VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System

• URL: http://arxiv.org/abs/2305.01898v1
• Date: Wed, 3 May 2023 05:08:56 GMT
• Title: VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System
• Authors: Tian Zhang, Wenshan Guan, Hao Miao, Xiujie Huang, Zhiquan Liu, Chaonan Wang, Quanlong Guan, Liangda Fang, Zhifei Duan
• Abstract summary: We develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems.
• Score: 6.499974038759507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems are most vulnerable to attacks, allowing them to conduct more thorough inspections and tests. In this paper, we develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We extract important indicators related to vehicle safety, use fuzzy cluster analys (FCA) combined with fuzzy analytic hierarchy process (FAHP) to mine the vulnerable components of the vehicle intelligent connected system, and conduct priority testing on vulnerable components to reduce risks and ensure vehicle safety. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems. The experiment fully complies with ISO 26262 and ISO/SAE 21434 standards, and our model has a higher accuracy rate than other models. These results provide a promising new research direction for predicting the security risks of vehicle intelligent connected systems and provide typical application tasks for VSRQ. The experimental results show that the accuracy rate is 94.36%, and the recall rate is 73.43%, which is at least 14.63% higher than all other known indicators.

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