Machine learning advancements for vehicle safety systems - Review of technical foundations and applications

This report summarizes the integration of Machine Learning (ML) in modern vehicle safety applications. Advances in ML have transformed vehicle safety, shifting from traditional rule-based systems to data-driven, adaptive technologies. These applications include advanced driver assistance, predictive maintenance, real-time traffic management, and autonomous driving. ML encompasses a broad spectrum of methodologies and offers flexibility for various implementations, enabling customization for a wide range of tasks. However, while modern ML approaches easily adapt to diversity in data, they also require substantial amounts of data to perform effectively. Furthermore, ML introduces several challenges, particularly the “black box” problem, which raises ethical and regulatory concerns, as well as issues related to privacy and cybersecurity. Addressing these challenges requires research to improve transparency of models, fairness, and trust in ML-driven safety systems. Importantly, the growing availability of vehicle- and traffic-generated data, enabled by Vehicle-to-Everything communication and smart city infrastructure, further highlights ML’s potential for enhancing vehicle safety.