Open Access

Varun Savitha

• As the automotive industry continues to evolve, the integration of automatic components driven by microcontrollers has become increasingly prevalent. In safety-critical systems such as seat belt mechanisms, the reliability and responsiveness of these components are paramount to ensuring the safety of both drivers and pedestrians. Central to this concern is the assessment of the CPU load ofAs the automotive industry continues to evolve, the integration of automatic components driven by microcontrollers has become increasingly prevalent. In safety-critical systems such as seat belt mechanisms, the reliability and responsiveness of these components are paramount to ensuring the safety of both drivers and pedestrians. Central to this concern is the assessment of the CPU load of real-time operating system (RTOS) microcontrollers, as their ability to execute tasks within specified time constraints directly impacts the system performance and, ultimately, safety outcomes. This research endeavors to address the need for rigorous evaluation methodologies for CPU load analysis in RTOS microcontrollers within automotive seat belt systems. The study commences by scrutinizing the current methodologies employed for CPU load analysis, with a particular focus on the prevalent pin toggling and Picoscope methods. While these methods have demonstrated efficacy in assessing CPU load, they often entail significant setup time and data extraction procedures, thus presenting a potential bottleneck in the evaluation process. To address these limitations, a novel methodology is proposed, leveraging the capabilities of the Lauterbach debugger and its Software Trace32 tool. This methodology aims to streamline the CPU load analysis process by enabling precise timing profiling of program functions and variables. By scripting within Trace32, data extraction is automated, reducing the time overhead associated with manual methods and enhancing overall efficiency. The proposed methodology holds promise for improving the accuracy and efficiency of CPU load analysis in RTOS microcontrollers. By providing a comprehensive understanding of the temporal behavior of program execution, it facilitates more nuanced insights into system performance and resource utilization, thereby enabling better-informed decision-making in the development and deployment of automotive safety systems. Empirical validation of the proposed methodology is conducted through a comparative analysis with existing approaches. Data is collected and analyzed from both the traditional pin toggling and Picoscope methods and the proposed Lauterbach Trace32-based methodology. By assessing the consistency and accuracy of results obtained from each approach, insights are gleaned into the efficacy of the proposed methodology and its potential for time savings. Through this research, I seek to contribute to the advancement of methodologies for CPU load analysis in RTOS microcontrollers, particularly within the context of automotive safety systems. By offering a more efficient and effective means of evaluating CPU load, my work aims to enhance the reliability and responsiveness of automotive seat belt systems, ultimately contributing to improved safety outcomes on the road.…