Ford Global Technolgoies, LLC

Argo Ai Gmbh

Jagjeet Singh

Andrew Thomas Hartnett

G Peter K Carr

Constantin Savtchenko

Deva K Ramanan

Slawomir W Bak

Siddhesh Shyam Khandelwal

William Junbo Qi

Greydon Foil

Lorenzo Nardi

Bangjie Liu

Andreas Eberlein

Elena Corina Grigore

Systems and methods of determining trajectories of an actor in an environment in which a vehicle is operating are provided. The method includes detecting an actor that may move within a scene in the environment by an object detection system of a vehicle in the environment, determining a kinematic history of the actor, and using context of the scene and the kinematic history of the actor to determine a plurality of reference polylines for the actor. The method further includes generating a contextual embedding of the kinematic history of the actor to generate a plurality of predicted trajectories of the actor, in which the generating conditions each of the predicted trajectories to correspond to one of the reference polylines. The method additionally includes using, by the vehicle, the plurality of predicted trajectories to plan movement of the vehicle.

Path-conditioned motion forecasting for vehicle motion planning

Systems and methods for planning a trajectory for a vehicle are disclosed. The methods include generating first predicted states of an object in an environment of the vehicle at one or more first time steps during a first prediction horizon using a machine learning model and sensor data about the environment. The first predicted states are used as an input to a physical model to generate second predicted states of the object at one or more second time steps during a second prediction horizon where the one or more second time steps are after the one or more first time steps. A trajectory for the vehicle is generated using the first predicted states or the second predicted states of the object.

Methods and systems for long-term trajectory prediction by extending a prediction horizon

Systems/methods for operating an autonomous vehicle. The methods comprise, by a computing device: using sensor data to track an object that was detected in proximity to the autonomous vehicle; classifying the object into at least one dynamic state class of a plurality of dynamic state classes; transforming the at least one dynamic state class into at least one goal class of a plurality of goal classes; transforming the at least one goal class into at least one proposed future intention class of a plurality of proposed future intention classes; determining at least one predicted future intention of the object based on the proposed future intention class; and/or causing the autonomous vehicle to perform at least one autonomous driving operation based on the at least one predicted future intention determined for the object.

Systems and methods for determining future intentions of objects

Systems and methods of determining trajectories of an actor in an environment in which a vehicle is operating are provided. The method includes, by an object detection system of a vehicle in an environment, detecting an actor that may move within a scene in the environment. The method further includes using context of the scene to determine a reference polyline for the actor and determining a kinematic history of the actor. The method additionally includes using the kinematic history to predict a waypoint, which is a predicted position of the actor at a conclusion of a waypoint time period, and identifying a segment of the reference polyline, the segment extending from a current location to a point along the reference polyline that is closest to the waypoint and determining a trajectory for the actor conditioned by the segment of the reference polyline.

Waypoint prediction for vehicle motion planning

Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is 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. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout

A method and a system for forecasting trajectories in an autonomous vehicle using recurrent neural networks. The method includes receiving a first set of data that comprises time series information corresponding to states of a plurality of objects, analyzing the first set of data to determine a plurality of object trajectory sequences corresponding to the plurality of objects, and using one or more of the plurality of object trajectory sequences as input to train a prediction model for predicting future trajectories of the plurality of objects. The predication model can be trained by defining a first prediction horizon, training the prediction model over the first prediction horizon to generate a semi-trained prediction model, defining a second prediction horizon that is longer than the first prediction horizon, and training the semi-trained prediction model to generate a trained prediction model.

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Pittsburgh, PA, United States of America

Jagjeet Singh