Redefining Automotive Safety using AI and ML

The evolution of safety on the road has undergone a significant transformation, shifting from reactive measures aimed at mitigating crash impacts to proactive systems focused on accident prevention. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into automotive safety frameworks powers this shift. By seamlessly blending passive safety features with active safety systems, AI and ML are revolutionizing how vehicles protect both occupants and vulnerable road users.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-27
DOI
https://doi.org/10.5281/zenodo.14604764
Primary Topic
Tuberculosis Research and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00
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article

Redefining Automotive Safety using AI and ML

Zenodo (CERN European Organization for Nuclear Research)
Tuberculosis Research and Epidemiology
article

Redefining Automotive Safety using AI and ML

article en

Abstract

The evolution of safety on the road has undergone a significant transformation, shifting from reactive measures aimed at mitigating crash impacts to proactive systems focused on accident prevention. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into automotive safety frameworks powers this shift. By seamlessly blending passive safety features with active safety systems, AI and ML are revolutionizing how vehicles protect both occupants and vulnerable road users.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 55%
Tuberculosis Research and Epidemiology
DOIOpen Access PDFOpenAlex
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