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Decision Anticipation for Driving Assistance Systems
2020
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Anticipating the correctness of imminent driver decisions is a crucial challenge in advanced driving assistance systems and has the potential to lead to more reliable and safer human-robot interactions. In this paper, we address the task of decision correctness prediction in a driver-in-the-loop simulated environment using unobtrusive physiological signals, namely, eye gaze and head pose. We introduce a sequence-to-sequence based deep learning model to infer the driver's likelihood of making
doi:10.1109/itsc45102.2020.9294216
fatcat:5l7lfem335gypeeuqu6ghg5g54