IEC Type 4 is an Akraino approved blueprint and part of Akraino Edge Stack. The project is focused on AR VR applications run on Edge Computing.
Use
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Cases
In general, there are multiple
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The use cases for the Connected Vehicle Blueprint are itemized below. For R2, we release the Microservice Platform Tars, which supports the multiple connected vehicle application deployment/management/orchestration/monitor.AR VR.
UseCases |
Value Proposition |
Accurate Location
The accuracy of location improved by over 10 times than today's system. Today’s GPS system is around 5-10meters away from your reallocation, <1 meter is possible with the help of edge computing.
Smarter Navigation
Real-time traffic information update, reduces the latency from minutes to seconds, figure out the most efficient way for drivers.
Safe DriveImprovement
Figure out the potential risks which can NOT be seen by the driver. See below.
Operation Guidance | Predict the next step for the operations(like assembling Lego blocks, cooking sandwiches, etc) and help people to achieve a goal. |
Virtual Classroom | Simulating a virtual classroom, which improves online education experiences for the teachers and students. |
Sports Live | Augment and simulate the sport live, which gives the audiences an amazing real watching experience. |
Gaming | Let the driverunderstand the traffic rule in some specific area. For instance, change the line prior to a narrow street, avoiding the opposite way drive in the one-way road, avoiding carpool lane when a single driver and so on. |
Overall Architecture
The following picture depicts the architecture of the Connect Vehicle Blueprint, which consists of the following key components:
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For R2, we deploy it in Amazon Web Service for Release. The detailed hardware is itemized below:
CPU+Memory | Drive | Deployment |
8Core * 16G | 15G | Jenkins Master |
8Core * 16G | 10+50G | TarsFramework |
8Core * 16G | 10G +20G | TarsNode + Application |
Beyond of AWS, we deploy it in Ampere POD 1 in CI Lab as well.
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