A device and a method for controlling autonomous driving are provided The device includes non-transitory memory storing instructions executable to control an autonomous driving; and a processor configured to execute the instructions to determine whether a collision has occurred using an acceleration sensor mounted on an airbag control unit during the autonomous driving, determine a collision direction based on a change in acceleration of three axes obtained by the acceleration sensor, and perform an emergency action by controlling at least one of longitudinal direction travel or transverse direction travel based on the collision direction.

A vehicle includes a transmission, a powerplant, an inertial measurement unit, and a controller. The transmission has an input shaft and an output shaft. The powerplant is configured to generate and deliver torque to the input shaft. The inertial measurement unit is configured to measure inertial forces exerted onto the vehicle. The controller is programmed to, in response to a demanded torque at the output shaft and a non-transient condition of the vehicle, control the torque at the output shaft based on a torque at the input shaft and a gear ratio of the step-ratio transmission. The controller is further programmed to, in response to the demanded torque at the output shaft and a transient condition of the vehicle, control the torque at the output shaft based on the inertial forces and a vehicle velocity.

Techniques described herein include detecting a degree of motion sickness experienced by a user within a vehicle. A suitable combination of physiological data (heart rate, heart rate variability parameters, blood volume pulse, oxygen values, respiration values, galvanic skin response, skin conductance values, and the like), eye gaze data (e.g., images of the user), vehicle motion data (e.g., accelerometer, gyroscope data indicative of vehicle oscillations) may be utilized to identify the degree of motion sickness experienced by the user. One or more autonomous actions may be performed to prevent an escalation in the degree of motion sickness experienced by the user or to ameliorate the degree of motion sickness currently experienced by the user.

A calibration system calibrates inertial sensor readings on a vehicle. The calibration system estimates an attitude of the ground from a series of height and position measurements and reads an attitude from an inertial sensor subsystem attached to the vehicle. The calibration system then calculates an attitude offset between the vehicle and inertial sensor subsystem based on a difference between the estimated attitude of the ground and the attitude reading of the inertial sensor subsystem. The calibration system may estimate a slope of the ground from a 3-dimensional terrain map. The slope of the ground is converted into an estimated roll and/or pitch of the vehicle which is then compared with the roll and pitch readings from the inertial sensor subsystem to determine the attitude offset.

A control system (10, 19, 185C) for a vehicle (100), the system comprising a processing means (10, 19) arranged to receive, from terrain data capture means (185C) arranged to capture data in respect of terrain ahead of the vehicle by means of one or more sensors, terrain information indicative of the topography of an area extending ahead of the vehicle (100), wherein the terrain information comprises data defining at least one 2D image of the terrain ahead of the vehicle, wherein the processing means (10, 19) is configured to: perform a segmentation operation on image data defining one said at least one 2D image and identify in the image data edges of a predicted path of the vehicle; calculate a 3D point cloud dataset in respect of the terrain ahead of the vehicle based on the terrain information; determine the 3D coordinates of lateral edges of the predicted path of the vehicle by reference to the point cloud dataset, based on the coordinates of edges of the predicted path identified in the 2D image, to determine a 3D predicted path of the vehicle; and control the direction of travel of the vehicle in dependence at least in part on the 3D predicted path.

A vehicle state estimation device for a vehicle provided with an inertial measurement sensor and a wheel speed sensor includes: a vehicle state estimation unit that estimates a vehicle state including a vehicle velocity based on an acceleration and an angular velocity acquired by the inertial measurement sensor and a wheel speed acquired by the wheel speed sensor; and a determination unit that determines whether a wheel is slipping. The estimation unit estimates a steady-state vehicle velocity based on the wheel speed and calculates a transient vehicle velocity by time integration based on the acceleration and the angular velocity. When the wheel is slipping, the estimation unit decides an estimated value of the vehicle velocity to be close to the transient vehicle velocity, and when the wheel is not slipping, the estimation unit decides the estimated value of the vehicle velocity to be close to the steady-state vehicle velocity.

A system for managing chassis and driveline actuators of a motor vehicle includes a control module executing program code portions that: cause sensors to obtain vehicle state information, receive a driver input and generate a desired dynamic output based on the driver input and the vehicle state information, and then estimate actuator actions based on the vehicle state information, generate one or more control action constraints based on the vehicle state information and estimated actuator actions, generate a reference control action based on the vehicle state information, the estimated actions of the one or more actuators and the control action constraints, and integrate the vehicle state information, the estimated actuator actions, desired dynamic output, reference control action and the control action constraints to generate an optimal control action that falls within a range of predefined actuator capacities and ensures driver control of the vehicle.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A vehicle includes a transmission, an engine, a clutch, and a controller. The transmission has an input. The engine is configured to generate and deliver torque to the input. The clutch is configured to connect and disconnect the engine from the input, and to crank the engine during an engine start. The controller is programmed to, in response to a command to adjust a torque of the clutch during an engine start and a presence of first condition of the clutch, drive a clutch actuator pressure to a first desired value based on a first transfer function. The controller is further programmed to, in response to a command to adjust the torque of the clutch during the engine start and a presence of a second condition of the clutch, drive the clutch actuator pressure to a second desired value based on a second transfer function.

An operational assistance method for a vehicle, in particular for a motor vehicle, is provided. A movement of an area of a body of a vehicle occupant is captured and sensor values representative of the movement are provided, sensor values for an area of the body of a vehicle occupant are weighted with one another and are combined to form an acceleration value, and the acceleration value is provided. A weighting factor for a respective sensor value, as the degree of the weighting of the sensor value in the acceleration value, is dependent on the magnitude of the sensor value.