Risk assessment in autonomous driving: a comprehensive survey of risk sources, methodologies, and system architectures
Year: 2025
Type: article
Abstract: As autonomous driving technology advances from assisted to higher levels of autonomy, the complexity of operational environments and the uncertainty of driving tasks continue to increase, posing significant challenges to system safety. The key to ensuring safety lies in conducting comprehensive and rational risk assessments to identify potential hazards and inform policy optimization. Consequently, risk assessment has emerged as a critical component for ensuring the safe operation of higher-level autonomous driving systems. This review focuses on research into risk assessment for autonomous driving. It systematically surveys the state-of-the-art literature from three key perspectives: risk sources, assessment methodologies, data foundations, and system architectures. For each perspective, the paper provides an in-depth analysis of representative technical approaches, modeling principles, and typical application scenarios, while summarizing their research characteristics and applicable boundaries. Finally, this paper synthesizes the three fundamental challenges that persist in current research and further explores future directions and development opportunities. It provides a theoretical foundation and methodological references for the development of autonomous driving systems that exhibit high safety and reliability.
Source: Autonomous Intelligent Systems
Authors: Dongyuan Lu, Haoyang Du, Zhengfei Wu, Shuo Yang
Institution: Tongji University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Award: G3821160350
OpenAlex ID: W4414391000