Toyota Research Institute, Inc.

Hiroyuki Funaya

Michael James Delp

Randall J St Romain

System, methods, and other embodiments described herein relate to simulating sensor data. In one embodiment, a method includes, in response to receiving a request to generate simulated information corresponding to the sensor data, acquiring the sensor data that includes at least range information about a perceived environment. The simulated information includes one or more attributes of the sensor data that are absent from the sensor data in a current format. The method includes computing simulated information of the sensor data using a machine learning model that accepts the sensor data and labels as an input and produces the simulated information as an output. The labels identify at least objects in the perceived environment that are depicted by the sensor data. The method includes providing the simulated information with the sensor data.

Systems and methods for inferring simulated data

System, methods, and other embodiments described herein relate to simulating sensor data for a scene. In one embodiment, a method includes, in response to receiving a request to generate simulated sensor data for the scene, acquiring simulation data about the scene. The simulation data includes at least simulated information about the scene that is computer-generated. The method includes computing the simulated sensor data using a generative neural network that accepts the simulation data as an input and produces the simulated sensor data as an output. The simulated sensor data is a simulated perception of the scene by a sensor. The method includes providing the simulated sensor data as part of the scene.

Systems and methods for simulating sensor data using a generative model

Systems and methods described herein relate to detecting objects. One embodiment receives a plurality of three-dimensional (3D) data points from a plurality of light beams emitted by one or more sensors; identifies, among the plurality of 3D data points, a first set of inlier points that satisfy a first predetermined error condition with respect to a plane hypothesis and a first set of outlier points that fail to satisfy the first predetermined error condition; identifying, among the first set of inlier points, a second set of outlier points, the second set of outlier points failing to satisfy a second predetermined error condition in a range domain with respect to a plurality of line hypotheses corresponding, respectively, to the plurality of light beams; and detecting an object based, at least in part, on at least one of the first set of outlier points and the second set of outlier points.

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Ann Arbor, MI, United States of America

Hiroyuki Funaya