OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs

Zhang, Qingyang; Wang, Yifan; Zhang, Xingzhou; Liu, Liangkai; Wu, Xiaopei; Shi, Weisong; Zhong, Hong

doi:10.1109/icdcs.2018.00131

Cited by 80 publications

(33 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been lots of efforts in developing edge computing platforms specialized for vehicular data analytics. Open Vehicular Data Analytics Platform (OpenVDAP) [26] is an open-source platform. It is a full-stack edge based platform, including an on-board computing/communication unit, an isolation-supported and security and privacy-preserved vehicle operation system, an edge-aware application library, as well as an optimal workload offloading and scheduling strategy.…”

Section: A Vehicle As a Clientmentioning

confidence: 99%

“…Due to limitations in budgets, size, weight, and power, as well as the complexity of public safety operations, analytic integration and optimization for VAPS is a significant challenge. In [123], built upon the OpenVDAP platform [26], an IoT-enabled public safety service, called AutoVAPS, was proposed. It integrates bodyworn cameras and other public safety sensors, and consists of three layers: A data layer for data management, a model layer for edge intelligence, and an access layer for privacypreserving data sharing and access.…”

Section: Mec-enabled Applications In Iovmentioning

confidence: 99%

“…Cloud computing platforms will certainly be an important supporter for IoV, but they are not sufficient. While cost and power consumption are the main limiting factors for onboard computation, the long latency and the massive data transmission are the bottlenecks for Cloud based processing [26]. The round trip time from a mobile client to a cloud center may easily be longer than 100 ms [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

View full text Add to dashboard Cite

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

show abstract

Section: A Vehicle As a Clientmentioning

confidence: 99%

Section: Mec-enabled Applications In Iovmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

View full text Add to dashboard Cite

show abstract

“…Compared to both on-board computation and cloud-based computation, edge computing can provide more effective data analytics with lower latency for the moving vehicles [21]. In [21], an open full-stack edge computing-based platform OpenVDAP is proposed for the data analytics of connected and autonomous vehicles (CAVs). OpenVDAP proposes sys-tematic mechanisms, including varied wireless interfaces, to utilize the heterogeneous computation resources of nearby CAVs, edge nodes, and the cloud.…”

Section: K Other Edge Computing Systemsmentioning

confidence: 99%

A Survey on Edge Computing Systems and Tools

Liu¹,

Tang²,

Li³

et al. 2019

Proc. IEEE

Self Cite

224

View full text Add to dashboard Cite

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. F. Liu is with the

show abstract

“…CAVBench is a benchmark suite for evaluating CAVs computing system performance. It takes six diverse real-world on-vehicle applications as evaluation workloads, covering four applications scenarios summarized in OpenVDAP: autonomous driving, real-time diagnostics, in-vehicle infotainment and third-party applications [16]. The six applications that we have chosen are simultaneous localization and mapping (SLAM), object detection, object tracking, battery diagnostics, speech recognition and edge video analysis.…”

Section: Introductionmentioning

confidence: 99%

CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles

Wang

Liu²,

et al. 2018

2018 IEEE/ACM Symposium on Edge Computing (SEC)

Self Cite

View full text Add to dashboard Cite

Connected and autonomous vehicles (CAVs) have recently attracted a significant amount of attention both from researchers and industry. Numerous studies targeting algorithms, software frameworks, and applications on the CAVs scenario have emerged. Meanwhile, several pioneer efforts have focused on the edge computing system and architecture design for the CAVs scenario and provided various heterogeneous platform prototypes for CAVs. However, a standard and comprehensive application benchmark for CAVs is missing, hindering the study of these emerging computing systems. To address this challenging problem, we present CAVBench, the first benchmark suite for the edge computing system in the CAVs scenario. CAVBench is comprised of six typical applications covering four dominate CAVs scenarios and takes four datasets as standard input. CAVBench provides quantitative evaluation results via application and system perspective output metrics. We perform a series of experiments and acquire three systemic characteristics of the applications in CAVBench. First, the operation intensity of the applications is polarized, which explains why heterogeneous hardware is important for a CAVs computing system. Second, all applications in CAVBench consume high memory bandwidth, so the system should be equipped with high bandwidth memory or leverage good memory bandwidth management to avoid the performance degradation caused by memory bandwidth competition. Third, some applications have worse data/instruction locality based on the cache miss observation, so the computing system targeting these applications should optimize the cache architecture. Last, we use the CAVBench to evaluate a typical edge computing platform and present the quantitative and qualitative analysis of the benchmarking results.

show abstract

OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs

Cited by 80 publications

References 19 publications

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Mobile Edge Intelligence and Computing for the Internet of Vehicles

A Survey on Edge Computing Systems and Tools

CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles

Contact Info

Product

Resources

About