Vision-Based Vehicle Safety Systems: Statistical Evaluation of Efficacy and Impact
DOI:
https://doi.org/10.32628/CSEIT25112544Keywords:
Computer Vision, Automotive Safety Systems, Crash Reduction Efficacy, Sensor Fusion, Autonomous Driving TechnologyAbstract
This article presents a comprehensive statistical evaluation of vision-based safety systems in modern vehicles, examining their efficacy in reducing crash frequency and severity across diverse operational conditions. The article synthesizes data from multiple sources, including NHTSA crash databases and naturalistic driving records, to quantify the real-world performance of various system implementations. The article establishes correlations between specific technical specifications and safety outcomes through rigorous statistical analysis while revealing significant performance variations across different environments, road types, and vehicle classes. The article encompasses the entire technological landscape, from camera configurations and deep learning architectures to sensor fusion approaches and processing requirements. Additionally, the article addresses regulatory frameworks across global markets, identifies gaps in current evaluation protocols, and explores emerging technologies that promise to advance automotive perception capabilities. Ethical considerations regarding privacy, responsibility allocation, and equitable technology distribution are examined alongside economic impact assessments. It provides evidence-based recommendations for manufacturers, regulators, insurers, and consumers to optimize vision system implementation and maximize safety benefits while navigating the transition toward higher levels of vehicular autonomy.
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