公开(公告)号：US11256983B2

公开(公告)日：2022-02-22

申请号：US15662031

申请日：2017-07-27

Applicant: Waymo LLC

Inventor： Abhijit Ogale ,  Mayank Bansal ,  Alexander Krizhevsky

IPC: G06N3/08 ,  G05D1/02 ,  G06N3/04 ,  G06N3/00 ,  G01C21/34 ,  G01S17/931 ,  G01S17/42 ,  G01S13/86 ,  G01S17/86 ,  G01S13/931

Abstract: Systems, methods, devices, and other techniques for training a trajectory planning neural network system to determine waypoints for trajectories of vehicles. A neural network training system can train the trajectory planning neural network system on the multiple training data sets. Each training data set can include: (i) a first training input that characterizes a set of waypoints that represent respective locations of a vehicle at each of a series of first time steps, (ii) a second training input that characterizes at least one of (a) environmental data that represents a current state of an environment of the vehicle or (b) navigation data that represents a planned navigation route for the vehicle, and (iii) a target output characterizing a waypoint that represents a target location of the vehicle at a second time step that follows the series of first time steps.

公开(公告)号：US10883844B2

公开(公告)日：2021-01-05

申请号：US15662007

申请日：2017-07-27

Applicant: Waymo LLC

Inventor： Abhijit Ogale ,  Mayank Bansal ,  Alexander Krizhevsky

IPC: G06N3/02 ,  G01C21/34 ,  G06N3/08 ,  G01C21/36 ,  G06N3/00 ,  G06N3/04

Abstract: Systems, methods, devices, and other techniques for planning a trajectory of a vehicle. A computing system can implement a trajectory planning neural network configured to, at each time step of multiple time steps: obtain a first neural network input and a second neural network input. The first neural network input can characterize a set of waypoints indicated by the waypoint data, and the second neural network input can characterize (a) environmental data that represents a current state of an environment of the vehicle and (b) navigation data that represents a planned navigation route for the vehicle. The trajectory planning neural network may process the first neural network input and the second neural network input to generate a set of output scores, where each output score in the set of output scores corresponds to a different location of a set of possible locations in a vicinity of the vehicle.

公开(公告)号：US20200174490A1

公开(公告)日：2020-06-04

申请号：US16628086

申请日：2018-07-27

Applicant: Waymo LLC

Inventor： Abhijit Ogale ,  Mayank Bansal ,  Alexander Krizhevsky

IPC: G05D1/02 ,  G06N3/04

Abstract: Systems, methods, devices, and other techniques for planning a trajectory of a vehicle. A computing system can implement a trajectory planning neural network configured to, at each time step of multiple time steps: obtain a first neural network input and a second neural network input. The first neural network input can characterize a set of waypoints indicated by the waypoint data, and the second neural network input can characterize (a) environmental data that represents a current state of an environment of the vehicle and (b) navigation data that represents a planned navigation route for the vehicle. The trajectory planning neural network may process the first neural network input and the second neural network input to generate a set of output scores, where each output score in the set of output scores corresponds to a different location of a set of possible locations in a vicinity of the vehicle.

公开(公告)号：US20190034794A1

公开(公告)日：2019-01-31

申请号：US15662031

申请日：2017-07-27

Applicant: Waymo LLC

Inventor： Abhijit Ogale ,  Mayank Bansal ,  Alexander Krizhevsky

IPC: G06N3/08 ,  G05D1/02

Abstract: Systems, methods, devices, and other techniques for training a trajectory planning neural network system to determine waypoints for trajectories of vehicles. A neural network training system can train the trajectory planning neural network system on the multiple training data sets. Each training data set can include: (i) a first training input that characterizes a set of waypoints that represent respective locations of a vehicle at each of a series of first time steps, (ii) a second training input that characterizes at least one of (a) environmental data that represents a current state of an environment of the vehicle or (b) navigation data that represents a planned navigation route for the vehicle, and (iii) a target output characterizing a waypoint that represents a target location of the vehicle at a second time step that follows the series of first time steps.