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Autonomous vehicles have the potential to lower the accident rate whencompared to human driving. Moreover, it is the driving force of the automatedvehicles' rapid development over the last few years. In the higher Society ofAutomotive Engineers (SAE) automation level, the vehicle's and passengers'safety responsibility is transferred from the driver to the automated system,so thoroughly validating such a system is essential. Recently, academia andindustry have embraced scenario-based evaluation as the complementary approachto road testing, reducing the overall testing effort required. It is essentialto determine the system's flaws before deploying it on public roads as there isno safety driver to guarantee the reliability of such a system. This paperproposes a Reinforcement Learning (RL) based scenario-based falsificationmethod to search for a high-risk scenario in a pedestrian crossing trafficsituation. We define a scenario as risky when a system under testing (SUT) doesnot satisfy the requirement. The reward function for our RL approach is basedon Intel's Responsibility Sensitive Safety(RSS), Euclidean distance, anddistance to a potential collision.

Critical concrete scenario generation using scenario-based falsification