In this paper we consider motion models cast in the Predictive Linear Gaussian (PLG) model, and propose two learning approaches for this framework: one based on ...

Abstract—Robot systems deployed in real-world environ- ments often need to interact with other dynamic objects, such as pedestrians, cars, bicycles or other ...

This paper considers motion models cast in the Predictive Linear Gaussian (PLG) model, and proposes two learning approaches for this framework: one based on ...

In this paper we consider motion models cast in the Predictive Linear Gaussian (PLG) model, and propose two learning approaches for this framework: one based on ...

In this paper, a deep-learning framework is proposed for modelling pedestrian movement uncertainty in large-scale indoor areas.

This project presents a novel incremental learning algorithm for pedestrian motion prediction, with the ability to improve the learned model over time when ...

Feb 6, 2022 · Broadly, road user trajectory prediction algorithms can be divided into theory-based or physics- based approaches and learning-based approaches.

Apr 29, 2024 · We propose Dynamic Target Driven Network for pedestrian trajectory prediction (DTDNet), which employs a multi-precision pedestrian intention analysis module to ...

We introduce a novel, online method to predict pedestrian tra- jectories using agent-based velocity-space reasoning for improved.

We propose a model named PTPGC based on graph attention and convolutional long short-term memory (ConvLSTM) network to predict multiple reasonable pedestrian ...