Vision-HD: road change detection and registration using images and high-definition maps

Hui, Nian; Jiang, Zijie; Cai, Zhongliang; Ying, Shen

doi:10.1080/13658816.2023.2293995

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…With the continuous development of intelligent driving, users' demands for the richness of high-definition map road environment information, the timeliness of information transmission, and the personalization of information are becoming increasingly strong. However, currently, high-precision maps still have problems such as large amounts of data, serious data redundancy, and weak data association, which make autonomous driving vehicles fall into difficulties such as difficult data query and low data transmission timeliness [4], which are not conducive to the efficient control and timely communication of autonomous driving vehicles and further make it difficult to improve user satisfaction. In response to these problems, a large number of experts and scholars have made efforts, especially in the aspect of autonomous vehicle control and communication technology.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios

Qian,

Ye,

Shi

2024

Sensors

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Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios

Qian,

Ye,

Shi

2024

Sensors

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EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps

Hui,

Jiang,

Cai

et al. 2024

Remote Sensing

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Accurate object registration is crucial for precise localization and environment sensing in autonomous driving systems. While real-time sensors such as cameras and radar capture the local environment, high-definition (HD) maps provide a global reference frame that enhances localization accuracy and robustness, especially in complex scenarios. In this paper, we propose an innovative method called enhanced object registration (EOR) to improve the accuracy and robustness of object registration between camera images and HD maps. Our research investigates the influence of spatial distribution factors and spatial structural characteristics of objects in visual perception and HD maps on registration accuracy and robustness. We specifically focus on understanding the varying importance of different object types and the constrained dimensions of pose estimation. These factors are integrated into a nonlinear optimization model and extended Kalman filter framework. Through comprehensive experimentation on the open-source Argoverse 2 dataset, the proposed EOR demonstrates the ability to maintain high registration accuracy in lateral and elevation dimensions, improve longitudinal accuracy, and increase the probability of successful registration. These findings contribute to a deeper understanding of the relationship between sensing data and scenario understanding in object registration for vehicle localization.

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Vision-HD: road change detection and registration using images and high-definition maps

Cited by 2 publications

References 34 publications

RETRACTED: Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios

RETRACTED: Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios

EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps

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