99 citations · 156 across the 7 of their papers we have counts for
7 papers
Learning from synthetic photorealistic raindrop for single image raindrop removal
Zhixiang Hao, Shaodi You, Yu Li +2
Raindrops adhered to camera lens or windshield are inevitable in rainy scenes and can become an issue for many computer vision systems such as autonomous driving. Because raindrop…
Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides
Kunming Li, Mao Shan, Stephany Berrio Perez +2
Traffic accidents are a global safety concern, resulting in numerous fatalities each year. A considerable number of these deaths are caused by animal-vehicle collisions (AVCs), whi…
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction
Yu Liu, Yuexin Zhang, Kunming Li +4
Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to th…
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles
Kunming Li, Mao Shan, Karan Narula +2
Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict…
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
Kunming Li, Stuart Eiffert, Mao Shan +3
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncert…
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network
Stuart Eiffert, Kunming Li, Mao Shan +3
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex…