3 accepted papers
The ability to adapt to varying observation lengths is crucial for human trajectory prediction tasks, particularly in scenarios with limited observation lengths or missing data. Existing approaches mainly focus on introducing novel architectures or additional structural components, which substantial…
Trajectory prediction is a critical technique for autonomous driving, robot navigation and intelligent surveillance. The recurrent neural networks (RNNs) models propagate temporal information through hidden states and have been widely applied in adapting to varying input lengths and generating multi
Pedestrian's future trajectory prediction is a key challenge in ego-centric view of autonomous driving system. Most of the current methods are flawed in capturing subtle change features in a lightweight model size. To solve this problem, we propose a multimodal forward generation transformer network