Artificial Intelligence–Driven Functional Safety Approaches for Autonomous Vehicles

Abstract

Autonomous vehicles are revolutionizing the modern world of transportation by the introduction of disruptive intelligent systems capable of perceiving the environment, making decisions and controlling the behavior of the transport unit with minimal participation by human drivers. However, the challenge of ensuring the safety and reliability of such systems is a critical one because autonomous driving is closely dependent on data-driven algorithms. Artificial intelligence techniques, and specifically machine learning and deep learning, have shown great potential to assist safety-related functions like object detection, environmental perception and risk assessment. Despite these advantages comes concerns of model transparency, reliability and if the AI will be compliant with established safety requirements set for automotive systems. This study discusses the role of artificial intelligence in enhancing safety mechanism of autonomous vehicles while taking into consideration of the functional safety regulations that are in place. A review of past studies involving artificial intelligence-based perception and decision-making models is carried out along with an experimental assessment in a simulated classification setting representing safety-critical and regular driving conditions. The performance of some of the AI models is measured by commonly used evaluation measures such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. Experimental results show that AI-based models show successful differentiation of critical situations from normal operation with high classification performance. The findings indicate that artificial intelligence can play a huge role in improving the safety capabilities of autonomous vehicle systems where proper validation strategies and safety frameworks are implemented. However, challenges around explainability, computational efficiency and regulatory certification to ensure deployment of the AI-driven autonomous driving technologies are facing.