Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Abstract

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation

“…When two faults are simultaneously injected into separate components of a system under test, it is referred to as Multi-Faults. Experience [69] shows that if there is a relationship between the features of these two components, the behavior of the system will differ from what would be observed if each fault were injected individually. To illustrate the methodology's efficacy, the experiment details the injection of 'noise' in the steering wheel angle signal and 'drift' in the acceleration pedal signal.…”

“…Focusing on the system integration phase of the V-model, the proposed framework aims to analyze the system behavior under faults to determine the critical faults that lead to the safety violations. The corresponding behavior of the identified critical faults was then used to collect a representative dataset [69].…”

Layered random fault injection method for the air braking system based on multiple Markov chains

“…The specific value of 𝜋 𝑡𝑖 (𝑖 = 0, 1, ..., 𝑘) can be determined by simultaneously solving Eqs. ( 8)- (10), representing the steady-state solution. At this point, the steady-state probabilities corresponding to the aforementioned 𝑘 +1 states have been acquired.…”

“…In the fault injection of automotive systems: Abboush et al [10] proposed a new method based on hardware in the loop (HIL) simulation and automatic real-time fault injection method for generating, analyzing, and collecting data samples in the presence of single and concurrent faults. In the fault injection of aviation control systems: Joshi et al [11] first proposed the application of Microsoft baseline security analyzer to the fault modeling and verification process of aviation control systems.…”

A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware in the Loop and Fault Injection

“…If the test drive is performed automatically by the machine, the faults can be injected automatically using an automation software tool, i.e., AutomationDesk (version 6.6). In this case, the execution process follows a sequence of block hierarchies with three phases, i.e., reading, manipulating signals and writing [ 54 ]. In the user-based virtual test drive, on the other hand, faults are injected manually during real-time HIL execution.…”