TRANSFORMER FRAMEWORK FOR TRAJECTORY PREDICTION
A computer-implemented method of trajectory prediction includes obtaining a first cross-attention between a vectorized representation of a road map near a vehicle and information obtained from a rasterized representation of an environment near the vehicle by processing through a first cross-attention stage; obtaining a second cross-attention between a vectorized representation of a vehicle history and information obtained from the rasterized representation by processing through a second cross-attention stage; operating a scene encoder on the first cross-attention and the second cross-attention; operating a trajectory decoder on an output of the scene encoder; obtaining one or more trajectory predictions by performing one or more queries on the trajectory decoder.
1 . A computer-implemented method of trajectory prediction, comprising:
determining a first cross-attention between a vectorized representation of a road map near a vehicle and information obtained from a rasterized representation of an environment near the vehicle by processing through a first cross-attention stage;
determining a second cross-attention between a vectorized representation of a vehicle history and information obtained from the rasterized representation by processing through a second cross-attention stage;
operating a scene encoder on the first cross-attention and the second cross-attention;
operating a trajectory decoder on an output of the scene encoder;
generating one or more trajectory predictions by performing one or more queries on the trajectory decoder.
2 . The computer-implemented method of claim 1 , wherein the information obtained from the rasterized representation comprises a multi-sourced, multi-grained feature map.
3 . The computer-implemented method of claim 2 , wherein the information is further based on a lidar point cloud obtained from a sensor located on the vehicle.
4 . The computer-implemented method of claim 1 , wherein the rasterized representation comprises traffic signal information near the vehicle.
5 . The computer-implemented method of claim 1 , wherein the information comprises a raw camera image obtained by a camera on the vehicle.
6 . The computer-implemented method of claim 1 , wherein the scene encoder comprises N encoding layers, where N is a positive integer.
7 . The computer-implemented method of claim 1 , wherein the trajectory decoder comprises M encoding layers, where N is a positive integer.
8 . The computer-implemented method of claim 1 , wherein the generating the one or more trajectory predictions includes generating a probability associated with each trajectory prediction.
9 . The computer-implemented method of claim 8 , wherein a Gaussian mixture model is used for generating the probability associated with each trajectory prediction.
10 . The computer-implemented method of claim 1 , wherein the scene encoder generates at least one token used for a query that is responsive to a pedestrian pose or a pedestrian gaze.
11 . An apparatus comprising one or more processors configured to implement a method, the processor configured to:
determine a first cross-attention between a vectorized representation of a road map near a vehicle and information obtained from a rasterized representation of an environment near the vehicle by processing through a first cross-attention stage;
determine a second cross-attention between a vectorized representation of a vehicle history and information obtained from the rasterized representation by processing through a second cross-attention stage;
operate a scene encoder on the first cross-attention and the second cross-attention;
operate a trajectory decoder on an output of the scene encoder;
generate one or more trajectory predictions by performing one or more queries on the trajectory decoder.
12 . The apparatus of claim 11 , wherein the information obtained from the rasterized representation comprises a multi-sourced, multi-grained feature map.
13 . The apparatus of claim 12 , wherein the information is further based on a lidar point cloud obtained from a sensor located on the vehicle.
14 . The apparatus of claim 11 , wherein the rasterized representation comprises traffic signal information near the vehicle.
15 . The apparatus of claim 11 , wherein the information comprises a raw camera image obtained by a camera on the vehicle.
16 . A non-transitory computer-storage medium having process-executable code that, upon execution, causes one or more processor to implement a method, comprising:
determining a first cross-attention between a vectorized representation of a road map near a vehicle and information obtained from a rasterized representation of an environment near the vehicle by processing through a first cross-attention stage;
determining a second cross-attention between a vectorized representation of a vehicle history and information obtained from the rasterized representation by processing through a second cross-attention stage;
operating a scene encoder on the first cross-attention and the second cross-attention;
operating a trajectory decoder on an output of the scene encoder;
generating one or more trajectory predictions by performing one or more queries on the trajectory decoder.
17 . The non-transitory computer-storage medium of claim 16 , wherein the scene encoder comprises N encoding layers, where N is a positive integer.
18 . The non-transitory computer-storage medium of claim 16 , wherein the trajectory decoder comprises M encoding layers, where N is a positive integer.
19 . The non-transitory computer-storage medium of claim 16 , wherein the generating the one or more trajectory predictions includes generating a probability associated with each trajectory prediction.
20 . The non-transitory computer-storage medium of claim 19 , wherein a Gaussian mixture model is used for generating the probability associated with each trajectory prediction.