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Encoding Consistency: Optimizing Self-Driving Reliability With Real-Time Speed Data

Published: 29 July 2024 Publication History

Abstract

Self-driving cars can revolutionize transportation systems, offering the potential to significantly enhance efficiency while also addressing the critical issue of human fatalities on roadways. Hence, there is a need to investigate methods to enhance self-driving technologies through end-to-end learning techniques. In this paper, we investigate methodologies that integrate Convolutional Neural Networks (CNNs) to enhance self-driving consistency through real-time velocity and steering estimation. We extend an end-to-end state-of-the-art learning solution with real-time speed data as additional model input to refine reliability. Specifically, our work integrates an optical encoder sensor system to record car speed during training data collection, ensuring the throttle can be regulated during model inference. An end-to-end experimental testbed is deployed on the Chameleon cloud using CHI@Edge infrastructure to manage a 1:18 scaled car, equipped with a Raspberry Pi as its onboard computer. Finally, we provide guidance that facilitates reproducibility and highlight the challenges and limitations of supporting such experiments.

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  1. Encoding Consistency: Optimizing Self-Driving Reliability With Real-Time Speed Data

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    cover image ACM Conferences
    FRAME '24: Proceedings of the 4th Workshop on Flexible Resource and Application Management on the Edge
    June 2024
    64 pages
    ISBN:9798400706417
    DOI:10.1145/3659994
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 29 July 2024

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    1. autonomous driving
    2. optimization
    3. machine learning

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