Nov 24, 2022 · In this work, we model traffic scenes in a form of spatial semantic scene graphs for various different predictions about the traffic participants.

In this work, we model traffic scenes in a form of spatial semantic scene graphs for various different predictions about the traffic participants, e.g., ...

This work model traffic scenes in a form of spatial semantic scene graphs for various different predictions about the traffic participants, e.g., ...

Oct 8, 2022 · Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving.

Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving.

Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving.

Relation-based Motion Prediction using Traffic Scene Graphs. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 825-831.

This approach constructs a traffic scene graph to represent vehicle layout and motion, thereby improving predictions in complex, dynamic environments [14,15].

Sep 27, 2020 · To capture this highly complex structure of interactions, we propose to use a hybrid graph whose nodes represent both the traffic actors as well ...

Graph Neural Networks (GNNs) have been widely used to capture and model the underlying spatial and temporal relationships of the agents in a traffic scene. Some ...