S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving

TL;DR

S-RAF is a simulation-based framework using CARLA to evaluate autonomous driving agents' robustness across diverse scenarios, helping improve safety, reduce costs, and streamline certification.

Contribution

The paper introduces S-RAF, a novel simulation-based robustness assessment framework specifically designed for autonomous driving systems, addressing evaluation inconsistencies.

Findings

Effective assessment of AD agents under varied conditions.

Reduced testing costs through simulation.

Ability to explore unsafe edge cases safely.

Abstract

As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness and its relationship with other safety-critical factors, such as carbon emissions, S-RAF aids developers and stakeholders in building safe and responsible driving agents, and streamlining safety certification processes. Furthermore, S-RAF offers significant advantages, such as reduced testing costs, and the ability to explore edge cases that may be unsafe to test in the real world. The code for this framework is available here: https://github.com/cognitive-robots/rai-leaderboard