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Risk Aware Warning and Control for Interactive Traffic Safety
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2024-07-31
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[PDF-26.42 MB]
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Edition:Final Report
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Abstract:In this project, we proposed a CBF-inspired risk assessment toolbox, measuring the aggregated risk faced by individual agents due to multi-agent interactions, to help empower the ego robot with a comprehensive understanding of the dynamic environment it is in. We also demonstrate two complementary methodologies of embedding the resulting risk assessment into robot controller design, especially for multi-robot scenarios. We show how to utilize such risk information to perform partition in robots’ joint state and action space respectively to construct robot controllers, resulting in the proposed Risk-aware WBVC and Risk-aware decentralized CBF. We further demonstrated the general applicability of our risk assessment in deployment to a physical F1-Tenth testing platform with existing MPPI-based vehicle controllers. A safe and efficient overtaking scenario is demonstrated. In the future, we are interested in extending these methods for safe navigation for self-driving cars with learning-enabled components.
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