Evaluating Economic Benefits of AI-Powered Crash Prevention Technologies

Economic impact of AI intervention in crash prevention systems through the use of computer simulations using digital twins done with Siemens NX and Simcenter. The research shows how the technology can virtually explore several angles of the crash and fine-tune its AI algorithms, as well as estimate practical risks and cost factors. Trial outcomes indicate an overall collision rate reduction by 40%, 30% lower medical expense, and 25% lower cost of vehicle repair in comparison to conventional as well as AI-based approaches separately. Further, this approach retained 85% of crash prediction accuracy besides cutting down false positive by 15% hence, increasing system credibility. The effectiveness of Digital Twins for scenario testing and cost calculation is underlined, thus the potential of the proposed approach for the future development of crash prevention systems is scalable. It is ascertained in the research that simulation-based assessments offer a stable paradigm for comparing AI-driven safety features in automobiles hence earning better road safety and economic impacts.

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