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Found 5 papers, 3 papers with code
Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases.
Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving
Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving.
Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention
This work addresses the above limitations by introducing a novel neural network architecture to fuse inherently different spatio-temporal features for pedestrian crossing intention prediction.
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
Automated driving in urban settings is challenging.
Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated.
