ENVIA - Simulation environment for AI-driven vehicles

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How do we learn the concepts of safety and how do we apply them to control algorithms for AI-driven vehicles? While deep reinforcement learning is a very common approach for modern artificial intelligence in terms of autonomous control (of robots, aircraft, factories, etc.), it carries by nature a fundamental safety risk that prevents its deployment in the context of many real industrial systems. Several researchers are trying to validate the safety of AI-driven systems. In this project, we use a different approach: we use classical control approaches to constrain AI-based systems and prevent actions that are not safe. We combine control theory and reinforcement learning to produce efficient and safe control algorithms, which can be initially trained in simulation, and then adapted and refined by meeting safety guarantees on the targeted real hardware. The principle is to use "barriers" to set strict safety limits for controllers trained by reinforcement learning (e.g., the controller of a drone). These barriers can themselves be learned automatically, and loosened or tightened as needed, trading off some level of risk to improve learning or performance. Imagine a person learning to ski with an instructor: they will take the risk of falling to learn, but this risk is limited and constrained by the instructor's knowledge and guidance. In a similar way, our barriers will prevent excessively risky actions taken by the AI. Our final goal is to design controllers that can be trained in simulation and then deployed on real hardware for adaptation and additional learning with tunable, calculated risk based on performance requirements. The economic and technical benefits for the industrial partners will be significant in the medium term, as the project will allow the application of deep reinforcement learning methods, which currently exceed the performance of all other known methods as well as that of human experts in more and more virtual environments where errors have no serious consequences, to real systems where safety is critical. Personnel working on the project will be highly sought after, and Canada will benefit in terms of scientific and industrialprestige, as well as by the economic impact of the new highly qualified personnel.