via supervised, self-supervised, and weakly supervised learning.
More than 90 percent of the HD map data in Tencent Map is labeled automatically by THMA, accelerating the traditional HD map labeling process by more than ten times.
arXiv Detail & Related papers (2022-12-14T08:36:31Z) - 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)
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.