Configuration and optimization of the operating system running on the infotainment ECU.

This thesis investigates the optimization of boot performance for the Android Auto 14 operating system. The rapid advancement of automotive technology, particularly in Infotainment systems, demands efficient and reliable performance to enhance user experience and safety. This study focuses on achieving swift startup, crucial for minimizing delays and improving overall system efficiency. The work is divided into several phases: initially, the architecture of infotainment systems and the integration of Android Auto within the Android ecosystem are analyzed. Subsequently, optimization methodologies are explored, with a specific focus on boot time optimization. A practical case study is presented, detailing the setup environment using Raspberry Pi 4 and Android 14, based on the existing project "raspberry-vanilla" on GitHub. Detailed log analysis using tools like Bootchart and Logcat from Android Studio identified key performance indicators (KPIs) related to boot performance. The optimization phase involved initially removing non-essential components within the operating system, such as the boot animation file, resulting in significant improvements in startup performance. Additionally, a critical aspect of the optimization process was the detailed study of Android kernel configurations, analyzing both active and inactive settings to understand their impact on system performance and boot time. Adjustments based on this analysis led to notable efficiency gains and reduced boot times. Furthermore, the study included an evaluation of kernel compression and decompression algorithms. Various algorithms were benchmarked to assess their impact on boot time and system responsiveness. This comprehensive analysis provided insights into selecting optimal compression techniques that contributed to enhanced performance during the boot process. The research demonstrates that targeted optimization efforts, including component removal, kernel tuning, and compression algorithm selection, can substantially improve Android Auto's boot performance on the Raspberry Pi 4. The results indicate that a well-optimized system not only starts faster but also operates more efficiently, providing a smoother user experience. This work underscores the potential for significant advancements in automotive technology through meticulous system optimization. Future research could explore additional optimization techniques and their application to other components of automotive infotainment systems, further enhancing overall system performance and user satisfaction.