A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

TL;DR

This survey reviews scenario-based testing for automated driving systems using high-fidelity simulation, highlighting frameworks, challenges, and future research directions to improve safety testing efficiency.

Contribution

It provides a comprehensive formulation of scenario-based testing in high-fidelity simulation and compares existing methods, identifying gaps and open challenges.

Findings

Many frameworks exist for scenario-based testing.

Comparison among methods reveals gaps and inconsistencies.

Open challenges include standardization and scalability.

Abstract

Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before their future mass deployment. Testing the system on the road is the closest to real-world and desirable approach, but it is incredibly costly. Also, it is infeasible to cover rare corner cases using such real-world testing. Thus, a popular alternative is to evaluate an ADS's performance in some well-designed challenging scenarios, a.k.a. scenario-based testing. High-fidelity simulators have been widely used in this setting to maximize flexibility and convenience in testing what-if scenarios. Although many works have been proposed offering diverse frameworks/methods for testing specific systems, the comparisons and connections among these works are still missing. To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works. We further compare them and present the open challenges as well as potential future research directions.