Toyota Motor Engineering & Manufacturing North America, Inc.

Raytheon Company

University of Michigan

Lorenzo Caminiti

Derek Stanley Caveney

Jeffrey Clark Lovell

Kenneth P Laberteaux

James Joseph Richardson

Michael Edward Samples

Christopher Thomas Higgins

Domitilla Del Vecchio

Michael R James

Michael Hafner

Andrew James Frantz

Evan Edward Quisenberry

Drew Cunningham

Christopher Peplin

Rajeev Verma

Jeff B Rogers

A system and method for transferring data between an object detection system and a collision processing circuit is provided. The object detection system includes sensors configured to provide coverage of and detect movement within a predetermined area. The object detection system further includes a path predicting circuit and a plotting circuit operable to predict and plot the location of detected objects. The system further includes a map definition of the predetermined area, a grid system plotted onto the predetermined area, and environmental information relating to the predetermined area, and a series of overlays. Each overlay is plotted with the grid system and the predicted location of the detected objects. The object detection system transmits the map definition and series of overlays to the collision processing circuit so as to determine a probability of a collision.

Site map interface for vehicular application

A back-propagating intersection collision avoidance system is provided. The system can include a first vehicle and a second vehicle, the first and second vehicles each operable to approach an intersection at a definable velocity and acceleration. In addition, the intersection can have a collision zone in which the first and second vehicles will collide if they are present there at the same time. The first vehicle can have a processing unit with a controller and a microprocessor, the microprocessor having an algorithm with a disturbance model. The processing unit is operable to back-propagate from the collision zone a capture set as a function of a disturbance for the first and second vehicles.

Computationally efficient intersection collision avoidance system

A system for detecting a pedestrian is provided. The system includes a base and a mast extending from the base, and a plurality of sensors mounted on the mast, the plurality of sensors for detecting objects and pedestrians within a predetermined range, and wherein the sensors include a GPS antenna, at least one camera, an alert mechanism, a radio with an integrated directional antenna, and a short range communication antenna. A path predicting circuit is in communication with a system vehicle and the pedestrian detection system. The path predicting circuit processes information from the system vehicle and pedestrian detection system to predict the path of the system vehicle and detected objects. The path predicting circuit is in communication with a path collision circuit and the predicted paths are mapped on the path collision circuit so as to determine if the system vehicle may possibly collide with a detected object.

Communication based vehicle-pedestrian collision warning system

The present invention discloses a back-propagating intersection collision avoidance (ICA) system for preventing two or more vehicles from colliding at an intersection. The ICA system can calculate predicted positions of the two or more vehicles in the near future, and both the current and future positions can be broadcast to surrounding vehicles using vehicle-to-vehicle communication. For each vehicle, a set of states, for example position, speed, acceleration, and the like, where a collision is imminent can be identified using state information for a local vehicle, a remote vehicle, and a known collision zone for the intersection. If the current states of the vehicles are determined to be in danger of entering the collision zone, the ICA system can control the vehicles to perform evasive driving maneuvers and/or alert the drivers.

A method for predicting future states of a vehicle including the steps of selecting a model having n states reflecting dynamic features of the vehicle; inputting noisy sensor measurements representing a current state of the vehicle to generate (2n+1) sigma points Xwhere i=0, . . . . 2n, each of the sigma points having n states; performing (2n+1) integrations, each integration includes propagating the n-states of the respective sigma points Xthrough the non-linear function Y=f(X); and combining the propagated sigma points to generate the predicted future states of the vehicle.

System and method for stochastically predicting the future states of a vehicle

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

Lorenzo Caminiti