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Leveraging Neural Networks for Consumer Trust in Safer Vehicles

Madinakhon RakhmedovaTashkent State University of Economics, UzbekistanDilnozakhon MukhitdinovaTashkent State University of Economics, UzbekistanAzamat BotirovTashkent State University of Economics, UzbekistanGulchekhra TangirberdievaTashkent State University of Economics, UzbekistanAkramjon KarimovTashkent State University of Economics, UzbekistanIslom KhurazovTashkent State University of Economics, Uzbekistan

Advances in computational intelligence and robotics book seriesbook series2025en

ABI

Annotatsiya

Consumer trust plays a pivotal role in the adoption of AI-enhanced vehicle safety systems. This study explores the application of neural networks, developed using TensorFlow, to analyze consumer sentiment and predict trust levels in response to safety features. By examining a dataset of 50,000 online reviews and surveys, the model identified key factors influencing trust, including transparency of AI operations and demonstrable reliability. Results show that vehicles emphasizing user-friendly interfaces and clear safety benefits achieved a 25% higher trust rating. Strategic insights for automotive manufacturers focus on building trust through AI-driven personalization and performance assurance.

Mavzular

Adversarial Robustness in Machine Learning Autonomous Vehicle Technology and Safety Advanced Neural Network Applications

Identifikatorlar

DOI: 10.4018/979-8-3373-0442-7.ch019

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