University of Virginia
DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data
Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements. While some systems determine the need for driver takeover using driver context and road condition to initiate a takeover request, studies show that the driver may not react to it. We present DeepTake, a novel deep neural network-based framework that predicts multiple aspects of takeover behavior to ensure that the driver is able to safely take over the control when engaged in non-driving tasks. Using features from vehicle data
The simulator records driver control actions and vehicle states with a sampling frequency of 20Hz and sent the captured data through our developed API using iMotions software. The simulated driving environments along with the tasks were created using PreScan Simulation Platform.
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