35 results about "Adhesion coefficient" patented technology

The invention relates to a vehicle braking method based on a road adhesion coefficient, and the method comprises the steps: 1, carrying out the real vehicle data collection, and punching a uniform timestamp through a clock source; 2, uniformly converting all the collected data into a vehicle coordinate system; 3, inputting the collected data into a preset deep learning regression model to obtain a predicted value of an attachment coefficient, generating a typical attachment coefficient representing the type of road, and adding the typical attachment coefficient into a preset road attachment coefficient query table; 4, the current road type is recognized, the corresponding typical attachment coefficient is called from the road attachment coefficient query table, the vehicle following distance is adjusted, and the braking deceleration is formulated; 5, analyzing to obtain the maximum adhesion potential of the current road surface; and 6, the original road adhesion coefficient is corrected, and the final braking deceleration is assigned to the current road so that the vehicle can conduct deceleration braking according to the assigned value.

This application relates to the field of vehicle control technology and discloses a predictive control method and system based on road surface adhesion coefficient. In this method, a cloud server acquires real-time information uploaded by vehicles on the road. The cloud server can combine the real-time information with historical information from each grid to form a road surface adhesion coefficient matrix. The cloud server sends the road surface adhesion coefficient matrix to the vehicle. Based on the road surface adhesion coefficient matrix, the vehicle's current position, and navigation path, the vehicle determines the predicted road surface adhesion coefficient value in its current direction of travel. Based on the predicted road surface adhesion coefficient value, the vehicle calculates the upper limit of its regenerative braking capacity and / or the upper limit of its driving torque, and then controls the vehicle accordingly. The beneficial effect is improved vehicle control efficiency on low-adhesion roads.

The invention discloses a pavement adhesion coefficient identification method, electronic equipment, a vehicle and a storage medium, relates to the technical field of pavement identification, and aims to solve the problem of low accuracy of a pavement adhesion coefficient. The road adhesion coefficient identification method comprises the following steps: acquiring a road type identification result of a road in front of a vehicle, wherein the road type is obtained by identifying a road image of the road in front of the vehicle shot by unmanned aerial vehicle equipment flying in front of the vehicle; determining a visual estimation value of a road adhesion coefficient of a road in front of the vehicle based on the road type recognition result and a preset corresponding relation; wherein the preset corresponding relation is used for indicating pavement adhesion coefficients corresponding to different pavement types; and determining the road adhesion coefficient of the road in front of the vehicle based on the visual estimation value of the road adhesion coefficient.

This invention discloses a method and system for dynamic distribution of axle control power in urban rail vehicles based on real-time adhesion estimation. The method includes: acquiring real-time operating state data of the vehicle; calculating the dynamic vertical load of each axle during braking by combining the vehicle's longitudinal dynamics model; constructing an extended state observer and using it to estimate the wheel-rail adhesion and wheel-rail contact state parameters of each axle in real time, and calculating the theoretical maximum available adhesion coefficient accordingly; applying an asymmetric rate of change constraint to the theoretical maximum available adhesion coefficient to obtain the safe adhesion limit for constraint; constructing a multi-objective optimization function for braking force distribution, including a braking force tracking error objective and an adhesion utilization rate objective, and weighting the two objectives using adaptive weighting coefficients; and solving the multi-objective optimization function to obtain the optimal braking force for each axle. This invention can significantly improve the adhesion utilization rate and active safety performance of urban rail vehicles under complex road conditions, effectively preventing wheel slippage.

The embodiment of the invention provides a train detection system, the train detection system comprises a detection device and a processing module, the detection device comprises a first mounting seat, a second mounting seat, a first sensor, a second sensor, a sliding part and an elastic guide rod, one side of the first mounting seat comprises a mounting plate, and the elastic guide rod is connected with the mounting plate; the second mounting seat is connected with one end, away from the first mounting plate, of the elastic guide rod; the sliding part is arranged on one side, back to the elastic guide rod, of the second mounting seat; the first sensor and the second sensor are arranged between the second mounting seat and the sliding part; one side, back to the mounting plate, of the first mounting seat is connected with the bottom of the train; the first sensor and the second sensor are connected with the processing module. Through the train detection system, the adhesion coefficient can be determined in real time, and the accuracy of the adhesion coefficient is improved.

The application discloses a kind of anti-cracking performance evaluation method and computer equipment for asphalt stress absorbing layer, it is related to pavement engineering technical field, the method includes: stress absorbing layer test piece is carried out oblique shear test, and index information in the process of oblique shear test is collected, the index information includes shear angle, stress load, vertical displacement, lateral displacement, interlayer dislocation displacement and stress absorbing layer adhesion force;According to the index information, the interlayer adhesion coefficient and interlayer stress absorbing coefficient of the stress absorbing layer test piece are calculated;According to the interlayer adhesion coefficient and interlayer stress absorbing coefficient, the anti-cracking performance of the stress absorbing layer test piece is evaluated.The application can introduce the interlayer adhesion coefficient and interlayer stress absorbing coefficient of stress absorbing layer test piece, and accurately evaluate the anti-cracking performance of asphalt stress absorbing layer.

A locomotive traction force upper limit limiting method, according to the environmental temperature, weather state and track object state, the adhesion coefficient is adaptively adjusted by using the direct reasoning calculation method to obtain the adhesion coefficient setting value, so that the maximum traction force limit of the locomotive can change in real time with the change of the road section road condition, and the locomotive traction is performed as much as possible without wheel pair idling. When the wheel pair idling cannot be avoided, the non-linear mathematical expression is used to calculate the idling risk value according to the creep rate change rate and the creep degree, to judge whether the wheel pair of the locomotive idles, and to decide whether to perform the idling traction force control on the locomotive traction force after the upper limit limiting control according to the idling judgment result, to realize the comprehensive judgment of multiple factors, so that the idling judgment and the idling traction force control are more comprehensive and accurate.

This application provides a method for controlling a vehicle and a vehicle in the field of vehicle control technology. The method incorporates environmental perception data, which considers the environmental sensitivity of adhesion coefficient recovery under complex operating conditions. A first feature is introduced, which directly relates to the physical root cause of adhesion coefficient changes. A second feature reflects the actual fluctuation trajectory of the adhesion coefficient between the tire and the road surface, taking into account the temporal dynamics of the adhesion coefficient under complex operating conditions. After determining the prediction result, the first feature provides physical justification for the adhesion coefficient prediction process, and the third feature provides an adjustment basis for the prediction process, thus accurately determining the adhesion coefficient after the vehicle exits a flooded road section. When the target adhesion coefficient at the current time point recovers to a normal state, the power output limitation is lifted, and the drive torque corresponding to the normal state is output. This enables precise vehicle control, avoiding the risk of slippage due to premature power recovery and eliminating the degradation in experience caused by conservative control.

The invention provides a pavement actual adhesion coefficient estimation method and device based on a tire slippage state and a storage medium, and relates to the technical field of vehicle motion control. According to the method, dynamics basic parameters during normal driving of a vehicle are collected, vertical loads and transverse and longitudinal forces of a front axle and a rear axle are calculated, after the longitudinal force is calibrated by adopting a correction coefficient, an adhesion coefficient is calculated and utilized in combination with the vertical loads, a complex tire model and a camera are not needed, the calculation force requirement is reduced, the transverse and longitudinal force coupling working condition is adapted, and the adaptability of a complex road surface is improved; tracking and utilizing the maximum value of the adhesion coefficient through a stepped memory storage mechanism, storing an effective peak value for a long time, performing gradient forgetting, and providing a stable reference basis; then, vehicle dynamic parameters during tire slippage are collected, and the flag bit activation state is determined; and finally, when the flag bit is activated, outputting a memory adhesion coefficient as an actual pavement adhesion coefficient, so that the output is more fit with a real pavement, and the transverse and longitudinal stability of the vehicle and the torque vector control accuracy are improved.

The invention discloses a method for testing a dry ground adhesion coefficient of an automobile tire. The method comprises the following steps: determining a test speed range; the tractor runs in the test road section at the first output power, and the surface temperature of tires and the speed of the tractor are detected; calculating a dynamic adhesive force coefficient, correcting the dynamic adhesive force coefficient, and recording the corrected dynamic adhesive force coefficient as a standard dynamic adhesive force coefficient; determining a correction coefficient; acquiring the surface roughness of the road surface on the driving path of the to-be-tested tire on the trailer, and the longitudinal force and the transverse force borne by the trailer; and calculating the standard dynamic adhesion coefficient of the tire to be tested, and correcting the standard dynamic adhesion coefficient by using the correction coefficient to obtain the dry ground adhesion coefficient of the tire. According to the method, the corresponding relation between the real-time roughness and the correction coefficient is established, the adhesive force deviation caused by pavement roughness changes is compensated in a targeted mode, and the high-precision requirements of tire basic performance evaluation and road safety monitoring can be met.

The present application relates to a kind of based on eddy current brake adhesion brake force enhancement method and system, comprising the following steps: when high-speed train implements emergency brake, linear eddy current brake is connected into excitation current and enters working state;On the basis of cooperating longitudinal non-adhesion brake force generated by linear eddy current brake, the surplus of maximum axle load and actual axle load is controlled excitation current to make vertical suction force in safe range, simultaneously using the vertical suction force generated, combined with the adhesion coefficient calculation obtains the increased high-speed train adhesion force available domain;Additional adhesion force available is fed back to adhesion brake system to improve adhesion brake force, and the braking effect of high-speed train is improved;With longitudinal non-adhesion brake force generated by linear eddy current brake, further realize the efficient matching use of brake force and adhesion force under multiple working conditions.The present application can be applied to any kind of linear eddy current brake, further improves total brake force on the basis of original high-speed train brake system, and improves brake efficiency.

This application discloses a method, device, equipment, and medium for rail vehicle operation control based on onboard edge computing, relating to the field of vehicle operation control technology. The method includes: acquiring pre-collected historical operation-related data of the rail vehicle; the historical operation-related data includes vehicle operation parameters, environmental monitoring parameters, and real-time track surface images; preprocessing the historical operation-related data and dividing it according to track surface conditions to construct a target training dataset; training an initial adhesion coefficient estimation model using the target training dataset to obtain a trained adhesion coefficient estimation model, and then performing trimming and fine-tuning to obtain a target adhesion coefficient estimation model; deploying the target adhesion coefficient estimation model on the onboard edge device of the target rail vehicle, so that the onboard edge device can use the target adhesion coefficient estimation model to estimate the wheel-rail adhesion coefficient of the target rail vehicle, and perform operation control of the target wheel-rail vehicle based on the target wheel-rail adhesion coefficient.

The invention relates to a vehicle control method and device, a vehicle, a storage medium and a program product, and belongs to the technical field of vehicles. Determining an estimated adhesion coefficient of the road according to the road information; taking the larger one of the estimated adhesion coefficient and the utilization adhesion coefficient of the wheels of the vehicle as a target adhesion coefficient corresponding to the wheels; and determining the upper limit value of the energy recovery torque of the vehicle according to the target adhesion coefficient of the wheels. Thus, the upper limit value suitable for the energy recovery torque of the current vehicle can be dynamically determined, wheel locking caused by the large energy recovery torque is avoided, and the stability of the vehicle is guaranteed.

A vehicle slip control method and a related apparatus, which relate to the technical field of vehicles. The method comprises: acquiring first information; on the basis of the first information, acquiring a target active force to be output by a suspension of a vehicle; and controlling the suspension to output the target active force. The first information comprises the type of a slip road surface that the vehicle is about to pass on a first road section ahead of the vehicle, and friction coefficients between different types of slip road surfaces and the wheels of the vehicle are different. The target active force comprises a downward force that acts on one or more wheels of the vehicle and is directed toward the ground. By means of the present solution, vehicle slippage can be effectively suppressed on a road surface having a low adhesion coefficient.

The application provides a multi-sensor-based road friction dynamic inversion Internet of Things system and method, and relates to the field of vehicle road safety. The system comprises a management platform, a vehicle-road cooperation network and a plurality of vehicle terminals. The method is executed by the system, and the method comprises: by each vehicle terminal, determining the adhesion coefficient of the wheel and the road surface contact based on multi-dimensional mechanical data; determining the confidence of the adhesion coefficient based on the wheel slip rate; by the management platform, acquiring the vehicle driving information of the vehicle terminal, the adhesion coefficient corresponding to the wheel and the confidence of the adhesion coefficient; based on the vehicle driving information of each vehicle terminal, the plurality of adhesion coefficients and the plurality of confidences, determining the fusion adhesion coefficient corresponding to the grid area and transmitting to the vehicle terminal; and by the vehicle terminal, determining the target driving speed of the vehicle based on the fusion adhesion coefficient, and driving the vehicle to the target driving speed within the target time. The application can provide reliable road surface state data support for automatic driving and intelligent traffic.

The invention provides a wheel-rail adhesion state identification method and system based on data driving, and the method comprises the steps: obtaining the operation data, line data and environment data of a to-be-analyzed locomotive, and obtaining corresponding feature data through feature extraction according to the operation data, the line data and the environment data; according to the data type of the feature data, an adhesion coefficient predicted value and a residual error correction value are obtained through analysis of a first gradient lifting and returning model constructed by using an XGBoost model and a second gradient lifting and returning model constructed by using a GBR model; and obtaining an adhesion state identification result according to the adhesion coefficient prediction value and the residual error correction value. Therefore, the dependence of a physical model on priori knowledge is broken through, and a data-driven adhesion state recognition framework is established; extra hardware equipment is not added, and multi-source data fusion processing and feature extraction are achieved; and a lightweight model for online dynamic identification is developed to adapt to different lines and weather changes.

The invention discloses a vehicle road adhesion coefficient determination method, system and device and a medium, and relates to the technical field of vehicle control, and the method comprises the following steps: obtaining tire dynamics response parameters of a vehicle; performing calculation and comparison based on the tire dynamic response parameters to obtain an axial longitudinal adhesion coefficient and an axial transverse adhesion coefficient of the vehicle; performing calculation and comparison on the basis of the axial longitudinal adhesion coefficient and the axial transverse adhesion coefficient to obtain a whole vehicle actual utilization adhesion coefficient of the vehicle; and performing retention attenuation analysis based on the actual utilization adhesion coefficient of the whole vehicle, and determining a target road adhesion coefficient of the vehicle. The problem that in the prior art, the road adhesion coefficient calculation mode is single, influences of external factors on vehicle driving cannot be handled, and consequently the vehicle control performance is limited is solved.

The invention relates to the technical field of automobile safety control, and discloses a traction control method and system for a high-low attachment butt joint road surface, and the method comprises the following steps: collecting vehicle dynamic information, and then carrying out the filtering processing of the vehicle dynamic information; calculating an initial docking road adhesion coefficient, compensating the initial docking road adhesion coefficient by using the filtered vehicle dynamic information to obtain a compensated road adhesion coefficient, and estimating a reference vehicle speed based on the filtered vehicle dynamic information and the compensated road adhesion coefficient; calculating a driving shaft speed control error according to the reference vehicle speed, and calculating a target driving torque based on the driving shaft speed control error; and calculating a driving wheel speed control deviation according to the reference vehicle speed, and calculating a target braking torque based on the driving wheel speed control deviation. The problem that in the prior art, traction control is difficult to carry out accurately is solved.

The application discloses a kind of test methods of the dry adhesion coefficient of automobile tire, comprising: determining test speed range;Towing vehicle travels in test section with first output power, the surface temperature of tire and the speed of towing vehicle are detected;Calculate dynamic adhesion coefficient, correct dynamic adhesion coefficient, and the corrected dynamic adhesion coefficient is recorded as standard dynamic adhesion coefficient;Determine correction coefficient;Obtain the surface roughness of the road on the driving path of the tire to be tested on trailer, the longitudinal force and lateral force received by trailer;Calculate the standard dynamic adhesion coefficient of the tire to be tested, and the standard dynamic adhesion coefficient is corrected using correction coefficient to obtain the dry adhesion coefficient of tire.The application establishes the corresponding relationship between real-time roughness and correction coefficient, compensates the adhesion deviation caused by the change of road roughness, and can meet the high-precision demand of tire basic performance evaluation and road safety monitoring.

A locomotive wheel-rail adhesion coefficient setting value setting method, first according to the environmental temperature and weather state reasoning to obtain weather environment factor value, then according to the track object state reasoning to obtain track object factor value, take the product of weather environment factor value and track object factor value as the locomotive wheel-rail adhesion coefficient setting value. The adhesion coefficient setting value is used for calculating the adhesion coefficient, and then the adhesion coefficient is used for the upper limit amplitude control of the locomotive traction force, so that the maximum traction force of the locomotive can be changed in real time with the change of the road section condition, and the wheel pair idling of the locomotive is avoided.

The application discloses a 4WIS-4WID integrated control method based on an attachment coefficient, which constructs an integrated control framework with a road surface attachment coefficient as the core in the integrated control of a four-wheel independent steering and a four-wheel independent driving system. The framework takes a steering trajectory tracking controller and a driving stability controller as collaborative subjects, realizes information interaction and function collaboration of steering and driving through global sharing of the attachment coefficient, and forms a "turning angle-torque" two-dimensional regulation and control mode. The designed integrated controller realizes further optimization in tracking accuracy and lateral stability, and fully embodies the technical advantages of multi-degree-of-freedom collaborative regulation and control.

A low adhesion road vehicle stability control method and device, the method comprising: determining a transient impact event according to the three-phase current of the wheel hub motor, and extracting a current transient impact feature from the corresponding synthesized current waveform of the transient impact event; extracting a power spectrum feature according to the vertical acceleration of the vehicle suspension; inputting the current transient impact feature, the power spectrum feature and the wheel speed fluctuation frequency into a preset road identification model to obtain a road adhesion coefficient; solving a control instruction according to the road adhesion coefficient and a preset constraint condition, and performing stability control on the vehicle. The method can quickly and accurately obtain the road adhesion coefficient at the initial stage of the low adhesion road working condition, and can effectively avoid the control conflict between different control systems, thereby improving the stability of the vehicle on the low adhesion road.

The invention discloses a method, a device and equipment for determining a lateral attachment coefficient of a vehicle motion control system, and relates to the technical field of vehicles. The method comprises the steps that a plurality of motion state parameter values and a plurality of corresponding identifications of a vehicle are obtained, the identifications of the motion state parameter values are valid identifications or invalid identifications, the valid identifications are used for representing that the motion state parameter values are valid, and the invalid identifications are used for representing that the motion state parameter values are invalid; adaptively updating the plurality of motion state parameter values of the vehicle according to the plurality of identifiers; performing analysis and calculation according to the multiple motion state parameter values of the vehicle after adaptive updating to obtain a lateral attachment coefficient estimation value; and performing analysis according to the plurality of identifiers and the lateral adhesion coefficient estimation value to determine a final value of the lateral adhesion coefficient. According to the method, the estimation precision of the lateral adhesion coefficient can be improved under the condition that analysis is carried out without depending on a tire model.

This application relates to a method, device, vehicle, and storage medium for identifying vehicle road surface information. The method includes: collecting vehicle driving data and determining the vehicle's current driving condition based on the driving data; if the vehicle's current driving condition is a first preset condition, calculating a first parameter of the road surface based on the vehicle driving data, and calculating a first adhesion coefficient based on the first parameter and a first preset strategy; if the vehicle's current driving condition is a second preset condition, calculating a second parameter of the road surface based on the vehicle driving data, obtaining a second adhesion coefficient based on the second parameter and a second preset strategy, and filtering the first or second adhesion coefficient to obtain a final adhesion coefficient value; using the final adhesion coefficient value as an index, searching a preset standard road condition adhesion coefficient-road condition information relationship table to obtain the current road condition information, thereby saving hardware costs while effectively improving the adhesion coefficient recognition accuracy and driving condition coverage.

The invention discloses a road adhesion coefficient prediction method, which comprises the following steps: arranging an acoustic sensor in a near field of a tire, collecting wheel-ground extrusion noise, and extracting 19-dimensional dimensionless time domain feature vectors after 2-2.5 kHz Butterworth high-pass filtering; the features are sent into an improved CNN-LSTM hybrid neural network model, multi-scale cavity convolution, negentropy weighting, temperature and sound pressure compensation and double-speed locking branches are adopted at the front end of the model, an output layer synchronously gives an upper limit and a lower limit of an adhesion coefficient through parallel double neurons, and a prediction interval is formed.

The present application relates to the road feature recognition technical field in vehicle engineering, disclose a kind of under non-structural road surface road surface peak adhesion coefficient estimation method and system, the method is through commonly used vehicle-mounted sensor collection vehicle pose and wheel rotation angle and other vehicle motion response signal, wheel vertical load and other motion parameters are calculated in traditional vehicle model by introducing equivalent suspension model;The above parameters are used as input, the coefficient matrix of extended Kalman filter observer is calculated by Dugoff tire model;Then considering the influence of gravity, the vehicle body acceleration is corrected in combination with vehicle pose data, and it is used as the observation signal to estimate the road surface peak adhesion coefficient.The present application improves the precision of vehicle dynamics model under non-structural road surface, and solves the problem of poor estimation effect of existing algorithm under uneven road surface and large slope conditions.

The invention provides an AMR robot slipping automatic recovery method and device and a storage medium, and the method comprises the steps: determining a road holding coefficient based on the contact pressure data of a driving wheel and the ground in combination with the torque data of the driving wheel; based on the actual rotating speed data of the driving wheel and preset expected rotating speed data, determining a rotating speed deviation value, and based on the rotating speed deviation value and the road holding coefficient, determining whether the vehicle is in a slipping state; if in the slipping state, performing power distribution on the left and right driving wheels based on the power adjusting quantity corresponding to the road holding coefficient in combination with the transverse inclination angle data and the longitudinal inclination angle data to obtain a power output value; and controlling a driving motor to execute power output adjustment based on the power output values of the left and right driving wheels, and collecting the adjusted road holding coefficient, the real-time attitude inclination parameter and the rotating speed deviation value until the rotating speed deviation value is reduced to a preset allowable range and the attitude is recovered to a preset stable range. The adaptive capacity of the AMR robot in a complex route environment is improved.

The application discloses a kind of vehicle adhesion coefficient pre-control method and system based on trajectory prediction, it is related to intelligent driving vehicle trajectory prediction technical field, wherein method includes: according to the current state of vehicle and driver intent, using hierarchical prediction architecture, four future driving trajectories are predicted and trajectory corridor is generated;Road surface image is collected and processed to the trajectory corridor area, road surface type is identified and is mapped as adhesion coefficient, and adhesion coefficient distribution along trajectory is generated;Combined with vehicle dynamics model forward simulation, the state evolution sequence of vehicle when passing through different adhesion road surface is predicted, and stability is evaluated, instability risk is judged accordingly;If there is risk, executor pre-control instruction is generated in advance.The application accurately perceives the adhesion coefficient of road surface on the driving path of wheel, and outputs instruction in advance through model predictive control, solves the problem of control lag and perception control disconnection, significantly improves the stability and safety of vehicle on adhesion mutation road surface.

An image sensor includes a substrate having a first surface and a second surface opposing the first surface and comprising photodiode regions spaced apart from each other, a light transmitting layer covering the second surface, a grid disposed on the light transmitting layer, the grid including first and second patterns and an air gap and an adhesion coefficient of the second pattern being greater than an adhesion coefficient of the first pattern, color filters filling openings defined by the grid, and microlenses disposed on the color filters.

The invention discloses a method and system for detecting the adhesion condition of a curve road surface in advance, and relates to the technical field of driving assistance, and the method comprises the steps: monitoring a front vehicle, a front vehicle center turning radius, a front vehicle rear wheel center turning radius and a front vehicle front wheel eyebrow center turning radius on a target curve road surface; the front vehicle center centrifugal acceleration, the front vehicle center absolute vehicle speed, the front vehicle rear wheel center centrifugal acceleration and the front vehicle front wheel eyebrow center centrifugal acceleration are obtained; obtaining a road adhesion coefficient threshold value based on the central centrifugal acceleration of the front vehicle and the central absolute speed of the front vehicle; and based on the numerical deviation between the central centrifugal acceleration of the rear wheels of the front vehicle and the central centrifugal acceleration of the front wheel eyebrow of the front vehicle and the central centrifugal acceleration of the front vehicle, the maximum value of the actual road adhesion coefficient is obtained by combining a road adhesion coefficient threshold value. According to the method, the driving state of the front vehicle is analyzed, the road adhesion limit capacity is pre-judged, and assistance is provided for vehicle driving.