Integrating the Simplex Architecture to Enhance Safety in Deep Learning Autonomous Systems

Integrating the Simplex Architecture to Enhance Safety in Deep Learning Autonomous Systems

연구 분야: Artificial Intelligence

학회: ICCPS '25: Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2025)

The rapid advancement of artificial intelligence (AI) has led to the development of deep neural networks (DNNs) that surpass human performance in various specialized tasks, including image classification [5], object detection [10], and control [8]. Numerous industries took notice of these findings and began implementing deep learning techniques in cyber-physical systems (CPSs), including automobiles, robots, and drones, to enhance their autonomy and carry out complex tasks. Those solutions are not free of drawbacks. The lack of trustworthiness of deep learning models in comparison to the stringent industrial-grade standards used in critical domains is one of the primary barriers to their deployment in CPSs. Generally speaking, trustworthiness refers to a collection of desirable attributes that a learning-based CPS ought to possess, such as safety, security, and temporal predictability.