Abstract: A system for providing weather conditions includes a server in communication with a weather data provision service. The server includes a middleware application operable to process weather data requests. The system also includes a gps module, and at least one component control system, operable to adjust at least one parameter associated with at least one vehicle component. Finally, the illustrative system includes a vehicle computing system, in communication with the GPS module, the at least one component control system, and the server. In response to a request for data from the component control system, the vehicle computing system passes GPS data to the server, including the request for data, and the vehicle computing system responsively receives the requested data from the server. The vehicle computing system relays the received data to the component control system that requested the data.

Abstract: A controller may indicate a low-traction mode of a vehicle when a longitudinal tracking accumulation exceeds a first threshold value and a lateral response accumulation exceeds a second threshold value. The longitudinal tracking accumulation may measure a tally of activation of a traction control system over time. The lateral response accumulation may measure a comparison of the vehicle yaw-rate to a driver-desired model-based prediction of the yaw-rate. The controller may indicate the low-traction mode by providing a recommendation to switch to the low-traction mode in a human-machine interface screen of the vehicle, or by automatically adjusting the operational mode of at least one electronic control unit of the vehicle to implement the low-traction mode.

Abstract: A demand is identified based on input from a vehicle operator. Data is received providing a history of vehicle velocities. An estimated reference velocity is generated based at least on the velocity data. A final demand is generated based at least in part on a reference velocity indicated by the velocity control signal and the estimated reference velocity.

Abstract: Example systems and methods for vehicle mode scheduling with learned user preferences are disclosed. The example disclosed method includes monitoring vehicle data when a vehicle changes from a first mode to a second mode. The example method also includes analyzing the vehicle data to identify a preference for the second mode during a driving context. Additionally, the example method includes generating a recommendation on whether to switch to the second mode while traversing a route based on the vehicle data, and the driving context, and presenting the recommendation to the driver.

Abstract: A system includes a processor configured to receive environmental context data upon which automatic engagement of a fuel economic driving mode (eco-mode) is conditioned. The processor is also configured to evaluate the context data to determine if the eco-mode should be automatically engaged based on a data correspondence to an engagement factor and engage the eco-mode upon correspondence of the data to an engagement factor.

Abstract: A vehicle system includes a computer in a vehicle, and the computer includes a processor and a memory. The computer is configured to determine an operational state of a vehicle, calculate a road grade value from data from a first sensor, calculate an effective road grade value from a torque demand value, update modeling variables to map the road grade value substantially to the effective grade value when the operation state is determined to be a steady-state condition, and calculate a road grade characteristic with the road grade value and the modeling variables.

Abstract: A system includes a processor configured to receive environmental context data upon which automatic engagement of a fuel economic driving mode (eco-mode) is conditioned. The processor is also configured to evaluate the context data to determine if the eco-mode should be automatically engaged based on a data correspondence to an engagement factor and engage the eco-mode upon correspondence of the data to an engagement factor.

Abstract: A vehicle selectively operates in a plurality of operational modes. The operational modes carry with them different commands for operating a controlled suspension system such as a continuously controlled damping suspension system, a controlled steering system such as an electronic power-assist steering system, and a powertrain of the vehicle. For example, in one operational mode, the powertrain might be more sensitive to output torque from a motor or engine quickly with little hesitation. The vehicle includes a sensing system, such as a plurality of suspension height sensors and a corresponding controller programmed to receive suspension height signals indicating the characteristic of the road, to categorize the signals, and to compute categorized vehicle characteristics such as vehicle pitch, heave, roll, yaw, etc. The controller can discretize the categorized signals into a discrete number of index values, and then command the vehicle to change operational mode based on the discrete index value.

Abstract: A curve length, a curve size, and a curve heading angle change of a roadway being traveled by a vehicle are determined. Each of the curve length, the curve size, and the curve heading angle are compared with one or more threshold values to obtain a driving mode request.

Abstract: A computer-implemented method of gathering data includes querying, via a vehicle computing system, a plurality of weather sensors included with a vehicle and in communication with a vehicle network. The method also includes determining whether or not appropriate conditions exist for storage of data from the sensor, for each of the sensors. Additionally, the method includes storing the data from the sensor if appropriate conditions exist. Finally, the method includes sending, from the vehicle computing system to a remote network, data from one or more queried sensors and current GPS coordinates of the vehicle.

Abstract: A vehicle system includes at least one sensor that collects infrastructure information in real time and outputs sensor signals representing the collected infrastructure information. A processing device processes the sensor signals, predicts a future road characteristic based on the infrastructure information, and controls at least one vehicle subsystem in accordance with the predicted future road characteristic. A method includes receiving infrastructure information collected in real time, processing the infrastructure information, predicting a future road characteristic based on the infrastructure information, and controlling at least one vehicle subsystem in accordance with the predicted future road characteristic.

Abstract: A vehicle selectively operates in a plurality of operational modes. The operational modes carry with them different commands for operating a controlled suspension system such as a continuously controlled damping suspension system, a controlled steering system such as an electronic power-assist steering system, and a powertrain of the vehicle. For example, in one operational mode, the powertrain might be more sensitive to output torque from a motor or engine quickly with little hesitation. The vehicle includes a sensing system, such as a plurality of suspension height sensors and a corresponding controller programmed to receive suspension height signals indicating the characteristic of the road, to categorize the signals, and to compute categorized vehicle characteristics such as vehicle pitch, heave, roll, yaw, etc. The controller can discretize the categorized signals into a discrete number of index values, and then command the vehicle to change operational mode based on the discrete index value.

Abstract: A rule-based vehicle cruise-control system includes a computer in a vehicle, the computer including a processor and a memory, and the computer is configured to control a vehicle speed within a first speed threshold according to a set point inputted to the cruise control system. The computer is configured to determine a current grade value is within a first grade threshold and adjust the set point to control the vehicle speed within a second speed threshold outside the first speed threshold. The computer is further configured to determine the current grade value is within a second grade threshold and adjust the set point to recover the vehicle speed to within the first speed threshold.

Abstract: A curve length, a curve size, and a curve heading angle change of a roadway being traveled by a vehicle are determined. Each of the curve length, the curve size, and the curve heading angle are compared with one or more threshold values to obtain a driving mode request.

Abstract: A method for controlling a vehicle includes automatically activating a first mode of operation of a vehicle, determining if a driver does not prefer the automatically activated first mode of operation, and automatically transitioning to a second mode of operation based on the determination.

Abstract: A system includes a processor configured to receive a vehicle location and to access driver-specific driving-mode-change data for the vehicle location. The processor is also configured to determine, based on the accessed data, if a vehicle driving-mode-change has previously occurred at the vehicle location and context a sufficient number of times to cross a predefined threshold and, if so, to automatically change a vehicle driving-mode to the driving-mode associated with the previous driving-mode-change.

Abstract: A demand is identified based on input from a vehicle operator. Data is received providing a history of vehicle velocities. An estimated reference velocity is generated based at least on the velocity data. A final demand is generated based at least in part on a reference velocity indicated by the velocity control signal and the estimated reference velocity.

Abstract: A predictive enhanced maneuverability system providing enhanced timely delivery of vehicle performance selection of chassis, and steering modes for potential predicted safety collisions is disclosed. The primary inputs of the disclosed invention include a determination of the proximity to a preceding vehicle, the density of the surrounding traffic, a forward collision warning alert, and the predictive enhanced maneuverability decision sub-system for vehicle mode selection. The system of the disclosed invention provides a customized vehicle dynamics chassis and steering dynamic mode output, based on a predicted decision about vehicle potential for collision, for improved driver maneuverability and safety. In addition, the disclosed invention provides an improved system and method for incorporating the time dependent headway, forward collision warning alert, and the traffic density for chassis collision-mode embedded decision-making.

Abstract: A vehicle system includes at least one sensor and a processing device. The sensor is configured to output sensor signals representing infrastructure information collected in real time. The processing device is configured to predict a future road characteristic based on the infrastructure information, evaluate a prediction error, adjust a prediction horizon until the prediction error is within a predetermined accuracy range, and control at least one vehicle subsystem in accordance with the predicted future road characteristic. A method includes receiving infrastructure information collected in real time, predicting a future road characteristic based on the infrastructure information, evaluating a prediction error, adjusting a prediction horizon until the prediction error is within a predetermined accuracy range, and controlling at least one vehicle subsystem in accordance with the predicted future road characteristic.

Abstract: Methods and systems are provided for adjusting an engine output delivered in response to an operator pedal actuation based at least on a grade of vehicle travel. During uphill travel, in the presence of headwinds, and/or in the presence of a vehicle payload, the output may be increased while during downhill travel or in the presence of tailwinds, the output may be decreased. In this way, driver fatigue during travel over varying elevations, varying ambient conditions, and varying loads can be reduced.