University of Michigan

Denso International America, Inc.

Neda Masoud

Ethan Zhang

Rajesh Kumar Malhan

Joseph Lull

Al-Thaddeus Avestruz

Wei Zhang

Chinedum E Okwudire

Mahdi Bandegi

A pedestrian tracking system includes: a buffer or a memory configured to store a trajectory sequence of a pedestrian; a step attention module and a control module. The step attention module iteratively performs a step attention process to predict states of the pedestrian. Each iteration of the step attention process includes the step attention module: learning the stored trajectory sequence to provide time-dependent hidden states, reshaping each of the time-dependent hidden states to provide two-dimensional tensors; condensing the two-dimensional tensors via convolutional networks to provide convolutional sequences; capturing global information of the convolutional sequences to output a set of trajectory patterns represented by a new sequence of tensors; learning time-related patterns in the new sequence and decoding the new sequence to provide one or more of the states of the pedestrian; and modifying the stored trajectory sequence to include the predicted one or more of the states of the pedestrian.

Sequential pedestrian trajectory prediction using step attention for collision avoidance

A ride-sourcing method including receiving a plurality of ride-sourcing requests; grouping a first set of the plurality of ride-sourcing requests into a first cluster and a second set of the plurality of ride-sourcing requests into a second cluster; computing a tour formation for each of the first cluster and the second cluster to define a series of stations and a cost function; applying trips to the first cluster and the second cluster under uniformity constraints; repeating the computing and applying steps until the cost function between consecutive iterations is below a threshold to define a first cluster of trips and a second cluster of trips; and assigning the first cluster of trips to a first driver and the second cluster of trips to a second driver.

Peer-to-peer ride-sourcing system

A system for predicting aggressive driving behavior for a driver of a vehicle includes a first edge computing device that can acquire spatial-temporal data for the vehicle from one or more sensors that are part of traffic infrastructure. The first edge computing device includes a processor and instructions executable by the processor that execute deep learning methods on the data from the sensors to cluster the data as a driving score. A trained model is applied to the driving score to determine an aggressive driving behavior risk level, and the first edge computing device is configured to predict the aggressive driving behavior based on the aggressive driving behavior risk level.

System for predicting aggressive driving

A vehicle-to-vehicle power transfer system for use between a first vehicle and at least a second vehicle. The system includes an electric power system disposed in each of the first and second vehicles configured to provide electrical drive power to a vehicle drive system for propulsion of the associated vehicle and a power transfer system configured to transfer electric power from at least the electric power system of the first vehicle to the electric power system of the second vehicle while the vehicles are in motion or stationary.

Growing community of inventors

Dexter, MI, United States of America

Neda Masoud