CN116872950A - Vehicle driving track prediction method and device, electronic equipment and storage medium - Google Patents
Vehicle driving track prediction method and device, electronic equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0097—Predicting future conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/146—Display means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2300/00—Indexing codes relating to the type of vehicle
- B60W2300/44—Tracked vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/12—Lateral speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/16—Ratio selector position
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Abstract
The application discloses a vehicle running track prediction method and device, electronic equipment and storage medium, wherein the method comprises the following steps: receiving input information of a vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information; judging the current running state of the vehicle according to the gear information; determining a current steering state of the vehicle according to the gear information, the yaw rate and the gear information; based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction. The application improves the driving safety of special vehicles and reduces the occurrence of collision accidents.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a method and apparatus for predicting a vehicle driving track, an electronic device, and a storage medium.
Background
The working environment of the special vehicle is complex and changeable, and in the daily use process, the problems that the visual field of a driver is limited, potential safety hazards exist when a commander commands the driver to operate the vehicle in a short distance, and the like exist.
In the related art, a driver is usually assisted in observing the surrounding environment of a vehicle by performing video display through common equipment such as a vehicle recorder and the like, but the current running track of the vehicle cannot be predicted, so that the driver knows the current running state of the vehicle in real time, and safe driving is ensured. In addition, in the running process of a special vehicle driven in a complex environment, the vehicle cannot calculate and feed back the predicted running track state of the vehicle in real time, so that the driver cannot evaluate the current running track state of the vehicle, and the risk of collision accidents exists.
Disclosure of Invention
The embodiment of the application provides a vehicle running track prediction method and device, electronic equipment and storage medium, so as to realize track prediction based on a vehicle kinematic model, thereby improving the driving safety of special vehicles and reducing the occurrence of collision accidents.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a vehicle driving track prediction method, which is applied to a special vehicle, where the method includes:
Receiving input information of a vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information;
judging the current running state of the vehicle according to the gear information;
determining a current steering state of the vehicle according to the gear information, the yaw rate and the gear information;
based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
In some embodiments, the method further comprises:
outputting a normal or abnormal working state in response to the input of the track speed, the yaw rate, the gear information, the vehicle track prediction result, the current steering state of the vehicle and the track characteristic coefficient;
when the output is normal, setting a fault code to be 1;
if the track characteristic coefficient exceeds a preset range, the predicted state value is abnormal or the steering state value is abnormal, outputting the abnormal track characteristic coefficient to be abnormal, and setting a fault code to be 0;
If the track speed, the yaw rate or the range of the gear information exceeds a preset range, outputting the abnormal signal, wherein the received sensor signal is abnormal and a fault code is set to be 2;
if the track speed, the yaw rate or the gear information does not change in a plurality of continuous sampling periods, the output is abnormal, the received sensor signal is abnormal, and the fault code is set to be 2.
In some embodiments, the method further includes obtaining a vehicle track prediction result based on a preset vehicle operation model according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least includes one of the following: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction, including:
taking the longitudinal direction of the vehicle as an X axis, the transverse direction as a Y axis and the mass center of the vehicle as a coordinate system, and establishing a vehicle kinematic model:where ω is the yaw rate of the vehicle, θ is the yaw angle of the vehicle, and V isThe centroid speed, V, is calculated as: />V l 、V r For the speed of the left and right tracks,
the track calculation formula of the mass center of the vehicle is as follows:
in some embodiments, the method further comprises:
The track calculation formula for determining the left and right track grounding points (x, y) of the vehicle is as follows:
where B is the vehicle width.
In some embodiments, the method further comprises:
calculating the length of the predicted distance which meets the requirement that the predicted distance of the vehicle is within a fixed length range:
in some embodiments, the vehicle trajectory prediction result includes:
and generating a track fitting result by using the input predicted centroid, track coordinates of the left track and the right track of the vehicle and using a cubic polynomial fitting to obtain corresponding cubic polynomial coefficients, wherein the track fitting result comprises the following steps: the track prediction state, the polynomial coefficient of the predicted track curve,
when the track prediction state is 0, the track is not predicted;
when the track prediction state is 1, the current prediction state is represented as forward running prediction;
when the predicted track state is 2, the current predicted state is a backward travel prediction;
and when the predicted track state is 3, the current predicted state is represented as central steering prediction.
In some embodiments, the receiving input information of the vehicle state sensing subsystem, wherein the input information includes at least track speed, yaw rate, gear information, includes:
And acquiring the track speed, the yaw rate and the gear information according to a vehicle-mounted ECU system, and transmitting the track speed, the yaw rate and the gear information through a CAN bus signal, wherein the track speed is not more than 72km/h, the value of the yaw rate is not more than 9rad/s, and the gear signal comprises 0,1,2 and 3 values and respectively represents stationary, forward, backward and in-situ steering.
In a second aspect, an embodiment of the present application further provides a vehicle driving track prediction apparatus, which is applied to a special vehicle, where the apparatus includes:
the receiving module is used for receiving input information of the vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information;
the judging module is used for judging the current running state of the vehicle according to the gear information;
the determining module is used for determining the current steering state of the vehicle according to the gear information, the yaw rate and the gear information;
the track prediction module is used for obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed based on a preset vehicle running model, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the above method.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the above-described method.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects: by receiving track speed, yaw rate, and gear information in the input information of the vehicle state sensing subsystem, the current running state of the vehicle can be determined according to the gear information, and the current steering state of the vehicle can be determined according to the gear information, the yaw rate, and the gear information. And finally, based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed. By means of the vehicle running track prediction, no matter whether a special vehicle is in forward or reverse, the running track of the vehicle can be predicted in real time according to the current speed, steering wheel rotation angle and other information, and the vehicle running track prediction system has the functions of system self-checking and fault diagnosis, so that the driving safety of the tracked vehicle is comprehensively improved, and the occurrence of collision accidents is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic hardware structure of a method for predicting a vehicle driving track according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for predicting a driving track of a vehicle according to an embodiment of the application;
FIG. 3 is a schematic diagram of a vehicle track prediction apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, according to the design requirements of the track prediction method of the special vehicle, the track prediction method can be divided into three modules, namely, state sensing, track prediction and track display, which are described in detail below.
a. State sensing module
The state sensing subsystem acquires vehicle speed (track speed), yaw rate and gear signals through the vehicle-mounted ECU system, and transmits the signals to the track prediction subsystem through the CAN bus. Wherein the value of the vehicle speed (track speed) should be less than 72km/h; the value of yaw rate should be less than 9rad/s; the gear signal may take values of 0,1,2,3 representing stationary, forward, reverse, and in-situ steering, respectively.
b. Track prediction subsystem
Based on a graphical modeling mode, a prediction model of track prediction is built, and the prediction model mainly comprises core control strategies such as system state switching, vehicle track prediction, fault diagnosis positioning and the like. The module graphical development environment provides a rich module library for real-time dynamic system development and finite state machine modeling, and can be used for off-line simulation of a track prediction algorithm and test and verification of different prediction strategies by combining a vehicle kinematics model.
c. Track display subsystem
And the track display subsystem switches the angles of view of the front camera and the rear camera by receiving the state signals and outputs predicted track images on the display screen in real time according to the track information.
In specific implementation, the track prediction module for the special vehicle mainly comprises six parts, namely an input interface module, a state switching module, a steering judgment module, a track prediction module, a fault diagnosis module and an output interface module. The track prediction module further comprises a track generation and track fitting module, and each part of software modules are designed in detail in sequence.
(1) Input interface module
The Input information of the track prediction system comprises left track/wheel speed, right track/wheel speed, yaw rate, gear information and the like, and the information is assembled into an Input information interface (Input) through a Bus Creator module to provide information Input for the gear switching and track prediction module.
(2) Gear switching module
The gear switching model judges the current running State of the vehicle through the input gear information, and finally outputs Systerm_State to provide basis for the predicted track type.
(3) Steering judging module
The Steering judging module judges the current Steering State of the vehicle through gear information, left crawler belt/wheel speed and right crawler belt/wheel speed, and finally outputs the current Steering State as a steering_State.
(4) Track prediction module
The track prediction module is the core of the whole software model, and the track prediction model consists of a prediction input module, a forward track prediction module, a reverse track prediction module, a central steering track prediction module and a prediction output module. Firstly, obtaining the state of a vehicle and a predicted core parameter by a predicted input module; then, determining a module capable of enabling calculation according to the vehicle state, namely, only one state module is in a calculation state at each moment, so that the calculation amount of a program is reduced; and finally, obtaining the final system output according to the system state and the predicted track under each state.
(5) Fault diagnosis module
Inputs to the fault diagnosis module are left track/wheel speed, right track/wheel speed, yaw rate, gear state, left side rail polynomial coefficient, right side rail polynomial coefficient, predicted track state, and steering state. When the ranges of the left crawler belt/wheel speed, the right crawler belt/wheel speed, the yaw rate and the gear state exceed the preset ranges or ten continuous sampling periods are unchanged, judging that the received sensor signals are abnormal and setting the fault code to be 2; when the polynomial parameter calculated by the system exceeds a preset range, the predicted state value is abnormal and the steering state value is abnormal, judging that the track predicted system is abnormal and setting a fault code to be 0; when the system is working normally, the fault code is 1.
(6) Output module
The output information of the track prediction system mainly comprises left side prediction track polynomial coefficients, right side prediction track polynomial coefficients, steering states, fault states and prediction states.
Because the driver visual field of the special vehicle is limited in the daily use process and the running track state of the vehicle cannot be seen in real time, if the commander is relied on to command the driver to operate the vehicle in a short distance, and the like, the vehicle running track prediction method in the embodiment of the application is adopted in combination with the actual application environment of the special vehicle, so that the driving safety of the special vehicle is comprehensively improved, and the occurrence of collision accidents is reduced.
The embodiment of the application provides a vehicle running track prediction method, as shown in fig. 2, and provides a flow chart of the vehicle running track prediction method in the embodiment of the application, wherein the method at least comprises the following steps S210 to S240:
step S210, receiving input information of a vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information.
The state sensing of special vehicles mainly comprises track speed, and usually left and right tracks.
The state awareness of the special vehicle also includes yaw rate as an important parameter for vehicle lateral control.
The state sensing of the special vehicle also comprises gear information, and as the special vehicle has a plurality of different gears, namely, static, forward, backward and in-situ steering, the track of the special vehicle can be affected differently under different gears.
Further, the state sensing information is transmitted to the track prediction module through the vehicle bus after passing through the vehicle ECU (ElecmalControlUnit) automobile electronic control unit.
Step S220, judging the current running state of the vehicle according to the gear information.
And judging the current running State of the vehicle through the input gear information, and finally outputting the current running State of the Systerm_State vehicle to provide a basis for the predicted track type.
And step S230, determining the current steering state of the vehicle according to the gear information, the yaw rate and the gear information.
And judging the current Steering State of the vehicle through the gear information, the left crawler belt/wheel speed and the right crawler belt/wheel speed, and finally outputting the current Steering State of the vehicle as the Steering State of the Steering State.
Illustratively, when the steering_State is 0, it indicates that the vehicle is currently in a left-Steering State; when the steering_State is 1, the vehicle is indicated to be currently in a right Steering State; when the assist_state is 2, it indicates that the vehicle is currently in a straight running State.
Step S240, obtaining a vehicle track prediction result based on a preset vehicle operation model according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
And deducing and calculating the predicted track point coordinates based on a vehicle dynamics model according to the obtained current running state of the vehicle and the current steering state of the vehicle and combining the crawler speed. The prediction model calculates a prediction track through the input left track speed, right track speed and yaw rate, and then inputs the track into the fitting model to obtain a fitted cubic polynomial coefficient.
In the specific implementation, the predicted track points of the vehicle can be calculated through a vehicle power model, and the fitting module fits the track points through a least square method to calculate coefficients of a fitting polynomial.
By adopting the method, the running track prediction algorithm is developed based on the high-definition image information by considering the kinematics and dynamics characteristics of the special vehicle, and the running track of the special vehicle is accurately predicted in real time. In addition, the method can also comprise forward track prediction (special vehicles such as crawler special vehicles run forwards), reverse track prediction (special vehicles such as crawler special vehicles run backwards), central steering track prediction (special vehicles such as crawler special vehicles steer in situ or steer along a certain center) and accords with the use scene characteristics of the special vehicles.
By adopting the method, the track prediction result is dynamically displayed on the display screen used by the driver of the special vehicle, so that the driver can evaluate the current motion state, and the occurrence of collision accidents is avoided or the damage degree of the collision accidents is reduced.
By adopting the method, the self-diagnosis function is realized, and the driving safety of the special vehicle can be comprehensively improved.
In one embodiment of the application, the method further comprises: outputting a normal or abnormal working state in response to the input of the track speed, the yaw rate, the gear information, the vehicle track prediction result, the current steering state of the vehicle and the track characteristic coefficient; when the output is normal, setting a fault code to be 1; if the track characteristic coefficient exceeds a preset range, the predicted state value is abnormal or the steering state value is abnormal, outputting the abnormal track characteristic coefficient to be abnormal, and setting a fault code to be 0; if the track speed, the yaw rate or the range of the gear information exceeds a preset range, outputting the abnormal signal, wherein the received sensor signal is abnormal and a fault code is set to be 2; if the track speed, the yaw rate or the gear information does not change in a plurality of continuous sampling periods, the output is abnormal, the received sensor signal is abnormal, and the fault code is set to be 2.
In order to match with the track prediction result, a normal or abnormal working state is obtained by responding to the input of the track speed (left track/wheel speed, right track/wheel speed), the yaw rate, the gear information, the vehicle track prediction result, the current steering state of the vehicle and the track characteristic coefficient.
In particular, the value of the track speed should be less than 72km/h; the value of yaw rate should be less than 9rad/s; the gear signal may take values of 0,1,2,3 representing stationary, forward, reverse, and in-situ steering, respectively. When the ranges of the left-side track/wheel speed, the right-side track/wheel speed, the yaw rate, and the gear state exceed a predetermined range, or ten consecutive sampling periods are unchanged, it is determined that the received sensor signal is abnormal and the failure code is set to 2.
When the polynomial parameter calculated by the fitted cubic polynomial coefficient exceeds a preset range, the predicted state value is abnormal and the steering state value is abnormal, the track prediction system is judged to be abnormal and the fault code is set to be 0.
When the system is working normally, the fault code is 1.
Through self-checking and fault diagnosis functions, the driving safety of the special vehicle can be comprehensively improved.
In one embodiment of the present application, the vehicle trajectory prediction result is obtained based on a preset vehicle operational model according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, where the vehicle trajectory prediction result at least includes one of the following: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction, including: taking the longitudinal direction of the vehicle as an X axis, the transverse direction as a Y axis and the mass center of the vehicle as a coordinate system, and establishing a vehicle kinematic model:wherein ω is the yaw rate of the vehicle, θ is the yaw rate of the vehicle, V is the speed of the centroid, and the calculation formula of V is: />V l 、V r For the speed of the left and right tracks,
the track calculation formula of the mass center of the vehicle is as follows:
the track prediction model consists of a prediction input module, a forward track prediction module, a reverse track prediction module, a central steering track prediction module and a prediction output module. Firstly, obtaining the state of a vehicle and a predicted core parameter by a predicted input module; then, determining a module capable of enabling calculation according to the vehicle state, namely, only one state module is in a calculation state at each moment, so that the calculation amount of a program is reduced; and finally, obtaining the final system output according to the system state and the predicted track under each state. The track prediction module consists of a prediction model and a fitting model. The prediction model calculates a prediction track through the input left crawler speed, the right crawler speed and the yaw rate; and inputting the track into a fitting model to obtain fitted cubic polynomial coefficients.
The calculation process of the predicted track is as follows:
and calculating predicted track points of the vehicle through a vehicle power model, and fitting the track points through a least square method by a fitting module to calculate coefficients of a fitting polynomial.
First, a vehicle dynamics model is built
Taking the longitudinal direction of the vehicle as an X axis, the transverse direction as a Y axis, and the mass center of the vehicle as a coordinate system, the kinematic model of the vehicle is as follows:
wherein x is the vehicle centroid abscissa, unit m; y-vehicle centroid ordinate, unit m; θ—vehicle yaw, unit rad; v—vehicle centroid speed; ω -vehicle yaw rate in rad/s.
Centroid speed calculation then
Wherein v is l Vehicle left track/wheel speed in m/s, V r -vehicle right track/wheel speed in m/s.
In one embodiment of the application, the method further comprises: the track calculation formula for determining the left and right track grounding points (x, y) of the vehicle is as follows:
where B is the vehicle width.
During specific implementation, the mass center and the track grounding points on the left and right sides are calculated:
wherein: x-vehicle centroid abscissa, unit m; y-vehicle centroid ordinate, unit m; x is x I -the left track ground point ordinate of the vehicle, unit m; y is l -left track ground point abscissa of vehicle, unit m; x is x r -the longitudinal coordinate of the ground point of the right track of the vehicle, unit m; y is r -the longitudinal coordinate of the ground point of the right track of the vehicle, unit m; b-vehicle width, unit m.
In one embodiment of the application, the method further comprises: calculating the length of the predicted distance which meets the requirement that the predicted distance of the vehicle is within a fixed length range:
based on the adaptive distance model, the speed changes at time during the running of the vehicle. The input of the self-adaptive distance model is the mass center speed of the vehicle, the output is the predicted track length, the predicted track length of the vehicle can be enabled to follow the self-adaptive change of the speed, the self-adaptive distance model is limited in a certain range, and the length of the predicted distance is calculated by adopting a formula.
The prediction module is used for calculating the track coordinates of the predicted mass center, the left track and the right track according to the left track speed, the right track speed and the yaw rate, then performing trinomial fitting with the width of the vehicle and the predicted track length as the input of the track fitting module, and outputting the corresponding trinomial coefficients, wherein the specific calculation formula is as follows
In the above formula, wherein: the prediction_dis-the vehicle predicts the track distance, in m; max_dis—maximum predicted trajectory distance, unit m; min_Dis—minimum predicted track distance, in m; max_V-maximum travel speed of the vehicle in m/s.
In one embodiment of the present application, the vehicle trajectory prediction result includes: and generating a track fitting result by using the input predicted centroid, track coordinates of the left track and the right track of the vehicle and using a cubic polynomial fitting to obtain corresponding cubic polynomial coefficients, wherein the track fitting result comprises the following steps: the track prediction state and the polynomial coefficient of the predicted track curve represent that the track is not predicted when the track prediction state is 0; when the track prediction state is 1, the current prediction state is represented as forward running prediction; when the predicted track state is 2, the current predicted state is a backward travel prediction; and when the predicted track state is 3, the current predicted state is represented as central steering prediction.
In the specific implementation, the input of the track fitting module is the predicted centroid, the calculated track coordinates of the left crawler belt/wheel and the right crawler belt/wheel, the corresponding cubic polynomial coefficients are obtained through cubic polynomial fitting, and finally the track prediction state and the polynomial coefficients of the predicted track curve are output.
Further, if the track prediction state is 0, it indicates that the system does not predict the track;
If the track prediction state is 1, the current prediction state of the system is indicated as forward running prediction;
if the predicted track state is 2, the current predicted state of the system is a backward travel prediction;
and if the predicted track state is 3, the current predicted state of the system is the central steering prediction.
In one embodiment of the present application, the receiving input information of the vehicle state sensing subsystem, wherein the input information includes at least track speed, yaw rate, and gear information, includes: and acquiring the track speed, the yaw rate and the gear information according to a vehicle-mounted ECU system, and transmitting the track speed, the yaw rate and the gear information through a CAN bus signal, wherein the track speed is not more than 72km/h, the value of the yaw rate is not more than 9rad/s, and the gear signal comprises 0,1,2 and 3 values and respectively represents stationary, forward, backward and in-situ steering.
The state sensing system acquires vehicle speed, yaw rate and gear signals through the vehicle-mounted ECU system, and transmits the signals to the track prediction subsystem through the CAN. The value of the crawler speed should be less than 72km/h; the value of yaw rate should be less than 9rad/s; the gear signal may take values of 0,1,2,3 representing stationary, forward, reverse, and in-situ steering, respectively.
The embodiment of the application also provides a vehicle running track prediction apparatus 300, as shown in fig. 3, and provides a schematic structural diagram of the vehicle running track prediction apparatus in the embodiment of the application, where the vehicle running track prediction apparatus 300 at least includes: a receiving module 310, a judging module 320, a determining module 330 and a track predicting module 340, wherein:
in one embodiment of the present application, the receiving module 310 is specifically configured to: the state sensing of special vehicles mainly comprises track speed, and usually left and right tracks.
The state awareness of the special vehicle also includes yaw rate as an important parameter for vehicle lateral control.
The state sensing of the special vehicle also comprises gear information, and as the special vehicle has a plurality of different gears, namely, static, forward, backward and in-situ steering, the track of the special vehicle can be affected differently under different gears.
Further, the state sensing information is transmitted to the track prediction module through the vehicle bus after passing through the vehicle ECU (ElecmalControlUnit) automobile electronic control unit.
In one embodiment of the present application, the determining module 320 is specifically configured to: and judging the current running state of the vehicle according to the gear information.
And judging the current running State of the vehicle through the input gear information, and finally outputting the current running State of the Systerm_State vehicle to provide a basis for the predicted track type.
In one embodiment of the present application, the determining module 330 is specifically configured to: and determining the current steering state of the vehicle according to the gear information, the yaw rate and the gear information.
And judging the current Steering State of the vehicle through the gear information, the left crawler belt/wheel speed and the right crawler belt/wheel speed, and finally outputting the current Steering State of the vehicle as the Steering State of the Steering State.
Illustratively, when the steering_State is 0, it indicates that the vehicle is currently in a left-Steering State; when the steering_State is 1, the vehicle is indicated to be currently in a right Steering State; when the assist_state is 2, it indicates that the vehicle is currently in a straight running State.
In one embodiment of the present application, the trajectory prediction module 340 is specifically configured to: based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
And deducing and calculating the predicted track point coordinates based on a vehicle dynamics model according to the obtained current running state of the vehicle and the current steering state of the vehicle and combining the crawler speed. The prediction model calculates a prediction track through the input left track speed, right track speed and yaw rate, and then inputs the track into the fitting model to obtain a fitted cubic polynomial coefficient.
In the specific implementation, the predicted track points of the vehicle can be calculated through a vehicle power model, and the fitting module fits the track points through a least square method to calculate coefficients of a fitting polynomial.
It can be understood that the above-mentioned vehicle travel track prediction apparatus can implement each step of the vehicle travel track prediction method provided in the foregoing embodiment, and the relevant explanation about the vehicle travel track prediction method is applicable to the vehicle travel track prediction apparatus, which is not repeated herein.
Fig. 4 is a schematic structural view of an electronic device according to an embodiment of the present application. Referring to fig. 4, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs, and the vehicle running track prediction device is formed on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
receiving input information of a vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information;
Judging the current running state of the vehicle according to the gear information;
determining a current steering state of the vehicle according to the gear information, the yaw rate and the gear information;
based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
The method executed by the vehicle travel track prediction apparatus disclosed in the embodiment of fig. 2 of the present application may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may further execute the method executed by the vehicle running track prediction apparatus in fig. 2, and implement the function of the vehicle running track prediction apparatus in the embodiment shown in fig. 2, which is not described herein again.
The embodiment of the present application also proposes a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform a method performed by the vehicle travel track prediction apparatus in the embodiment shown in fig. 2, and specifically configured to perform:
receiving input information of a vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information;
judging the current running state of the vehicle according to the gear information;
determining a current steering state of the vehicle according to the gear information, the yaw rate and the gear information;
based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (trans itory med ia), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A vehicle travel track prediction method applied to a special vehicle, wherein the method comprises the following steps:
receiving input information of a vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information;
judging the current running state of the vehicle according to the gear information;
Determining a current steering state of the vehicle according to the gear information, the yaw rate and the gear information;
based on a preset vehicle running model, obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
2. The method of claim 1, wherein the method further comprises:
outputting a normal or abnormal working state in response to the input of the track speed, the yaw rate, the gear information, the vehicle track prediction result, the current steering state of the vehicle and the track characteristic coefficient;
when the output is normal, setting a fault code to be 1;
if the track characteristic coefficient exceeds a preset range, the predicted state value is abnormal or the steering state value is abnormal, outputting the abnormal track characteristic coefficient to be abnormal, and setting a fault code to be 0;
if the track speed, the yaw rate or the range of the gear information exceeds a preset range, outputting the abnormal signal, wherein the received sensor signal is abnormal and a fault code is set to be 2;
If the track speed, the yaw rate or the gear information does not change in a plurality of continuous sampling periods, the output is abnormal, the received sensor signal is abnormal, and the fault code is set to be 2.
3. The method of claim 1, wherein the obtaining a vehicle trajectory prediction result based on a preset vehicle dynamics model according to the current running state of the vehicle, the current steering state of the vehicle, and the track speed, wherein the vehicle trajectory prediction result includes at least one of: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction, including:
taking the longitudinal direction of the vehicle as an X axis, the transverse direction as a Y axis and the mass center of the vehicle as a coordinate system, and establishing a vehicle kinematic model:wherein ω is the yaw rate of the vehicle, θ is the yaw rate of the vehicle, V is the speed of the centroid, and the calculation formula of V is: />V l 、V r For the speed of the left and right tracks,
the track calculation formula of the mass center of the vehicle is as follows:
4. a method as claimed in claim 3, wherein the method further comprises:
the track calculation formula for determining the left and right track grounding points (x, y) of the vehicle is as follows:
where B is the vehicle width.
5. A method as claimed in claim 3, wherein the method further comprises:
Calculating the length of the predicted distance which meets the requirement that the predicted distance of the vehicle is within a fixed length range:
6. the method of claim 3, wherein the vehicle trajectory prediction result comprises:
and generating a track fitting result by using the input predicted centroid, track coordinates of the left track and the right track of the vehicle and using a cubic polynomial fitting to obtain corresponding cubic polynomial coefficients, wherein the track fitting result comprises the following steps: the track prediction state, the polynomial coefficient of the predicted track curve,
when the track prediction state is 0, the track is not predicted;
when the track prediction state is 1, the current prediction state is represented as forward running prediction;
when the predicted track state is 2, the current predicted state is a backward travel prediction;
and when the predicted track state is 3, the current predicted state is represented as central steering prediction.
7. The method of claim 1, wherein the receiving input information from the vehicle state awareness subsystem, wherein the input information includes at least track speed, yaw rate, gear information, comprises:
and acquiring the track speed, the yaw rate and the gear information according to a vehicle-mounted ECU system, and transmitting the track speed, the yaw rate and the gear information through a CAN bus signal, wherein the track speed is not more than 72km/h, the value of the yaw rate is not more than 9rad/s, and the gear signal comprises 0,1,2 and 3 values and respectively represents stationary, forward, backward and in-situ steering.
8. A vehicle travel track prediction apparatus applied to a special vehicle, wherein the apparatus comprises:
the receiving module is used for receiving input information of the vehicle state sensing subsystem, wherein the input information at least comprises track speed, yaw rate and gear information;
the judging module is used for judging the current running state of the vehicle according to the gear information;
the determining module is used for determining the current steering state of the vehicle according to the gear information, the yaw rate and the gear information;
the track prediction module is used for obtaining a vehicle track prediction result according to the current running state of the vehicle, the current steering state of the vehicle and the track speed based on a preset vehicle running model, wherein the vehicle track prediction result at least comprises one of the following steps: forward trajectory prediction, reverse trajectory prediction, and center steering trajectory prediction.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
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| CN117068185B (en) * | 2023-10-18 | 2024-01-02 | 中汽研（天津）汽车工程研究院有限公司 | Track vehicle track prediction method, track vehicle track prediction equipment and track vehicle track prediction medium |
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