Kunming Li — accepted AI-conference papers

2021

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

ICRA 2021poster

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…

Cited by 37 Source ↗Scholar ↗

2020

Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction Using a Graph Vehicle-Pedestrian Attention Network

Stuart Eiffert, Kunming Li, Mao Shan, Stewart Worrall, Salah Sukkarieh, Eduardo M. Nebot

RA-L 2020

Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between m

Cited by 88 Source ↗Scholar ↗