The transition from manual to autonomous driving introduces new safety
challenges, with road obstacles emerging as a prominent threat to driving
safety. However, existing research primarily focuses on vehicle-to-vehicle risk
assessment, often overlooking the significant risks posed by static or dynamic
road obstacles. In this context, developing a system capable of real-time
monitoring of road conditions, accurately identifying obstacle positions and
characteristics, and assessing their associated risk levels is crucial. To
address these gaps, this study proposes a comprehensive process for rapid
obstacle identification and risk quantification, composed of three main
components: road obstacle event detection and feature extraction, risk
quantification and level assessment, and output of warning information and
countermeasures. First, a rapid detection method suited for highway scenarios is
proposed based on the YOLOv5 model, enabling fast detection and classification
of obstacles in highway environments. Second, a customized risk assessment model
tailored to highway scenarios is developed using potential field theory,
considering multiple influencing factors, including obstacle type, location, and
road attributes. The proposed model provides a complete process for rapid
obstacle identification and quantitative risk assessment. This system not only
allows for early detection of potential hazards but also timely issuance of
warnings, enabling intelligent connected vehicles (ICVs) to perform appropriate
evasive maneuvers. This real-time decision-making enhances the safety of
autonomous driving. These findings make a significant contribution to the
development of intelligent warning systems and strengthen the deployment of ICVs
in highway scenarios, supporting a safer and more reliable transportation
system.