In some examples, a vehicle suspension for supporting, at least in part, a sprung mass, includes a damper connected to the sprung mass, the damper including a movable piston. The vehicle suspension further includes an actuator and a controller. The controller may be configured to determine a frequency of motion associated with the sprung mass. When the frequency of motion is below a first frequency threshold, the controller may send a control signal to cause the actuator to apply a deceleration force to the sprung mass. Further, when the frequency of motion associated with the sprung mass exceeds the first frequency threshold, the controller may send a control signal to cause the actuator to apply a compensatory force to the sprung mass. For instance, a magnitude of the compensatory force may be based on a friction force determined for the damper.

A method of controlling autonomous driving is provided. The method includes collecting driving information of a driver and curvature information of a road and generating a driving pattern of the driver, defined by associating behaviors in longitudinal and lateral directions of the vehicle based on the driving information. The driving pattern is then set to a constraint condition for driving torque and brake pressure and the vehicle is operated based thereon.

Evasive steering assist (ESA) systems and methods for a vehicle utilize a set of vehicle perception systems configured to detect an object in a path of the vehicle, a driver interface configured to receive steering input from a driver of the vehicle via a steering system of the vehicle, a set of steering sensors configured to measure a set of steering parameters, and a controller configured to determine a set of driver-specific threshold values for the set of steering parameters, compare the measured set of steering parameters and the set of driver-specific threshold values to determine whether to engage/enable an ESA feature of the vehicle, and in response to engaging/enabling the ESA feature of the vehicle, command the steering system to assist the driver in avoiding a collision with the detected object.

A method includes identifying a desired path for an ego vehicle. The method also includes determining how to apply steering control and torque vectoring control to cause the ego vehicle to follow the desired path. The determination is based on actuator delays associated with the steering control and the torque vectoring control and one or more limits of the ego vehicle. The method further includes applying at least one of the steering control and the torque vectoring control to create lateral movement of the ego vehicle during travel. Determining how to apply the steering control and the torque vectoring control may include using a state-space model that incorporates first-order time delays associated with the steering control and the torque vectoring control and using a linear quadratic regulator to determine how to control the ego vehicle based on the state-space model and the one or more limits of the ego vehicle.

The present disclosure provides methods and systems for monitoring a driving automation system of a vehicle. An example system comprises a vehicle interface for connecting to a forward-facing camera mounted on the vehicle to receive video data, a microphone for recording audio signals from inside the vehicle, an internal measurement unit (IMU) for generating vehicle motion signals comprising at least a lateral acceleration signal, a longitudinal acceleration signal, and a yaw angular acceleration signal, and a processor connected to process the video data, audio signals and vehicle motion signals to determine whether the driving automation signal is engaged or disengaged.

A method includes identifying a path to be followed by an ego vehicle. The method also includes determining a desired yaw rate and a desired yaw acceleration for the ego vehicle based on the identified path. The method further includes determining a desired yaw moment for the ego vehicle based on the desired yaw rate and the desired yaw acceleration. In addition, the method includes distributing the desired yaw moment to multiple wheels of the ego vehicle such that the distributed desired yaw moment creates lateral movement of the ego vehicle during travel along the identified path. In some cases, the desired yaw rate and the desired yaw acceleration for the ego vehicle may be determined based on nonlinear kinematics of the ego vehicle, and the desired yaw moment for the ego vehicle may be determined based on a single-track dynamic model of the ego vehicle.

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: A method of controlling an autonomous vehicle, comprising: receiving, by a processor, a first set of data obtained from an inertial measurement unit of the vehicle; receiving, by the processor, a second set of data obtained from a global positioning system of the vehicle; receiving, by the processor, a third set of data obtained from a camera of the vehicle; determining, by the processor, at least two vehicle states relative to markings of a lane by processing the first set of data, the second set of data, and the third set of data as measurement with an extended Kalman filter; and controlling, by the processor, the vehicle based on the at least two vehicle states.

A method of estimating a load on a vehicle (10), the method comprising: obtaining a first load estimate using a first load estimation technique; obtaining a second load estimate using a second load estimation technique; analysing characteristics of the first load estimate and the second load estimate; and, based on the analysis selecting either the first load estimate or the second load estimate as an output load estimate.

An apparatus for controlling a direction and speed of travel an autonomous vehicle or driver assisted autonomous vehicle (AV). A GPS and map module receive a start location and a destination location for the AV. A plurality of sensors identify a current and a proposed lane for the AV. A database of AV baseline maneuver profiles used to control one or more of direction and speed of travel of the AV is provided. A trajectory profile generator module generates a planned path for the AV with lateral acceleration less than 2 Hz, based on the start location, the destination location, the current and proposed lane for the AV, and a selected AV baseline maneuver profile from the database. A steering control module controls the direction of travel of the AV based on the generated AV planned path, and a supervisory control module controls the speed of the AV based on the generated AV planned path and inner ear constraints.

The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the “under-test” path planning module's output is evaluated in closed-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning development-module.