OCTNet: Trajectory Generation in New Environments from Past Experiences

by   Weiming Zhi, et al.

Being able to safely operate for extended periods of time in dynamic environments is a critical capability for autonomous systems. This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings. Many motion prediction methods in the literature can learn a function, mapping position and time to potential trajectories taken by people or other dynamic entities. However, these predictions depend only on previously observed trajectories, and do not explicitly take into consideration the environment. Trends of motion obtained in one environment are typically specific to that environment, and are not used to better predict motion in other environments. In this paper, we address the problem of generating likely motion dynamics conditioned on the environment, represented as an occupancy map. We introduce the Occupancy Conditional Trajectory Network (OCTNet) framework, capable of generalising the previously observed motion in known environments, to generate trajectories in new environments where no observations of motion has not been observed. OCTNet encodes trajectories as a fixed-sized vector of parameters and utilises neural networks to learn conditional distributions over parameters. We empirically demonstrate our method's ability to generate complex multi-modal trajectory patterns in different environments.


page 1

page 5

page 6


Probabilistic Trajectory Prediction with Structural Constraints

This work addresses the problem of predicting the motion trajectories of...

Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction

Understanding the dynamics of an environment, such as the movement of hu...

CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction

Human motion prediction is important for mobile service robots and intel...

Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories

In this paper, we propose a novel trajectory learning method that exploi...

Topological Trajectory Prediction with Homotopy Classes

Trajectory prediction in a cluttered environment is key to many importan...

Generating people flow from architecture of real unseen environments

Mapping people dynamics is a crucial skill, because it enables robots to...

Personalized Trajectory Prediction via Distribution Discrimination

Trajectory prediction is confronted with the dilemma to capture the mult...

Please sign up or login with your details

Forgot password? Click here to reset