This section contains the upstream information related to release 6 of this blueprint.
Summary
MEC-based Stable Topology Prediction for Vehicular Networks is planned to include the implementation of a platform that can enable stable topology prediction for vehicular networks. It aims at providing vehicle-friendly environment such that the future use-case scenarios can be built upon the provided information.
Components of the release
- Road-aware location rectifier
- vTrachea-Store
Dependencies of the release (upstream version, patches)
Operating system:
Name | Description |
---|
Distributor ID: | Ubuntu |
Description: | Ubuntu 20.04.3 LTS |
Release: | 20.04 |
Codename: | focal |
Note: |
Software used:
For | Name | Version/Info |
---|
Running Notebooks | Conda: | 4.9.2 |
Python: | 3.8.10 |
Jupyter Core: | 4.7.1 |
Jupyter Notebook | 6.4.0 |
Conda Environment File: | env_kf_model (file) |
Pushing CD Logs | Jenkins: | 2.303.1 |
Docker-hub image link: | mehmoodasif/jenkins |
pip3 | 20.0.2 |
lftools: | 0.35.10 |
Running Containers | Docker: | 20.10.8 |
Docker build: | 3967b7d |
Map and Data-set Generation | SUMO: | 1.10.0 |
TraceExporter.py | traceExporter (file) |
Netedit: | Netedit - SUMO |
Netconvert: | Netconvert - SUMO |
Note: List of software (shown below) are used after release 6 |
Database | PostgreSQL server: | 12.8 |
DBeaver client: | 21.2.2 |
Repository:
Differences from previous version
- Previous versions did not include the the road information
- Previous version did not have the process of rectification included
Upgrade Procedures
None.
Release Data
Module version changes
None.
Document Version Changes
Initial versions.
Software Deliverable
Documentation Deliverable
Installation Documentation - R6 (MEC-based)
API Documentation - R6 (MEC-based)
Release Notes - R6 (MEC-based) - this document
Fixed Issues and Bugs
None
Enhancements
- Improvement in the accuracy of estimated/predicted location of a vehicle
- Support of geo-coordinates rather than using simple x, y coordinates
Functionality changes
- Previously, the location prediction was done by the use of a basic Kalman filter
- Now, we have added the step of rectification with the help of road information extracted from OpenStreetMap
- This procedure enhances the accuracy of estimated/predicted location of a vehicle
New Features
- Enhancement in accuracy of predicted location, i.e., rectified location
Deliverable
- Road-aware rectification (explained here)
Known Limitations, Issues and Workarounds
System Limitations
Known Issues
Workarounds
References