A method and related apparatus for determining an orientation of a sensor unit in a vehicle, the sensor unit having at least one acceleration sensor, including: capturing a first sensor signal from the acceleration sensor in an acceleration-free state of the vehicle; capturing a second sensor signal from the acceleration sensor in response to a linear acceleration of the vehicle; and ascertaining the orientation of the sensor unit relative to the vehicle based on the first sensor signal and the second sensor signal.

An apparatus for controlling pressure of a braking system including a pressure sensor configured to detect a pressure value within the braking system mounted in a vehicle, and collect the detected pressure value as an analog pressure signal; and a control device configured to calibrate the analog pressure signal received from the pressure sensor, convert the calibrated analog pressure signal into a digital pressure signal, and output the digital pressure signal.

Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.

The present invention provides a vehicle motion detecting apparatus that can satisfy a required detection accuracy with a simple architecture and at a low cost, and also maintain reliability of an applied external apparatus. The vehicle motion detecting apparatus 100 of the present invention has a motion detecting section 10 that detects a motion of a vehicle and a malfunction detecting section 20 that detects a malfunction of the motion detecting section 10, and is characterized in that the motion detecting section 10 is a 6-axis inertial sensor as the first multi-axis inertial sensor that is capable of detecting accelerations in directions of three axes and angular velocities about three axes.

A method of providing an additive offset of a longitudinal acceleration signal of a traveling motor vehicle. The signal being measured by an inertial sensor is ascertained. At least the longitudinal acceleration signal, a braking signal, and a drive signal are detected. A force balance of the longitudinal dynamic of the motor vehicle is analyzed. The signals are detected both during at least one acceleration process as well as during at least one braking process. The signals during the acceleration processes are detected and/or analyzed separately from the signals during the braking processes, and the additive offset is ascertained by comparing the signals detected during the acceleration processes or the values calculated therefrom with the signals detected during the braking processes or the values calculated therefrom. The invention further relates to an electronic controller.

The present disclosure includes the use of a smart load cell in a system for controlling an electromechanical actuator. A load cell may be positioned along the outer surface of the electromechanical actuator. Further, the load cell may utilize strain gages and a microcontroller. The load cell may be configured to transmit data to an electric brake actuator controller which includes calibration for operating temperature of the electromechanical actuator.

A vehicle disturbance detection apparatus includes an electronic control unit. The electronic control unit determines whether a disturbance occurs in a vehicle based on detection signals from a sensor device. The disturbance is a lateral external force that causes the vehicle to veer in a direction different from a direction expected by a driver. The electronic control unit determines that the disturbance occurs in the vehicle when a disturbance determination condition is established in a relationship between a calculated yaw rate and an actual yaw rate. The disturbance determination condition includes a cant traveling exclusion condition that is not established when the vehicle veers by traveling along a cant road but is established when the vehicle veers by receiving a crosswind.

An emergency braking system of a single-track vehicle configured to intervene in a braking process of the single-track vehicle is provided. The emergency braking system includes a plurality of sensors that determine various physical variables. From the physical variables an accident risk actual value is determined, compared with an accident risk target value using an emergency braking system control unit, and if the accident risk actual value exceeds the accident risk target value, the single-track vehicle's brake is actuated by the emergency braking system control unit.

A sensor system has a plurality of sensor elements and a signal processing device in communication with the plurality of sensor elements. The signal processing device is configured to evaluate more than one of at a substantially similar time and assign time information which includes information on the time of the respective measurement to the measurement data of physical variables, wherein the signal processing device takes into consideration time information at least during the generation of a fusion data set in a fusion filter.

Methods and systems for autonomously steering a moving vehicle are disclosed. A processor determines a longitudinal velocity, a longitudinal acceleration, a lateral acceleration, and a yaw rate of the vehicle. The processor estimates, based on the longitudinal velocity, lateral acceleration, and yaw rate of the vehicle, a change in lateral velocity over time. The processor estimates, based on the change in the lateral velocity over time, the yaw rate, a distance between the front axle of the vehicle and a center of gravity of the vehicle, and a distance between the rear axle of the vehicle and the center of gravity of the vehicle, a lateral front velocity of the vehicle and a lateral rear velocity of the vehicle. Using calculations, a state estimation model for the vehicle is updated by the processor using a lateral acceleration bias. The updated state estimation model is used to autonomously steer the vehicle.