Building Public Trust in Autonomous Industry Through Machine Learning (Addressing Public Concerns on Safety and Reliability with Advanced Machine Learning)

Abstract: As autonomous vehicles (AVs) advance toward mainstream adoption, public trust remains a critical barrier to their widespread acceptance. Despite significant progress in machine learning (ML) technologies that power AVs, concerns about safety, reliability, and adaptability in unpredictable environments continue to hinder consumer confidence. This study investigates how advancements in ML can address these trust-related challenges, focusing on enhancing AV performance in safety, environmental adaptability, and response to unexpected scenarios. A survey was conducted to explore public attitudes toward AVs, examining trust levels, safety concerns, and willingness to pay for improved features. The results revealed moderate trust in AVs, with participants identifying malfunctioning technology, unpredictable road scenarios, and poor weather adaptability as primary concerns. Advanced ML models, such as reinforcement learning and deep neural networks, were identified as critical tools for addressing these challenges. The findings underscore the potential of ML to bridge the trust gap by improving AV safety and performance in real-world conditions. By addressing public concerns thr