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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles.

Miguel de Prado1,2, Manuele Rusci3, Alessandro Capotondi4

  • 1He-Arc Ingenierie, HES-SO, 2800 Delemont, Switzerland.

Sensors (Basel, Switzerland)
|March 6, 2021
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Summary
This summary is machine-generated.

This study introduces a new learning method for autonomous mini-vehicles, enhancing their robustness in dynamic environments using compact deep learning models and specialized hardware for efficient, real-time control.

Keywords:
autonomous drivingmicro-controllersrobustnesstinyML

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Area of Science:

  • Robotics
  • Computer Vision
  • Embedded Systems

Background:

  • Deep learning has advanced standard autonomous vehicles.
  • Mini-vehicles face challenges in autonomous driving due to limited resources and environmental variability.
  • Existing autonomous systems struggle with robustness in dynamic, real-world conditions.

Purpose of the Study:

  • To develop a robust closed-loop learning system for autonomous mini-vehicles.
  • To address limitations in on-board storage, computing power, and environmental adaptability.
  • To improve the performance and energy efficiency of autonomous mini-vehicle control.

Main Methods:

  • Implemented a closed-loop learning flow with the target environment in-the-loop.
  • Utilized compact, high-throughput tiny Convolutional Neural Networks (tinyCNNs) trained via imitation learning.
  • Integrated an online predictor for runtime model selection and a GAP8 RISC-V microcontroller for real-time inference.

Main Results:

  • Achieved robustness to lighting conditions and continuous improvement over time for tinyCNNs.
  • Reduced energy consumption by up to 3.2× using the online predictor.
  • Outperformed traditional microcontrollers (STM32L4, NXP k64f) on latency (13× reduction) and energy consumption (92% reduction) using the GAP8 platform.

Conclusions:

  • The proposed system enables robust and efficient autonomous driving for resource-constrained mini-vehicles.
  • The combination of tinyCNNs, online prediction, and specialized hardware (GAP8) is effective for real-time autonomous control.
  • This approach significantly enhances the practical deployment of autonomous technology in smaller robotic platforms.