Clothoid-Based Lane-Level High-Definition Maps: Unifying Sensing and Control Models

Cudrano, Paolo; Gallazzi, Barbara; Frosi, Matteo; Mentasti, Simone; Matteucci, Matteo

doi:10.1109/mvt.2022.3209503

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…The academic community, map vendors, and auto companies have continuously increased their attention on the HDMIS but failed to unify their understanding of its data logical structure [12]. For example, in China, He Yong and others believed that the HDMIS can be divided into the traffic sign information layer, the lane line information layer, the lane network information layer, and the road network information layer [13].…”

Section: High-definition Map Information System Model (Hdmism)mentioning

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: High-definition Map Information System Model (Hdmism)mentioning

confidence: 99%

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

Qian,

Ye,

Shi

2024

Sensors

View full text Add to dashboard Cite

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“…3 with a further step for road mark identification and HD lanes reconstruction. A multi-stage pipeline relying on clothoidal spline models is proposed in [14] for the definition of lane-level high-definition maps. We endowed the SUMO vehicle motion model with a kinematic bicycle model for the estimation of the front wheels' rotation.…”

Section: B Datasets Generationmentioning

confidence: 99%

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

Cazzella

Tagliaferri

Mizmizi

et al. 2022

IEEE Trans. Antennas Propagat.

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Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment. At high-frequency bands, DTs can help to overcome the challenges emerging in high mobility conditions featuring vehicular environments. In this paper, we propose a novel data-driven workflow for the creation of the DT of a Vehicle-to-Everything (V2X) communication scenario and a multi-modal simulation framework for the generation of realistic sensor data and accurate mmWave/sub-THz wireless channels. The proposed method leverages an automotive simulation and testing framework based on the Unreal Engine game engine and an accurate ray-tracing channel simulator. Simulations over an urban scenario show the achievable realistic sensor and channel modelling both at the infrastructure and at ego-vehicles.

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A lane-level localization method via the lateral displacement estimation model on expressway

Guo,

Zhou,

Dong

et al. 2024

Expert Systems with Applications

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Clothoid-Based Lane-Level High-Definition Maps: Unifying Sensing and Control Models

Cited by 8 publications

References 12 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

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

A lane-level localization method via the lateral displacement estimation model on expressway

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