  This paper explores the role and challenges of Artificial Intelligence (AI)
algorithms, specifically AI-based software elements, in autonomous driving
systems. These AI systems are fundamental in executing real-time critical
functions in complex and high-dimensional environments. They handle vital tasks
like multi-modal perception, cognition, and decision-making tasks such as
motion planning, lane keeping, and emergency braking. A primary concern relates
to the ability (and necessity) of AI models to generalize beyond their initial
training data. This generalization issue becomes evident in real-time
scenarios, where models frequently encounter inputs not represented in their
training or validation data. In such cases, AI systems must still function
effectively despite facing distributional or domain shifts. This paper
investigates the risk associated with overconfident AI models in
safety-critical applications like autonomous driving. To mitigate these risks,
methods for training AI models that help maintain performance without
overconfidence are proposed. This involves implementing certainty reporting
architectures and ensuring diverse training data. While various
distribution-based methods exist to provide safety mechanisms for AI models,
there is a noted lack of systematic assessment of these methods, especially in
the context of safety-critical automotive applications. Many methods in the
literature do not adapt well to the quick response times required in
safety-critical edge applications. This paper reviews these methods, discusses
their suitability for safety-critical applications, and highlights their
strengths and limitations. The paper also proposes potential improvements to
enhance the safety and reliability of AI algorithms in autonomous vehicles in
the context of rapid and accurate decision-making processes.
