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.

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.

A controller includes a calculation processing portion configured to make a predetermined calculation based on an input vehicle state amount and output a target damping force, and a damping force map configured to acquire a control instruction value for controlling a variable damper based on the target damping force. The calculation processing portion makes the calculation using a learning result that is acquired by causing the calculation processing portion to learn pairs of a plurality of target amounts acquired using a predetermined evaluation method prepared in advance with respect to a plurality of different vehicle state amounts as pairs of pieces of input and output data.

A neural network control system and method includes vehicle sensors in communication with a neural network controller in a vehicle. The neural network (NN) operates in at least two modes: a training mode and a control mode. The NN consists of at least computational five layers the layers containing a plurality of neurons. Sensor data is received by an NN controller and processed through the layers where each of the neurons applies a weight to the sensor data. In the training mode the weights are continuously adjusted until a threshold difference between a known reference signal and a plant output is achieved. In the control mode, the NN controller continuously and recursively sends a control signal commanding the plant to adjust an actuator position in response to the sensor data until a disturbance in the sensor data is substantially eliminated.

A sensory evaluation system performs sensory evaluation on a plurality of sensory indexes corresponding to respective feelings of an occupant according to traveling of a moving body, and includes: a data adjustment unit that generates evaluation data to be used for the sensory evaluation based on information acquired according to the traveling of the moving body; an evaluation index determination unit that selects at least any one sensory index as an evaluation index from among the plurality of sensory indexes based on the information; an evaluation unit that calculates an evaluation value for the evaluation index from the evaluation data using an evaluation circuit corresponding to the evaluation index; and an aggregation unit that aggregates the evaluation value calculated by the evaluation unit.

A controller controls a suspension apparatus including a variable damper that adjusts a force between a vehicle body and a wheel of a vehicle. The controller includes a first instruction calculation portion and a control instruction output portion. The first instruction calculation portion outputs a first instruction value of a damping force, which corresponds to a first target amount, using a learning result acquired from machine learning in advance by inputting a plurality of different pieces of information. The control instruction output portion limits the first instruction value and outputs it as a control instruction in a case where the first instruction value works in a direction leading to a greater vehicle state amount than a predetermined amount due to control of the variable damper based on the first instruction value.

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.

The invention relates to a method for controlling a flow from a source of pressurized air to an air bag of a pneumatic suspension arrangement in a vehicle. The method comprises obtaining a set of vehicle condition signals comprising at least two vehicle condition signals, each vehicle condition signal being indicative of an individual current condition associated with said vehicle. The method further comprises, on the basis of said set of vehicle condition signals, determining whether or not there is a need to supply the air bag with air from the source of pressurized air. The method further comprises, in response to determining that there is not a need to supply the air bag with air from the source of pressurized air, preventing pressurized air to be fed from said source of pressurized air to said air bag.

Disclosed are a device and method for detecting an abnormality of a solenoid valve of an electronically controlled suspension (ECS) system, and a non-transitory computer-readable storage medium storing a program for performing the method. The device for detecting the abnormality of the solenoid valve of the ECS system is a device for detecting an abnormality of a solenoid valve of an ECS system, which detects an abnormality of a solenoid valve disposed in an ECS system of a vehicle, and includes a memory configured to store one or more instructions, and a processor configured to execute the one or more instructions, wherein the processor executes the one or more instructions to input input data representing a state of the ECS system into an artificial neural network model, obtain an estimated value of a physical quantity representing an output of the ECS system that is output by the artificial neural network model, compare the estimated value with a measurement value of the physical quantity, and detect the abnormality of the solenoid.

A controller controls a suspension apparatus including a variable damper that adjusts a force between a vehicle body and a wheel of a vehicle. The controller includes a first instruction calculation portion and a control instruction output portion. The first instruction calculation portion outputs a first instruction value of a damping force, which corresponds to a first target amount, using a learning result acquired from machine learning in advance by inputting a plurality of different pieces of information. The control instruction output portion limits the first instruction value and outputs it as a control instruction in a case where the first instruction value works in a direction leading to a greater vehicle state amount than a predetermined amount due to control of the variable damper based on the first instruction value.