9 results about "Road surface roughness" patented technology

This application discloses a road surface roughness identification method, a multi-axle vehicle, a medium, and a product, relating to the field of vehicle driving technology. The method includes determining the road surface grade pre-classification result for the current period based on the acquired vertical acceleration time-series data set for the current period; combining the road surface grade pre-classification result of the previous period to determine the forgetting factor and process noise covariance matrix of the unknown input Kalman filter algorithm for the current period, thereby determining the Kalman gain for the current period; combining the system's discrete state-space equation and the unknown input Kalman filter algorithm to determine the road surface excitation signal for the current period; when the current period is greater than a preset value, calculating the road surface roughness power spectral density for the current period based on the road surface excitation signals of each period within a preset time window, and determining the road surface grade for the current period based on the road surface roughness classification criteria. This application can improve the response sensitivity and accuracy of multi-axle vehicles in identifying road surface grades.

Method comprising: Receiving a variety of sensor data at a road roughness algorithm, determining, via the road roughness algorithm, that the wheel data exceeds a wheel threshold and a severity level exceeds the wheel threshold, executing a wheel flag, determining, based on the wheel flag, that the ride height data exceeds a ride height threshold, executing a ride height flag, determining, based on the wheel flag and the ride height flag, that a wheel pushdown time exceeds a time threshold, identifying, via the road roughness algorithm, an IMU change exceeding an IMU threshold, executing, based on the IMU change exceeding the IMU threshold, an IMU flag, adjusting, based on the IMU flag, parameters of the road roughness algorithm, and combining the wheel flag, the ride height flag, and the IMU flags to define a road anomaly.

A method comprising receiving a plurality of sensor data at a road roughness algorithm, determining, via the road roughness algorithm, that wheel data exceeds a wheel threshold and a severity level exceeds a wheel threshold, executing a wheel flag, determining, based on the wheel flag, that a ride height data exceeds a ride height threshold, executing a ride height flag, determining, based on the wheel flag and the ride height flag, that a duration of a wheel jerk of the wheel data exceeds a time threshold, identifying, via the road roughness algorithm, that a change in an IMU exceeds an IMU threshold, executing an IMU flag based on the change in the IMU exceeding the IMU threshold, adjusting a parameter of the road roughness algorithm based on the IMU flag, and fusing the wheel flag, the ride height flag, and the IMU flag to define a road anomaly.

The application discloses a kind of visual touch fusion and dynamics compensation intelligent vehicle trajectory tracking method and device.The method synchronously collects the road surface visual image data of the pre-foresight area in front of vehicle and the inertial vibration data of vehicle body chassis, respectively extracts road surface texture semantic features and road surface roughness features, and the features are spatiotemporally aligned and fused to obtain real-time road dynamics parameters;Then, according to the real-time road dynamics parameters, the vehicle dynamics prediction model and the dynamics boundary constraint condition are dynamically reconstructed, the optimization control problem is solved, and the vehicle actuator control instruction is output, so as to realize the intelligent vehicle trajectory tracking under complex road conditions.The method can effectively improve the intelligent vehicle trajectory tracking accuracy and environmental adaptability.

A method including receiving, at a road roughness algorithm, a plurality of sensor data, determining, via the road roughness algorithm, the wheel data exceeds a wheel threshold and a severity level exceeding the wheel threshold, executing a wheel flag, determining, based on the wheel flag, the ride height data exceeds a ride height threshold, executing a ride height flag, determining, based on the wheel flag and the ride height flag, a time duration of a wheel jerk of the wheel data exceeds a time threshold, identifying, via the road roughness algorithm, a change in IMU that exceed an IMU threshold, executing, based on the change in the IMU exceeding the IMU threshold, an IMU flag, adjusting, based on the IMU flag, parameters of the road roughness algorithm, and fusing the wheel flag, the ride height flag, and the IMU flag to define a road anomaly.

This invention relates to a method for multidimensional electromechanical coupling dynamics modeling and analysis of an external rotor hub motor, comprising: constructing a six-degree-of-freedom vertical dynamic model (VDMAD) considering the axial deflection of the stator and rotor; constructing a multidimensional eccentric unbalanced electromagnetic force model (MUEF); and constructing a motor control model; performing transient electromechanical coupling on the VDMAD, MUEF, and motor control models to establish a multidimensional eccentric electromechanical coupling model (MDEEC); inputting the vehicle axle load and road surface roughness as external excitations into the MDEEC; and obtaining the transient multidimensional eccentricity, unbalanced electromagnetic force, and vibration characteristics of the external rotor hub motor through closed-loop solution of the dynamic equations. Compared with existing technologies, this invention can accurately characterize the axial structural features and transient multidimensional eccentricity coupling effect of the external rotor hub motor, accurately solve the dynamic characteristics of the external rotor hub motor under coupled excitation, and improve the model prediction accuracy.

The application relates to the field of steer-by-wire, and particularly discloses a steer-by-wire road feeling simulation method, which comprises the following steps: collecting vehicle driving state parameters, steering operation parameters and road surface texture time domain signals; performing frequency domain decomposition on the road surface texture time domain signals to extract road surface roughness features and road surface adhesion correlation features; constructing a dynamic steering inertia compensation model based on the road surface features and the driving state and outputting compensation coefficients; combining the compensation model and a preset torque curve to calculate a basic road feeling torque; converting the road surface roughness into a high-frequency vibration excitation signal and superimposing the high-frequency vibration excitation signal to obtain a composite road feeling target torque; and finally outputting the composite road feeling target torque to a road feeling execution motor to drive a steering wheel to feedback road feeling. The application realizes real road feeling reproduction through road surface feature calculation and dynamic inertia compensation, introduces high-frequency vibration to improve road surface perception, has the advantages of sensor multiplexing, no additional hardware cost and strong robustness, is suitable for different road surfaces and driving scenes, and meets the road feeling reality and safety requirements of a steer-by-wire system.

The application discloses a roughness prediction method for pavement based on a hybrid tern algorithm and a bidirectional gate, and comprises the following steps: obtaining pavement performance data from an LTPP database for preprocessing to form a sample data set; adopting an HSTOA algorithm to optimize the learning rate and the number of neurons of an L2 and a BiGRU and the key value of a self-attention mechanism; a TCN extracts local features of data through dilated convolution; a BiGRU layer analyzes context information in a sequence through a forward GRU and a reverse GRU; a SA mechanism performs weighted operation according to the importance of BiGRU output data; a full connection layer performs nonlinear transformation on input results to generate a final prediction result; and evaluation indexes of an HSTOA-BiGRU model and a comparison model are calculated. The application ensures that the prediction model can adaptively lock a global optimal super parameter combination, thereby improving the prediction accuracy of the model for the roughness of the pavement.