#Go map for pokemon go driver#

We provide extensive experimental studies over multiple baseline classification models on an eye gaze pattern and head pose dataset collected from simulated driving. We introduce a sequence-to-sequence based deep learning model to infer the driver's likelihood of making correct/wrong decisions based on the corresponding cognitive state. 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.

#Go map for pokemon go driver#

We also conduct additional experiments to validate the robustness of our self-supervised network with respect to different amounts of labeled data used for training.Īnticipating 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. We demonstrate the performance of our method on the EYEDIAP dataset in two different evaluation settings and achieve state-of-the-art results, outperforming all the existing benchmarks on this dataset. Finally, this pretrained eye encoder, along with two additional encoders for visible eyeball region and iris, are used in parallel in our multistream framework to extract salient features for gaze estimation from real-world images. Next, we pretrain an eye image encoder in the real domain with self-supervised contrastive learning to learn generalized eye representations. We then perform eye region segmentation with a U-Net type model which we later use to generate eye region masks for real-world eye images. We first create a synthetic dataset containing eye region masks detailing the visible eyeball and iris using a simulator. We present a novel multistream network that learns robust eye representations for gaze estimation. This suggests that single modality stimulation induces higher cognitive load on users, hindering their decision-making abilities. We show that DecNet’s performance gap between audio and visual scenarios is consistent with user perceived difficulty. DecNet anticipates cognitive overload events in both scenarios and can perform in time-constrained scenarios, anticipating cognitive overload events up to 2s before they occur. We train and test DecNet on a dataset collected in a simulated driving setup from a cohort of 20 users on two dual-tasking decision-making scenarios, with either visual or auditory decision stimuli. We propose DecNet, an end-to-end sequence-to-sequence deep learning model that infers in real-time the likelihood of user mistakes on the secondary task, i.e., the practical impact of cognitive overload, from eye-gaze and head-pose data. Our framework builds on the assumption that decision mistakes on the cognitive secondary task of dual-tasking users correspond to cognitive overload events, wherein the cognitive resources required to perform the task exceed the ones available to the users. Anticipating cognitive overload is a pivotal challenge in interactive cognitive systems and could lead to safer shared-control between users and assistance systems. In this paper, we address cognitive overload detection from unobtrusive physiological signals for users in dual-tasking scenarios.