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PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction., , , and . ICCV, page 6261-6270. IEEE, (2019)NeurIPS 2022 Competition: Driving SMARTS., , , , , , , , , and 8 other author(s). CoRR, (2022)Understanding and Modeling the Effects of Task and Context on Drivers' Gaze Allocation., and . IV, page 1337-1344. IEEE, (2024)Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment., and . IV, page 1381-1388. IEEE, (2024)A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications., , and . CoRR, (2016)Active Fixation Control to Predict Saccade Sequences., , and . CVPR, page 3184-3193. Computer Vision Foundation / IEEE Computer Society, (2018)Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs., , and . BMVC, page 171. BMVA Press, (2019)Do They Want to Cross? Understanding Pedestrian Intention for Behavior Prediction., , and . IV, page 1688-1693. IEEE, (2020)Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior., , and . ICCV Workshops, page 206-213. IEEE Computer Society, (2017)Agreeing to cross: How drivers and pedestrians communicate., , and . Intelligent Vehicles Symposium, page 264-269. IEEE, (2017)