Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving

Yu, Ting; Huang, He; Jiang, Nana; Acharya, Tri Dev

doi:10.3390/ijgi10110761

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Given the importance of accuracy of spatial data in maps, many map features have been studied in detail, such as evaluating the relative accuracy of lane lines for highdefinition maps (HDM) for autonomous driving (AD) [17] and matching entities in different data sources for residential areas [18].…”

Section: Additional Related Workmentioning

confidence: 99%

RDQS: A Geospatial Data Analysis System for Improving Roads Directionality Quality

Salama

Hampshire

Lee

et al. 2022

IJGI

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With the increasing availability of smart devices, billions of users are currently relying on map services for many fundamental daily tasks such as obtaining directions and getting routes. It is becoming more and more important to verify the quality and consistency of route data presented by different map providers. However, verifying this consistency manually is a very time-consuming task. To address this problem, in this paper we introduce a novel geospatial data analysis system that is based on road directionality. We investigate our Road Directionality Quality System (RDQS) using multiple map providers, including: Bing Maps, Google Maps, and OpenStreetMap. Results from the experiments conducted show that our detection neural network is able to detect an arrow’s position and direction in map images with >90% F1-Score across each of the different providers. We then utilize this model to analyze map images in six different regions. Our findings show that our approach can reliably assess map quality and discover discrepancies in road directionality across the different providers. We report the percentage of discrepancies found between map providers using this approach in a proposed study area. These results can help determine areas needs to be revised and prioritized to improve the overall quality of the data within maps.

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Section: Additional Related Workmentioning

confidence: 99%

RDQS: A Geospatial Data Analysis System for Improving Roads Directionality Quality

Salama

Hampshire

Lee

et al. 2022

IJGI

View full text Add to dashboard Cite

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A Novel Lane Line Detection Algorithm for Driverless Geographic Information Perception Using Mixed-Attention Mechanism ResNet and Row Anchor Classification

Song

Huang

et al. 2023

IJGI

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Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult to effectively balance accuracy and efficiency. To improve the comprehensive perception ability of lane line geographic information in a natural traffic environment, a lane line detection algorithm based on a mixed-attention mechanism residual network (ResNet) and row anchor classification is proposed. A mixed-attention mechanism is added after the backbone network convolution, normalization and activation layers, respectively, so that the model can focus more on important lane line features to improve the pertinence and efficiency of feature extraction. In addition, to achieve faster detection speed and solve the problem of no vision, the method of lane line location selection and classification based on the row direction is used to detect whether there are lane lines in each candidate point according to the row anchor, reducing the high computational complexity caused by segmentation on a pixel-by-pixel basis of traditional semantic segmentation. Based on TuSimple and CurveLane datasets, multi-scene, multi-environment, multi-linear road image datasets and video sequences are integrated and self-built, and several experiments are designed and tested to verify the effectiveness of the proposed method. The test accuracy of the mixed-attention mechanism network model reached 95.96%, and the average time efficiency is nearly 180 FPS, which can achieve a high level of accuracy and real-time detection process. Therefore, the proposed method can meet the safety perception effect of lane line geographic information in natural traffic environments, and achieve an effective balance between the accuracy and efficiency of actual road application scenarios.

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Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving

Cited by 2 publications

References 9 publications

RDQS: A Geospatial Data Analysis System for Improving Roads Directionality Quality

RDQS: A Geospatial Data Analysis System for Improving Roads Directionality Quality

A Novel Lane Line Detection Algorithm for Driverless Geographic Information Perception Using Mixed-Attention Mechanism ResNet and Row Anchor Classification

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