Trajectory predicting methods and systems

A system for determining a plurality of predicted trajectories of a moving object, the method comprising:

• memory;
• and
• at least one processor in communication with the memory,
• wherein the memory stores machine-readable instructions for causing the
• at least one processor to:
• obtain historical trajectory data for the moving object and for one or more neighbouring objects;
• pass the historical trajectory data to an agent encoder to generate dynamic features for the moving object and the one or more neighbouring objects;
• pass the historical trajectory data and candidate centre-lines (CCLs) of the moving object and the neighbouring objects to a CCL encoder to generate sequential features for the CCLs of the moving object and the neighbouring objects;
• construct a graph representing interactions between the moving object and the one or more neighbouring objects, between the moving object and the moving object's candidate centre-lines, and between each neighbouring object and each neighbouring object's respective candidate centre-lines, wherein each node of the graph represents the moving object, or one of the moving object's candidate centre-lines, or one of the neighbouring objects, or one of each neighbouring object's respective candidate centre-lines,
• wherein each node of the graph comprises the respective dynamic features of the moving object or the neighbouring objects, and comprises respective sequential features of the moving object's candidate centre-lines or each neighbouring objects' respective candidate centre-lines,
• wherein each edge of the graph represents an effect of the moving object on a neighbouring object or vice versa, or an effect of the moving object and the moving object's candidate centre-lines or vice versa, or an effect of each neighbouring object and said each neighbouring object's respective candidate centre-lines or vice versa;
• pass the graph, the dynamic features, and the sequential features to a plurality of graph neural network (GNN) encoders to generate a plurality of interaction features;
• and
• pass the dynamic features, the interaction features, and the sequential features to a decoder to generate the predicted trajectories.

A method of determining a predicted trajectory of a moving object, the method comprising:

• obtaining historical trajectory data for the moving object and for one or more neighbouring objects;
• passing the historical trajectory data to a recurrent neural network (RNN) encoder to generate dynamic features for the moving object and the one or more neighbouring objects;
• constructing a graph representing interactions between the moving object and the one or more neighbouring objects, wherein each node of the graph represents one of the moving object or one of neighbouring objects, and comprises the respective dynamic features of the moving object or the one or more neighbouring objects, and each edge represents an effect of the moving object on a neighbouring object or vice versa, or an effect of a neighbouring object on another neighbouring object;
• passing the graph and the dynamic features to a graph neural network (GNN) encoder to generate a plurality of interaction features;
• and
• passing the dynamic features and the interaction features to a RNN decoder to generate the predicted trajectory.