A vehicle may receive sensor data captured by a sensor of the vehicle, determine that the sensor data represents an object in the environment, and determine an impact location between the vehicle and the object. The impact location may be associated with a predicted collision between the vehicle and the object. The vehicle may also determine an object type corresponding to the object and/or a characteristic of the object. Based at least in part on the impact location, object type, and/or the characteristic, a ride height of the vehicle may be adjusted.

This application relates to a suspension control method and system, a vehicle, and a storage medium. The suspension control method includes: acquiring a pavement image in a traveling direction; identifying a variation type corresponding to a pavement smoothness variation according to the pavement image; generating a control signal according to the identified variation type, to adjust a suspension parameter; detecting, by using a sensor coupled to a suspension, pavement characteristic information corresponding to the variation type; and generating a correction signal based on the pavement characteristic information, to correct the control signal. The suspension control method can identify the pavement smoothness variation more accurately and set the suspension damping parameter according to an identification result.

A modular electronic damping control system is described and includes a damping component located at a vehicle suspension location. The modular electronic damping control system also includes a control system configured to control the damping component, and determine the type of damping component present. Also, the control system is configured to automatically tune a vehicle's suspension based on the type of damping component present, and automatically monitor the damping component and determine when a change has been made to the damping component so that the control system can then automatically re-tune the vehicle's suspension based on the change to the damping component.

Disclosed are a system, a method, and an apparatus for controlling a suspension using image data captured by an image sensor. The suspension control apparatus includes at least one image sensor mounted to an own vehicle to capture image data of the surrounding environment of the own vehicle, and a controller configured to control the own vehicle based on the image data captured by the at least one image sensor, wherein the controller receives the image data from the at least one image sensor, extracts suspension control reference information including at least one of road environment information, own vehicle surrounding object information, and own vehicle state information based on the image data and/or an in-vehicle sensor of the own vehicle, and generates a control signal for controlling the height of a suspension according to the suspension control reference information.

A host vehicle-based feature harvester is disclosed. In one implementation, the feature harvester includes memory and a processor configured to receive a plurality of images captured by a camera onboard the host vehicle, the plurality of images being representative of an environment of the host vehicle; analyze at least one image from the plurality of images to identify a representation of a lane mark; select at least one sample area of the representation of the lane mark, wherein the at least one sample area is associated with an image location of at least a portion of the representation of lane mark; determine a location identifier of the at least one sample area; determine a surface quality indicator associated with the at least one sample area; and cause transmission of the location identifier and the surface quality indicator to an entity remotely-located relative to the host vehicle.

In some embodiments, methods and systems may be used to control operation of various systems of the vehicle based on road features included in an upcoming portion of a road surface located along a path of travel of the vehicle. This control may either be based on a probability of encountering a road feature on the road surface 5 and/or frequency information related to the upcoming portion of the road surface.

The present disclosure relates to a method and an apparatus for controlling an electronic control suspension using a deep learning-based road surface classification model. The method for controlling an electronic control suspension in a vehicle including a camera and a GPS receiver may include collecting location information of the vehicle using the GPS receiver while driving, identifying whether there is a previously generated road surface classification model corresponding to a front obstacle when the front obstacle is detected, determining a first control value based on a first characteristic value corresponding to the road surface classification model when there is the road surface classification model as a result of the identification, controlling the electronic control suspension with the determined first control value when entering the obstacle, and collecting new sensing data through a physical sensor, and correcting the first characteristic value based on the new sensing data.

An electronic control suspension system for a vehicle is proposed. The electronic control suspension system includes a detection unit detecting information on a road in front of the vehicle, an electronic control suspension which damping force is controlled by current, and a control unit is configured to adjust the damping force of the electronic control suspension according to the information detected by the detection unit.

Techniques for using ball joint sensor data to determine conditions relevant to a vehicle are described in this disclosure. For example, in one example, the ball joint sensor data may be used to determine estimated steering data. The estimated steering data may be directly used to navigate through an environment, such as by the vehicle relying on the estimated steering data when planning, tracking, or executing a driving maneuver. Also, the estimated steering data may be used to verify the reliability of other steering sensor data used to navigate through the environment.

A vehicle position estimation method includes: acquiring time-series data of a parameter related to a vertical motion of a wheel while the vehicle is traveling; acquiring the parameter around the vehicle, as a reference parameter, from a parameter map indicating a correspondence relationship between the parameter and a position; estimating a vehicle position based on a comparison between the time-series data of the parameter and time-series data of the reference parameter. Meanwhile, road surface roughness around the vehicle in a lateral direction and a lateral position of the vehicle in a road are recognized by using a recognition sensor installed on the vehicle. When the road surface roughness is less than a threshold, a lateral position component of the estimated vehicle position is replaced with the lateral position recognized by using the recognition sensor.