Abstract: Disclosed is a number of variations that may include a method, system, or computer product useful in determining an intended yaw or yaw rate that a driver desires using a model, comparing the yaw or yaw rate with the actual vehicle yaw or yaw rate to determining a yaw error or yaw rate error, using the yaw error or yaw rate error in a model predictive control to determine the brake pressure required to minimize or reduced to zero the yaw error or the yaw rate error.

Abstract: A number of variations are disclosed including a system and method for modifying, in real-time, at least one brake or powertrain application to individual roadwheels of a vehicle to increase lateral maneuver capability in a vehicle having an operational, partially operational, failing, or failed electronic steering system. The system and method may include modifying at least one brake or powertrain command to individual roadwheels where vehicle instability is detected.

Abstract: A number of variations are disclosed including a system and method for modifying, in real-time, at least one brake or powertrain application to individual roadwheels of a vehicle to increase lateral maneuver capability in a vehicle having an operational, partially operational, failing, or failed electronic steering system. The system and method may include modifying at least one brake or powertrain command to individual roadwheels where vehicle instability is detected.

Abstract: A number of variations may include a method or system using human driving characteristics in autonomous vehicle driving method or system.

Abstract: A system and method of hands-off signature detection may include applying a unique sinusoidal motor command to an EPS or handwheel actuator of a vehicle and monitoring attenuation of the unique sinusoidal motor command to determine if a user's hands are on or off of a handwheel of a vehicle. Attenuation of the unique sinusoidal motor command may occur due to the physical presence of a driver's hand(s) on the steering wheel.

Abstract: A number of variations may include a method or system using human driving characteristics in autonomous vehicle driving method or system.

Abstract: A number of variations may include a system and method may include autonomous driving systems including an algorithm analyzing motion controller performance in real time with respect to a set of attributes. The system and method may include an algorithm monitoring conditions such as monitoring actuations, vehicle dynamics, vehicle operating environment, and vehicle capability. The system and method may include an algorithm evaluating said conditions to determine which controller is best suited to the current situation during live vehicle operations.

Abstract: A number of variations may include a product including a body mounted air brake stabilizing and steering a vehicle having autonomous driving capabilities or functions, and a method of using the same, wherein the air brake is power actuated.

Abstract: A number of variations may include a vehicle, system and method of modulating power steering or torque from a lane keep assist system based on driver attentiveness to avoid accidental land changes.

Abstract: A number of variations may include a vehicle, system and method of estimating roadwheel angle based on camera input. A number of variations may include at least one of camera based angle detection of roadwheels; tire alignment problem detection using available cameras; or detection of gross roadwheel/tire problems using cameras.

Abstract: A number of variations may include a system or method of monitoring vehicle control actions and communicating upcoming control actions, such as deceleration or lateral movement, to vehicle occupants prior to occurrence of the control action such that occupants are aware of upcoming vehicle control actions and occupant stress or anxiety may be reduced during operation of a vehicle.

Abstract: A system and method of hands-off signature detection may include applying a unique sinusoidal motor command to an EPS or handwheel actuator of a vehicle and monitoring attenuation of the unique sinusoidal motor command to determine if a user's hands are on or off of a handwheel of a vehicle. Attenuation of the unique sinusoidal motor command may occur due to the physical presence of a driver's hand(s) on the steering wheel.

Abstract: A system and method of mu estimation may include the steps of collecting vehicle travel data on a road surface via a plurality of a sensors including at least one of an accelerometer or microphone; collecting external source data over a network; and aggregating the vehicle travel data and external source data to form an aggregated data set. The method may include performing feature extraction processing of the aggregated data set to transform the aggregated data set and into a processed aggregated data set; communicating the processed aggregated data set to a machine learning model; and generating at least one of an estimated mu value of the road surface or road surface classification via the machine learning model.

Abstract: A number of illustrative variations may include a method or product for sensing driver intervention in an autonomous steering system.

Abstract: In a number of illustrative variations, an always-on motion controller may include a path planner system; a driver monitoring system, and a motion controller system. The always-on motion controller may provide for an always present autonomous driving system which may seamlessly take control of the vehicle where driver attention is lost, distracted, or momentarily absent. The always-on motion controller may seamlessly release control of the vehicle to the driver or driver attention is regained.

Abstract: In a number of illustrative variations, a method may include providing a first motion control algorithm and a second motion control algorithm, the first motion control algorithm and a second motion control algorithm being constructed and arranged to provide motion control requests. The method may further include determining if the first motion control algorithms motion control requests are within a plausible range and performing a switchover from the first motion control algorithm to the second motion control algorithm if necessary.

Abstract: A number of variations may include a method of training a neural network vehicle motion controller that more closely replicates how a human would drive a vehicle using seat of pants vehicle dynamics variables and look ahead parameters in order to determine how a motion controller should direct the steering angle, throttle and break inputs to the vehicle to navigate the vehicle.

Abstract: A number of illustrative variations may include a method or product for sensing driver intervention in an autonomous steering system.