Toyota Research Institute, Inc.

Toyota Jidosha Kabushiki Kaisha

Paul Drews

Guy Rosman

Stephen G McGill

Stephen G Mcgill

A vehicle system includes one or more sensors configured to capture aspects of an environment and a computing device. The computing device is configured to receive information about the environment captured by the one or more sensors, determine one or more structures within the environment based on the received information, select a kernel that is parameterized for predicting a vehicle trajectory based on the one or more structures determined within the environment, and perform a convolution of the selected kernel and an array defining the environment, wherein the convolution predicts a future trajectory of a vehicle within the environment.

Convolution operator selection

System, methods, and other embodiments described herein relate to coordinating and optimizing prediction by a model using intermediate data and vehicle-to-vehicle (V2V) communications that improves bandwidth utilization. In one embodiment, a method includes processing acquired data, by a subject vehicle using a prediction model, to execute a vehicle task associated with navigating a driving scene. The method also includes extracting processed data according to the acquired data from intermediate activations of layers within the prediction model according to a cooperation model. The method also includes quantifying relevancy of the processed data, for a target vehicle, to select a subset using the cooperation model. The method also includes communicating the subset to a selected vehicle having the prediction model for additional prediction according to the relevancy and available resources to transmit the subset.

Systems and methods for optimizing coordination and communication resources between vehicles using models

A method includes receiving data relating to pedestrian activity at one or more locations outside of a crosswalk, analyzing the data, based on the data, identifying at least one location of the one or more locations as a constructive crosswalk, and controlling operation of an autonomous vehicle based on the at least one location of the constructive crosswalk.

Systems and methods for modeling pedestrian activity

A method including generating a curvilinear coordinate system from a current position of the vehicle. The method further includes generating a probability distribution from the curvilinear coordinate system and predicting a future vehicle behavior of the vehicle based on the probability distribution.

Curvilinear coordinate systems for predicting vehicle behavior

A method includes encapsulating a current state of a road portion into a voxel grid, a first dimension of the voxel grid being associated with a first spatial dimension of the road portion, a second dimension of the voxel grid being associated with a second spatial dimension of the road portion, and a third dimension of the voxel grid comprising a plurality of feature layers, wherein each feature layer is associated with a feature of the road portion. The voxel grid may be input into a trained neural network and a future state of the road portion may be predicted based on an output of the neural network.

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Watertown, MA, United States of America

Paul Drews