Toyota Motor Engineering & Manufacturing North America, Inc.

Michael Edward Samples

Michael R James

Kenneth P Laberteaux

Derek Stanley Caveney

Lorenzo Caminiti

Steven F Kalik

Avdhut S Joshi

Jeff B Rogers

Michael Robert James

A vehicle system includes a first sensor and a second sensor, each having, respectively, different first and second modalities. A controller includes a processor configured to: receive a first sensor input from the first sensor and a second sensor input from the second sensor; detect, synchronously, first and second observations from, respectively, the first and second sensor inputs; project the detected first and second observations onto a graph network; associate the first and second observations with a target on the graph network, the target having a trajectory on the graph network; select either the first or the second observation as a best observation based on characteristics of the first and second sensors; and estimate a current position of the target by performing a prediction based on the best observation and a current timestamp.

Tracking on-road vehicles with sensors of different modalities

An object tracking system and method operable to minimize processing time for tracking objects is provided. The system includes a pair of filters operable to make associations between dynamic objects newly detected and previously detected. One of the pair of filters makes an association when the predicted location of the previously detected objects is within a predetermined distance of the newly detected object. The other of the pair of filters makes an association based upon the probability that a newly detected dynamic object is a previously detected dynamic object. The remaining unassociated dynamic objects are then localized so as to form discrete matrices for optimization filters.

Method and system for tracking objects

Training a strong classifier by classifying point cloud data with a first classifier, inferring a first terrain map from the classified point cloud data, reclassifying the point cloud data with the first classifier based on the first terrain map, and training a second classifier based on the point cloud data reclassified with the first classifier based on the terrain map. The point cloud data is then classified with the second classifier, and the procedure followed with the first classifier is iteratively repeated until a strong classifier is determined. A strong classifier is determined when a probability of a terrain map matching a given terrain for the strong classifier is approximately equal to a probability of a terrain map matching the given terrain for a prior trained classifier.

Real-time 3D point cloud obstacle discriminator apparatus and associated methodology for training a classifier via bootstrapping

Method, storage medium and system of optimizing a destination for a vehicle by obtaining a map corresponding to a desired destination of the vehicle and identifying objectives of the map based on multiple parameters including collision avoidance, driver time, legal constraints and social consensus. A cost function is constructed to determine an optimal destination based on a proximity to the desired destination and the identified objectives, and an optimal destination is identified by minimizing a value of the cost function.

Method for safely parking vehicle near obstacles

An apparatus for assisting safe operation of a vehicle includes an environment sensor system detecting hazards within the vehicle environment, a driver monitor providing driver awareness data (such as a gaze track), and an attention-evaluation module identifying hazards as sufficiently or insufficiently sensed by the driver by comparing the hazard data and the gaze track. An alert signal relating to the unperceived hazards can be provided.

Combining driver and environment sensing for vehicular safety systems

A computing platform for multiple intelligent transportation systems in an automotive vehicle having a plurality of sensors which generate output signals representative of various vehicle operating parameters. The platform includes a vehicle data center which receives input signals from the vehicle sensors and the vehicle data center is configured to transform these input signals into output signals having a predetermined format for each of the vehicle operating parameters. A central processing unit receives the output signal from the vehicle data and is programmed to process the vehicle data center output signals for each of the intelligent transportation systems and generate the appropriate output signals as a result of such processing.

Computing platform for multiple intelligent transportation systems in an automotive vehicle

There is disclosed a method for avoiding a collision in a vehicle including the steps of: providing a transmitting vehicle, providing a receiving vehicle, creating data information in the transmitting vehicle, sending the data information to the receiving vehicle, and determining the relevancy of the data information to the receiving vehicle using a current position and heading of the receiving vehicle.

Growing community of inventors

Ann Arbor, MI, United States of America

Michael Edward Samples