HD Map Update for Autonomous Driving With Crowdsourced Data

Kim, Kitae; Cho, Soohyun; Chung, Woojin

doi:10.1109/lra.2021.3060406

Cited by 63 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the important parts of autonomous driving in cars is the display of high-definition maps. Past studies (Kim Ko) [3] have used many different approaches to autonomous driving, and one of them is using HD map update frequently to let the vehicle to obtain more precision autonomous driving. High-precision maps improve the reaction time of existing Automated Diving (AD) systems, making them a requirement for safe driving automation.…”

Section: Autonomous Drivingmentioning

confidence: 99%

“…cars. As an improvement, in Kim's research [3] emphasize the importance of High-Definition television (HD) Satellite map as an approach to support automation driving; Analyzing road observation data through a proposed algorithm to update HD map from Standard-Definition television(SD). However, this HD map only contains traffic and road conditions without traffic signs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real time road sign detection based on YOLO and Swift

Shi

Xu²

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

View full text Add to dashboard Cite

In this research, we find out current traffic road sign detection is mostly based on high precision detection device to collect road data for machine learning to train model for autonomous driving. For most people, createML makes it easier to use and create models such as our road sign detection than using scientific equipment in the lab. If more and more people have access to machine learning, then it will cost us less to develop a model. The point of doing this research is to provide a solution to reduce the cost of future road sign detection and development models that are valuable for real-life applications, such as Carplay. We are using an easier approach from using current exist road sign pictures, and use create ML to do the model training and find out if could be potential solutions for transplanting models into phone apps to connect car device for autonomous driving.

show abstract

Section: Autonomous Drivingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real time road sign detection based on YOLO and Swift

Shi

Xu²

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

View full text Add to dashboard Cite

show abstract

“…Research pertaining to HD map updating focuses on real-time systems that use a crowdsourcing framework. (2)(3)(4)(5) Although a significant amount of time and money is required to complete HD maps, updates to reflect volatile road conditions in real time are necessary to ensure safety during autonomous driving. This framework can be categorized into two stages: the stage of recognizing objects to identify changes and the stage of updating HD map features via an Internet server.…”

Section: Introductionmentioning

confidence: 99%

Object Detection of Road Facilities Using YOLOv3 for High-definition Map Updates

Lee¹,

Jeong²,

Peter³

2022

Sensors and Materials

View full text Add to dashboard Cite

Autonomous driving technology is significantly based on the fusion of high-definition (HD) maps and sensors. Therefore, the construction and update of HD maps must be emphasized to achieve full driving automation. Herein, a method is proposed to detect road facilities using object detection with images, particularly for HD map updates utilizing the You Only Look Once version 3 (YOLOv3) algorithm. The proposed approach, a deep-learning-based object detection method, utilizes transfer learning, which can detect objects in road facilities and record road sections that require maintenance. To test the effectiveness of the detection method, we analyze video footage captured in the Korean road environment. The experimental results show that this method achieves a mean average precision (mAP) of 58 and can update HD maps using a crowdsourcing framework.

show abstract

“…HD maps play an important role for autonomous navigation [18], [10], in particular in semi-structured and dynamic environments such as urban areas. In this work, we are interested in learning to infer geometric annotations for lane centerlines which are integral to HD maps and which allow autonomous vehicles to perform planning tasks.…”

Section: Introductionmentioning

confidence: 99%

Lane Graph Estimation for Scene Understanding in Urban Driving

Zürn¹,

Vertens²,

Burgard³

2021

Preprint

View full text Add to dashboard Cite

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird's-eyeview images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird'seye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving.

show abstract

HD Map Update for Autonomous Driving With Crowdsourced Data

Cited by 63 publications

References 9 publications

Real time road sign detection based on YOLO and Swift

Real time road sign detection based on YOLO and Swift

Object Detection of Road Facilities Using YOLOv3 for High-definition Map Updates

Lane Graph Estimation for Scene Understanding in Urban Driving

Contact Info

Product

Resources

About