University of Michigan

Robert Bosch

Toyota Motor Engineering & Mfg North America, Inc.

Huei Peng

Ziheng Pan

Nikhil Ravi

Shyam Jade

John K Lenneman

Jing Sun

Jason Schwanke

Tyler S Worman

Viktor Rill

Gregory J McGUIRE

Matt Thorington

Caihao Weng

Geunseob Oh

Elizabeth Pulver

Songan Zhang

Maani Ghaffari

Minghan Zhu

Systems and methods are provided for point cloud processing with an equivariant neural network and implicit shape learning that may produce correspondence-free registration. The systems and methods may provide for feature space preservation with the same rotation operation as a Euclidean input space, due to the equivariance property, which may provide for solving the feature-space registration in a closed form.

Systems and methods for point cloud registration

An augmented virtual vehicle testing system and method for presenting graphics to a vehicle operator during operation of a vehicle. The method includes: determining a position of a vehicle operator within a vehicle testing environment; executing an augmentative simulation of the vehicle testing environment, wherein the augmentative simulation is used to provide a position of one or more virtual objects within the vehicle testing environment; generating graphics representing the one or more virtual objects based on the position of the vehicle operator and the position of the one or more virtual objects within the vehicle testing environment; and presenting the graphics on an electronic display and to the vehicle operator during operation of the vehicle.

Augmented vehicle testing environment

A roadmanship system comprises a computational device and a vehicle comprising a plurality of sensors and a vehicle control system in communication with the computational device and the plurality of sensors. The computational device can be configured to: (i) receive driving data from a group of vehicles; (ii) calculate a regression curve based on the driving data; (iii) calculate a threshold value of an engineering parameter based on the regression curve and a predetermined roadmanship level; and (iv) output the threshold value to the vehicle control system. The vehicle control system can be configured to: (a) receive the threshold value from the computational device; (b) receive operational information associated with at least one of the vehicle and a driving environment surrounding the vehicle from the plurality of sensors; and (c) cause the vehicle to perform a vehicle maneuver based on the threshold value and the operational information.

Roadmanship systems and methods

A system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance dand a longitudinal host vehicle speed vthat are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase Pand an traffic light signal timing T; obtaining a time of day TOD; providing the host vehicle-traffic light distance d, the longitudinal host vehicle speed v, the traffic light signal phase P, the traffic light signal timing T, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.

Vehicle trajectory prediction near or at traffic signal

Methods and systems for calibrating a hybrid vehicle system for simplified control of the powertrain to optimize fuel efficiency of the hybrid vehicle and for operating a hybrid vehicle powertrain accordingly. The optimization mechanism reduces the optimized control problem to a single degree-of-freedom. Accordingly, during real-time operation of the hybrid vehicle, the system is able to quickly identify and apply optimized operating settings for a particular driver demand and, in some implementations, to provide a particular rate of change of the state-of-charge of the battery of the hybrid vehicle.

Simplified control for optimized hybrid vehicle powertrain operation

An All-Wheel-Drive (AWD), multi-mode, power-split hybrid vehicle employing a drive combining all electric motors and an internal combustion engine, together collocated on a 2-planetary-gear (PG) set. The present teachings are capable of delivering competitive performance while maximizing fuel economy through power-split hybrid design.

Hybrid all-wheel drive system having dynamic clutches

A multi-mode, power-split hybrid transmission system having two planetary gear (PG) sets connected to one engine, two electric motors, one output shaft, and each other by several clutches, brakes, and direct connection elements. Depending on the specific location and actuation of the various clutch and brake elements, the multi-mode, power-split hybrid transmission system can be run in one of several modes (e.g. electric drive, power-split, parallel hybrid, series hybrid, electronic continuously variable transmission (eCVT), generator, neutral, and the like).

Multi-mode power split hybrid transmission with two planetary gear mechanisms

A method is provided for determining capacity of a battery. The method includes: defining a model for a battery, where the model relates terminal voltage of the battery to charged capacity of the battery; taking a plurality of voltage measures of the battery, where the voltage measures are taken through a range of states of charge and the range excludes the battery being fully charged and fully discharged; determining the parameters of the model by fitting the plurality of voltage measures to the model; determining an incremental capacity curve for the battery by taking derivative of the model; and quantifying a peak of the incremental capacity curve to thereby determine a capacity of the battery.

Growing community of inventors

Ann Arbor, MI, United States of America

Huei Peng