775 results about "Speedometer" patented technology

The invention discloses a rapid measurement method and system for geometric irregularity of a track in a turnout zone. The method comprises the steps that complete three-dimensional point cloud data of the surface of a railway turnout steel rail are obtained in real time through a multi-angle line structure laser scanner on a special detection vehicle body; accurate attitude and position data of the detected vehicle body in the measurement process are obtained in real time through an inertial navigation measurement unit and an odometer; obtaining a high-precision pose of a detected vehicle body through a robust dead reckoning method of an IMU (Inertial Measurement Unit) assisted by a speedometer dynamic lever arm value based on a variable wheel-rail contact point; constructing a turnout area three-dimensional point cloud model; extracting geometric irregularity key points of turnout steel rail point cloud from the turnout area three-dimensional point cloud model; calculating parameters of track gauge, horizontal and rail direction irregularity and unevenness; the special detection vehicle body can quickly and stably pass through a turnout area to complete continuous and accurate measurement of geometric irregularity of a track.

The invention provides a quad-rotor unmanned aerial vehicle trajectory tracking control method and device, equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: determining a translation system state variable and an attitude system state variable of an unmanned aerial vehicle based on the real-time trajectory data of the quad-rotor unmanned aerial vehicle; calculating a translation disturbance estimated value based on the translation system state variable, the disturbance observer constant and the expected speed; thrust data is determined based on the translation system state variable, the translation disturbance estimated value, the expected speed, the expected position and outer ring controller parameters of the unmanned aerial vehicle; determining a desired attitude angle based on the thrust data and the desired yaw angle; calculating a control moment based on the expected attitude angle, the attitude system state variable and an inner ring controller parameter of the unmanned aerial vehicle; and based on the thrust data, the control torque and the physical parameters of the unmanned aerial vehicle, calculating to obtain a rotor expected rotating speed, and controlling the unmanned aerial vehicle based on the rotor expected rotating speed. The accuracy of flight control of the unmanned aerial vehicle can be improved.

The invention discloses a multi-signal fusion yaw velocity credibility calculation method and electronic equipment. The method includes calculating a first yaw velocity based on a vehicle model; calculating a second yaw velocity based on the lateral acceleration; calculating a third yaw velocity based on the geometric constraint of the wheel speed difference; determining whether a credibility updating condition is satisfied; and if yes, determining the credibility of the actually measured yaw velocity of the sensor according to the first yaw velocity, the second yaw velocity, the third yaw velocity and the actually measured yaw velocity of the sensor. Multi-dimensional signals such as the wheel speed, the steering angle and the acceleration are fused, and fault tolerance is improved. And through threshold screening and weighted fusion, noise and sudden change interference are suppressed. And front wheel calculation can be switched when a rear wheel breaks down, so that the system continuity is guaranteed.

System and method for monitoring vehicular traffic on a bridge, including receiving digital data representing the response of the bridge to a traffic event, where the digital data has been collected during the traffic event, from displacement sensors and accelerometers mounted on the superstructure of the bridge, wherein the digital data from the displacement sensors embody bending responses of the bridge, the digital data from the accelerometers embody acceleration responses of the bridge, and the digital data from the sensor are synchronised in the same time space, providing a parametric model which uses modal parameters to simulate generalized boundary conditions and two-dimensional behaviour of the bridge; using the parametric model to process the digital data to solve for deformation of the bridge and characteristics of the vehicle traffic, and generating an output that describes the deformation of the bridge and characteristics of the vehicle traffic.

The invention provides a train attitude precision keeping method and system based on inertial quaternion modeling, and belongs to the technical field of train operation positioning. Through a quaternion modeling train attitude feedback compensation model, auxiliary calibration of a gyroscope is realized by using an accelerometer, and calculation of an inertial attitude is realized; converting the acquired wheel speed sensor information into three-dimensional speed under a navigation coordinate system through attitude information resolved by a quaternion modeling train attitude feedback compensation model, and further integrating to obtain a position; and fusing the speed and position information of the wheel speed sensor after attitude conversion with the speed and position information obtained by inertial navigation dead reckoning to realize error correction of the inertial navigation system. According to the method, the problem that the train state sensing precision is degraded due to the fact that a satellite navigation / inertial navigation system positioning model is difficult to overcome inertial device error accumulation when a satellite fails is solved, the train attitude precision is kept, and then error divergence of a combined positioning system during satellite signal denial is effectively restrained.

A system (1) for efficiently determining a driving behaviour of a driver driving a vehicle. The system includes a 3-axis accelerometer (2) generating accelerometer data (3), a direction sensor (4) generating direction data (5) and orientation sensor (6) generating orientation and angular velocity data (7), all placed inside the vehicle. The system (1) also includes a processing unit (8) which processes the accelerometer data (3), the direction data (5) and the orientation data (7), and calibrates the accelerometer data (3) to generate a calibrated accelerometer data (9). The calibrated accelerometer data (9) is used in determining the driving behaviour of the driver of the vehicle and generate Driving Deficiency Alerts, including but not limited to Over-speeding. Hard acceleration. Sudden Braking, Fast Cornering, Quick Lane Changing, Distracted Driving, Dangerous Driving over bumps and Crash Detection.

The invention discloses a method for calculating a vehicle speed based on a moving unmanned aerial vehicle video, and belongs to the technical field of vehicle speed calculation methods through videos, and the method comprises the steps: extracting global feature points of a video frame through employing an SIFT operator, calculating a homography matrix H through combining with an RANSAC algorithm, projecting a previous frame image to a current frame, and carrying out the calculation of the current frame image; the method effectively eliminates the interference of the motion (such as flight attitude change and displacement) of the unmanned aerial vehicle on the vehicle speed calculation, solves the problem of speed measurement failure caused by the deviation of the motion directions of the unmanned aerial vehicle and the vehicle in the traditional method, enables the algorithm to be suitable for complex scenes such as curves and multi-angle flight of the unmanned aerial vehicle, improves the adaptability of the system in a dynamic environment, and improves the reliability of the system. According to the method, the YOLOv5-1 model is combined with the DeepSORT algorithm, high-precision detection and stable tracking of the vehicle are achieved, even under the complex conditions that the vehicle is partially shielded, illumination changes and the like, continuous detection frame association can still be kept, and reliable time sequence data support is provided for vehicle displacement calculation.

The invention discloses a road condition early warning method and system based on an automobile data recorder, and relates to the technical field of road condition early warning, and the method comprises the steps: starting a vehicle, recording the driving road condition through the automobile data recorder, collecting the driving speed of the vehicle, and detecting the recorded video data to recognize road condition obstacles; the actual distance and speed of an obstacle are calculated based on the recognized road condition obstacle, the movement track of the obstacle is predicted, and the dynamic TTC is calculated through the predicted movement track of the obstacle in combination with the vehicle running speed. According to the method, the motion state vector of the obstacle is constructed, and the attention mechanism and the LSTM model are combined to predict the trajectory and calculate the dynamic TTC value, so that the future position and relative trend of the obstacle can be accurately judged, the potential collision risk can be recognized in advance and graded early warning can be realized, and the accuracy of road condition early warning can be effectively improved.

The invention relates to a non-inductive FOC open-loop to closed-loop switching method and device for an electronic water pump and a vehicle. The method comprises the steps that a rotor is pre-positioned; starting an open loop I-F; switching condition judgment; estimating the rotor speed based on an observer, calculating a normalization factor, and obtaining a mixed scale factor through cubic polynomial operation; acquiring an open-loop current instruction of a q axis and a closed-loop current instruction output by a closed-loop speed PI regulator; performing weighted synthesis on the two paths of current instructions according to the mixing scale factor to obtain a temporary target current instruction, and applying a change rate limit to obtain a final current instruction output in the current period; the original angle error of the observer estimation angle and the open-loop slope angle is calculated and normalized, the allowable adjustment step length and direction of the round are dynamically determined, and the angle correction amount of the round is generated and superposed to the open-loop slope angle to update the rotor angle reference value; and closed-loop switching is completed. According to the invention, the problems of electromagnetic torque impact and rotating speed fluctuation caused by instruction step change are solved.

The invention provides a speedometer sideslip online compensation integrated navigation method based on FPGA (Field Programmable Gate Array) hardware acceleration. Relates to the technical field of train navigation positioning. According to the SINS / DR integrated navigation system, an IMU inertial measurement unit is integrated in an SINS inertial navigation module and specifically comprises a three-axis gyroscope and a three-axis accelerometer, in SINS / DR integrated navigation, mapping from sensor data of the gyroscope, the accelerometer, an odometer and the like to a slip mode is achieved by adopting a Sheng differential equation model, and the SINS / DR integrated navigation system is obtained. Two speedometers are installed on non-steering wheels on the two sides respectively, a speedometer speed error model is established to compensate and correct the actual speed, and acceleration of a Kalman filtering algorithm is achieved through an FPGA.

The invention relates to the field of automatic driving, in particular to a laser radar and IMU fusion-based unmanned mine car pose calculation method, which comprises the following steps of: calculating an estimated pose of an unmanned mine car based on point cloud data; acquiring sensor data of an IMU and a speedometer; generating map information based on the point cloud data and the sensor data; calculating the zero deviation of the gyroscope according to the zero deviation estimation target function of the gyroscope, and correcting the data of the sensor; based on a sliding window, solving a predefined cost function, and optimizing the estimated pose and the corrected sensor data to obtain an optimized state vector; carrying out closed-loop detection; and when a closed loop appears, correcting all the optimized state vectors by solving a preset error function according to the optimized state vectors to obtain the real pose of the unmanned mine car. According to the invention, the limitation of single laser radar positioning in a feature-deficient or repetitive environment can be overcome, and the positioning accuracy is improved.

The invention provides a lane lighting method based on vehicle visual perception, a storage medium and a system. The method comprises the following steps: analyzing a road image collected by a vehicle front-view camera; when the lane type in front of the vehicle is identified as a curve, determining the position of a center line of a curve entrance; an initial illumination angle is calculated according to the road curvature and the current illumination angle of the headlamp, and the transverse offset of the vehicle in the lane is calculated according to the position of the center line and the real-time position of the vehicle; and calculating an angle adjustment coefficient of the headlamp according to the transverse offset and the vehicle speed, adjusting the initial illumination angle according to the angle adjustment coefficient to obtain a target illumination angle, and controlling the headlamp to illuminate according to the target illumination angle. According to the method, the illumination angle of the headlamp in the curve scene is accurately adjusted, so that a driver can clearly see the curve boundary at night, the driver is assisted in judging the distance between the vehicle and the curve boundary, and the driving safety is improved.

The present application provides a vehicle cruise control method and system, a medium, an electronic device, and in-vehicle infotainment. The vehicle cruise control method comprises: calculating a speed deviation on the basis of a preset speed of a vehicle and an ego-vehicle speed; on the basis of a speed deviation range where the speed deviation is located, selecting variable control parameters, and performing segmented control on a requested acceleration, wherein the speed deviation range is a range obtained by division on the basis of the magnitude of speed deviation, and includes a negative large deviation zone, a negative small deviation zone, a positive small deviation zone, and a positive large deviation zone; and calculating a PID control quantity of vehicle cruise control on the basis of the variable control parameters and the speed deviation. The present application can improve the control precision of cruise control, and reduce the speed fluctuation generated in the control process.

The embodiment of the invention provides a traffic signal-based vehicle path planning method and related equipment, and belongs to the technical field of automatic driving. The method comprises the following steps: acquiring traffic light data of each intersection of a target driving path; according to the green light time interval of the traffic light data, time constraint calculation of multi-intersection continuous passing is carried out, and a time constraint of continuous green wave passing is obtained; performing speed calculation on each road section according to the time constraint to obtain a speed interval of each road section; performing quadratic programming on the speed interval by taking the energy consumption and the driving time as optimization objectives to obtain a global speed sequence; and performing multi-dimensional constraint and multi-target optimization on a speed controller of the vehicle according to the global speed sequence so as to control the vehicle to run on the target running path. According to the embodiment of the invention, the vehicle can be guided to realize continuous green light passing, and the driving efficiency and the driving comfort are improved while the driving energy consumption of the vehicle is reduced.

The present disclosure provides a camera calibration method based on vehicle localization, mainly considering that a vehicle is not of an ideal rigid structure, and shaking of a vehicle body during operation, uneven road surfaces, or different loads will cause variations in extrinsic parameters of a camera system, resulting in perceptual information errors. Therefore, through perceptual information from a fused image, an inertial measurement unit, a speedometer, and the like, combined with the road and marking information provided by a high-definition map, the relative motion between the vehicle body and a vehicle chassis is simultaneously described with a mass-spring-damper model, so as to complete the determination of six-degree-of-freedom positions of the vehicle body and the vehicle chassis, and finally the extrinsic parameters of cameras are immediately calibrated with the vehicle chassis as reference coordinates.

The invention provides a monorail crane rail deformation monitoring method based on an inertia measurement technology, and belongs to the technical field of coal mine underground rail monitoring. According to the technical scheme, the method comprises the following steps: S1, deploying a plurality of IMU modules at equal intervals in the extension direction of a monorail crane track; s2, collecting initial IMU data of each IMU module and a monorail crane operation speed signal; s3, resolving initial spatial position data of the track, and generating an undeformed monorail crane track reference value; s4, in the coal mine transportation process, current IMU data and speedometer speed signals of all IMU modules are collected regularly in real time; s5, calculating the current spatial position data of the track; s6, calculating a displacement difference value and an attitude angle deviation of each measuring point; and S7, determining that the region is a monorail crane track deformation point. The system has the beneficial effects that high-resolution real-time monitoring is realized through high-frequency data acquisition and multi-sensor fusion, the external interference resistance is high, and a deformation area can be accurately positioned without interrupting operation.

The invention discloses a track non-contact intelligent detection system and method based on multi-source data fusion, relates to the technical field of track detection, and aims at efficiently completing track detection with low cost. The system comprises a frame body, a plurality of line structured light 3D cameras, a synchronous controller, a processing computer and a speedometer; the linear structured light 3D cameras are arranged on the two sides of the frame body, and the optical axes of the linear structured light 3D cameras are arranged to be perpendicular to the axis direction of the steel rail to be measured; the synchronous controller, the processing computer and the speedometer are integrated at the bottom of the frame body, and the synchronous controller and the speedometer are respectively connected with the structured light 3D camera through cables.

The invention provides a semi-active suspension control method and device, a storage medium and an electronic device.The method comprises the steps that the road surface feature elevation of a road surface abnormal object of a vehicle in the driving direction is recognized, and the driving speed of the vehicle in the driving direction is detected; calculating a front wheel shock-absorbing opportunity of the vehicle for the road surface abnormal object according to the driving speed, and calculating a shock-absorbing damping coefficient of the vehicle for the road surface abnormal object according to the driving speed and the road surface feature elevation; determining that the front wheels of the vehicle enter a shock absorption control state when the front wheel shock absorption time arrives, and controlling a shock absorber of a front axle suspension of the vehicle based on the shock absorption damping coefficient; judging whether the front wheel is switched from the damping control state to the aftershock control state or not; if the front wheels are switched to the aftershock control state, the aftershock elimination damping coefficient of the vehicle for the road surface abnormal object is calculated according to the running speed, and a shock absorber of a front axle suspension of the vehicle is controlled based on the aftershock elimination damping coefficient; and the driving safety and stability of the vehicle are improved.

The invention relates to a safety constraint vehicle formation distributed sliding mode control method based on a preset performance function. Compared with the prior art, the defect that vehicle formation control is difficult to meet safety constraint, preset performance and distributed cooperative control at the same time is overcome. The method comprises the following steps: establishing a vehicle formation three-order state space model; generating a following vehicle smooth reference trajectory; a preset performance constraint of the following vehicle is designed; calculating the expected speed of the following vehicle; calculating the expected torque of the following vehicle; a following vehicle driving system is controlled; and formation cooperative control is realized. According to the invention, through a reference generation mechanism of constraint perception, a preset performance function with a self-adaptive boundary and a topological structured reaching law, high-performance tracking of the track of the pilot vehicle is realized on the premise of ensuring that all following vehicles meet the time-varying safety boundary.

The invention relates to an unmanned surface vehicle formation control method and device, and belongs to the technical field of unmanned surface vehicle automatic driving, and the unmanned surface vehicle formation control method comprises the steps: building a mathematical model of each unmanned surface vehicle in an unmanned surface vehicle formation based on the initial position and initial speed of each unmanned surface vehicle in the unmanned surface vehicle formation; estimating the real-time speed and disturbance of each unmanned ship in the unmanned ship formation based on the mathematical model and a preset extended state observer; based on a preset relative threshold event triggering mechanism, calculating a measurement error between the unmanned ships in combination with the initial position information and the real-time speed of each unmanned ship in the unmanned ship formation; and calculating a generalized dynamic error of the unmanned ship formation based on the measurement error between the unmanned ships, and calculating control input of the unmanned ship formation by adopting a preset sliding mode algorithm based on the generalized dynamic error. According to the invention, the unmanned ship formation can be accurately and stably controlled.

The invention provides a vehicle travel calculation method and device, electronic equipment and a storage medium, and the method comprises the steps: marking a travel starting point and setting an activation state if a vehicle enters a travel starting stage; if the vehicle in the activated state is in a static state, determining idling duration based on time information of an idling starting point corresponding to a static moment and time information of a target track point corresponding to an idling judgment moment; if the idle speed lasting time meets the preset time condition, a track point between the idle speed starting point and a target track point serves as an idle speed track point, and whether the vehicle enters a travel ending stage or not is determined based on the actual driving speed at each speed calculation moment and the current state of the travel state; and if yes, marking the track point at the travel ending moment as a travel ending point, setting the travel state as an inactive state, and determining a vehicle travel result based on the travel starting point, the idle speed track point and the travel ending point. According to the invention, the accuracy of stroke calculation is improved.

A vehicle alert system (10) for instructing observers as to movement of the vehicle. A scoop (12) adheres against an inside surface of a windshield (2) and supports a plurality of illuminating elements (43) communicating with a PCBA (44), along with each of an accelerometer (74), on board diagnostic system and a power supply. In response to a determined acceleration of the structure, the accelerometer (74) inputs a signal to the PCBA (44) representative of the vehicle movement and the PCBA (44) in turn provides an output signal causing illuminating elements to generate a visual output corresponding to said input signal and further representative of a decelerating or braking condition of the vehicle. An ambient light sensor (ALS) can be incorporated into the assembly for managing lighting / LED intensity based upon exterior lighting conditions.

The invention relates to the technical field of intelligent driving, in particular to a vehicle collision risk dynamic assessment method based on multi-modal perception. In the vehicle driving process, driving information and environment characteristic information of a vehicle are collected through a sensor array, and vehicle increment movement prediction is carried out according to the environment characteristic information; acquiring a second driving speed of the vehicle, performing vehicle incremental movement identification, and acquiring an identified vehicle incremental movement distance; according to the first driving speed and the second driving speed, calculating to obtain a fusion driving speed, and carrying out weighted calculation on the predicted vehicle increment moving distance and the identified vehicle increment moving distance to obtain a vehicle increment moving distance; according to the first monitoring distance and the vehicle increment moving distance, a second monitoring distance is obtained through calculation, correction is conducted through the fused driving speed, a third monitoring distance is obtained, collision risk assessment is conducted, and an assessment result is obtained. The technical effect of accurately and quickly evaluating the collision risk is achieved.

The application discloses a road condition early warning method and system based on a driving recorder, relates to the technical field of road condition early warning, and comprises the following steps: starting a vehicle, recording a driving road condition by using a driving recorder, collecting a vehicle driving speed, and detecting and identifying road condition obstacles from video data; calculating actual distances and speeds of the obstacles based on the identified road condition obstacles, predicting motion trajectories of the obstacles, and calculating a dynamic TTC by combining the predicted motion trajectories of the obstacles with the vehicle driving speed. The application can accurately determine future positions and relative trends of the obstacles by constructing a motion state vector of the obstacles and predicting the trajectories by combining an attention mechanism and an LSTM model, and can calculate a dynamic TTC value, so that early identification and graded early warning of potential collision risks are realized, and the accuracy of road condition early warning is effectively improved.

The invention provides a wheel slip suppression control method and device, a vehicle and a storage medium, the method is applied to the field of vehicles, and the method comprises the steps that by obtaining the drive axle speed and the drive axle acceleration of the vehicle, a first difference value between the drive axle speed and a first preset speed and a second difference value between the drive axle speed and a second preset speed are calculated; if the first difference value is larger than a preset threshold value, it is judged that the vehicle is in a slip state, a slip torque intervention mark is activated, a target torque slope is determined according to a second difference value between the drive axle speed and a second preset speed, and a slip suppression control strategy is generated according to an intervention request torque corresponding to the target torque slope to conduct slip suppression control. According to the method, the starting time of a slip torque intervention mark can be judged by monitoring the slip state of wheels, so that after the slip torque intervention mark is started, the intervention request torque is calculated in different slip stages and output to the wheel end, and the slip rate of the wheels is reduced.

Various implementations include audio systems and related approaches for providing road noise cancelation (RNC). Certain implementations include a method of training a machine learning (ML) based road noise cancelation (RNC) system for a vehicle, the method including: providing inputs to the ML based RNC system, the inputs obtained from: a set of ear-mounted microphones on a user of the vehicle, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, wherein the inputs from the set of ear-mounted microphones on the user approximate detected road noise by the user; adapting a set of parameters defining noise cancelation signals in the ML based RNC system based on the inputs; and generating noise cancelation signals for output by the at least one transducer based on the adapted set of parameters.

The invention discloses a meter weight control device and method for a cable crosslinking procedure, and belongs to the technical field of cable manufacturing, the meter weight control device comprises an extruder and a weighing unit, the extruder adopts a three-layer co-extrusion system and comprises a feeding device, a traction device and an extrusion device, and the meter weight control device further comprises a speed detection unit, a calculation unit and a control unit. The speed detection unit can detect the production line traction speed of the extruder, and the calculation unit can receive a speed signal and calculate a real-time meter weight value and an actual glue thickness value; the control unit controls the working parameters of the extruder according to the comparison between the data calculated by the calculation unit and the set data; the weighing unit, the speed detection unit, the calculation unit and the control unit work cooperatively to control the weight per meter of the extruder. According to the invention, the weighing unit is arranged, and the weight of the extruded material entering the extruder is adjusted through the cooperation of the speed detection unit, the calculation unit and the control unit. The situation that the thickness exceeds the standard can be avoided, and trial production and starting waste products are remarkably reduced.

The invention provides a vehicle motion constraint dual-adjustment integrated navigation method based on machine learning, which comprises the step of realizing accurate prediction of lateral and vertical speeds of a vehicle in different motion states by fusing an inertial navigation system, a speedometer and non-integrity constraint and combining a random forest regression model. The predicted value is used as an observed value in NHC Kalman filtering, and meanwhile, a covariance matrix of a Kalman filter is dynamically adjusted by utilizing speed variance obtained by calculating different motion state data when GNSS signals are available. According to the method, the precision of vehicle navigation can be improved under the condition of not increasing additional sensors, and particularly, the high-precision positioning capability can be kept in complex environments such as cities and the like. Through experimental verification, the algorithm significantly improves the horizontal positioning precision when the GNSS fails, and provides an efficient and low-cost solution for a vehicle navigation system.

The invention discloses a dynamic pressure-based carrier rocket grid rudder control method, which comprises the following steps of: according to a pre-launch binding wind field and the speed of a carrier rocket at the current moment, calculating to obtain the relative airflow speed of the carrier rocket at the current moment; according to the current relative airflow velocity of the carrier rocket, calculating to obtain a current dynamic pressure estimated value; according to the dynamic pressure estimation value at the current moment, solving a control law parameter in the grid rudder online variable gain control law to obtain an optimal control law parameter at the current moment; according to the optimal control law parameters at the current moment, a grid rudder swing angle control instruction at the current moment is obtained through calculation, and real-time control over the grid rudder of the carrier rocket is completed. The method can effectively adapt to the rapid change pneumatic environment of the reentry flight section, remarkably reduces the flight deviation, is suitable for various carrier rocket reentry recovery scenes, and has the advantages of being high in engineering practicability, high in control efficiency and the like.

A method of rationalizing a sensor of a vehicle is provided. The method includes the steps of receiving an output from a wheel speed sensor, receiving an output from a lateral accelerometer, receiving an output from a steering angle sensor, determining whether an evaluation event exists based on the output from the wheel speed sensor, evaluating one of the steering angle sensor output or the lateral accelerometer sensor output during the evaluation event, and determining whether the other of the steering angle sensor output or the lateral accelerometer sensor output exceeds a test threshold value during the evaluation event.