OpenVDAP at Wayne State University

With the burgeoning of Sensing, Edge Computing, AI, and 5G technologies, we envision future vehicles will serve as a computing platform for a variety of services like advanced driver assistance system (ADAS) and Autonomous Driving (AD), remote real-time diagnostics, in-vehicle infotainment, and a variety of third-party services, such as public safety and weather alert. 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.

Partners and Sponsors

OpenVDAP

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

VCU

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.

HydraOS

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

DDI

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

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

People

Faculty

Weisong Shi

Students

Jiamin Chen

Liangkai Liu

Samira Taghavi

Sheng Tan

Yifan Wang

Qingyang Zhang

Xingzhou Zhang

Former Members

Xiaopei Wu

Publications

Sidi Lu, Yongtao Yao and Weisong Shi, Collaborative Learning on the Edges: A Case Study on Connected Vehicles, in Proceedings of the 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge), July 9, 2019, Renton, WA, USA.
Xingzhou Zhang, Yifan Wang, Sidi Lu, Liangkai Liu, Lanyu Xu, Weisong Shi, OpenEI: An Open Framework for Edge Intelligence, in Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS), Vision/Blue Sky Track, July 7-10, 2019, Dallas, USA.
Yifan Wang, Shaoshan Liu, Xiaowei Wu, and Weisong Shi, CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles, in Proceedings of the 3nd ACM/IEEE Symposium on Edge Computing (SEC), Oct. 25-27, 2018. Bellevue, WA.
Liangkai Liu, Xingzhou Zhang, Mu Qiao, and Weisong Shi, SafeShareRide: Edge-based Attack Detection in Ridesharing Services, in Proceedings of the 3nd ACM/IEEE Symposium on Edge Computing (SEC), Oct. 25-27, 2018. Bellevue, WA.
Jie Tang, Shaoshan Liu, Liangkai Liu, Bo Yu, and Weisong Shi, π-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services, Technical Report MIST-TR-2018-061.
Jie Tang, Shaoshan Liu, Songwen Pei, Stephane Zuckerman, Chen Liu, Weisong Shi, and Jean-Luc Gaudiot, Teaching Autonomous Driving Using a Modular and Integrated Approach, in Proceedings of the 42nd IEEE International Conferences on Computers, Software & Applications (COMPSAC), July 23-27, 2018. Tokyo, Japan.
Liangkai Liu, Xingzhou Zhang, Mu Qiao, and Weisong Shi, SafeShareRide: Edge-based Attack Detection in Ridesharing Services, in Proceedings of USENIX Workshop on Hot Topics in Edge Computing (HotEdge), July 10, 2018.
Xingzhou Zhang, Yifan Wang, and Weisong Shi, pCAMP: Performance Comparison of Machine Learning Packages on the Edges, in Proceedings of USENIX Workshop on Hot Topics in Edge Computing (HotEdge), July 10, 2018.
Qingyang Zhang, Yifan Wang, Xingzhou Zhang,Liangkai Liu, Xiaopei Wu, Weisong Shi and Hong Zhong, OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs, in Proceedings of the 38th IEEE International Conference on Distributed Computing Systems (ICDCS), Vision/Blue Sky Track, July 2-5, 2018, Vienna, Austria.
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.
Mohit Kumar, Youhuizi Li and Weisong Shi, Energy Consumption in Java: An Early Experience, in Proceedings of the 8th International Green and Sustainable Computing Conference (IGSC), Orlando, FL, Oct 23-25, 2017.
Shanhe Yi, Zijiang Hao, Qingyang Zhang, Quan Zhang, Weisong Shi and Qun Li, LAVEA: Latency-aware Video Analytics on Edge Computing Platform , in Proceedings of 2nd ACM/IEEE Symposium on Edge Computing (SEC), San Jose, Oct 12-14, 2017.
Qingyang Zhang, Quan Zhang, Weisong Shi and Hong Zhong, Poster: Enhancing AMBER Alert using Collaborative Edges , in Proceedings of 2nd ACM/IEEE Symposium on Edge Computing ( SEC),San Jose, Oct 12-14, 2017.
Zhenyu Ning, Fengwei Zhang, Weisong Shi and Larry Shi, Position Paper: Challenges Toward Securing Hardware-assisted Execution Environments, in Proceedings of Hardware and Architectural Support for Security and Privacy (HASP), in conjunction with ISCA, Toronto, Canada, June 25, 2017.
Quan Zhang, Qingyang Zhang, Weisong Shi and Hong Zhong, Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics, Technical Report MIST-TR-2017-002, January 2017.
Songqing Chen, Tao Zhang and Weisong Shi, Editorial on Fog Computing, IEEE Internet Computing, Vol. 21, No. 2, March/April 2017.
Qingyang Zhang, Zhifeng Yu, Weisong Shi and Hong Zhong, Demo Abstract: EVAPS: Edge Video Analysis for Public Safety, in Proceedings of 1st IEEE/ACM Symposium on Edge Computing (SEC), Washington DC, Oct 27-28, 2016.
Quan Zhang, Xiaohong Zhang, Qingyang Zhang, Weisong Shi and Hong Zhong, Firework: Big Data Sharing and Processing in Collaborative Edge Environment, in Proceedings of 4th IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), Washington DC, Oct. 24-25,2016.
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li and Lanyu Xu, Edge Computing: Vision and Challenges, IEEE Internet of Things Journal, Vol. 3, No. 5, October 2016, pp. 637-646.

Contact Us

Address: 5057 Woodward Ave, Detroit, MI 48202
Email: contact AT thecarlab dot org

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