S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916359599816704 |
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| author | Omeiza, Daniel Somaiya, Pratik Pattinson, Jo-Ann Ten-Holter, Carolyn Stilgoe, Jack Jirotka, Marina Kunze, Lars |
| author_facet | Omeiza, Daniel Somaiya, Pratik Pattinson, Jo-Ann Ten-Holter, Carolyn Stilgoe, Jack Jirotka, Marina Kunze, Lars |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_08584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving Omeiza, Daniel Somaiya, Pratik Pattinson, Jo-Ann Ten-Holter, Carolyn Stilgoe, Jack Jirotka, Marina Kunze, Lars Robotics Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Machine Learning 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 |
| title | S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2408.08584 |