  Existing safety assurance approaches for autonomous vehicles (AVs) perform
system-level safety evaluation by placing the AV-under-test in challenging
traffic scenarios captured by abstract scenario specifications and investigated
in realistic traffic simulators. As a first step towards scenario-based testing
of AVs, the initial scene of a traffic scenario must be concretized. In this
context, the scene concretization challenge takes as input a high-level
specification of abstract traffic scenes and aims to map them to concrete
scenes where exact numeric initial values are defined for each attribute of a
vehicle (e.g. position or velocity). In this paper, we propose a traffic scene
concretization approach that places vehicles on realistic road maps such that
they satisfy an extensible set of abstract constraints defined by an expressive
scene specification language which also supports static detection of
inconsistencies. Then, abstract constraints are mapped to corresponding numeric
constraints, which are solved by metaheuristic search with customizable
objective functions and constraint aggregation strategies. We conduct a series
of experiments over three realistic road maps to compare eight configurations
of our approach with three variations of the state-of-the-art Scenic tool, and
to evaluate its scalability.
