Fault Injection in Model-Based System Failure Analysis of Highly Automated Vehicles
The active safety control systems of highly automated vehicles for SAE level 3 and higher are still not fully developed and facing some unresolved issues. The deployment of automated driving systems and the functional safety development present challenges in driver – machine control relat...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/8f8e43bbd10241efa10bd869a6bec10e |
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| Sumario: | The active safety control systems of highly automated vehicles for SAE level 3 and higher are still not fully developed and facing some unresolved issues. The deployment of automated driving systems and the functional safety development present challenges in driver – machine control relationship when there is a system failure or malfunction. The current definition of the product development and controllability classes of the road vehicles functional safety (ISO26262) are not feasible in highly automated vehicles (HAV). This research developed an overview of fault or disturbance injection on the steering system of highly automated model to study the impact of steering system sensors malfunction. The approach was to study the fault propagation using a model-based engineering development in a virtual environment of MATLAB. Subsequently, the steering control system of automated vehicle was developed using an adaptive model predictive control structure to study the control system sensors failures on a system-feature level of the vehicle. It was concluded that the steering wheel angle sensor failure has a significant impact on the planned trajectory of the vehicle and thus it was classified as ASIL D, which represents the highest critical safety component and requires comprehensive safety mechanisms to meet the safety goals of the system. The study also introduced a new criterion for controllability classes suitable for highly automated systems based on the global vehicle position relative of the lane marker lines, to deal with the active safety systems and risk handling strategies. The drivers – vehicle control systems are changing significantly in SAE level 3 automated vehicle and above that driving functions are controlled by the vehicle control systems. This presents human factors challenge in this interactive system with moving to SAE levels 4 and 5. Hence, several human machine interfaces and scenario-based testing are introduced to mitigate any risk or safety uncertainty resulting from control handing-over between the driver and the vehicle control system. |
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