Conservative Estimation of Perception Relevance of Dynamic Objects for
  Safe Trajectories in Automotive Scenarios
Ken Mori Kai Storms Steven Peters
Having efficient testing strategies is a core challenge that needs to be
overcome for the release of automated driving. This necessitates clear
requirements as well as suitable methods for testing. In this work, the
requirements for perception modules are considered with respect to relevance.
The concept of relevance currently remains insufficiently defined and
specified. In this paper, we propose a novel methodology to overcome this
challenge by exemplary application to collision safety in the highway domain.
Using this general system and use case specification, a corresponding concept
for relevance is derived. Irrelevant objects are thus defined as objects which
do not limit the set of safe actions available to the ego vehicle under
consideration of all uncertainties. As an initial step, the use case is
decomposed into functional scenarios with respect to collision relevance. For
each functional scenario, possible actions of both the ego vehicle and any
other dynamic object are formalized as equations. This set of possible actions
is constrained by traffic rules, yielding relevance criteria. As a result, we
present a conservative estimation which dynamic objects are relevant for
perception and need to be considered for a complete evaluation. The estimation
provides requirements which are applicable for offline testing and validation
of perception components. A visualization is presented for examples from the
highD dataset, showing the plausibility of the results. Finally, a possibility
for a future validation of the presented relevance concept is outlined.