A control device includes a processor. The processor is configured to perform a route acquisition process for acquiring route information indicating a target route of a vehicle. The processor is configured to perform a behavior optimization process for correcting, based on at least one of a plurality of state quantities indicating a behavior of the vehicle during traveling, each of a left turning command value and a right turning command value such that the behavior of the vehicle becomes a target behavior. The processor is configured to perform a locus stabilization process for correcting, based on at least one of the state quantities indicating the behavior of the vehicle during traveling, a steering command value such that the vehicle travels on the target route.

The technology employs a model structure for motion control in a vehicle configured to operate in an autonomous driving mode. The model structure has components including a vehicle dynamics system module, a column dynamics module, a rack dynamics module, and an actuation control module. A virtual validation and verification model is configurable based on the components of the model structure. Configuration is performed according to a set of operational requirements based on at least one of a vehicle type, occupant loading information, a center of gravity, or tire pressure as per a cold nominal setpoint. The virtual validation and verification model can be executed so that an electric power steering (EPS) module of the model structure components is configured for at least one of: a software-in-loop model, functional EPS assist, angle control, or to emulate an EPS controller.

A method of maintaining stability of a motor vehicle having a first axle, a second axle, and a steering actuator configured to steer the first axle includes determining localization and heading of the vehicle. The method also includes determining a current side-slip angle of the second axle and setting a maximum side-slip angle of the second axle using the friction coefficient at the vehicle and road surface interface. The method additionally includes predicting when the maximum side-slip angle would be exceeded using the localization, heading, and determined current side-slip angle as inputs to a linear computational model. The method also includes updating the model using the prediction of when the maximum side-slip angle would be exceeded to determine impending instability of the vehicle. Furthermore, the method includes correcting for the impending instability using the updated model and the maximum side-slip angle via modifying a steering angle of the first axle.

An apparatus and method for controlling motor driven power steering system, the apparatus including: a column torque sensor detecting column torque applied to a steering column of a vehicle; an MDPS logic unit deciding a first command current based on the column torque and vehicle speed; a steering angle position control unit outputting a second command current for driving an MDPS motor in an autonomous driving mode, based on a command steering angle and a current steering angle inputted from an autonomous driving system and a steering angle sensor, respectivley; a driver steering intervention determination unit monitoring the column torque of the column torque sensor in an autonomous driving mode, and determining whether a driver has intervened in steering; and an output control unit deciding a final command current by applying the weight decided by the driver steering intervention determination unit to the first and second command currents.

A control apparatus and method of a motor-driven power steering system are provided. The apparatus and method are capable of suppressing catch-up that occurs at a high steering speed even without using an expensive motor, thereby contributing to improving commercial value and reducing the manufacturing cost of a vehicle. The method includes determining a target steering speed using driver-steering input information and vehicle state information and determining the target steering speed corresponds to a condition for avoiding catch-up using the determined target steering speed. A current compensation value is then determined for reducing catch-up using the target steering speed in response to determining that is the target steering speed corresponds a condition for avoiding catch-up and motor current is compensated with the current compensation value.

Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

A dip angle-steering median filtering method based on a niche differential evolution algorithm, comprising the following steps: dividing a data to be processed into a series of overlapping time-space windows; obtaining an event energy curve in a time-space window and obtaining an event position according to a local maximum value of the event energy curve; obtaining event dip angles and coherence values of the event dip angles through the niche differential evolution algorithm at the event position; filtering the event dip angles according to the event dip angles and the coherence values of the event dip angles; and performing a median filtering sequentially along a filtering dip angle. The disclosure can simultaneously obtain all dip angles of intersecting events and a true three-dimensional feature enable the present disclosure to obtain a better filtering effect.

Aspects of the disclosure relate to a vehicle having an autonomous driving mode and a manual driving mode. The vehicle may include a steering system having one or more processors configured to control the orientation of the one or more wheels based on control commands. The vehicle may also include an autonomous driving control system configured to control the vehicle in an autonomous driving mode by generating the control commands and to send the control commands to the steering system. In addition, the steering system may thermally derate the steering system based on first temperature information for the steering system when the vehicle is operating in a manual drive mode, and the autonomous driving control system may thermally derate the steering system based on second temperature information for the steering system when the vehicle is operating in the autonomous driving mode.

The power management unit supplies first power that is continuous power to the second sensor when the power switch is on, supplies second power that is intermittent power having a voltage lower than the first power to the second sensor when the power switch is off, and outputs rotation number information representing a rotation number of the motor rotation shaft based on the second sensor signal. The power management unit includes a comparator that operates using the second power as the power source when the power switch is off and compare the second sensor signal and the reference voltage, and a counter that detects the rotation number of the motor rotation shaft by counting the output of the comparators.

In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for estimating a Hitch Articulation Angle (HAA) using Ultra-Sonic Sensors (USSs) while ensuring quality detected echo signal performance using plausibility filtering, generating at least one set of USS data based on detecting a set of echo signals generated by a plurality of USSs configured about a vehicle coupled to a trailer; determining based on a set of USS data using a selected set of geometric equations in a plausibility filtering process for an arbitrary frontal shape of the trailer; and generating at least one comparison based on at least one set of USS data estimations to a kinematic model at low speeds for ensuring that results of the kinematic model to the HAA associated with the determined trailer shape is based on a pair of detected echo signals that are deemed to have a higher signal performance.