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.
