SafeExplain helps organizations use artificial intelligence safely in autonomous systems such as vehicles, robots, satellites and industrial machines. The project provides methods and software tools that allow AI to be integrated into systems where safety is critical, while meeting both regulatory and engineering requirements.

Problem

AI systems are developed very differently from traditional safety-critical software. They learn from data, can behave unpredictably and the often do not follow fixed rules defined in advance. This creates a mismatch with safety-critical development processes, which require strict verification, clear requirements and evidence that systems behave safely in all situations. As a result, using AI in autonomous systems such as cars, aircraft or space missions poses a certain risk if there aren’t new methods in place to  address these differences.

Solution

SafeExplain provides software technologies that make AI suitable for use in safety-critical autonomous systems by combining explainable AI with built-in safety mechanisms.  The approach starts by clearly defining the operating conditions and limits of the AI, then uses this information to guide how data is selected, models are trained and behavior is evaluated. Throughout development, the system is continuously tested and validated to collect evidence of safe and reliable behavior. Safety functions such as monitoring, supervision and redundancy are integrated directly into the system architecture, allowing the AI to detect and respond to unsafe situations at runtime. This combination of explainability, validation and runtime safety enables AI systems to operate reliably in real-world environments while meeting safety and legal requirements.

Success Story

• Industry: Automotive 
• Results: SafeExplain supported real-time object detection in simulated driving conditions while ensuring safety.
• Impact: Enabled higher levels of autonomy for self-driving vehicles.