Impact Analysis of Data Drift Towards The Development of Safety-Critical Automotive System
A significant part of contemporary research in autonomous vehicles is dedicated to the development of safety critical systems where state-of-the-art artificial intelligence (AI) algorithms, like computer vision (CV), can play a major role. Vision models have great potential for the real-time detecti...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
07.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | A significant part of contemporary research in autonomous vehicles is
dedicated to the development of safety critical systems where state-of-the-art
artificial intelligence (AI) algorithms, like computer vision (CV), can play a
major role. Vision models have great potential for the real-time detection of
numerous traffic signs and obstacles, which is essential to avoid accidents and
protect human lives. Despite vast potential, computer vision-based systems have
critical safety concerns too if the traffic condition drifts over time. This
paper represents an analysis of how data drift can affect the performance of
vision models in terms of traffic sign detection. The novelty in this research
is provided through a YOLO-based fusion model that is trained with drifted data
from the CARLA simulator and delivers a robust and enhanced performance in
object detection. The enhanced model showed an average precision of 97.5\%
compared to the 58.27\% precision of the original model. A detailed performance
review of the original and fusion models is depicted in the paper, which
promises to have a significant impact on safety-critical automotive systems. |
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DOI: | 10.48550/arxiv.2408.04476 |