Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
Kunming Li,Stuart Eiffert,Mao Shan,Francisco Gomez-Donoso,Stewart Worrall,Eduardo Nebot,Kunming Li,Stuart Eiffert,Mao Shan,Francisco Gomez-Donoso,Stewart Worrall,Eduardo Nebot
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated sampling of generative models. Additionally, most current predictive models are trained on datasets...
