Research on Dual-Safety Collaborative Testing and Risk Assessment Framework for Vehicles

In order to solve the problems of incomplete risk assessment and blind areas of test coverage caused by the separation of functional safety and cybersecurity of intelligent connected vehicles, this paper proposes a dual-safety collaborative test and risk assessment framework. The framework builds an attack-hazard correlation model by integrating Hazard Analysis and Risk Assessment (HARA) and Threat Analysis and Risk Assessment (TARA) to accurately identify high-priority 'attack-hazard' scenarios, and then relies on the test environment that integrates functional safety and cybersecurity tools to generate and execute collaborative test cases. Finally, the strategy of the design and test will be optimized through quantitative evaluation feedback. Taking the intelligent cockpit controller as a case, the results show that this framework can find out deep security vulnerabilities missed by traditional independent testing, achieve more comprehensive risk identification and accurate assessment, and provide effective guidance for the practice of intelligent connected vehicle safety engineering. The framework achieves a 42% improvement in defect detection rate for cross-domain security vulnerabilities, and the test coverage rate of attack-hazard coupling scenarios reaches 96%, which is significantly higher than the 61% coverage rate of traditional testing methods.