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Found 4 papers, 1 papers with code
ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction
Predicting trajectories of pedestrians based on goal information in highly interactive scenes is a crucial step toward Intelligent Transportation Systems and Autonomous Driving.
SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories
Autonomous robots and vehicles are expected to soon become an integral part of our environment.
Modeling Interactions of Multimodal Road Users in Shared Spaces
In shared spaces, motorized and non-motorized road users share the same space with equal priority.
On the Generalizability of Motion Models for Road Users in Heterogeneous Shared Traffic Spaces
The results indicate that our model can realistically simulate a wide range of motion behaviors and interaction scenarios, and that adding different motion patterns of pedestrians into our model improves its performance.
