Crowd-sourced Semantic Edge Mapping for Autonomous Vehicles

Herb, Markus; Weiherer, Tobias; Navab, Nassir; Tombari, Federico

doi:10.1109/iros40897.2019.8968020

Cited by 20 publications

(5 citation statements)

References 18 publications

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“…However, this approach cannot directly capture depth information of the scene. To achieve more accurate 3D road reconstruction, single-camera systems typically integrate with Inertial Navigation Systems (INS) or wheel encoders [27,28]. For instance, Guo et al proposed a low-cost scheme for extracting lane information for HD maps.…”

Section: Camera-based Data-collection Methodsmentioning

confidence: 99%

A Review of Crowdsourcing Update Methods for High-Definition Maps

Guo,

Zhou,

et al. 2024

IJGI

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High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

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Section: Camera-based Data-collection Methodsmentioning

confidence: 99%

A Review of Crowdsourcing Update Methods for High-Definition Maps

Guo,

Zhou,

et al. 2024

IJGI

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“…This technical challenges leads to bringing artificial intelligence closer to the edge using distributed learning, in this context edge 11. Some of the proposed collaborative applications and approaches includes perception [44], SLAM [103], [348], [10], HD map [383], collision warning systems [58], [81] and path planning [308].…”

Section: A Edge Computing and Intelligencementioning

confidence: 99%

“…To overcome computational requirement cloud-based SLAM has been proposed [265], however some drawbacks in centralized approach are the extreme low latency requirement and the current uplink bandwidth. Edge assisted SLAM [103], [348], [10] approaches includes efficient computation, task scheduling algorithms, data offloading and sharing strategies. The backbone used in [348], [10] is ORB-SLAM [216] and ORB-SLAM2 [217] which provides the algorithm centimeter level localization accuracy.…”

Section: A Edge Computing and Intelligencementioning

confidence: 99%

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

Katare

Perino

Nurmi

et al. 2023

IEEE Commun. Surv. Tutorials

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Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.

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“…Meanwhile, some researches [19]- [22] focused on building the road map. Regder et al [19] detected lanes on the image and used odometry to generate local grid maps.…”

Section: B Road-based Localizationmentioning

confidence: 99%

“…The autonomous parking application was performed based on this semantic map. Herb et al [22] proposed a crow-sourced way to generate semantic map. However, it was difficult to be applied since the inter-session feature matching consumed great computation.…”

Section: B Road-based Localizationmentioning

confidence: 99%

RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving

Qin¹,

Zheng²,

Chen³

et al. 2021

Preprint

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Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving on the street autonomously, which rely on high-accurate sensors (e.g. Lidar and RTK GPS) and high-resolution map. However, low-cost production cars cannot afford such high expenses on sensors and maps. How to reduce costs? How do sensor-rich vehicles benefit low-cost cars? In this paper, we proposed a light-weight localization solution, which relies on low-cost cameras and compact visual semantic maps. The map is easily produced and updated by sensor-rich vehicles in a crowd-sourced way. Specifically, the map consists of several semantic elements, such as lane line, crosswalk, ground sign, and stop line on the road surface. We introduce the whole framework of on-vehicle mapping, on-cloud maintenance, and user-end localization. The map data is collected and preprocessed on vehicles. Then, the crowd-sourced data is uploaded to a cloud server. The mass data from multiple vehicles are merged on the cloud so that the semantic map is updated in time. Finally, the semantic map is compressed and distributed to production cars, which use this map for localization. We validate the performance of the proposed map in real-world experiments and compare it against other algorithms. The average size of the semantic map is 36 kb/km. We highlight that this framework is a reliable and practical localization solution for autonomous driving.

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Crowd-sourced Semantic Edge Mapping for Autonomous Vehicles

Cited by 20 publications

References 18 publications

A Review of Crowdsourcing Update Methods for High-Definition Maps

A Review of Crowdsourcing Update Methods for High-Definition Maps

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving

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