US 11,726,477 B2
Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout
Jagjeet Singh, Pittsburgh, PA (US); Andrew T. Hartnett, West Hartford, CT (US); G. Peter K. Carr, Allison Park, PA (US); and Slawomir W. Bak, Pittsburgh, PA (US)
Assigned to ARGO AI, LLC, Pittsburgh, PA (US)
Filed by Argo AI, LLC, Pittsburgh, PA (US)
Filed on Jul. 12, 2021, as Appl. No. 17/373,090.
Application 17/373,090 is a continuation of application No. 16/425,132, filed on May 29, 2019, granted, now 11,131,993.
Prior Publication US 2021/0341920 A1, Nov. 4, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01); B60W 50/00 (2006.01)
CPC G05D 1/0088 (2013.01) [B60W 50/0097 (2013.01); G05D 1/0221 (2013.01); B60W 2554/00 (2020.02); G05D 2201/0213 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining a prediction model trained to predict future trajectories of objects, the prediction model trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory; and
generating a planned trajectory of an autonomous vehicle, the generating comprising:
receiving state data corresponding to the autonomous vehicle;
receiving perception data corresponding to an object;
predicting, using the prediction model, a future trajectory of the object based on the perception data, and
generating the planned trajectory of the autonomous vehicle based on the predicted future trajectory of the object and the state data.