Year: 2024
Type: conference-paper
Abstract: 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.
Source: International Symposium ELMAR
Institution: University of Siegen
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403125772