Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a generative adversarial network (GAN) to reconstruct a missing portion of an image by determining a reconstructed portion of the image based on data from portions of the image surrounding the missing portion and compare an acquired image with the reconstructed portion of the image to determine a damaged portion. The instructions further include instructions to determine estimated damage based on the damaged portion.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to identify an initial lateral distance and an initial longitudinal distance of a host vehicle in a turn at an initiation of the turn, predict a heading angle of the host vehicle at a specified time after the initiation of the turn, predict a final lateral distance and a final longitudinal distance between the host vehicle and a target at the specified time based on the identified lateral distance, the identified longitudinal position, and the predicted heading angle, determine a lateral offset at a longitudinal time to collision based on the final lateral distance and the final longitudinal distance, and actuate a brake of the host vehicle according to a threat assessment based on the lateral offset.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a generative adversarial network (GAN) to reconstruct a missing portion of an image by determining a reconstructed portion of the image based on data from portions of the image surrounding the missing portion and compare an acquired image with the reconstructed portion of the image to determine a damaged portion. The instructions further include instructions to determine estimated damage based on the damaged portion.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to identify an initial lateral distance and an initial longitudinal distance of a host vehicle in a turn at an initiation of the turn, predict a heading angle of the host vehicle at a specified time after the initiation of the turn, predict a final lateral distance and a final longitudinal distance between the host vehicle and a target at the specified time based on the identified lateral distance, the identified longitudinal position, and the predicted heading angle, determine a lateral offset at a longitudinal time to collision based on the final lateral distance and the final longitudinal distance, and actuate a brake of the host vehicle according to a threat assessment based on the lateral offset.

Abstract: A system, comprising a first computer that includes a processor and a memory. The memory stores instructions executable by the processor to input an expected control input to a second computer, and then, to determine a response resulting from the expected control input. The memory stores instructions to determine a compensated control input based on the expected control input and the response, and to input the compensated control input to the second computer to achieve the expected control input. The second computer is provided to actuate a vehicle component to achieve the expected control input.

Abstract: An object exterior to a vehicle can be detected, and an augmented image of the detected object can be output to a vehicle display device, wherein at least one of dimensions and a motion of the augmented image is adjusted based on an environmental condition and physical attribute of the detected object including at least one of a speed, an acceleration, a location, and a direction of the detected object.

Abstract: Methods and systems for adjusting vehicle operation in response to vehicle weight are described. In one example, an adaptive driver demand correction is adjusted in response to vehicle weight. The methods and systems may provide for more consistent powertrain response and lower vehicle emissions at lower vehicle weights.

Abstract: A computing device in a vehicle, programmed to pilot a vehicle by adaptive cruise control and determine a lane change maneuver based on the adaptive cruise control and determined traffic in an adjacent lane. The computing device can display a prompt to an occupant based on the lane change maneuver.

Abstract: A computing device in a vehicle can determine a time bound for a lane change maneuver based on determining a reaction time, an occupant acceptance time, a vehicle maneuver time and a maneuver cancellation time and request authorization to perform the lane change maneuver. The computing device can determine the time bound has expired, and cancel the authorization request based on the expired time bound.

Abstract: A system includes a processor configured to receive weather data including a plurality of measured data points in an area around a vehicle, from a remote weather server. The processor is also configured to receive a path from a vehicle subsystem. The processor is further configured to combine the data points and path to form a predictive model reflecting expected weather conditions along the path at future times, the model based on predefined parameters defining expected weather behavior and send the model to a requesting vehicle subsystem.

Abstract: An object exterior to a vehicle can be detected, and an augmented image of the detected object can be output to a vehicle display device, wherein at least one of dimensions and a motion of the augmented image is adjusted based on an environmental condition and physical attribute of the detected object including at least one of a speed, an acceleration, a location, and a direction of the detected object.

Abstract: A system, comprising a first computer that includes a processor and a memory. The memory stores instructions executable by the processor to input an expected control input to a second computer, and then, to determine a response resulting from the expected control input. The memory stores instructions to determine a compensated control input based on the expected control input and the response, and to input the compensated control input to the second computer to achieve the expected control input. The second computer is provided to actuate a vehicle component to achieve the expected control input.

Abstract: A system includes a processor configured to receive weather data including a plurality of measured data points in an area around a vehicle, from a remote weather server. The processor is also configured to receive a path from a vehicle subsystem. The processor is further configured to combine the data points and path to form a predictive model reflecting expected weather conditions along the path at future times, the model based on predefined parameters defining expected weather behavior and send the model to a requesting vehicle subsystem.

Abstract: A computing device in a vehicle can be programmed to determine a virtual steerable path polynomial including a lane change maneuver, update the virtual steerable path polynomial by controlling a vehicle trajectory, and, pilot the vehicle based on the virtual steerable path polynomial. The computer further programmed to determine the virtual steerable path polynomial based on the vehicle trajectory.

Abstract: A computing device in a vehicle, programmed to pilot a vehicle by adaptive cruise control and determine a lane change maneuver based on the adaptive cruise control and determined traffic in an adjacent lane. The computing device can display a prompt to an occupant based on the lane change maneuver.

Abstract: A computing device in a vehicle can be programmed to determine a virtual steerable path polynomial including a lane change maneuver, update the virtual steerable path polynomial by controlling a vehicle trajectory, and, pilot the vehicle based on the virtual steerable path polynomial. The computer further programmed to determine the virtual steerable path polynomial based on the vehicle trajectory.

Abstract: A computing device in a vehicle can determine a time bound for a lane change maneuver based on determining a reaction time, an occupant acceptance time, a vehicle maneuver time and a maneuver cancellation time and request authorization to perform the lane change maneuver. The computing device can determine the time bound has expired, and cancel the authorization request based on the expired time bound.

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 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: Systems and methods for vehicle mode scheduling with learned user preferences include monitoring vehicle data when a vehicle changes from a first mode to a second mode. The method also includes analyzing the vehicle data to identify a preference for the second mode during a driving context. Additionally, the 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.