A method for controlling a vehicle, an electronic device, a storage medium, and a vehicle are provided, related to a field of artificial intelligence technology, in particular to a field of autonomous driving and a field of computer vision. The method for controlling a vehicle includes: determining, in response to a request of switching to an autonomous driving mode, whether the vehicle is in a safe state; and controlling, in response to the vehicle being in the safe state, the vehicle to switch from a manual driving mode to the autonomous driving mode during travelling.

A method can be used to control a steer-by-wire steering system for a motor vehicle that has two axles each with two wheels. Two front wheels can be steered by front-wheel steering and two rear wheels can be steered by rear-wheel steering. The motor vehicle includes a single wheel drive that is assigned to one of the two axles and drives the two wheels of the corresponding axle via a differential. The motor vehicle comprises an inboard braking system. The method involves checking the motor vehicle speed and activating rear-axle steering when a motor vehicle speed should be slower than 40 km/hr. With rear-axle steering active, the following steps are performed: deactivating front-wheel steering and rear-wheel steering, determining a reference position of a first steering rod via a reference wheel steering angle, determining a differential drive torque between the rear wheels to reach the reference position via a control unit.

A vehicular driver assistance system includes a front camera module (FCM) disposed at a vehicle. The system, responsive to processing captured image data, generates FCM lane information including information regarding a traffic lane the vehicle is currently traveling along. An e-Horizon module (EHM) generates EHM lane information including information regarding the traffic lane the vehicle is currently traveling along. The vehicular driver assistance system determines an FCM correlation using the FCM lane information and sensor data captured by at least one exterior sensor. The vehicular driver assistance system determines an EHM correlation using the EHM lane information and the sensor data captured by the at least one exterior sensor. Responsive to determining the FCM correlation and the EHM correlation, the system controls lateral movement of the vehicle based on one selected from the group consisting of (i) the FCM lane information and (ii) the EHM lane information.

Techniques are described for using variables associated with vehicle wheels (e.g., linear velocity at a wheel and orientation of the wheel) to estimate velocity of a vehicle during a turn maneuver. In examples of the disclosure, in association with one or more wheels, a wheel orientation during the maneuver and a linear speed during the maneuver may be determined, and well as a yaw rate (e.g., from an inertial measurement unit, gyroscope, etc.) of the vehicle. Examples of the present disclosure include, based on the variables associated with the wheel(s) and the yaw rate associated with the turn maneuver, estimating a vehicle velocity, which may be used by various downstream components, such as to determine or update a pose of a vehicle as part of a localization operation.

An embodiment device for controlling autonomous driving includes a roll angle estimated value calculation device configured to calculate a roll angle estimated value of a vehicle based on a height of a center of gravity of the vehicle, a sprung mass, a spring constant of a suspension, a target speed, and a target turning radius, and a controller configured to compare a roll angle of the vehicle with a preset reference roll angle to adjust the target speed or the target turning radius of the vehicle.

An object of the present invention is to enhance the reliability of an autonomous driving system. The autonomous driving system includes: a higher-level control device 1 that outputs a control target value of an actuator group based on an action plan of a vehicle; and a lower-level control device 2 that controls the actuator group of the vehicle based on a command from the higher-level control device 1. The lower-level control device 2 holds the control target value of the vehicle provided by the higher-level control device 1 over a specific period. When the higher-level control device 1 does not satisfy a desired function, the lower-level control device 2 is configured to be controlled based on the held control target value. The action plan is followed by determining and correcting a difference between an actual action value and the control target value of the vehicle.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate dynamic curve speed control adaptive to road conditions. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components can comprise a curvature component, a road condition component, and a safety component. The curvature component can generate composite curvature data for a curve of a road preceding a vehicle using digital map data and lane marker data. The road condition component can generate friction data for a surface of the road using sensor data obtained from an on-board sensor of the vehicle. The safety component can determine a safe operational profile for traversing the curve using the composite curvature data and the friction data.

Systems and methods are provided herein for operating an electric vehicle in a vehicle yaw mode. The electric vehicle includes a normal driving mode where the electric vehicle is steered by turning the steerable wheels (e.g., left or right) and vehicle yaw mode where the vehicle controls the torque applied to each wheel. In response to receiving input to initiate vehicle yaw mode and yaw direction, the system determines the inner wheels and the outer wheels and provides forward torque to the outer wheels of the vehicle and backward torque to the inner wheels of the vehicle to rotate the vehicle.

In one embodiment, static-state curvature error compensation control logic for autonomous driving vehicles (ADV) receives planning and control data associated with the ADV, including a planned steering angle and a planned speed. A steering command is generated based on a current steering angle and the planned steering angle of the ADV. A throttle command is generated based on the planned speed in view of a current speed of the ADV. A curvature error is calculated based on a difference between the current steering angle and the planned steering angle. The steering command is issued to the ADV while withholding the throttle command, in response to determining that the curvature error is greater than a predetermined curvature threshold, such that the steering angle of the ADV is adjusted in view of the planned steering angle without acceleration.

Four-wheel steering of a vehicle, e.g., in which leading wheels and trailing wheels are steered independently of each other, can provide improved maneuverability and stability. A first vehicle model may be used to determine trajectories for execution by a vehicle equipped with four-wheel steering. A second vehicle model may be used to control the vehicle relative to the determined trajectories. For instance, the second vehicle model can determine leading wheels steering angles for steering leading wheels of the vehicle and trailing wheels steering angles for steering trailing wheels of the vehicle, independently of the leading wheels.