A driving assistance apparatus includes a controller programmed to perform a deceleration assistance process of assisting in decelerating a vehicle before the vehicle arrives at a deceleration object, and to control a display apparatus to display, in a first display area, first notification information for notifying an occupant of the vehicle of the deceleration object that is a target for the deceleration assistance process. When a first object and a second object that is different from the first object are both detected as the deceleration object and the second object is the target for the deceleration assistance process but the first object is not the target for the deceleration assistance process, the controller is programmed to control the display apparatus to display, in a second display area, second notification information for notifying the occupant of the first object, the second display area is different from the first display area.

A vehicle control device includes a plurality of IC units, while maintaining the operational reliability. The vehicle control device includes an IC unit for performing image processing on outputs from cameras; an IC unit for performing recognition processing of an external environment of the vehicle; and an IC unit for performing judgment processing for cruise control of the vehicle. A control flow is provided so as to allow the IC unit to transmit a control signal to the IC units and. The control flow is provided separately from a data flow configured to transmit the output from the cameras, the image data, and the external environment data.

A method of operating a personal mobility is provided. The method includes receiving, by a processor in the personal mobility, user identification information for a user authentication and transmitting the received user identification information to a server. A use approval message is received from the server when the user is a user registered in the server and the personal mobility is operated by the user according to the received use approval message.

A method detects unintended acceleration of a motor vehicle during a closed-loop speed control mode by determining external forces on the vehicle via a controller, and then calculating a desired acceleration using a measured vehicle speed and the external forces. The method includes determining an actual acceleration of the vehicle, including filtering a speed signal as a first actual acceleration value and/or measuring a second actual acceleration value using an inertial measurement unit (IMU). During the speed control mode, the method includes calculating an acceleration delta value as a difference between the desired acceleration and the actual acceleration, and then using the acceleration delta value to detect the unintended acceleration during the speed control mode. A powertrain system for the motor vehicle, e.g., an electric vehicle, includes the controller and one or more torque generating devices coupled to road wheels of the vehicle.

A method detects unintended acceleration of a motor vehicle during a closed-loop speed control mode by determining external forces on the vehicle via a controller, and then calculating a desired acceleration using a measured vehicle speed and the external forces. The method includes determining an actual acceleration of the vehicle, including filtering a speed signal as a first actual acceleration value and/or measuring a second actual acceleration value using an inertial measurement unit (IMU). During the speed control mode, the method includes calculating an acceleration delta value as a difference between the desired acceleration and the actual acceleration, and then using the acceleration delta value to detect the unintended acceleration during the speed control mode. A powertrain system for the motor vehicle, e.g., an electric vehicle, includes the controller and one or more torque generating devices coupled to road wheels of the vehicle.

In an aspect, a self-balancing two-wheeled vehicle is provided, having a body, and first and second wheels rotatably coupled to the body. The second wheel has at least one lateral roller rotatable about an axis that is one of oblique and orthogonal to a rotation axis of the second wheel. At least one motor is coupled to the second wheel to control rotation of the second wheel and the at least one lateral roller. At least one sensor is coupled to the body to generate orientation data therefor. A control module is coupled to the at least one motor to control operation thereof at least partially based on the orientation data generated by the at least one sensor.

Controlling the speed of a motor vehicle with an adaptive cruise control system in a curve can include dynamically adjusting the speed of the motor vehicle depending on the planned exit from the roundabout and the current position of the motor vehicle.

In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated from real-world data. For example, one or more vehicles may collect image data and LiDAR data while navigating through a real-world environment. To generate input training data, 3D surface structure estimation may be performed on captured image data to generate a sparse representation of a 3D surface structure of interest (e.g., a 3D road surface). To generate corresponding ground truth training data, captured LiDAR data may be smoothed, subject to outlier removal, subject to triangulation to filling missing values, accumulated from multiple LiDAR sensors, aligned with corresponding frames of image data, and/or annotated to identify 3D points on the 3D surface of interest, and the identified 3D points may be projected to generate a dense representation of the 3D surface structure.

A system for a vehicle is provided. The system may include a memory and at least one processor configured to: access a plurality of images of a forward-facing view from the vehicle, the plurality of images corresponding to image data obtained by a camera; determine from the images a first lane marking on a first side of a lane, the lane through which the vehicle can navigate, and a second lane marking on a second side of the lane opposite of the first side; navigate the vehicle autonomously relatively centered between the first and second lane markings; determine from the plurality of images that an object is on the first side or the second side of the lane, and the object beyond the first or second lane marking; and navigate the vehicle autonomously to travel over a driving path that is offset from a center of the lane.

Vehicles may be composed of a relatively few number of “modules” that are assembled together during a final assembly process. An example vehicle may include a body module, a first drive module coupled to a first end of the body module, and a second drive module coupled to a second end of the body module. One or both of the drive modules may include a pair of wheels, a battery, an electric drive motor, and/or a heating ventilation and air conditioning (HVAC) system. One or both of the drive modules may also include a crash structure to absorb impacts. If a component of a drive module fails or is damaged, the drive module can be quickly and easily replaced with a new drive module, minimizing vehicle down time.