Page 838 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2�3 Future work

With funding, we will proceed with:

• Extensive Data Collection: Real-world driving data from EV fleets to improve model
accuracy.
• Advanced ML Techniques Integrating Graph Neural Networks (GNNs) to improve terrain-
aware battery predictions.
• Deployment & Real-Time Testing: Deploying AI models in edge devices inside EVs to
provide instant predictions.
• Collaboration with Charging Networks, Integrating AI-driven booking & reservation
systems for charging slots

3 Use Case Requirements
• REQ-01: It is mandatory that the system continuously acquires, processes, and integrates
real-time data from vehicle telemetry, GPS, traffic APIs, road gradient, environmental
sensors, and charging infrastructure through standardized APIs or onboard modules for
context-aware decision-making.
• REQ-02: It is critical to implement an AI-based terrain-aware energy forecasting model
(Chronos) that predicts and analyzes energy consumption using historical data, real-time
telemetry, terrain profiles, and driver behavior patterns.
• REQ-03: It is critical that the reinforcement learning-driven optimization engine provides
adaptive, personalized driving and charging recommendations by learning continuously
from live driving conditions, battery status, environmental data, and historical feedback.
• REQ-04: It is mandatory that the system supports an edge-optimized, low-latency
deployment architecture with in-vehicle edge devices capable of sub-second latency for
data ingestion, prediction, and feedback, incorporating fallback lightweight models for
offline operability where the Mistral (RL-based NLP model) calculates the loss function.
• REQ-05: It is critical to integrate a context-aware driver assistance and visualization interface
using the HUD, cluster, and center console to deliver actionable recommendations and
visualize live and predicted battery status, range, route, and upcoming terrain changes.
• REQ-06: It is mandatory that resilient, redundant, and highly available APIs are used for
route, traffic, weather, and charging data, ensuring secure, standardized, and anonymized
data handling aligned with data privacy guidelines, even under connectivity loss.
• REQ-07: It is critical to include continuous model evaluation and a scalable simulation
environment with a retraining pipeline and loss function monitoring to detect model drift
and validate AI model behavior.

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