Camera to map alignment for accurate low-cost lane-level scene interpretation

Cao, Gaoya; Damerow, Florian; Flade, Benedict; Helmling, Markus; Eggert, Julian

doi:10.1109/itsc.2016.7795601

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…The development of intelligent transportation and advanced driver assistance system (ADAS) has attracted significant attention in academia and industry [1][2][3]. High-definition (HD) maps can provide detailed map information to assist smart cars with HD positioning [4][5][6], which can solve problems with sensor failures under certain circumstances, correct the shortcomings of environmental sensors and improve the sensing ability of smart cars [7][8][9]. Based on prior knowledge of maps and dynamic transportation information, HD maps help self-driving vehicles determine the best driving path and a reasonable driving strategy using global path planning [10][11][12], effectively enhancing driving safety and reducing driving complexity [13].…”

Section: Introductionmentioning

confidence: 99%

A High-Definition Road-Network Model for Self-Driving Vehicles

Zheng

Zhang

et al. 2018

IJGI

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High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network layer and a road-network layer. It includes road segments, attributes and geometric topological relations between lanes, as well as relations between road segments and lanes. We define the place in a road segment where the attribute changes as a linear event point. The road segment serves as a linear benchmark, and the linear event point from the road segment is mapped to its lanes via their relative positions to segment the lanes. Then, the HDRN is automatically generated from road centerlines collected by a mobile mapping vehicle through a multi-directional constraint principal component analysis method. Finally, an experiment proves the effectiveness of this HDRNM.

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Section: Introductionmentioning

confidence: 99%

A High-Definition Road-Network Model for Self-Driving Vehicles

Zheng

Zhang

et al. 2018

IJGI

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“…Curve estimation models [22] and lane type classification [23] have been explored by utilizing LiDAR signals along with images, which provide a better contextual understanding of the road. Digital map data is also be used to render virtual images and achieve lane-level vehicle localization [24]. With the development of deep learning models, many autonomous driving tasks have shown great progress.…”

Section: A Lane-change Detection From Steering Signalmentioning

confidence: 99%

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Liu¹,

Hansen

2021

IEEE Open J. Intell. Transp. Syst.

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Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending more effort trying to understand the interaction between vehicles with various levels of automation in large-scale traffic scenarios. Next-generation vehicles are expected to have the capacity of evaluating driver conditions, vehicle capabilities, surrounding traffic contexts, and take advantage of such knowledge to ensure safe and efficient driving. Three general research questions are raised to achieve this goal, which are (i) how can we acquire sufficient data, (ii) how to evaluate and understand driving behavior, and (iii) how to deliver information effectively to drivers. In this article, we present a review of previous studies from the UTDrive project attempts to answer above questions.

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“…Instead of explicitly mapping and storing each marking, we exploit the lane boundaries that have been generated in the map enhancement process. In early research, we applied common edge-detection algorithms, e.g., Canny edge detection, to the camera image [30]. However, such approaches tend to overestimate image features, which can lead to a large number of false positives.…”

Section: Lane Detection-based Feature Extractionmentioning

confidence: 99%

Vision-Enhanced Low-Cost Localization in Crowdsourced Maps

Flade

Koppert

Vélez

et al. 2020

IEEE Intell. Transport. Syst. Mag.

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show abstract

Camera to map alignment for accurate low-cost lane-level scene interpretation

Cited by 15 publications

References 11 publications

A High-Definition Road-Network Model for Self-Driving Vehicles

A High-Definition Road-Network Model for Self-Driving Vehicles

A Review of UTDrive Studies: Learning Driver Behavior From Naturalistic Driving Data

Vision-Enhanced Low-Cost Localization in Crowdsourced Maps

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