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Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing.

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Summary

This study introduces neural network models for indoor autonomous driving, using LiDAR data for navigation. The research evaluates model performance and resource usage, guiding the selection of optimal models for resource-constrained mobile environments.

Keywords:
LiDAR sensorindoor autonomous drivingmobile edge computingneural network modelresource usage

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Engineering

Background:

  • Mobile edge computing addresses cloud latency issues, crucial for real-time applications like autonomous driving.
  • Indoor autonomous driving presents unique challenges, including the absence of GPS and the need for real-time sensor data processing for safety.

Purpose of the Study:

  • To propose and evaluate neural network models for indoor autonomous driving.
  • To determine the impact of input data quantity on model performance and resource consumption.

Main Methods:

  • Developed six neural network models utilizing LiDAR range data for indoor navigation.
  • Built a Raspberry Pi-based autonomous vehicle and an indoor test track for data collection and evaluation.
  • Assessed models based on accuracy, response time, battery usage, and resource consumption.

Main Results:

  • Evaluated the performance of six distinct neural network models.
  • Demonstrated the influence of input data volume on resource utilization during neural network learning.
  • Identified trade-offs between model complexity, accuracy, and resource efficiency.

Conclusions:

  • Neural network models show promise for indoor autonomous driving applications.
  • The number of input data points significantly affects resource usage and model performance.
  • Findings provide guidance for selecting appropriate neural network models for indoor autonomous vehicles.