Abstract

With the advancement of autonomous vehicle technologies, embedded vision systems play a crucial role in ensuring safety, particularly in scenarios like airbag deployment. System-on-Chip (SoC) architectures are pivotal for the efficient execution of vision algorithms in real-time. This study explores various SoC architectures tailored for embedded vision applications in autonomous vehicles, focusing on airbag deployment scenarios. The methodology involves a comprehensive review of existing SoC designs, analysis of their suitability for vision tasks, and evaluation through simulations and prototyping. Our contribution lies in identifying the key requirements and challenges specific to airbag deployment in autonomous vehicles and proposing optimized SoC architectures to address them. We consider factors such as computational efficiency, power consumption, and real-time processing capabilities to design SoCs that meet the stringent demands of embedded vision systems. Through extensive experimentation and evaluation, we demonstrate the efficacy of the proposed SoC architectures in achieving high-performance vision processing for airbag deployment applications. The results indicate significant improvements in terms of processing speed and energy efficiency compared to conventional approaches. Moreover, our findings highlight the importance of customized hardware designs tailored to the unique requirements of autonomous vehicle safety systems.