Details on the grant funded by the PRIN 2022 MEDITATE project
Referring Prof. V. Cortellessa
Title: Model-based generation and optimization of advanced driver assistance systems (ADAS) testing scenarios in co-simulation
Abstract: Advanced Driver Assistance Systems (ADAS) play a crucial role in improving road safety, reducing traffic congestion, and decreasing fuel consumption, thus paving the way to safer and more environmentally friendly mobility. Novel ADAS mostly relies on AI techniques to “understand” the current situation around the vehicle (e.g., a pedestrian in front of the car), plan, and execute appropriate behavior (e.g., emergency braking). The validation of AI-based ADAS is a pain point for automotive companies, due to the sheer number of scenarios that need to be considered. In this PhD program, we aim at improving cosimulation-based validation of ADAS with an integrated framework, supporting both the automated generation of a suite of testing scenarios and their optimization.