In-Cabin Gesture Control with Embedded ML for Airbag Activation

This paper proposes a perspective on the integration of smart technology within embedded automotive systems, em phasizing the role of machine learning and artificial intelligence in enhancing in-cabin interactions and safety mechanisms. These technologies have transformed sectors that rely on embedded systems as controllers, driving advancements in performance and efficiency while fostering groundbreaking innovations. By leveraging an array of sensors, industries are enabling sophisticated, real-time responsiveness that improves both safety and operational efficiency. Gesture sensors, for instance, detect hand movements to support contact-free interactions across various domains such as gaming, smartphone applications,and automotive interfaces, where they enhance user experience and interaction efficiency. In the automotive sector, gesture-based systems are increasingly used not only for infotainment but also to control other in-cabin functionalities, reflecting a broader trend toward intuitive, contact-free vehicle interactions. This study introduces a gesture-based control system for vehicle functions, streamlining task initiation and usability. Additionally, we examine an accident detection system integrated with airbag control, powered by embedded machine learning. This integration facilitates real time decision-making and data processing, minimizing latency, enhancing data privacy, and boosting efficiency, contributing to the evolution of intelligent, user-centric, and safer automotive environments.