Progress and Consideration of High Precision Road Navigation Map

Liu, Jingnan; Wu, Hangbin; Zhang, Hongmin; Zuo, W; Yang, Cheng

doi:10.15302/j-sscae-2018.02.015

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…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]. Therefore, the creation of HD maps is important, and they are currently in high demand [14].…”

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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“…In addition, we define a calibration session as a 'success' when the RRE and RTE are below the predefined threshold [26]. In this paper, we choose the threshold as 1m, which is sufficient for the requirements of autonomous driving navigation [27]. The success rate is then defined as the average rate a session is successful.…”

Section: B Metrics and Baseline Algorithms 1) Metrics:mentioning

confidence: 99%

TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

Ren¹,

Zhang²,

Li³

et al. 2023

Preprint

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Recently, it has become popular to deploy sensors such as LiDARs on the roadside to monitor the passing traffic and assist autonomous vehicle perception. Unlike autonomous vehicle systems, roadside sensors are usually affiliated with different subsystems and lack synchronization both in time and space. Calibration is a key technology which allows the central server to fuse the data generated by different location infrastructures, which can deliver improve the sensing range and detection robustness. Unfortunately, existing calibration algorithms often assume that the LiDARs are significantly overlapped or that the temporal calibration is already achieved. Since these assumptions do not always hold in the real world, the calibration results from the existing algorithms are often unsatisfactory and always need human involvement, which brings high labor costs.In this paper, we propose TrajMatch -the first system that can automatically calibrate for roadside LiDARs in both time and space. The main idea is to automatically calibrate the sensors based on the result of the detection/tracking task instead of extracting special features. More deeply, we propose a mechanism for evaluating calibration parameters that is consistent with our algorithm, and we demonstrate the effectiveness of this scheme experimentally, which can also be used to guide parameter iterations for multiple calibration. Finally, to evaluate the performance of TrajMatch , we collect two dataset, one simulated dataset LiDARnet-sim 1.0 and a real-world dataset. Experiment results show that TrajMatch can achieve a spatial calibration error of less than 10cm and a temporal calibration error of less than 1.5ms.

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“…The AD HDMs can be roughly divided into several groups, including road networks, lane networks, road markings, and road facilities. The classification of maps in different groups is shown in Figure 4 [18].…”

Section: Elemental Classification and Decomposition Of Hdmsmentioning

confidence: 99%

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

Huang

Jiang

et al. 2021

IJGI

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High-definition maps (HDM) for autonomous driving (AD) are an important component of AD systems. HDMs accurately provide a priori information, including lane lines, and road signs, for AD systems. It is an important task to make a reasonable accuracy assessment of the HDM. The current methods for relative accuracy evaluation of general maps in the field of mapping are not fully applicable to HDMs. In this study, a method based on point set alignment and resampling is used to evaluate the relative accuracy of lane lines, and experiments are conducted based on relevant real HDM data. The results show that the relative accuracy of the lane lines is more detailed and relevant than the traditional method. This has implications for the quality control of HDM production.

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Progress and Consideration of High Precision Road Navigation Map

Cited by 15 publications

References 7 publications

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

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

TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

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

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