Page 818 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Use Case 7: AI-Driven 6G-Enabled V2X Communications for Smart

and Sustainable Mobility

Country: Nigeria

Organization: AI4Africa Research Group

Contact Person(s): `

Emmanuel Aaron (aaronemmanuel054@ gmail .com, +2348077200689), Dr. Houda Chihi
(houda.chihi@ supcom .tn), Blessed Guda (gudablessed@ gmail .com), Emmanuella Sule
(suleemmanuella@ yahoo .com), Chidi Ebube (chidizack24@ gmail .com), Emmanuel Ani (ani.
mlengineer@ outlook .com), Okafor Miracle Uche (okaformiracle212@ gmail .com)

1 Use Case Summary Table

Item Details
Category Intelligent transport, v2x, AI

Problem Addressed Network interference and mobility, resource allocation among
vehicular nodes

Key Aspects of Solution Integer Linear Programming (ILP) is employed for network resource
allocation in environments with fixed conditions, while Deep
Reinforcement Learning (DRL) is utilized for adaptive resource
allocation in vehicular networks with varying mobility patterns [1].
Technology Keywords Autonomous Vehicles, V2X, network resource allocation, vehicular
mobility.
Data Availability Data Generation is extracted using public access for Open Street
Map[2] for DRL training.
Metadata (Type of Data) Matrices (for the network states), numeric data from RSU logs for
latency, load, or network fluctuation, LiDAR or sensor readings
from vehicles.

Model Training and Combining Integer Linear Programming and Deep Reinforcement
Fine-Tuning Learning (Proximal Policy Optimization).

Testbeds or Pilot Deploy- No Deployments at the moment, but the intended toolkits include
ments the Sumo-Gym toolkit[3].

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