Patent ID: 11960290
Assignee: UATC, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC G

Claim 13:
14. A computer-implemented method for training a trajectory prediction network model from end-to-end, the network model comprising a plurality of subnetworks configured to extract one or more features from input data and a trajectory prediction machine-learned model, the method comprising:
inputting training data into the network model, the training data comprising radar data comprising a plurality of sweeps, LIDAR data comprising a plurality of sweeps, and map data;
generating, by the network model, one or more features for each of the LIDAR data; the radar data, and the map data, wherein generating the one or more features for the radar data comprises determining a feature vector for each cell in a coordinate frame and concatenating per sweep feature vectors for each cell;
combining, by the network model, the one or more generated features for each of the LIDAR data, the radar data; and the map data to generate fused feature data;
predicting, by the network model, a respective future trajectory for one or more objects detected by the network model;
determining a loss function for the network model based at least in part on a comparison between the respective predicted future trajectory for the one or more Objects and a ground truth future trajectory; and training the network model based at least in part on the loss function.