Motivation

With the burgeoning of Internet of Things (IoT) and 5G technologies, we envision future vehicles will serve as a computing platform for a variety of services like advanced driver assistance system (ADAS), remote diagnostics, on-board entertainment, and a variety third-party services, such as public safety. As the amount of data generated by these services on one vehicle increases dramatically and has the potential to reach four terabytes per day, it is a huge challenge to guarantee performance through the limited on-board computing power. Therefore, an efficient data analytics platform, including both hardware and software, is needed to enable future vehicle computing. Many auto vendors are working on their proprietary platform, however, an open platform that provide interfaces for researchers and developers is missing. In this project, we aim to develop such a platform that provides a full stack solution and dynamically senses services status and adjusts services to guarantee service quality and user experience.

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OpenVDAP


We are developing on an open vehicular data analytics platform (OpenVDAP), which consists of four components: heterogeneous vehicle computing unit (VCU), operation system (EdgeOSv), driving data collector and integrator (DDI), and application library (libvdap).

VCU: heterogeneous vehicle computing unit: We intend to manage and utilize the heterogeneous computing hardware resources in a future vehicle, e.g., CPU, GPU and FPGA, as well as computing devices from passengers,  to support real-time data processing. We also aim to manage the multiple  communication  components, e.g., 3G/4G/5G and DSRC, available in the vehicle.

EdgeOSv: edge operating system for vehicle: The core of OpenVDAP is an edge operating system for vehicles, call EdgeOSv, in which each service offers multiple execution pipelines in response to various network and computational constraints. EdgeOSv employs an elastic management model that automatically chooses an optimal pipeline to reduce the lowest end-to-end latency.

DDI: driving data collector and integrator: We build a driving data collector and integrator. This service will collect all data generated by the vehicle, including the data read by OBD reader and other sensors' data, such as dash camera. Moreover, this service will integrate related data from Internet, such as weather, road condition and so on. 

libvdap: application library: We provide a library for developers who want to build third-party services on our EdgeOSv. Using libvdap, developers can access all vehicle data by communicating with DDI service. 

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Partners

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People

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Publication


  • Qingyang Zhang, Yifan Wang, Xingzhou Zhang, Liangkai Liu, Xiaopei Wu, Weisong Shi and Hong Zhong, OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs, Technical Report MIST-TR-2018-003, February 2018.
  • Quan Zhang, Qingyang Zhang, Weisong Shi and Hong Zhong, Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics, accepted by IEEE Transactions on Parallel and Distributed Systems (TPDS), February 2018.
  • Qingyang Zhang, Quan Zhang, Weisong Shi and Hong Zhong, Distributed Collaborative Execution on the Edges and Its Application on AMBER Alert, accepted by IEEE Internet of Things Journal. Feb. 2018.
  • Kewei Sha, Wei Wei, Andrew T. Yang, Zhiwei Wang and Weisong Shi, On Security Challenges and Open Issues in Internet of Things, accepted by Future Generation Computer Systems, January 2018.

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Contact US

weisong@wayne.edu

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