Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving a first surface value associated with a first road surface area in an upcoming environment of the vehicle; receiving a second surface value associated with a second road surface area in the upcoming environment of the vehicle; determining a change in surface value based on the first surface value and the second surface value; and in response to the change in surface value being greater than a threshold, adapting at least one of vehicle collision warning messages, vehicle braking control, vehicle steering control, and path planning based on the second surface value.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving a first surface value associated with a first road surface area in an upcoming environment of the vehicle; receiving a second surface value associated with a second road surface area in the upcoming environment of the vehicle; determining a change in surface value based on the first surface value and the second surface value; and in response to the change in surface value being greater than a threshold, adapting at least one of vehicle collision warning messages, vehicle braking control, vehicle steering control, and path planning based on the second surface value.

Abstract: Systems and methods use an image of a road surface that is generated by a camera. The image includes a pattern from a light source. A region of interest in the image is determined based on the pattern from the light source. A total area is determined that includes at least part of the region of interest and an area adjacent the region of interest. A feature vector is extracted based on the region of interest and the total area. A road condition is determined based on the feature vector and a classifier.

Abstract: Systems and methods use an image of a road surface that is generated by a camera. The image includes a pattern from a light source. A region of interest in the image is determined based on the pattern from the light source. A total area is determined that includes at least part of the region of interest and an area adjacent the region of interest. A feature vector is extracted based on the region of interest and the total area. A road condition is determined based on the feature vector and a classifier.

Abstract: A lane departure detection/avoidance and data fusion system adapted for use with a vehicle and by an operator, includes at least one lane-marking sensor, at least one condition sensor, and a controller communicatively coupled to the sensors, and configured to determine a condition deviation, and compare the condition deviation to a pre-determined condition threshold, so as to improve system identification of operator engagement, lane departure detection upon curves, and detection of performance degradation.

Abstract: A method for map-aided vision-based lane sensing. The method includes receiving map information corresponding to a current geographic position of a vehicle on a roadway. The map information includes the number of lanes on the roadway. Information about the number of lanes crossed by the vehicle on the roadway is received. It is determined which of the lanes on the roadway is currently occupied by the vehicle based on the map information and the number of lanes crossed by the vehicle on the roadway.

Abstract: A method of collision avoidance. The method includes receiving a signal indicative of a potential collision of a vehicle. The method also includes generating a haptic collision alert in response to the signal.

Abstract: A vehicle speed estimation system estimates vehicle speed from wheel speed prior to an incipient wheel lockup condition. When an incipient wheel lockup condition is sensed, the estimated speed is increased by a determined amount and then decreased based on an estimated vehicle deceleration. The estimated vehicle deceleration during the incipient wheel lockup condition is based on wheel deceleration at a time preceding the time the incipient wheel lockup condition is sensed. When in incipient wheel lockup condition is sensed, a forgetting period during which wheel deceleration is not representative of vehicle deceleration is determined. The estimated vehicle deceleration during the incipient wheel lock condition is then based on wheel deceleration prior to the forgetting period.