OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs

Citations Over TimeTop 10% of 2018 papers

Abstract

In this paper, we envision the future connected and autonomous vehicles (CAVs) as a sophisticated computer on wheels, with substantial on-board sensors as data sources and a variety of services running on top to support autonomous driving or other functions. In general, these services are computationally expensive, especially for the machine learning based applications (e.g., CNN-based object detection). Nevertheless, the on-board computation unit possess limited compute resources, raising a huge challenge to deploy these computation-intensive services on the vehicle. On the contrary, the cloud-based architecture conceptually with unconstrained resources suffers from unexpected extended latency that attributes to the large-scale Internet data transmission; thus, adversely affecting the services' real-time performance, quality of services and user experiences. To address this dilemma, inspired by the promising edge computing paradigm, we propose to build an Open Vehicular Data Analytics Platform (OpenVDAP) for CAVs, which is a full-stack edge based platform including an on-board computing/communication unit, an isolation-supported and security & privacy-preserved vehicle operation system, an edge-aware application library, as well as an optimal workload of?oading and scheduling strategy, allowing CAVs to dynamically detect each service's status, computation overhead and the optimal of?oading destination so that each service could be finished within an acceptable latency and limited bandwidth consumption. Most importantly, contrast to the proprietary platform, OpenVDAP is an open-source platform that offers free APIs and real-?eld vehicle data to the researchers and developers in the community, allowing them to deploy and evaluate applications on the real environment.

Related Papers

• → Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks(2019)784 cited
• → Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing(2017)148 cited
• → Computation Offloading for Workflow in Mobile Edge Computing Based on Deep Q-Learning(2019)34 cited
• → Learning for Smart Edge: Cognitive Learning-Based Computation Offloading(2018)19 cited
• → Joint Scheduling of Communication and Computation Resources for Efficient Computation Offloading in MEC-based V2X Systems(2023)2 cited