Abstract: In a feature, a road condition detection system includes: a combination module configured to generate a combined image based on at least two images, each of the two images including a road and generated based on one of: (a) an image captured using a camera, (b) light detection and ranging (LIDAR) data, (c) radar data, and (d) ultrasonic data; a feature extraction module configured to generate a first feature map based on the combined image; an information map module configured to generate a second feature map based on at least one operating parameter; a joining module configured to generate a joint feature map based on the first and second feature maps; and a condition module configured to set a road condition of the road in front of a vehicle based on the joint feature map.

Abstract: A system and method of automatically activating a windshield wiper system of a vehicle having a front windshield with a front wiper and a rear windshield with a rear wiper are provided. The method comprises assessing at least one windshield classification of road conditions based on original information and capturing a front image of the front windshield, a rear image of the rear windshield, and an environment image of the environment. The method further comprises classifying the images to define a first windshield class. The method further comprises determining a front windshield perception, a rear windshield perception, and an environment perception of the first windshield class to define a first combination of detection sources. The method further comprises fusing the front windshield perception, the rear windshield perception and the environment perception, defining a first front probability of the first windshield class.

Abstract: Systems and methods for determining whether a vehicle is in an understeer or oversteer situation. The system includes a controller circuit coupled to an IMU and an EPS, and programmed to: calculate, for a steered first axle, an axle-based pneumatic trail for using IMU measurements and EPS signals and estimate a saturation level as a function of a distance between the axle-based pneumatic trail and zero. The system estimates, for an unsteered second axle, an axle lateral force curve with respect to a slip angle of the second axle, and a saturation level as a function of when the axle lateral force curve with respect to the slip angle transitions from positive values to negative values. The saturation level of the first axle and the second axle are integrated. The system determines that the vehicle is in an understeer or oversteer situation as a function of the integrated saturation levels.

Abstract: A driver command predictor includes a controller, multiple sensors, and a command prediction unit. The controller is configured to command an adjustment of multiple motion vectors of a vehicle relative to a roadway in response to multiple actual driver commands and multiple future driver commands. The actual driver commands are received at a current time. The future driver commands are received at multiple update times. The update times range from the current time to a future time. The sensors are configured to generate sensor data that determines multiple actual states of the vehicle in response to the motion vectors as commanded. The command prediction unit is configured to generate the future driver commands at the update times in response to a driver model. The driver model operates on the actual driver commands and the actual states to predict the future driver commands at the update times.

Abstract: A system includes a primary control module, a stability status module, and a supervisory control module. The primary control module is configured to determine at least one control action for at least one of an electronic limited slip differential and an aerodynamic actuator of a vehicle based on a driver command. The stability status module is configured to determine whether at least one component of the vehicle is stable or unstable based on an input from a sensor on the vehicle. The at least one component includes at least one of a vehicle body, a front axle, a rear axle, front wheels, and rear wheels. The supervisory control module is configured to adjust the at least one control action when the at least one component is unstable.

Abstract: A method of controlling a vehicle includes obtaining a linear representation of a vehicle dynamics model that includes actuator dynamics u integrated with vehicle dynamics x. The actuator dynamics u include a road wheel angle at rear wheels ?r and a torque Mz. The method also includes obtaining an objective function based on a function of the vehicle dynamics x and the actuator dynamics u and formulating a cost function to minimize the objective function. The actuator dynamics u including the torque Mz are determined for a next time sample based on minimizing the objective function. The vehicle is controlled to implement the torque Mz.

Abstract: A vehicle, and a method and system for operating the vehicle. The system includes a processor. The processor receives a driver input at the vehicle, determines a current lateral force on a tire of the vehicle for the driver input, determines a desired yaw rate and lateral velocity for the vehicle based on the current lateral force on the tire that operates the vehicle at a maximum yaw moment, and operates the vehicle at the desired yaw rate and lateral velocity.

Abstract: A system includes a clutch state module and a clutch torque module. The clutch state module is configured to determine whether a clutch of an electronic limited slip differential is locked or unlocked. The electronic limited slip differential couples an engine of a vehicle to left and right wheels of the vehicle. The clutch torque module is configured to estimate an actual torque transferred by the clutch using a first clutch torque model when the electronic limited slip differential is unlocked, and estimate the actual clutch torque using a second clutch torque model when the electronic limited slip differential is locked. The second clutch torque model is different than the first clutch torque model.

Abstract: Methods and systems are provided for controlling a vehicle action based on a condition of a road on which a vehicle is travelling, including: obtaining first sensor data as to a surface of the road from one or more first sensors onboard the vehicle; obtaining second sensor data from one or more second sensors onboard the vehicle as to a measured parameter pertaining to operation of the vehicle or conditions pertaining thereto; generating a plurality of road surface channel images from the first sensor data, wherein each road surface channel image captures one of a plurality of facets of properties of the first sensor data; classifying, via a processor using a neural network model, the condition of the road on which the vehicle is travelling, based on the measured parameter and the plurality of road surface channel images; and controlling a vehicle action based on the classification of the condition of the road.

Abstract: A method of controlling stability of a vehicle and a stability control system for the vehicle. A driver command is determined based on driver input data. At least one output command is sent to one or more vehicle systems to control stability of the vehicle based on the driver command. A controller sends the output command based on a control hierarchy that provides an order in which the controller controls body motion of the vehicle, wheel slip of the vehicle, and standard stability of the vehicle to control stability of the vehicle. The order dictates that the controller controls the body motion of the vehicle and the wheel slip of the vehicle before the controller controls the standard stability of the vehicle. A state of one or more of the vehicle systems is controlled based on the sent output command as dictated via the control hierarchy.

Abstract: A vehicle, system and method of operating the vehicle. A sensor measures a dynamic parameter of the vehicle. A processor determines a lateral force on a first tire based on the dynamic parameter of the vehicle, determines a longitudinal force on the first tire that achieves a maximal grip of the first tire for the lateral force, and adjusts a first torque on the first tire in order to achieve the determined longitudinal force at the first tire.

Abstract: Systems and methods for controlling an autonomous vehicle are described. A trajectory planner module provides a first trajectory to a trajectory control module. The trajectory control module determines parameters of the first trajectory. The trajectory control module compares the parameters to a respective threshold value. The trajectory control module obtains one or more alternative trajectories, determines parameters of each alternative trajectory, and compares the parameters of the alternative trajectory to a respective threshold value. The trajectory control module selects a trajectory for controlling the autonomous vehicle that has parameters which are within a range defined by the threshold values and controls the autonomous vehicle based on the selected trajectory. Thus, before handing back control to a driver, the trajectory control module selects from alternate trajectories for controlling the autonomous vehicle.

Abstract: Systems and methods are provided for generating a downforce on a vehicle. An aerodynamic deflector on the vehicle is repositionable. An actuator is coupled with the aerodynamic deflector. A controller configured to: detect a performance mode of operation of the vehicle; determine a requested lateral acceleration; calculate a control adjustment of the aerodynamic deflector to generate a downforce to achieve the requested lateral acceleration and maximize lateral grip of the vehicle; and operate the actuator to effect the control adjustment of the aerodynamic deflector to generate the downforce on the vehicle.

Abstract: A tire radius monitoring system for dynamically determining a tire effective radius for each of the wheels on a vehicle is described. The system includes a GPS sensor, a plurality of wheel speed sensors, and a controller. The controller determines, via the GPS sensor, a velocity vector related to longitudinal velocity of the vehicle. The controller determines wheel speeds for the plurality of vehicle wheels, and detects a no-wheel-slip state for the vehicle wheels and the velocity vector from the GPS sensor. The controller determines tire effective radii for the plurality of vehicle wheels based upon the velocity vector for the vehicle and the wheel speeds for the plurality of vehicle wheels during the no-wheel-slip state, and controls vehicle operation based upon the tire effective radii.

Abstract: An axle torque distribution system includes a memory and a control module. The memory stores a steering angle and a toque distribution algorithm. The control module executes the torque distribution algorithm to: obtain the steering angle; based on the steering angle, determine total lateral force requested for axles of a vehicle; based on the total lateral force requested, determine lateral forces requested for the axles while constraining lateral force distribution between the axles, where the constraining of the lateral force distribution includes, based on maximum lateral force capacities of tires of the vehicle, limiting the lateral forces requested for the axles; determine available longitudinal capacities for the axles based on the lateral forces requested respectively for the axles; determine torque capacities of the axles based on the lateral forces requested respectively for the axles; and control distribution of torque to the axles based on the torque capacities of the axles.

Abstract: Methods and systems are provided for controlling a vehicle action based on a condition of a road on which a vehicle is travelling, including: obtaining first sensor data as to a surface of the road from one or more first sensors onboard the vehicle; obtaining second sensor data from one or more second sensors onboard the vehicle as to a measured parameter pertaining to operation of the vehicle or conditions pertaining thereto; generating a plurality of road surface channel images from the first sensor data, wherein each road surface channel image captures one of a plurality of facets of properties of the first sensor data; classifying, via a processor using a neural network model, the condition of the road on which the vehicle is travelling, based on the measured parameter and the plurality of road surface channel images; and controlling a vehicle action based on the classification of the condition of the road.

Abstract: Presented are automated driving systems for executing intelligent vehicle operations in mixed-mu road conditions, methods for making/using such systems, and vehicles with enhanced headway control for transitional surface friction conditions. A method for executing an automated driving operation includes a vehicle controller receiving sensor signals indicative of road surface conditions of adjoining road segments, and determining, based on these sensor signals, road friction values for the road segments. The controller determines whether the road friction value is increasing or decreasing, and if a difference between the road friction values is greater than a calibrated minimum differential. Responsive to the friction difference being greater than the calibrated minimum differential and the road friction value decreasing, the vehicle controller executes a first vehicle control action.

Abstract: A method and associated system for selecting a charging station for a subject vehicle is described, and includes determining a state of charge of an on-vehicle DC power source arranged to supply electric power to a propulsion system for the subject vehicle. A travel route to a destination point is determined, and locations of a plurality of charging stations proximal to the travel route are identified. Desired states and corresponding weighting factors for a plurality of user-selectable parameters are determined, and a sorting routine is executed to rank the plurality of charging stations proximal to the travel route based upon the desired states and corresponding weighting factors for the plurality of user-selectable parameters and the state of charge of the on-vehicle DC power source. One of the charging stations is selected based upon the ranking, and a charging reservation is scheduled.

Abstract: A vehicle, system and method of operating a vehicle. A sensor for measures a dynamic variable of the vehicle. A processor determines a location of a perceived yaw center (PYC) of the vehicle from the dynamic variable, tracks a desired location of the PYC, and adjusts a control parameter of the vehicle to reduce a difference between the location of the PYC and the desired location of the PYC.

Abstract: A vehicle including an advanced driver-assistance system (ADAS) and operator-adjustable devices is described. Controlling the vehicle includes identifying a vehicle operator, and capturing a plurality of operator-selectable settings associated with the plurality of operator-adjustable devices for the vehicle operator. A base profile and a second profile are determined for the vehicle operator based upon the first subset associated with non-autonomous operation of the vehicle and the second subset associated with ADAS, respectively. The plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the base profile when the vehicle is operating in the non-autonomous mode, and the plurality of operator-adjustable devices are controlled to the operator-selectable settings associated with the second profile when the vehicle is being controlled at least in part by one or more of the subsystems of the ADAS.