Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Research Institute, Inc.

University of Michigan

Michael James Delp

Danil V Prokhorov

Randall J St Romain

Katsuhiro Sakai

John-Michael McNew

Christopher M Higgins

Yusuke Kanzawa

Kazutoshi Ebe

Hiroyuki Funaya

Shan Bao

Ruijia Feng

Kentaro Ichikawa

Nobuhide Kamata

Michael R James

Xue Mei

Derek Stanley Caveney

Peiyan Gong

Matthew T Kliemann

Antonio Prioletti

John H Marcoux

Christian Potthast

A system and method are provided for making a probabilistic determination of the state of a lane transition which may be controlled by a traffic signal, such as a traffic cue. A determination of the state of transition of a traffic signal may be used by a vehicle to determine a course of action to take. In making the determination, the states of multiple traffic signals may be combined into one collection of elements that has values associated with the likelihood of the traffic signals being in a given state. Optionally, a determination may be made of whether a traffic signal is occluded.

Probabilistic modular lane transition state estimation

An autonomous vehicle configured to autonomously pass a cyclist includes an imaging device and processing circuitry configured to receive information from the imaging device. Additionally, the processing circuitry of the autonomous vehicle is configured to identify a cyclist passing situation based on the information received from the imaging device, and plan a path of an autonomous vehicle based on the cyclist passing situation. The autonomous vehicle also includes a positioning system and the processing circuitry is further configured to receive information from the positioning system, determine if the cyclist passing situation is sufficiently identified, and identify the cyclist passing situation based on the information from the imaging device and the positioning system when the cyclist passing situation is not sufficiently identified based on the information received from the imaging device.

System, method, and computer-readable medium for an autonomous vehicle to pass a bicycle

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 improving clustering of points within a point cloud. In one embodiment, a method includes grouping the points into cells of a grid. The grid divides an observed region of a surrounding environment associated with the point cloud into the cells. The method includes computing feature vectors for the cells that use cell features to characterize the points in the cells and relationships between the cells. The method includes analyzing the feature vectors according to a clustering model to identify clusters for the cells. The clustering model evaluates the cells to identify which of the cells belong to common entities. The method includes providing the clusters as assignments of the points to the entities depicted in the point cloud.

Systems and methods for clustering using a smart grid

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 registering three-dimensional (3D) data with two-dimensional (2D) image data. One embodiment receives 3D data from one or more sensors and 2D image data from one or more cameras; identifies a 3D segment in the 3D data and associates it with an object; classifies pixels in the 2D image data; determines a speed and a heading for the object; and registers the 3D segment with a portion of the classified pixels by either (1) shifting the 3D segment to a position that, based on the associated object's speed and heading, corresponds to a 2D image data capture time and projecting the time-shifted 3D segment onto 2D image space; or (2) projecting the 3D segment onto 2D image space and shifting the projected 3D segment to a position that, based on the associated object's speed and heading, corresponds to a 2D image data capture time.

Systems and methods for registering 3D data with 2D image data

Systems and methods described herein relate to registering three-dimensional (3D) data with two-dimensional (2D) image data. One embodiment receives 3D data from one or more sensors and 2D image data from one or more cameras; identifies a 3D segment in the 3D data and associates it with an object; identifies 2D boundary information for the object; determines a speed and a heading for the object; and registers the 3D segment with the 2D boundary information by adjusting the relative positions of the 3D segment and the 2D boundary information based on the speed and heading of the object and matching, in 3D space, the 3D segment with projected 2D boundary information.

An autonomous vehicle can plan a path of the autonomous vehicle at an intersection where cyclists are also present. The autonomous vehicle includes an imaging device and processing circuitry configured to receive information from the imaging device and then identify a position of a cyclist in a traffic intersection based on the information from the imaging device. Additionally, a future path of the cyclist is predicted based on the position of the cyclist in the traffic intersection and a path of the autonomous vehicle is planned based on the predicted future path of the cyclist. Further, it can be determine if the cyclist followed the predicted future path, and the planned path of the autonomous vehicle is updated in response to the cyclist not following the predicted future path.

System, method, and computer-readable medium for autonomous vehicle response to bicycles at intersections

Systems and methods for publishing LIDAR cluster data are described. The vehicle can identify one or more clusters based on data from the LIDAR sensor. The identified one or more clusters can be representative of at least one object in an external environment of the vehicle. Additionally, the vehicle can publish the identified one or more clusters before the LIDAR sensor completes a full 360 degree rotation about the axis of rotation.

Publishing LIDAR cluster data

A computing system for an autonomous engine module stores a self-driving capability and is configured to determine, by communication between an autonomous engine module in which the computing system resides and other autonomous engine modules, and responsive to a user request for use of an autonomous engine module, at least one suitable autonomous engine module use candidate. The computing system may also generate a notification directed to the user including information relating to the at least one suitable autonomous engine module use candidate. The computing system may also receive a selection by the user of an autonomous engine module use candidate. The computing system may also, using the self-driving capability, autonomously drive the autonomous engine module to a designated pickup location responsive to selection by the user of the autonomous engine module as the autonomous engine module use candidate.

Transportation system including autonomous detachable engine modules and passenger module

A navigation apparatus for an autonomous vehicle includes circuitry configured to receive at least one route between a start location and a destination, display the at least one route on a first screen that allows selection of a first set of routes from the at least one route, receive a plurality of characteristics corresponding to each of the at least one route. Each characteristic of the plurality of characteristics is associated with a measure and a longest block within which each characteristic can be performed continuously. The circuitry further configured to divide a route of the at least one route to generate a plurality of segments based on the plurality of characteristics, and display the first set of routes, the plurality of characteristics corresponding to the first set of routes, the measure and the longest block corresponding to the plurality of characteristics on a second screen.

Apparatus and method transitioning between driving states during navigation for highly automated vechicle

A system for adjusting the position of one or more sensors of an autonomous vehicle includes a plurality of sensor arms, a plurality of sensor manipulators attached to each of the plurality of sensor arms. The system can be configured to receive output from the one or more sensors, and determine if an occlusion zone is detected. Based on the objects creating the occlusion zone, the sensors can be adjusted to decrease the size of the occlusion zone.

Systems and methods for adjusting the position of sensors of an automated vehicle

A transportation system includes at least one passenger module and at least one autonomous engine module configured to operatively couple with the at least one passenger module to form an autonomous passenger vehicle.

Arrangements related to sensing systems and methods are described. A sensing system can include a sensor track and one or more sensors. The sensor track can be operatively connected to a vehicle surface, such as at least a portion of a perimeter of a roof of a vehicle. The one or more sensors can be operatively connected to the sensor track such that the one or more sensors move along the sensor track while scanning an environment of the vehicle. The sensing systems can include a controller operatively connected to the one or more sensors to control the movement of the one or more sensors along the sensor track. The controller can also receive signals from the one or more sensors relating to the environment. In one or more arrangements, the one or more sensors can include LIDAR sensors.

Track for vehicle environment sensors

System, methods, and other embodiments described herein relate to determining rendezvous locations and autonomously swapping a resupply carriage with a vehicle along a route. In one embodiment, a method includes in response to a request for resupplying the vehicle with energy, determine a rendezvous point for meeting the vehicle with a resupply carriage. The method includes dispatching the resupply carriage to meet the vehicle at the rendezvous point by providing an electronic signal to the resupply carriage to cause the resupply carriage to autonomously travel to the rendezvous point. The method includes controlling the vehicle to swap a current carriage for the resupply carriage to supply energy to the vehicle. The vehicle is comprised of a passenger compartment that is removably attached to the current carriage. The current carriage and the resupply carriage are interchangeable propulsion sources for the vehicle.

Growing community of inventors

Ann Arbor, MI, United States of America

Michael James Delp