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MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning.

, , , , , and . ICCV, page 13158-13168. IEEE, (2021)

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The effects of augmented reality on improving spatial problem solving for object assembly., and . Adv. Eng. Informatics, (2018)Chiroptical Metasurfaces: Principles, Classification, and Applications., , , , , , , and . Sensors, 21 (13): 4381 (2021)Deep-Learning Enabled Assessment of Neurocognitive Performance in Object Following in Mixed Reality., , , , and . CHASE, page 203-207. IEEE, (2022)Can Intelligent Agent Improve Human-Machine Team Performance Under Cyberattacks?, , , and . IHSI, volume 903 of Advances in Intelligent Systems and Computing, page 725-730. Springer, (2019)Performance Comparisons Between Thumb-Based and Finger-Based Input on a Small Touch-Screen Under Realistic Variability., and . Int. J. Hum. Comput. Interaction, 31 (11): 746-760 (2015)EyeCar: Modeling the Visual Attention Allocation of Drivers in Semi-Autonomous Vehicles., , , , , and . CoRR, (2019)Toward Minimum Startle After Take-Over Request: A Preliminary Study of Physiological Data., , , , , and . AutomotiveUI (adjunct), page 27-29. ACM, (2020)MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning., , , , , and . ICCV, page 13158-13168. IEEE, (2021)Operator Suspicion and Human-Machine Team Performance Under Mission Scenarios of Unmanned Ground Vehicle Operation., , , , and . IEEE Access, (2019)Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning., , and . CoRR, (2019)