US 11,912,271 B2
Trajectory prediction from precomputed or dynamically generated bank of trajectories
Tung Minh Phan, Garden Grove, CA (US); Eric Wolff, Boston, MA (US); Emilio Frazzoli, Newton, MA (US); Elena Corina Grigore, Boston, MA (US); and Freddy Boulton, Boston, MA (US)
Assigned to Motional AD LLC, Boston, MA (US)
Filed by Motional AD LLC, Boston, MA (US)
Filed on May 26, 2020, as Appl. No. 16/883,899.
Claims priority of provisional application 62/932,164, filed on Nov. 7, 2019.
Prior Publication US 2021/0139026 A1, May 13, 2021
Int. Cl. B60W 30/095 (2012.01); G01S 19/01 (2010.01); G01S 17/89 (2020.01); G01S 17/931 (2020.01); G01S 7/481 (2006.01); G05B 13/02 (2006.01); G06F 17/18 (2006.01); G05D 1/02 (2020.01); G05D 1/00 (2006.01)
CPC B60W 30/0956 (2013.01) [G01S 7/4814 (2013.01); G01S 17/89 (2013.01); G01S 17/931 (2020.01); G01S 19/01 (2013.01); G05B 13/027 (2013.01); G05D 1/0221 (2013.01); G06F 17/18 (2013.01); B60W 2420/42 (2013.01); B60W 2554/4044 (2020.02); B60W 2556/10 (2020.02); G05D 2201/0213 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer implemented method comprising:
receiving, by one or more processors, location data and past trajectory data for one or more objects detected by one or more sensors and training set data comprising a trajectory traveled by an agent;
determining, by the one or more processors, a set of features for the one or more objects based on the location data and the past trajectory data, wherein the set of features comprises information associated with the one or more objects;
combining, by the one or more processors, the set of features, the trajectory traveled by the agent, and motion data of the agent to form a concatenated data set;
generating, by the one or more processors, predicted trajectories based on the concatenated data set, wherein the predicted trajectories are associated with coordinates and corresponding probabilities;
calculating, by the one or more processors, angles between the predicted trajectories and the trajectory traveled by the agent;
selecting, by the one or more processors, a selected trajectory from the predicted trajectories when the angles are within a threshold using a function that selects the selected trajectory from the predicted trajectories based on template trajectories;
calculating, by the one or more processors, a difference between the selected trajectory and the trajectory traveled by the agent using a multi-modal loss function;
adjusting, by the one or more processors, weights of a model based on the difference between the selected trajectory and the trajectory traveled by the agent; and
controlling, by the one or more processors, operation of at least one actuator of a vehicle according to at least one driving command generated based on the model.