Page 833 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Innovative Technological approach: The proposed framework introduces a multi-layered AI
architecture that integrates deep learning, time-series forecasting, and reinforcement learning
to enable real-time, adaptive energy management in autonomous electric vehicles. At the core
of this innovation lies a hybrid prediction engine combining LSTM and Transformer models to Transport 4.10: Intelligent
perform granular battery consumption forecasting by dynamically analysing sequential inputs
such as road gradient, elevation, traffic density, environmental conditions, and driver-specific
behavioural data. These predictions are further refined through a terrain-aware processing
module that translates topographic and environmental metadata into energy expenditure
coefficients. The system continuously ingests real-time telematics to fine-tune predictions and
employs a reinforcement learning agent to optimize charging decisions by learning from
historical feedback, user behaviour, and system performance metrics. This enables the vehicle
to recommend context-aware charging stations by evaluating route efficiency, congestion
levels, and real-time charger availability. The proposed architecture not only advances battery
endurance prediction but also delivers actionable, in-journey driving recommendations
through in-vehicle interfaces such as HUDs, creating a closed-loop decision-support system
that adapts autonomously to evolving conditions.

Types of Models :

We use a multi-model pipeline, including:

Table II – Models

Model type Usecase

Chronos Transformer , LSTM
Mistral NLP,RL (loss function )

Transformer Size & On-Vehicle Deployment

• We use a compressed transformer model—Chronous(T5) with 7B parameters.
• Fine-tuned for battery forecasting and real-time sequence understanding.
• Models are optimized using quantization + pruning.
• In-vehicle edge deployment using a runtime such as TensorRT on an NVIDIA Jetson
Nano/Xavier.

Mistral for Natural Language Processing?

Mistral AI has developed models like Mistral 7B, which, despite having fewer parameters than
some competitors, deliver performance that rivals larger models. This efficiency is achieved
through innovative architectural choices, such as Grouped-Query Attention (GQA) and Sliding
Window Attention (SWA), enabling faster inference and reduced memory usage.

Key Advantages:

• Efficiency: Mistral models are designed to be lightweight, making them suitable for
deployment in environments with limited computational resources.
• Open-Source Flexibility: Being open-source, Mistral allows for greater customization and
transparency, facilitating integration into various systems while maintaining data privacy.
• Competitive Performance: In benchmarks, Mistral 7B has demonstrated performance on
par with or exceeding that of larger models like LLaMA 2 13 B.

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