The present disclosure relates to a method and an apparatus for controlling an electronic control suspension using a deep learning-based road surface classification model. The method for controlling an electronic control suspension in a vehicle including a camera and a GPS receiver may include collecting location information of the vehicle using the GPS receiver while driving, identifying whether there is a previously generated road surface classification model corresponding to a front obstacle when the front obstacle is detected, determining a first control value based on a first characteristic value corresponding to the road surface classification model when there is the road surface classification model as a result of the identification, controlling the electronic control suspension with the determined first control value when entering the obstacle, and collecting new sensing data through a physical sensor, and correcting the first characteristic value based on the new sensing data.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

A vehicle power output control method, apparatus and system are provided. The method includes: collecting an image of a road surface on which a vehicle drives currently, and recognizing, according to the image of the road surface, the type of the road surface on which the vehicle drives currently; starting, according to the current type of the road surface, a terrain mode corresponding to an all-terrain adaptive mode; determining, in the terrain mode, a power output strategy corresponding to the current terrain mode; and adjusting an output torque of an engine according to a power output curve corresponding to the current power output strategy, the power output curve being a function curve using a stepping depth of an accelerator pedal as a variable and the output torque of the engine as an output. The method facilitates outputting an adaptive power by an engine when a vehicle drives on different road surfaces.

A vehicle control device includes a memory configured to store relationship definition data that defines a relationship between a state of a vehicle and an action variable, which is a variable relating to an operation of electronic equipment in the vehicle, and a processor. The processor is configured to execute acquisition processing of acquiring a detection value of a sensor, operation processing of operating the electronic equipment, reward calculation processing, update processing of updating the relationship definition data, detection processing, and switching processing of switching the relationship definition data to post-treatment data. The processor is configured to, based on the update mapping, output the relationship definition data updated to increase an expected return on the reward when the electronic equipment is operated in compliance with the relationship definition data.

A vehicle controller, a vehicle control system, a vehicle learning device, a vehicle control method, and a memory medium are provided. A switching process switches relationship defining data used in an operation process to post-measure data, when a detection process detects that a functional recovery measure has been taken. The switching process includes a process that uses, as the post-measure data, initial data that is the relationship defining data of a state before an update process is executed as the vehicle travels.

Provided is a method of generating vehicle control data. The method is applied to a vehicle configured to select one of a plurality of traveling control modes and is executed by a processor in a state in which relationship definition data defining a relationship between a state of the vehicle and an action variable as a variable relating to an operation of electronic equipment in the vehicle is stored in a memory. The method includes operation processing for operating the electronic equipment, acquisition processing for acquiring a detection value of a sensor configured to detect the state of the vehicle, reward calculation processing for providing reward, and update processing for updating the relationship definition data.

A method of generating vehicle control data includes: storing, with a storage device, relationship prescription data; operating, with an execution device, an operable portion of an internal combustion engine; acquiring, with the execution device, a detection value from a sensor that detects the state of the vehicle; calculating, with the execution device, a reward; and updating, with the execution device, the relationship prescription data using update mapping determined in advance, the update mapping using the state of the vehicle based on the detection value, an operation amount used to operate the operable portion, and the reward corresponding to the operation as arguments, and returning the relationship prescription data which have been updated such that an expected profit for the reward calculated when the operable portion is operated in accordance with the relationship prescription data increases.

An update process updates relationship defining data by inputting, to a predetermined update map, a state of a vehicle obtained by a state obtaining process, a value of an action variable used to operate an electronic device, and a reward corresponding to an operation of an electronic device. A range in which an operation process uses, as the action variable, a value different from a value that maximizes an expected return related to the reward is defined as a return non-maximizing range. In a case in which a degree of deterioration of the vehicle is greater than or equal to a predetermined degree, a changing process changes the return non-maximizing range to a side on which the return non-maximizing range is expanded as compared to a case in which the degree of deterioration is less than the predetermined degree.