Autonomous test fleet cars collect ground truth data through various sensors while driving through city roads. Once a test vehicle reaches test centres, on-board data stores upload data to the server hosted locally at test sites. Test engineer cleans (semi-automated) data files and uploads it to the local shared file location, to enable access to all users across the globe.
Once the file is available in the shared file location, AV RCP’s Optima metadata extractor extracts meta data of the file. It also provides a facility to assign user tags. Metadata information is stored in the central database, which is searchable by any global user. Let’s say, an user from the India lab searches for some data file and finds that it is available in the US lab.
There are several ways the user can access this file. User can download the file to local for processing or have a mechanism where they can perform required operation without downloading file locally. Downloading the file is straightforward but requires huge network resources. Here, file size is in gigabytes to petabytes. Downloading such huge files will consume precious network resources and time. Typically, a 50-60 GB file takes around 5-6 hours over a regular network.
AV RCP distributed architecture provides a different approach without the physical movement of the file. User clicks on the desired file; system redirects VDI enabled desktop (for Azure Cloud, Azure Desktop is an option) where user can access and process file remotely. As the data file is available locally to compute engine (remote), simulation and other data processing can be done seamlessly.
AV RCP solution also provides tools to slice files so that user can download the data-of-interest, typically a smaller size file locally for any hardware simulation/ test. This approach saves precious network bandwidth and saves on file upload and download time. It thus improves productivity of the data engineer.
AV RCP can be deployed in hybrid and cloud PaaS environments:
- Hybrid: AV RCP supports Hybrid cloud deployment model. In this model, high computation (Simulation, AI / ML) and storage archival are leveraged from cloud based services and the rest of the functions are retained at on-premise lab infrastructure. This is the recommended deployment. Many Azure cloud services can be leveraged for scalability and performance in this option.
- Cloud PaaS: AV RCP is available as a PaaS service. It exposes RESTful APIs so the customer can use RCP platform services in any application. Due to multitenancy feature, AV RCP can support multiple customers.
AV RCP framework uses various Azure PaaS / IaaS services for achieving higher scalability and reliability (See Table 1).