The present disclosure relates to a control system for selectively controlling a vehicle, ensuring high integrity of decisions taken by the control system when controlling the vehicle. The present disclosure also relates to a corresponding computer implemented method and to a computer program product.

A road friction coefficient of a vehicle is estimated by obtaining substantially contemporaneous values associated with a steering angle for a steered axle of the vehicle, a lateral acceleration, a yaw acceleration, an alignment torque and an axle load on the steered axle; estimating a lateral tire force on the basis of the steering angle, lateral acceleration, and yaw acceleration; deriving a pneumatic trail from the alignment torque and estimated lateral tyre force; and estimating a road friction coefficient from the lateral tire force, the axle load, and the pneumatic trail. In embodiments, the derivation of the road friction coefficient includes evaluating a nonlinear function of the pneumatic trail.

A LIDAR-to-LIDAR alignment system includes a memory and an autonomous driving module. The memory stores first and second points based on outputs of first and second LIDAR sensors. The autonomous driving module performs a validation process to determine whether alignment of the LIDAR sensors satisfy an alignment condition. The validation process includes: aggregating the first and second points in a vehicle coordinate system to provide aggregated LIDAR points; based on the aggregated LIDAR points, performing (i) a first method including determining pitch and roll differences between the first and second LIDAR sensors, (ii) a second method including determining a yaw difference between the first and second LIDAR sensors, or (iii) point cloud registration to determine rotation and translation differences between the first and second LIDAR sensors; and based on results of the first method, the second method or the point cloud registration, determining whether the alignment condition is satisfied.

A vehicle travel control device executes trajectory following control to make the vehicle follow a target trajectory. A delay time represents control delay of the trajectory following control. A delay compensation time is at least a part of the delay time. The trajectory following control includes: displacement estimation processing that estimates a displacement of the vehicle in the delay compensation time; and delay compensation processing that corrects a deviation between the vehicle and the target trajectory based on the estimated displacement to compensate the control delay. The displacement estimation processing is effective in an effective period and ineffective in an ineffective period. When the ineffective period is included in the delay time of the trajectory following control, the displacement estimation processing is executed in a temporary mode by using sensor-detected information in the effective period without using the sensor-detected information in the ineffective period.

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 a vehicle, GV control and M+ control are executed by generating braking/driving forces from a brake hydraulic pressure control device and a drive device during steering. A controller estimates (calculates), by a posture estimation unit, a pitch amount and a roll amount (predicted pitch rate and predicted roll rate) that occur in the vehicle through use of a moment command of the M+ control and a longitudinal G command of the GV control. The controller adjusts damping forces of damping force variable dampers through use of the estimated pitch amount and the estimated roll amount (predicted pitch rate and predicted roll rate) so that a pitch amount calculated by a pitch control unit and a roll amount calculated by a roll suppression unit approach respective target values.

A manager is installed in a vehicle. The manager includes: an accepting unit that accepts, from a plurality of advanced driver assistance system applications, a plurality of kinematic plans including first information that is information representing lateral-direction motion of the vehicle; an arbitration unit that performs arbitration of the kinematic plans; a first output unit that distributes a motion request based on a result of arbitration performed by the arbitration unit to at least one of a plurality of actuator systems; and a second output unit that outputs second information used for generating the first information to at least one of the ADAS applications.

Systems and methods for coordinating steering controls between towing vehicles and towed vehicles provide more cohesive parking experiences during towing events, including bidirectional charging towing events. The towed vehicle may be controlled to provide assistive parking steering maneuvers to assist the towing vehicle when parking during the towing event. The assistive parking steering maneuver may include maneuvering a drive wheel of the towed vehicle either toward or away from a detected curb or detected traffic, for example.

A vehicle height adjustment control apparatus is provided for compensating for vehicle pulls including a recognition device that recognizes that a vehicle is driven straight, a determination device that determines whether the vehicle pulls of the vehicle occur, in response to recognizing that the vehicle is driven straight, and a controller that generates a warning message and calculates compensation height control information of the vehicle in response to determining that the vehicle pulls occur.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.