Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search

TL;DR

This paper presents a novel metaheuristic search method for concretizing abstract traffic scene specifications into realistic, numerically defined scenarios for autonomous vehicle safety testing, improving over existing tools.

Contribution

It introduces a flexible approach that maps high-level scene constraints to concrete scenarios using metaheuristic search, supporting static inconsistency detection and customizable objectives.

Findings

Outperforms state-of-the-art Scenic tool variations in experiments

Demonstrates scalability on three realistic road maps

Provides a flexible, constraint-aware scene concretization method

Abstract

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.