A Self-Adaptive Framework for Advanced Driver Assistance Systems

Self-adaptive systems that can handle faults and changing environmental conditions are increasingly important for the automotive industry, particularly for next-generation Advanced Driver Assistance Systems (ADAS). The ability to manage faults is essential for ensuring the system reaches a safe state. In this paper, we propose a novel self-adaptive system approach for ADAS, addressing the evolving needs and challenges faced by current solutions. We analyze existing ADAS technologies and identify gaps that limit their adaptability and responsiveness. Our proposed approach leverages advanced machine learning techniques, real-time data processing, and adaptive control systems to provide a robust and scalable solution. We present detailed analyses of various components of self-adaptive ADAS, demonstrate its implementation through use cases such as Collision Avoidance and Adaptive Cruise Control, and discuss the challenges and future directions for research and development in this field. Our findings underscore the transformative potential of self-adaptive ADAS, paving the way for safer and more intelligent transportation systems.