Automated Driving Systems (ADS) pose significant challenges for testing within their operational design domains (ODD), especially at Levels 4 and 5 of autonomy. Testing these systems requires evaluating their functionality within the defined ODD in a simulation environment, making necessary improvements and corrections, and subsequently implementing them in real-world scenarios. My goal is to develop a testing method that allows for a comprehensive evaluation of scenarios in a simulation environment, especially for scenarios that are challenging or expensive to replicate in the real world. The proposed method will allow for comparative safety analysis by evaluating ADS performance against human drivers, enabling a thorough assessment of its safety profile.

Testing and verification of self-driving cars are essential for ensuring their safety and reliability. In the context of the ICST 2024 self-driving cars testing tool competition, we present ITS4SDC, our tool for selecting roads that challenge lane-keeping assist systems by leading the car off the road. ITS4SDC leverages a long short-term memory-based model for the classification of roads as safe and unsafe and subsequently selects unsafe roads for testing.