Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

A device for determining hands off of a driver includes a torque sensor for sensing a torque caused by turning of a steering wheel and generating a torque signal, a first frequency filter and a second frequency filter for filtering the torque signal, a representative value generating device for generating a first representative value based on a frequency component of a first filtered signal output by the first frequency filter, and generating a second representative value based on a frequency component of a second filtered signal output by the second frequency filter, and a control module that determines the hands off of the driver based on a ratio of the first representative value and the second representative value.

Apparatus for estimating a grip factor of at least one wheel comprising: a control unit configured to process a current slip angle of said wheel; a storage unit, within which, in a consultation table, a plurality of grip curves correlating a plurality of values of a steering parameter comprising the rack force with a plurality of values of the slip angle are recorded; along a same curve, the value of the grip factor remains unchanged; wherein the control unit is configured to cyclically estimate at least one raw value of the grip factor of said at least one wheel based on the position of a current condition within the consultation table, as a function of the current slip angle and of the current rack force.

A semiconductor device capable of performing filter processing while suppressing an increase in processing time is provided. The semiconductor device includes a microcontroller. The microcontroller comprises a CPU, a memory and a CAN-controller. The memory stores software. The CPU executes the software stored in the memory. The CAN controller is configured to add label information to the message information. The CAN routing software stored in the memory implements a filtering function for performing a filter processing for determining whether or not to route the message information by using the label information.

An active vibration reduction control apparatus for a hybrid electric vehicle includes: a reference signal generator generating a reference signal and a first phase based on a first rotational angle of a first motor; a vibration extractor extracting a vibration signal from a second motor; a coefficient determiner determining a filter coefficient which minimizes a phase difference between the reference signal and the vibration signal; a phase determiner detecting a second phase which corresponds to the phase difference using a first speed signal of the first motor and the filter coefficient; a phase deviation amount detector detecting a third phase for compensating for a phase delay; and a synchronization signal generator generating an antiphase signal of a shape of an actual vibration in order to determine a compensating force of the first motor.

Stochastic nonlinear model predictive control (SNMPC) allows to directly take uncertainty of the dynamics and/or of the system's environment into account, e.g., by including probabilistic chance constraints. However, SNMPC requires the approximate computation of the probability distributions for the state variables that are propagated through the nonlinear system dynamics. This invention proposes the use of Gaussian-assumed density filters (ADF) to perform high-accuracy propagation of mean and covariance information of the state variables through the nonlinear system dynamics, resulting in a tractable SNMPC approach with improved control performance. In addition, the use of a matrix factorization for the covariance matrix variables in the constrained optimal control problem (OCP) formulation guarantees positive definiteness of the full trajectory of covariance matrices in each iteration of any optimization algorithm. Finally, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is described that considerably reduces the computational cost and allows a real-time feasible implementation of the proposed ADF-based SNMPC method to control nonlinear dynamical systems under uncertainty.

A method for detecting a standstill of a vehicle includes detecting the standstill of the vehicle using at least one sensor, and, when the standstill is detected using the at least one sensor, carrying out a test routine for checking the standstill of the vehicle. The test routine uses at least one signal from at least one rate-of-rotation sensor as an input value. The method further includes rejecting the detected standstill of the vehicle when the at least one signal from the at least one rate-of-rotation sensor indicates that there is no standstill of the vehicle.

An apparatus of controlling an engine including an electric supercharger includes: an engine to combust fuel to generate power; a drive motor to assist the power of the engine and selectively operate as a generator to generate electrical energy; a battery configured to supply electrical energy to the drive motor and to be charged by the electrical energy generated from the drive motor; a plurality of electric superchargers respectively installed in a plurality of intake lines through which an ambient air flows to be supplied to a combustion chamber of the engine; and a controller that based on a determined driving tendency, adjusts a target speed of the electric superchargers of the plurality of electric superchargers, determine a driving mode of the electric superchargers, limits a maximum output of the engine, and variably adjusts a SOC electricity-generating region where the engine charges the battery.

Provided are methods for vehicle state estimation based on sensor data, which can include receiving the sensor data generated by one or more sensors, calculating a cornering stiffness value associated with the vehicle, predicting a lateral velocity value associated with the vehicle based on the cornering stiffness value, and outputting a set of vehicle state variables indicative of a current state of the vehicle at least by inputting the lateral velocity value into a recursive filter. Some methods described also include updating the cornering stiffness value based on the set of vehicle state variables, updating the lateral velocity value based on the updated cornering stiffness value, and updating the set of vehicle state variables based on the updated lateral velocity value. Systems and computer program products are also provided.

A method for predicting a future action of an object for a driving assistance system for a highly automated mobile vehicle. At least one sensor signal from at least one vehicle sensor of the vehicle is read in, the sensor signal representing at least one piece of kinematic object information concerning the object that is detected by the vehicle sensor at an instantaneous point in time. A planner signal from a planner of the autonomous driving assistance system is read in, the planner signal representing at least one piece of semantic information concerning the object or the surroundings of the object at a point in time in the past. The kinematic object information is fused with the semantic information to obtain a fusion signal. A prediction signal is determined using the fusion signal, the prediction signal representing the future action of the object.