A vehicle control device includes a travel environment recognition device configured to recognize travel environment, a vehicle travel state detection device, and an autonomous/manual driving mode controller. The autonomous/manual driving mode controller includes a learning correction unit configured to store at least one of a plurality of control parameters indicating the vehicle travel state by operation of the driver in the autonomous driving mode, and to correct the control parameter in the autonomous driving mode according to the stored control parameter. When the stored control parameter is the control parameter changed by an operation by the driver midway in passing or after passing, the learning correction unit is configured to correct the control parameter in the autonomous driving mode so that an acceleration level or the deceleration level is increased midway in passing or after passing.

Provided is a control apparatus for a vehicle configured to execute driving force suppression control when a predetermined erroneous operation condition is satisfied, the erroneous operation condition being satisfied when a first condition including at least an operation velocity condition is satisfied and a second condition is satisfied, the operation velocity condition being satisfied when an operation velocity which is an amount of change in an operation amount of an accelerator operation element per unit time is equal to or higher than an operation velocity threshold, and the second condition being satisfied when the operation amount becomes equal to or larger than a first operation amount threshold within a first time threshold from a time point at which the first condition is satisfied.

A monitoring area setting apparatus is mountable to an own vehicle and sets a monitoring area that indicates an area for monitoring objects in a vicinity of the own vehicle. The monitoring area setting apparatus acquires (i) at least either of a vehicle-width movement amount that indicates an amount of movement in a vehicle-width direction of the own vehicle and an inclination amount that indicates a degree of inclination of the own vehicle relative to an extending direction of a road on which the own vehicle is traveling, and (ii) information that the own vehicle has changed traffic lanes. The monitoring area setting apparatus sets the monitoring area taking into consideration at least either of the vehicle-width movement amount and the inclination amount in response to the own vehicle changing traffic lanes.

A method of controlling operation of a vehicle includes monitoring at least one of a location and a route of the vehicle when the vehicle is in a first operating state, receiving map data related to an area around at least one of the location and the route, and identifying, based on the map data, one or more map attributes indicative of one or more features of the area. The method also includes comparing the one or more map attributes to at least one reference attribute, and based on the one or more map attributes matching the at least one reference attribute, causing the vehicle to enter a second operating state when the vehicle is in the area.

An apparatus of controlling regenerative braking for battery charging according to driving information may include a driving information generation device that generates the driving information of a vehicle, a controller that is configured to control regenerative braking of the vehicle according to the generated driving information, and a charging device that controls charging of a battery of the vehicle according to the controlled regenerative braking.

Disclosed in the present disclosure are a method and an apparatus for determining a vehicle speed control model training sample. The method includes: a lane in map data is divided into a first lane area and a second lane area, and to-be-measured vehicle speed control features indicated by the two lane areas are different; the to-be-measured vehicle speed control features are determined as target vehicle speed control feature when a difference between a first empirical highest vehicle speed distribution and a second empirical highest vehicle speed distribution is greater than or equal to a preset difference threshold; and starting coordinates and ending coordinates of each preset lane section in the map data, and the target vehicle speed control features and a vehicle speed control label in the preset lane section are taken as a vehicle speed control model training sample.

Disclosed are a device and a method for controlling a driving mode of a vehicle. The device includes a sensor for collecting information about an access road to a controlled-access highway, and a controller which determines a switching point on the access road at which the driving mode of the vehicle is switched to a sport mode, based on the information about the access road to the controlled-access highway, and switches the driving mode of the vehicle to the sport mode when the vehicle reaches the switching point on the access road. Thus, the vehicle accesses the controlled-access highway naturally without interfering with flow of other vehicles.

Systems and methods are provided herein for operating a vehicle in a vehicle yaw mode. In response to initiating vehicle yaw mode, the system engages an open-loop mode, that provides open-loop forward torque to the outer wheels of the vehicle and open-loop backward torque to the inner wheels of the vehicle until a sufficient number of wheels are slipping. In response to determining that a sufficient number of wheels are slipping, engaging a closed-loop mode. While operating in the closed-loop mode, one or both of the wheel rotation and vehicle yaw rate are monitored to adjust the torques provided to the wheels of the vehicle to control the vehicle yaw rate.

The present invention provides an iterative joint estimation method of vehicle mass and road gradient based on MMRLS and SH-STF, which includes the following steps: establishment of a dynamic model considering steering, MMRLS/SH-STF iterative joint estimation algorithm architecture, improved slope estimation algorithm based on SH-STF. It is an iterative joint estimation method of vehicle mass and road slope based on MMRLS and SH-STF, which is designed reasonably, and the slow-variation characteristics of vehicle mass and the time-varying characteristics of road gradient are analyzed. According to the characteristics of gradual change and time change, based on the longitudinal dynamics model of the vehicle and the steering single-track model, the system identification algorithm of multi-model fusion recursive least squares is used to calculate the vehicle mass, and the noise adaptive strong tracking based on extended Kalman filter is used.

When an inclination angle is greater than or equal to a first threshold, a controller determines a displacement of a hydraulic pump from a command vehicle speed based on second pump data and determines a displacement of a hydraulic motor from the command vehicle speed based on second motor data. The second pump data defines the displacement of the hydraulic pump that is smaller than that of first pump data with respect to the command vehicle speed. The second motor data defines the displacement of the hydraulic motor that is smaller than that of first motor data with respect to the command vehicle speed.