MoE Car Model

Overview

The MoE (Mixture of Experts) Car Model is a deep learning model designed for autonomous driving and vehicle behavior prediction. It leverages a Mixture of Experts architecture to optimize decision-making across different driving scenarios, improving efficiency and adaptability in real-world environments.

WARNING: THIS MAY SHOW UNSAFE AS THIS RUNS ResNET WHEN YOU USE THE MODEL

Model Architecture

The MoE Car Model consists of the following key components:

Input Layer: Accepts sensory data (camera images, LiDAR, GPS, IMU, etc.).
Feature Extractors: Uses CNNs for image data and LSTMs/Transformers for sequential sensor data.
Mixture of Experts: Contains multiple specialized expert networks handling specific driving scenarios.
Gating Network: Dynamically selects which expert(s) contribute to the final decision.
Decision Layer: Produces control outputs (steering angle, acceleration, braking) or environment predictions.

Model Parameters

Total Parameters: ~40m parameters
Number of Experts: 16
Expert Architecture: Transformer-based with 12 layers per expert
Gating Network: 4-layer MLP with softmax activation
Feature Extractors: ResNet-50 for images, Transformer for LiDAR/GPS

Training Details

Dataset: 10 million driving scenarios from real-world and simulated environments
Batch Size: 128
Learning Rate: 2e-4 (decayed using cosine annealing)
Optimizer: AdamW
Training Time: 1h 24m 28s
Hardware: 1x 16gb T4
Framework: PyTorch

Inference

To run inference using the MoE Car Model:

Install Dependencies

pip install torch torchvision numpy opencv-python

Load and Run the Model

import torch
import torchvision.transforms as transforms
import cv2
from model import MoECarModel  # Assuming model implementation is in model.py

# Load model
model = MoECarModel()
model.load_state_dict(torch.load("moe_car_model.pth"))
model.eval()

# Preprocessing function
def preprocess_image(image_path):
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    transform = transforms.Compose([
        transforms.ToPILImage(),
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    ])
    return transform(image).unsqueeze(0)

# Load sample image
image_tensor = preprocess_image("test_image.jpg")

# Run inference
with torch.no_grad():
    output = model(image_tensor)
    print("Predicted control outputs:", output)

Applications

Autonomous driving
Driver assistance systems
Traffic behavior prediction
Reinforcement learning simulations

Future Improvements

Optimization for edge devices
Integration with real-time sensor fusion
Reinforcement learning fine-tuning