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.

翻译：现有的自主车辆（AV）安全保证方法通过在现实交通模拟器中，将待测AV置于由抽象场景规范捕获的具有挑战性的交通场景中，来执行系统级安全评估。作为基于场景的AV测试的第一步，交通场景的初始画面必须被具体化。在此背景下，场景具体化挑战将抽象交通场景的高级规范作为输入，旨在将其映射为具体场景，其中为车辆的每个属性（例如位置或速度）定义精确的数值初始值。本文提出了一种交通场景具体化方法，该方法将车辆放置在现实道路地图上，使其满足由一种表达性场景规范语言定义的可扩展抽象约束集，该语言还支持静态不一致性检测。然后，抽象约束被映射为相应的数值约束，这些约束通过具有可定制目标函数和约束聚合策略的元启发式搜索来求解。我们在三个现实道路地图上进行了一系列实验，将我们方法的八种配置与最新Scenic工具的三种变体进行比较，并评估其可扩展性。