Quantitative Evaluation and Modeling of Action-Reaction Cycles in Interactive Human Driving Behaviors

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This project aims to provide a framework to quantify the interactivity between human drivers in traffic, enabling autonomous vehicles to be more innovative and safer in real traffic by leveraging interaction-awareness into decision-making algorithm design. To this end, we develop a module that can predict the human driver's behavior, which allows us to evaluate how dependent one human driver's reaction is on the other human driver's action using statistic machine learning. Finally, we integrate the quantified dependency into an interaction-aware trajectory prediction model or controller to validate the algorithm performance in real traffic. This project will benefit Canada's development of autonomous vehicles and other related domains such as artificial intelligence and human-robot interaction in Canada's Natural Sciences and Engineering. This research will systematically and solidly bridge an enhanced understanding and characterization of human interaction-aware behaviors toward advanced autonomous cars in academia and related products in industries. Moreover, this research can account for the influence of human driver behavior, thus making Canada's daily transportation systems safer and more efficient.