Benchmark and Applications
AutoVAPS is an IoT-Enabled public safety service which integrates body-worn cameras and other sensors on the vehicle for public safety. In AutoVAPS, we propose a reference architecture that consists of the data layer for data management, the model layer for edge intelligence, and the access layer for privacy-preserving data sharing and access. Object detection is implemented as a case study of AutoVAPS.
CAVBench is a benchmark suite to evaluate the connected and autonomous vehicles (CAVs) computing platforms performance. It takes six diverse realworld on-vehicle applications as evaluation workloads, which covering four applications scenarios summarized in OpenVDAP. We collect four real-world datasets for CAVBench as the standard input to the six application including three types data: image, audio, and text. And CAVBench has two categories of output metrics, the execution time breakdown and service-resource utilization curve (QoS-RU curve) for each application.
An edge-based attack detection in ridesharing services, namely SafeShareRide, which can detect dangerous events happening in the vehicle in near real time. The detection of SafeShareRide consists of three stages: speech recognition, driving behavior detection, and video capture and analysis. We implemented SafeShareRide system by leveraging open source algorithms.
Firework is a framework for big data processing and sharing among multiple stakeholders in collaborative edge environment, in which data are owned by multiple stakeholders, and is rarely shared due to various reasons, such as security concern and privacy issue. Thought the Firework interfaces, the programmer can easily and fastly develop the application on the edge by only implementing the functions and setting up the configuring of the application.
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).
We make a comprehensive performance comparison and analysis of several state-of-the-art deep learning packages on the edges, including TensorFlow, Caffe2, MXNet, PyTorch, and TensorFlow Lite. We focus on evaluating the latency, memory footprint and energy of these tools with four popular deep learning models on different edge devices, including MacBook, Intel FogNode, NVIDIA Jetson TX2, Raspberry Pi, and Nexus 6P.