  Conventional vehicles are certified through classical approaches, where
different physical certification tests are set up on test tracks to assess
required safety levels. These approaches are well suited for vehicles with
limited complexity and limited interactions with other entities as last-second
resources. However, these approaches do not allow to evaluate safety with real
behaviors for critical and edge cases, nor to evaluate the ability to
anticipate them in the mid or long term. This is particularly relevant for
automated and autonomous driving functions that make use of advanced predictive
systems to anticipate future actions and motions to be considered in the path
planning layer. In this paper, we present and analyze the results of physical
tests on proving grounds of several predictive systems in automated driving
functions developed within the framework of the BRAVE project. Based on our
experience in testing predictive automated driving functions, we identify the
main limitations of current physical testing approaches when dealing with
predictive systems, analyze the main challenges ahead, and provide a set of
practical actions and recommendations to consider in future physical testing
procedures for automated and autonomous driving functions.
