In an embodiment a control apparatus includes a processor configured to calculate a target curvature depending on a target path of a vehicle, calculate a first lateral control value based on a feedforward control by using the target curvature, calculate a second lateral control value based on a feedback control by using vehicle information collected from a sensing device of the vehicle, estimate a disturbance by using the vehicle information collected from the sensing device of the vehicle, and calculate a final lateral control command value by summing the first lateral control value, the second lateral control value, and the disturbance and a storage configured to store data and algorithms driven by the processor.

Systems and methods for real time and predictive diagnostics of vehicle electronic components. Circuit boards and similar vehicle electronic devices have input/output speeds on the order of tens of gigahertz (GHz). Signal integrity can be affected when operating in real time while a high speed signal travels across circuit boards. To maintain signal integrity, hardware around high speed signals is designed to match impedance and transmission lengths. Additionally, differences can occur from circuit board to circuit board due to aging during vehicle operation, and tolerances in the manufacturing process. Systems and methods are provided herein to compensate for the tolerances in the manufacturing process. The real time and predictive diagnostics of vehicle electronic components provided herein can prevent on-road vehicle failures.

A filtering system is described for automotive application, designed to implement filtering of a raw detection signal provided by a sensor installed on board a motor vehicle. The system envisages a neural network stage configured to receive, as an input, the raw detection signal and to implement a neural network architecture trained to generate, in real time and as a function of the raw detection signal, a filtered detection signal with a zero-phase filtering.

A vehicle control apparatus and method are disclosed. A vehicle control apparatus for autonomous driving includes a receiver configured to receive a local path based on a trajectory planner, and a controller configured to generate a steering command for lateral control of the vehicle based on the local path, generate a compensated steering command by compensating the steering command based on a movement state of a vehicle to be controlled, and control the vehicle based on the compensated steering command.

A method for vehicle guidance. The method includes: receiving and/or retrieving sensor data, a termination criterion of a vehicle assistance system, and a destination; recognizing and/or tracking a traffic object on the basis of the sensor data, recognizing a driving maneuver of the traffic object on the basis of the tracking of the traffic object; recognizing a traffic jam situation in the vehicle environment using the recognized driving maneuver; and taking over autonomous longitudinal guidance and lateral guidance of the vehicle toward the destination, wherein longitudinal guidance and lateral guidance of the vehicle toward the destination are taken over as long as a termination criterion of the driver assistance system is fulfilled.

In an embodiment, a method of controlling a vehicle by altitude prediction based on identification of a high-energy area is provided. The method can include identifying a high-energy spot based on a reference altitude change and map information including altitude data, clustering high-energy spots for each area to set a high-energy area and generating area information, and performing driving power control based on at least location information of the vehicle and the area information in response to the vehicle travel path including the high-energy area.

A method for attenuating interference in the measurement of an angle of sideways tilt of a motorized two-wheeled vehicle including an electronic control unit configured to compute an angle of sideways tilt of the vehicle from the measurements of a tilt sensor. The method includes: reception of the value of the speed of the vehicle measured by the speed-measuring module; reception of the number of revolutions per minute of the engine; computation of the ratio between the received speed value and the received number of revolutions per minute; comparison of the ratio between the received speed value and the number of revolutions per minute with an attenuation interval; if the ratio is outside the attenuation interval, computation of the tilt angle without attenuation; if the ratio is within the attenuation interval, computation of the tilt angle with attenuation.

Vehicle dynamics related parameter(s) of an autonomous vehicle model can be calibrated so that the autonomous vehicle model can more accurately determine the driving related behaviors of the autonomous vehicle. An example method comprises obtaining, from sensor data, vehicle related parameters; performing a first determination of a slope of a road and a banking angle of the road based on a pitch angle of the vehicle and a roll angle of the vehicle, respectively; performing a second determination of a set of parameters that describe a driving-related operation of the vehicle; performing a third determination that at least one difference between at least one value from the set of parameters and a corresponding parameter from the plurality of vehicle related parameters exceeds at least one threshold value; and obtaining, in response to the third determination, a calibrated longitudinal dynamic-related parameter or a calibrated lateral dynamic-related parameter.

A method for evaluating sensor signals in a vehicle is disclosed. A sensitivity error is determined as part of the evaluation, and the method includes the following: (i) in the event of a low-dynamic state of the vehicle, performing an offset correction on the sensor signals, (ii) evaluating the dynamic state of the vehicle based on the sensor signals and comparing the dynamic state with threshold values in order to evaluate the activation of a monitoring function, and (iii) calculating relative deviations and comparing these relative deviations with limit values in order to evaluate the sensor signals.

Embodiments of the present disclosure a privacy-preserving defensive driving system that can detect abnormal following vehicles during driving. An example system may be configured to: continuously capture video data of the camera's field-of-view, detect following vehicles in the captured video data, and determine whether one or more following vehicles is exhibiting abnormal following behavior with respect to a first vehicle.