Ford Global Technolgoies, LLC

Anya Lynn Tascillo

Ronald Hugh Miller

Irving Toivo Salmeen

George Carver Davis

David Michael DiMeo

Mark Anthony Tascillo

Jie Cheng

Charles Edward Newman, Jr

Stephanie Mary LaCrosse

A warning system () for a source vehicle () is illustrated. A camera () generates a plurality of images. A controller () is coupled to an indicator (). The controller () generates a size signal and position signal for a rear-approaching vehicle. The controller () activates an indicator when a rear-approaching vehicle enters a blind spot () in response to the size signal and position signal.

Blind spot warning system for an automotive vehicle

A system for sensing a potential collision of a first vehicle ( ) with a second vehicle ( ) that transmits a second position signal. The first vehicle has a pre-crash sensing system ( ) includes a memory ( ) that stores vehicle data and generates a vehicle data signal. A first global positioning system ( ) generates a first position signal corresponding to a position of the first vehicle. A first sensor ( ) generating sensor data signals from the first vehicle. A receiver ( ) receives a second position signal and a road condition signal from the second vehicle. A countermeasure system ( ) is also coupled within the first vehicle. A controller ( ) is coupled to the memory ( ), the global positioning receiver ( ) the first sensor ( ) and the counter measure system ( ). The controller ( ) determines a distance to the second vehicle in as a function of the second position signal. The controller determines a first vehicle trajectory from the sensor data signals and the position signal. The controller ( ) determines a threat level as a function of the distance, the first vehicle trajectory and the road condition signal and activates the counter-measure system in response to the threat level.

Method and apparatus for activating a crash countermeasure in response to the road condition

A method for operating a pre-crash sensing system for a vehicle having an object detecting system and an associated data storage. The method includes partitioning the vehicle operating environment into a plurality of zones wherein each zone represents a different area surrounding the vehicle. In response to detecting an object within any one of the zones, the method activates the zone, and modifies an state of the object detection system and the contents of,the data storage as a function. the active zone. In one embodiment, three zones are disclosed wherein each zone represents a spheroidal area surrounding the vehicle. When the furthest zone is active, all data within the data storage is given approximately equal processing priority. When the middle zone is active, the content of the data storage is modified to prioritize data regarding the detected object for processing. Finally, when the nearest zone is active, the content of the data storage is further modified to provide highest priority to data regarding the detected object.

Method and apparatus for pre-crash threat assessment using spheroidal partitioning

An apparatus for predicting cylinder pressure for on-vehicle control of an internal combustion engine includes a piston sensor, a cylinder pressure sensor, and a controller. The piston sensor is coupled to a piston located in the engine. The piston sensor detects the piston position and generates a piston position signal. The cylinder pressure sensor is coupled to a cylinder located in the engine. The cylinder pressure sensor detects the cylinder pressure and generates a cylinder pressure signal. The controller receives both the piston position signal and the cylinder pressure signal. A neural network, located in the controller, uses this data to predict an undesirable cylinder pressure during a future combustion event. The controller then modifies the future combustion event in response to the predicted undesirable cylinder pressure.

Predicting cylinder pressure for on-vehicle control

A virtual vehicle sensor includes a neural network which produces a sensor output based on a linear combination of non-linear physical signals generated by conventional physical sensors. Instead of determining an output directly, the neural network determines the polynomial coefficients as functions of the physical signals indicative of other engine operating parameters. The sensor is manufactured using relatively limited data collection to calibrate a simulation model. The output of the simulation model is used for model-based mapping to generate more comprehensive maps used for training the neural network. The trained neural network is embedded in a controller and acts as the virtual sensor to monitor engine parameters which are difficult to measure or for which conventional physical sensors do not currently exist. The virtual sensor may be used to sense parameters such as in-cylinder residual mass fraction, emission levels, in-cylinder pressure rise during combustion, and exhaust gas temperature.

Virtual vehicle sensors based on neural networks trained using data generated by simulation models

A motor vehicle related operating signal, such as a torque signal produced by operation of an engine of the motor vehicle, is monitored to produce a frequency signature for the vehicle. The frequency signature is filtered by a fuzzy spectral filter to extract a frequency membership for the vehicle which is utilized to generate characteristic signals representative of the vehicle. Signals representative of vehicle inertia (J), vehicle horsepower (HP) and relative vehicle temperature (RVT) are extracted by a radial basis function (RBF) neural network. These characteristic signals are then used to control the vehicle directly via a powertrain control module (PCM) or via a robot driver for vehicle test purposes. For robot control, an anticipated throttle lag and an anticipated brake lag are generated and used to more accurately and repeatedly control the robot to simulate a human driver in following acceleration curves for vehicle testing purposes.

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Belleville, MI, United States of America

Anya Lynn Tascillo