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FRINATEK-Fri prosj.st. mat.,naturv.,tek

Theory, methods, and tools for testing autonomous systems

Alternative title: Theory, methods, and tools for testing autonomous systems

Awarded: NOK 8.0 mill.

Autonomous systems (AS) are emerging technologies that have the potential to impact many areas of human life nowadays. In T3AS, we are exploring autonomous cars (AC) as one popular type of AS. In the past few years, there has been a wide increase of interest in testing ACs. The main reason is that despite the technological progress, we still see fatal failure scenarios of ACs. Unlike testing of traditional software, testing of AI-based AC software is a less mature area. We are addressing the challenge of testing AC using a deep reinforcement learning approach. In particular, we explore adversarial deep reinforcement learning for testing and improving driving policies in a multi-agent AC environment. The main idea is to use adversarial examples beyond testing purposes to improve the robustness of ACs, since adversarial attacks are usually not considered when autonomous driving models are being trained in urban driving scenarios.

Autonomous systems are emerging technologies that are impacting a range of industries and many areas of human life nowadays. Driverless cars have started appearing on public roads, and collaborative robots have started working with human workers on the factory floor. Autonomous technologies offer a significant opportunity to enhance economy and society, but they may also cause fatal harm if they malfunction. There are great open challenges of testing these autonomous systems, to prevent their malfunctioning, and to ensure their safe and fault-free behavior. T3AS project will develop a novel theoretical foundation based on artificial intelligence, with a set of methods and tools, for addressing the great challenges of testing autonomous systems, to make them dependable and safe for their users and environment.

Activity:

FRINATEK-Fri prosj.st. mat.,naturv.,tek