Toyota Research Institute, Inc.

Massachusetts Institute of Technology

Waymo LLC

Xin Huang

Guy Rosman

Stephen G McGill

Luke S Fletcher

John J Leonard

Yusuke Sawamura

Brian C Williams

Dragomir Dimitrov Anguelov

Alexander Patrick McCauley

Jiyang Gao

Pei Sun

Benjamin Sapp

Igor Gilitschenski

Hang Zhao

Stephen G Mcgill

Jonathan A DeCastro

Kyriacos Christoforos Shiarlis

Ashkan Mohammadzadeh Jasour

Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.

Systems and methods for predicting the trajectory of a moving object

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using temporal-spatial interaction predictions.

Multi-modal multi-agent trajectory prediction

A system and method for generating a predicted vehicle trajectory includes a generative adversarial network configured to receive a trajectory vector of a target vehicle and generate a set of latent state vectors using the received trajectory vector and an artificial neural network. The latent state vectors each comprise a high-level sub-vector, Z. The GAN enforces Zto be correlated to an annotation coding representing semantic categories of vehicle trajectories. The GAN selects a subset, from the set of latent state vectors, using farthest point sampling and generates a predicted vehicle trajectory based on the selected subset of latent state vectors.

Methods and systems for diversity-aware vehicle motion prediction via latent semantic sampling

Systems and methods described herein relate to controlling the operation of a vehicle. One embodiment generates predicted trajectories of the vehicle using first trajectory predictors based, at least in part, on first inputs; generates predicted trajectories of a road agent that is external to the vehicle using second trajectory predictors based, at least in part, on second inputs; integrates the predicted trajectories of the road agent into the first inputs to iteratively update the predicted trajectories of the vehicle and integrates the predicted trajectories of the vehicle into the second inputs to iteratively update the predicted trajectories of the road agent; and controls operation of the vehicle based, at least in part, on at least one of (1) the iteratively updated predicted trajectories of the vehicle and (2) the iteratively updated predicted trajectories of the road agent.

Systems and methods for controlling the operation of a vehicle

Systems and methods described herein relate to predicting a trajectory of a road agent external to a vehicle. One embodiment generates first and second predicted road-agent trajectories using respective first and second trajectory predictors based, at least in part, on a plurality of inputs including past road-agent trajectory information and vehicle sensor data; generates a confidence score for each predicted road-agent trajectory using a confidence estimator that includes a deep neural network, wherein generating the confidence scores includes computing them as a function of time within a predetermined temporal horizon; outputs the first and second predicted road-agent trajectories and their respective confidence scores; and controls operation of the vehicle based, at least in part, on one or more of the first predicted road-agent trajectory, the second predicted road-agent trajectory, the confidence score for the first predicted road-agent trajectory, and the confidence score for the second predicted road-agent trajectory.

Systems and methods for predicting the trajectory of a road agent external to a vehicle

Systems and methods described herein relate to predicting a trajectory of a vehicle. One embodiment generates first and second predicted vehicle trajectories using respective first and second trajectory predictors based, at least in part, on a plurality of inputs including past trajectory information and vehicle sensor data; generates a confidence score for each of the first and second predicted vehicle trajectories using a confidence estimator that includes a first deep neural network, wherein generating the confidence scores includes computing the confidence scores as a function of time within a predetermined temporal horizon; outputs the first and second predicted vehicle trajectories and their respective confidence scores; and controls operation of the vehicle based, at least in part, on one or more of the first predicted vehicle trajectory, the second predicted vehicle trajectory, the confidence score for the first predicted vehicle trajectory, and the confidence score for the second predicted vehicle trajectory.

Systems and methods for predicting vehicle trajectory

Growing community of inventors

Cambridge, MA, United States of America

Xin Huang