Identification and Quantitative Analysis Technique of Road Obstacle Safety Risks Southeast University

Format:

Book

Conference/Event

Author/Creator:

Chen, Tingting, author.

Contributor:

Chen, Daoxie

Chen, Leilei

Yu, Wenlu

Conference Name:

2024 International Conference on Smart Transportation Interdisciplinary Studies (2024-12-13 : Nanjing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2025-01-7201

Access Restriction:

Restricted for use by site license