  Autonomous vehicles have the potential to lower the accident rate when
compared to human driving. Moreover, it is the driving force of the automated
vehicles' rapid development over the last few years. In the higher Society of
Automotive 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 and
industry have embraced scenario-based evaluation as the complementary approach
to road testing, reducing the overall testing effort required. It is essential
to determine the system's flaws before deploying it on public roads as there is
no safety driver to guarantee the reliability of such a system. This paper
proposes a Reinforcement Learning (RL) based scenario-based falsification
method to search for a high-risk scenario in a pedestrian crossing traffic
situation. We define a scenario as risky when a system under testing (SUT) does
not satisfy the requirement. The reward function for our RL approach is based
on Intel's Responsibility Sensitive Safety(RSS), Euclidean distance, and
distance to a potential collision.
