Driving tomorrow: the bold road ahead for autonomous vehicles

This is the authors' abstract. We don't add key points for this paper.

This paper presents a comprehensive literature review of recent advances in autonomous vehicle (AV) technologies, focusing on artificial intelligence, multi-sensor fusion, automated control, and safety assurance frameworks. Autonomous vehicles promise to transform mobility by integrating AI-driven perception and decision-making systems, with the potential to significantly reduce human-driver errors, which are responsible for over 90% of road crashes, and to improve transportation safety and accessibility. We review recent advances in AV perception, planning, and safety, with particular emphasis on industry safety standards such as ISO 26262 for functional safety. The review highlights that purely empirical validation is impractical, as prior studies estimate that billions of test miles would be required to achieve statistical confidence, motivating hybrid safety approaches that combine formal analysis (FTA/FMEA) with large-scale simulation and targeted on-road testing. Key enabling technologies, including sensor platforms, multi-modal fusion, AI-based planners, and validation workflows such as simulators, hardware-in-the-loop testing, and vehicle trials, are surveyed. Finally, emerging regulatory and deployment trends, including UNECE initiatives and public trust considerations, are discussed, along with open challenges such as robust handling of rare edge-case scenarios and the need for continuous, data-driven safety feedback loops. By integrating technical advances with safety assurance frameworks, ethical considerations, and regulatory developments, this review provides actionable insights for engineers and policymakers supporting safe and scalable AV deployment.