Abstract: A vehicle includes vehicle systems each having driver-selectable vehicle system settings (VSS), and a control system for statistically modeling the VSS to determine an identity of a driver. The control system automatically controls a setting of at least one of the vehicle systems using or based on the identity. The control system statistically models the VSS for the driver over time to produce a historical driver profile (HDP) for the driver, and can automatically update the HDP when said driver manually changes any one of the VSS. An optional driver identification device can verify the identity. A method for controlling a predetermined onboard system of a vehicle includes collecting a set of VSS for a plurality of onboard systems, processing the VSS through a statistical modeling algorithm to determine an identity of a driver of the vehicle, and automatically controlling a predetermined onboard system using the identity of the driver.

Abstract: A system and method for providing proactive vehicle system management and maintenance using diagnostic and prognostic information. Vehicle information is collected from vehicle sensors and/or sub-systems by an on-board module on the vehicle and/or at a remote facility where the information is wirelessly transmitted to the remote facility. The collected information is analyzed to determine the health of various systems, sub-systems and components so that the remaining useful life of the systems, sub-systems and components can be predicted. By utilizing the diagnostic and prognostic information, a vehicle control strategy can be reconfigured to minimize customer impact. Further, if a software problem is detected, temporary or permanent software fixes can be provided automatically and remotely through a remote service garage.

Abstract: An adaptive control VGR steering system that varies the steering gear ratio between a vehicle hand-wheel angle and the road wheel angle based on vehicle speed and one or more of hand-wheel angle, driver attentiveness and a driver's driving style and skill.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on vehicle passing maneuvers. A process determines whether the vehicle has started a lane change from a first lane to a second lane as a first phase of the passing maneuver. If so, the process determines whether the lane change to the second lane has been completed as a second phase of the passing maneuver. If the lane change to the second lane has been completed, the process determines whether there has been a lane change back to the first lane to complete the passing maneuver as a third phase of the passing maneuver. Further, the process determines whether a time threshold has been exceeded after the passing maneuver is in a second phase, but no lane change back to the first lane has occurred.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on characteristic maneuvers and road and traffic conditions. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the maneuver. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on highway on/off ramp maneuvers. Once the maneuver has been determined to be an on-ramp maneuver or an off-ramp maneuver, a style characterization processor can classify the maneuver using selected discriminant features obtain or derived from the maneuver.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on characteristic maneuvers and road and traffic conditions. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the characteristic maneuver. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle. In one embodiment, the style characterization processor generates a maneuver model for the characteristic maneuvers and compares it with driver input data in the frequency domain to generate the driver style classification.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on characteristic curve-handling maneuvers and road and traffic conditions. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the maneuver. The system also includes a traffic and road condition recognition processor that receives the sensor signals, and provides traffic condition signals identifying traffic conditions and road condition signals identifying road conditions. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on characteristic maneuvers and road and traffic conditions. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the maneuver. The system also includes a traffic condition recognition processor that receives the sensor signals, and provides traffic condition signals identifying traffic conditions. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the traffic condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle.

Abstract: An adaptive vehicle control system that classifies a drivers driving style based on vehicle headway control. The system reads sensor signals to identify the range and range rate between a subject vehicle and a preceding vehicle, where the range rate is close to zero if the distance between the subject vehicle and the preceding vehicle is relatively steady, the range rate is negative when the subject vehicle is closing in on the preceding vehicle and the range rate is positive when the subject vehicle is falling behind the preceding vehicle. The range rate, the subject vehicle speed and other signals are used to classify the drivers driving style based on how fast the subject vehicle closes in on the preceding vehicle or falls behind, and the following distance between the subject vehicle and the preceding vehicle. The system can then classify the headway-control maneuver using selected discriminant features.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on lane-change maneuvers. A maneuver identification classification process reads sensor signals for vehicle speed, vehicle yaw rate and vehicle heading angle. The process determines if the vehicle is turning based on whether the yaw rate signal is greater than a yaw rate threshold and whether the heading angle is greater than a heading angle threshold. The process then determines whether the maneuver is an ordinary curve-handling maneuver or a lane-change maneuver by determining whether the yaw rate signal is greater than another yaw rate threshold, the difference between the heading angle and an initial heading angle is greater than another heading angle threshold and the lateral deviation is greater than a lateral deviation threshold. If the curve-handling maneuver is a lane-change maneuver, a maneuver identifier signal is provided to a style characterization processor.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on vehicle U-turn maneuvers. A process determines if the vehicle has started a turn if the yaw rate is greater than a first yaw rate threshold, and determines a vehicle heading angle based on the yaw rate and a sampling time if the vehicle has started a turn. The process then determines whether the vehicle maneuver has been completed by determining if the yaw rate is less than a second yaw rate threshold. The process then determines that the completed maneuver was a U-turn maneuver if the yaw rate is less than a third yaw rate threshold during the maneuver, the final vehicle heading angle is within a heading angle range and the duration of the maneuver is less than a predetermined time threshold. In one non-limiting embodiment, the heading angle range is between 165° and 195°.

Abstract: A vehicle control system that classifies a driver's driving style based on characteristic maneuvers. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the maneuver. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the traffic and road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on left/right-turn maneuvers. A process determines if the vehicle has started a turn if the yaw rate is greater than a first yaw rate threshold, and determines a vehicle heading angle based on the yaw rate and a sampling time if the vehicle has started a turn. The process then determines whether the vehicle maneuver has been completed by determining if the yaw rate is less than a second yaw rate threshold. The process then determines that the completed maneuver was a left/right-turn maneuver if the yaw rate is less than a third yaw rate threshold over the sampling time and the vehicle heading angle is within a heading angle range for a time period less than a predetermined time threshold. In one non-limiting embodiment, the heading angle range is between 75° and 105°.

Abstract: An adaptive vehicle control system that classifies a driver's driving style based on characteristic maneuvers and road and traffic conditions. The system includes a plurality of vehicle sensors that detect various vehicle parameters. A maneuver identification processor receives the sensor signals to identify a characteristic maneuver of the vehicle and provides a maneuver identifier signal of the maneuver. The system also includes a road condition recognition processor that receives the sensor signals, and provides road condition signals identifying road conditions. A style characterization processor receives the maneuver identifier signals, sensor signals from the vehicle sensors and the road condition signals, and classifies driving style based on the signals to classify the style of the driver driving the vehicle.

Abstract: An adaptive vehicle control system that classifies a drivers driving style based on vehicle launching maneuvers. A process determines whether the vehicle speed signal during a predetermined time window is greater than a speed threshold, whether the vehicle speed signal before the time window is less than the speed threshold and whether the average of the vehicle longitudinal acceleration during the time window is greater than a first longitudinal acceleration threshold and, if so, determines if the vehicle is in a vehicle launching maneuver. The process then determines that the vehicle launching maneuver has ended if the average of the vehicle longitudinal acceleration during a second time window is less than the longitudinal acceleration threshold. The style characterization processor can then classify the vehicle launching maneuver using selected discriminant features.

Abstract: An embodiment contemplates a method for estimating a capacity of a battery. A state of charge is determined at a first instant of time and at a second instant of time. A difference in the state of charge is determined between the first instant of time and the second instant of time. A net coulomb flow is calculated between the first instant of time and the second instant of time. The battery capacity is determined as a function of the change in the state of charge and the net coulomb flow.

Abstract: A method is provided for determining a state-of-health of a battery in a vehicle-during an engine cranking phase. An engine cranking phase is initiated. Characteristic data is recorded that includes battery voltage data and engine cranking speed data during the engine cranking phase. The characteristic data is provided to a pre-processing unit. The pre-processing unit normalizes the characteristic data for processing within a classifier. The normalized data is input o the classifier for determining the vehicle battery state-of-health. The classifier has a trained state-of-health decision boundary resulting from a plurality of trials in which predetermined characterization data is collected with known classes. The battery state-of-health is classified based on the trained state-of-health decision boundary.

Abstract: A vehicle steering control system for a vehicle/trailer combination that provides rear-wheel steering assist to improve the vehicle/trailer combination stability and handling performance. The control system provides an open-loop feed-forward control signal based on hand-wheel angle and vehicle speed. The control system includes a vehicle yaw rate command interpreter that provides a vehicle closed-loop feedback control signal based on the yaw rate of the vehicle, a trailer yaw rate command interpreter that provides a trailer closed-loop feedback control signal based on the yaw rate of the trailer, and a vehicle lateral acceleration command interpreter that provides a closed-loop feedback control signal based on the lateral acceleration of the vehicle. The closed-loop feedback signals are added together and then combined with the open-loop feed-forward control signal to provide the rear-wheel steering assist.

Abstract: A system and method for determining the root cause of a fault in a vehicle system, sub-system or component using models and observations. In one embodiment, a hierarchical tree is employed to combine trouble or diagnostic codes from multiple sub-systems and components to get a confidence estimate of whether a certain diagnostic code is accurately giving an indication of problem with a particular sub-system or component. In another embodiment, a hierarchical diagnosis network is employed that relies on the theory of hierarchical information whereby at any level of the network only the required abstracted information is being used for decision making. In another embodiment, a graph-based diagnosis and prognosis system is employed that includes a plurality of nodes interconnected by information pathways. The nodes are fault diagnosis and fault prognosis nodes for components or sub-systems, and contain fault and state-of-health diagnosis and reasoning modules.