A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to: determine whether a difference between a friction coefficient label and a determined friction coefficient corresponding to an image depicting a surface is greater than a label threshold; modify the determined friction coefficient to equal the friction coefficient label when the difference is greater than the label threshold; and retrain a neural network using the image and the friction coefficient label.

A method of controlling driving of a hybrid electric vehicle having an engine connected to main drive wheels via a transmission and a motor connected to auxiliary drive wheels includes setting a drive mode, controlling an input torque of the transmission in response to an extent of depression of an accelerator pedal (APS) according to the drive mode, performing distribution of drive power to the main drive wheels and the auxiliary drive wheels and variable shift-pattern control based on a result of a comparison between an amount of slip of the main drive wheels and the amount of slip of the auxiliary drive wheels, and determining whether to perform variable shift-time control in consideration of a type of the variable shift-pattern control or a number of revolutions per minute (RPM) of the engine at beginning of a shift.

An embodiment vehicle includes a collecting device for collecting environment information, a user input for automatic parking of the vehicle, an automatic parking controller for performing the automatic parking based on the environment information and the user input, and a behavior controller for controlling a behavior of the vehicle in response to control of the automatic parking controller. The automatic parking controller calculates a first progress corresponding to determination of whether the user input is a wake-up request, a second progress corresponding to acquisition of a control right for the behavior controller, a third progress corresponding to whether the user input is an execution request for the automatic parking, a fourth progress corresponding to generation of control information for the behavior controller, and a fifth progress corresponding to control of the vehicle's behavior.

The technology employs a variable motion control envelope that enables an on-board computing system of a self-driving vehicle to estimate future vehicle driving behavior along an upcoming path, in order to maintain a desired amount of control during autonomous driving. Factors including intrinsic vehicle properties, extrinsic environmental influences and road friction information are evaluated. Such factors can be evaluated to derive an available acceleration model, which defines an envelope of maximum longitudinal and lateral accelerations for the vehicle. This model, which may identify dynamically varying acceleration limits that can be affected by road conditions and road configurations, may be used by the on-board control system (e.g., a planner module of the processing system) to control driving operations of the vehicle in an autonomous driving mode.

The present invention relates to a control unit for determining a value indicative of a load bearing capability of a ground segment supporting a vehicle. The control unit is configured to issue a control signal to the vehicle to thereby impart a motion change of the vehicle, and receive response information from the vehicle indicative of the vehicle's response to the imparted motion change. The control unit is further configured to, based on the response information, determine a vertical position change of at least one wheel of the vehicle, and based on the determined vertical position change and the imparted motion change, determine the value indicative of the load bearing capability of the ground segment.

A method for operating a vehicle configured for highly automated or autonomous driving involves adapting control of the driving mode to the weather conditions of a vehicle's environment. Moreover, control of the driving mode is adapted by reference control parameters if the weather conditions deviate from a specified criterion. For generating the reference control parameters, driving behaviors of a plurality of vehicles in the vehicle's environment, adjacent to the vehicle, are determined. From the determined driving behaviors, an average speed, an average distance, an average acceleration, and an average deceleration are determined and are taken into account when generating the reference control parameters.

In the present invention, when a road surface condition is determined based on information acquired by a camera installed in a vehicle, a route of a host vehicle is predicted and the is determined. A road surface condition determination method and device determines a road surface condition of a predicted route based on information acquired by a camera installed in a host vehicle. A controller predicts a route of the host vehicle by determining a road surface friction coefficient of the predicted route based on information acquired by the camera. The determining of the road surface friction coefficient of the predicted route includes: dividing an ahead-of-vehicle image acquired by the camera in a left-right direction and determining a road surface condition for each of the determination areas, and determining the road surface friction coefficient in the determination areas through which the predicted route will pass.

Systems and methods for verifying whether vehicle operators are paying attention are disclosed. Exemplary implementations may: generate output signals conveying information related to a first vehicle operator; make a first type of determination of at least one of an object on which attention of the first vehicle operator is focused and/or a direction in which attention of the first vehicle operator is focused; make a second type of determination regarding fatigue of the first vehicle operator; make a third type of determination of at least one of a distraction level of the first vehicle operator and/or a fatigue level of the first vehicle operator; and effectuate a notification regarding the third type of determination to at least one of the first vehicle operator and/or a remote computing server.

Systems, apparatuses and methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle. Additionally, the technology may determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount. In one example, a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition.

The present invention obtains a controller and a control method capable of appropriately assisting with driving of a motorcycle by a rider.

A controller that controls operation of the motorcycle, to which a surrounding environment detector is mounted, includes: an acquisition section that acquires information on the surrounding environment of the motorcycle on the basis of output of the surrounding environment detector during travel of the motorcycle; and an adaptive cruise operation performing section that makes the motorcycle perform adaptive cruise operation on the basis of the surrounding environment information acquired by the acquisition section. The acquisition section acquires road traffic information that is acquired by a road facility via wireless communication. The controller further includes a safety operation performing section that makes the motorcycle currently performing the adaptive cruise operation perform safety operation on the basis of the road traffic information acquired by the acquisition section.