CN115511222A - Vehicle state prediction method, vehicle state prediction device, electronic device and storage medium - Google Patents
Vehicle state prediction method, vehicle state prediction device, electronic device and storage medium Download PDFInfo
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Abstract
The application discloses a vehicle state prediction method, a vehicle state prediction device, an electronic device and a storage medium, wherein the method comprises the following steps: establishing a control quantity prediction model of the target vehicle according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment; establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle. According to the method and the device, the running state of the target vehicle at the next moment can be predicted by constructing a double-level model of control quantity prediction and state prediction, and accurate and effective basis can be provided for the braking control decision of the AEB system of the self vehicle on the basis of ensuring the accuracy of the vehicle state prediction result.
Description
Technical Field
The present disclosure relates to the field of driving assistance technologies, and in particular, to a method and an apparatus for predicting a vehicle state, an electronic device, and a storage medium.
Background
In an intelligent driving scene, the motion state of a target vehicle in the surrounding environment of the vehicle needs to be predicted in real time, so that the purposes of behavior decision and emergency risk avoidance of the vehicle are achieved. In the prior art, when the prediction task is completed, a machine learning model is usually adopted and combined with a historical motion trajectory to predict the trajectory of the target vehicle and generate a probability distribution of a prediction result, so as to be used by a subsequent module.
However, due to the complex diversity of the driving environment, the traditional prediction model has limited perception accuracy and stability for the target vehicle, the precision of the prediction result is poor, and the requirement of safe driving cannot be met; meanwhile, the existing prediction model has low training efficiency and large calculation amount, and cannot realize real-time prediction of the motion state of the vehicle, so that the driving efficiency of the vehicle is influenced.
Disclosure of Invention
The embodiment of the application provides a vehicle state prediction method and device, electronic equipment and a storage medium, so as to achieve the technical effects of accurately and efficiently predicting a vehicle running state and improving the safety and stability of an AEB system.
The embodiment of the application adopts the following technical scheme:
according to a first aspect of the present application, there is provided a vehicle state prediction method including:
establishing a control quantity prediction model of a target vehicle according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment;
establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and
and predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle.
Optionally, the variable of the running state of the target vehicle at the next time includes at least one of: the method for predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle comprises the following steps of:
and predicting the real-time longitudinal distance, the real-time transverse distance, the real-time longitudinal speed, the real-time transverse speed and the real-time offset angle of the target vehicle at the next moment according to the state prediction model of the target vehicle to obtain a real-time longitudinal distance predicted value, a real-time transverse distance predicted value, a real-time longitudinal speed predicted value, a real-time transverse speed predicted value and a real-time offset angle predicted value of the target vehicle at the next moment.
Optionally, the step of establishing a control quantity prediction model of the target vehicle according to the control quantity of the target vehicle at the previous time, where the control quantity prediction model is used to predict the control quantity of the target vehicle at the current time includes:
and establishing an acceleration prediction model of the target vehicle according to the acceleration of the target vehicle at the last moment in the control quantity and the brake light signal information of the target vehicle in the measurable input quantity, wherein the acceleration prediction model is used for changing into a nonlinear model when the change of the brake light signal information of the target vehicle is detected, and carrying out linear interpolation according to the change of the acceleration at the last moment when the change of the brake light signal information of the target vehicle is not detected so as to predict the change of the acceleration at the current moment.
Optionally, the measurable input quantities include a real-time speed of the host vehicle, an acceleration of the host vehicle, and a bias angle of the host vehicle, and the output quantities include a collision time, and the state prediction model of the target vehicle is established according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the host vehicle at the current moment, and the output quantity of the target vehicle at the current moment, and includes:
and establishing a state prediction model of the target vehicle according to the acceleration of the target vehicle at the current moment, the real-time speed of the vehicle, the acceleration of the vehicle, the offset angle of the vehicle and the collision time of the target vehicle at the current moment.
Optionally, the control amount prediction model of the target vehicle further includes a first calculation factor, a second calculation factor, a third calculation factor, a fourth calculation factor, and a fifth calculation factor required for prediction by the state prediction model,
the first calculation factor is related to a vehicle offset angle and brake lamp signal information, the second calculation factor is related to the maximum braking deceleration of the vehicle, the third calculation factor is related to the deceleration output by an ABS system when the vehicle steers, the fourth calculation factor is a constant, and the fifth calculation factor is related to the vehicle offset angle.
Optionally, the state prediction model of the target vehicle further includes a sixth calculation factor, a seventh calculation factor, an eighth calculation factor, a ninth calculation factor, and a tenth calculation factor required for prediction by the control amount prediction model,
the sixth calculation factor is related to vehicle acceleration, vehicle real-time speed and vehicle offset angle, the seventh calculation factor is based on a first matrix relation established by the vehicle offset angle, the eighth calculation factor is based on a second matrix relation established by the vehicle offset angle, the ninth calculation factor is related to the vehicle offset angle and the vehicle acceleration, and the tenth calculation factor is related to the vehicle offset angle and the vehicle real-time speed.
Optionally, the method further comprises: the method comprises the steps of obtaining real-time state information of a target vehicle and a vehicle, forming a preset system based on the target vehicle and the vehicle, and establishing a steady-state prediction model for the preset system.
According to a second aspect of the present application, there is provided a vehicle state prediction apparatus comprising:
the control quantity prediction module is used for predicting the control quantity of the target vehicle at the current moment;
the second model establishing module is used for establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and the prediction module is used for predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle.
In accordance with a third aspect of the present application, there is provided an electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a vehicle state prediction method as in any one of the above.
According to a fourth aspect of the present application, there is provided a computer readable storage medium storing one or more programs which, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform the vehicle state prediction method according to any one of the above.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
establishing a control quantity prediction model of a target vehicle according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment; establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle. The accuracy of the vehicle state prediction result is ensured by constructing a double-level model of control quantity prediction and state prediction, the running state of the target vehicle at the next moment can be efficiently and stably predicted, and meanwhile, an accurate and effective basis can be provided for the braking control of the AEB system of the self vehicle.
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 application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart diagram of a vehicle condition prediction method in one embodiment of the present application;
FIG. 2 is a schematic configuration diagram of a vehicle state prediction apparatus according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer-readable storage medium in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
As described above, due to the complexity and diversity of driving scenes, the sensing accuracy and stability of a target vehicle in the prior art are limited, a traditional prediction model cannot meet the safety requirement, and a prediction result has an error, so that the early warning and Braking timings of an AEB (automatic Emergency Braking) system deviate, and the safety and comfort of the system are poor.
Based on the above, the embodiment of the application provides a vehicle state prediction method, so as to achieve the technical effects of accurately and efficiently predicting the vehicle running state and simultaneously improving the safety and stability of the AEB system.
The technical idea of the application is that a control quantity prediction model of a target vehicle is established according to the control quantity of the target vehicle at the previous moment, and a state prediction model of the target vehicle is established according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; the running state of the target vehicle at the next moment is predicted by constructing a double-level model of control quantity prediction and state prediction, so that accurate and effective basis can be provided for the braking control of the AEB system of the self vehicle while the accuracy and the precision of the vehicle state prediction result are ensured.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method includes steps S110 to S130 as follows:
step S110, establishing a control quantity prediction model of the target vehicle according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment.
In one embodiment of the present application, since the current control amount of the target vehicle is not available, in this embodiment, the control amount of the target vehicle at the previous time is obtained by the sensing module of the host vehicle, and a control amount prediction model for the target vehicle is established according to the control amount of the target vehicle at the previous time.
In the control quantity prediction model, t k-1 Indicates the last time, t k Indicates the current time, t k+1 Indicating the next time instant.
Preset target vehicle at t k-1 The control amount at the time is u (k-1), and the target vehicle is at t k The control amount at time is u (k), and the target vehicle is at t k+1 The control quantity at the moment is u (k + 1), and m (t) represents the signal of the brake lamp of the target vehicleThe method comprises the following steps of (1) establishing a control quantity prediction model of a target vehicle, wherein the information is related to a relational expression, n (t) represents the relational expression related to the steering lamp signal information of the target vehicle, p (k) represents the relational expression related to the lane changing probability of the target vehicle, and the control quantity prediction model of the target vehicle is established as follows:
wherein u = [ a ] o ]，n＝[ρ t ]，m＝[ρ b ]，p＝[δ l ]；
The meaning of each parameter in the above model is as follows:
a o representing the real-time acceleration, p, of the target vehicle b Representing target vehicle brake light signal information, p t Representing target vehicle turn signal information, δ l Representing the lane change probability of the target vehicle;
A d denotes a first calculation factor, B du Represents a second calculation factor, B dv Representing a third calculation factor, C d Denotes a fourth calculation factor, D dv A fifth calculation factor is represented, wherein,
representing the real-time offset angle of the own vehicle,
representing the real-time offset angle, tau, of the target vehicle d Stands for EPS (Electric Power Steering)System) can provide a maximum braking deceleration, τ t Represents the deceleration output by the ABS system (antilock brake system) during steering.
The control amount u (k) of the target vehicle at the present time can be predicted by using the control amount prediction model, and the operation state of the target vehicle at the next time can be predicted by substituting the control amount u (k) into the state prediction model of the target vehicle.
And step S120, establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment.
In the state prediction model, t k-1 Indicates the last time, t k Indicates the current time, t k+1 Indicating the next time instant. Preset target vehicle at t k The variable of the vehicle running state at the time is x (k), and the state prediction model of the target vehicle is as follows:
in the formula, relational expressions relating to x (k), u (k), v (k), and y (k) are as follows:
the meaning of each parameter in the above model is as follows:
x (k) + 1) is t k+1 Predicted values of variables of the running state of the target vehicle at the moment, u (k), v (k), and y (k) are respectively the predicted values of the target vehicle system at t k A control amount of the target vehicle, a measurable input amount of the host vehicle, and an output amount of the target vehicle in the model predictive control processor at the time;
d p representing the real-time lateral distance, d, of the target vehicle H Representing real-time longitudinal distance, V, of a target vehicle p Representing the real-time lateral velocity, V, of the target vehicle H Representing the real-time longitudinal speed of the target vehicle,
A real-time offset angle representative of the target vehicle; a is o Representing the real-time acceleration, v, of the target vehicle m Representing the real-time speed of the own vehicle, a m Representing the real-time acceleration of the own vehicle,
representing the real-time offset angle of the own vehicle,
representing the difference value of the real-time offset angles of the self vehicle and the target vehicle (namely the included angle formed by the heading directions of the self vehicle and the target vehicle), and TTC represents the collision time of the target vehicle;
a represents a sixth calculation factor, B u Representing a seventh calculation factor, B v Denotes an eighth calculation factor, C denotes a ninth calculation factor, D v Represents the tenth calculation factor, y (k-1) represents the output quantity of the target vehicle at the last time, and y (k) represents the output quantity of the target vehicle at the present time.
And step S130, predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle.
In the embodiment of the application, a control quantity prediction model of a target vehicle is established according to the control quantity u (k-1) of the target vehicle at the previous moment, and the control quantity u (k) of the target vehicle at the current moment is calculated; according to the current time of the target vehicleEstablishing a state prediction model of the target vehicle by using the control quantity u (k) of the moment, the measurable input quantity v (k) of the vehicle at the current moment and the output quantity y (k) of the target vehicle at the current moment; by constructing the two-level model of the control quantity prediction and the state prediction, the prediction of the running state of the target vehicle at the next moment can be realized, namely, the target vehicle is predicted at t k+1 Prediction of variable x (k + 1) of the running state at the moment.
The double-layer prediction model is adopted, and strategies such as feedback correction and rolling optimization are combined, so that the running state of the target vehicle at the next moment can be efficiently and stably predicted, and the accuracy of a vehicle state prediction result is ensured; meanwhile, an accurate and effective basis is provided for the braking control of the AEB system of the self-vehicle, the self-vehicle can make an emergency braking decision more accurately and efficiently on the premise of ensuring safety, and the driving comfort is effectively improved.
In one embodiment of the present application, the variables of the operating state of the target vehicle at the next time include at least one of: the method for predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle comprises the following steps:
and predicting the real-time longitudinal distance, the real-time transverse distance, the real-time longitudinal speed, the real-time transverse speed and the real-time offset angle of the target vehicle at the next moment according to the state prediction model of the target vehicle to obtain a real-time longitudinal distance predicted value, a real-time transverse distance predicted value, a real-time longitudinal speed predicted value, a real-time transverse speed predicted value and a real-time offset angle predicted value of the target vehicle at the next moment.
Specifically, in the foregoing control amount prediction model and state prediction model, the variable of the vehicle behavior of the target vehicle at the time tk is x (k), and the matrix relational expression referred to in x (k) includes:
real-time lateral distance d of target vehicle at current moment p Real-time longitudinal distance d of target vehicle H Real-time lateral velocity V of target vehicle p Real-time longitudinal speed V of target vehicle H Real-time offset angle of target vehicle
Therefore, in the embodiment, the predicted value x (k + 1) of the running state variable of the target vehicle at the next moment can be predicted according to the running state variable of the target vehicle at the current moment in the state prediction model; meanwhile, the predicted value comprises a real-time transverse distance, a real-time longitudinal distance, a real-time transverse speed, a real-time longitudinal speed and a real-time offset angle.
Further, in an embodiment of the present application, the control amount includes a real-time acceleration of the target vehicle, and the establishing a control amount prediction model of the target vehicle according to the control amount of the target vehicle at a previous time, where the control amount prediction model is used for predicting the control amount of the target vehicle at a current time includes: and establishing an acceleration prediction model of the target vehicle according to the acceleration of the target vehicle at the last moment in the control quantity and the brake light signal information of the target vehicle in the measurable input quantity, wherein the acceleration prediction model is used for changing into a non-linear model when the change of the brake light signal information of the target vehicle is detected, and performing linear interpolation according to the change of the acceleration at the last moment when the change of the brake light signal information of the target vehicle is not detected so as to predict the change of the acceleration at the current moment.
Specifically, in the control quantity prediction model, since the current control quantity of the target vehicle is not available, the control quantity of the target vehicle at the previous moment is obtained through the sensing module of the vehicle, and the control quantity u (k) of the target vehicle at the current moment can be predicted by using the control quantity prediction model, namely the real-time acceleration a representing the target vehicle in the relational expression related to the predicted control quantity u (k) o And substituting the predicted control quantity u (k) at the current moment into the state prediction model of the target vehicle, so as to predict the running state of the target vehicle at the next moment.
It will be appreciated that the acceleration of the target vehicle is non-linear only at vehicle braking and non-braking moments; in other scenarios, the acceleration of the target vehicle may be considered a linearly varying model. Therefore, the brake light signal information of the target vehicle which can be observed by the vehicle is introduced in the embodiment.
In the present embodiment, as described above, in the control amount prediction model of the target vehicle, u = [ a = o ]，n＝[ρ t ]，m＝[ρ b ]，p＝[δ l ]；a o Representing the real-time acceleration, p, of the target vehicle b Representing the target vehicle brake light status, p t Representing the target vehicle turn signal state, δ l Representing the lane change probability of the target vehicle. Tau is b Representing the maximum braking deceleration, τ, that eps can provide t Representing the deceleration output by the abs system during steering. The measurable input can be designed in such a way that, for example, the brake light is set to 1 when it is on and set to 0 when it is off. Thus, an acceleration prediction model of the target vehicle is established, and when the brake light signal information changes, the acceleration model is changed into nonlinearity in a short time; when the change of the brake light signal information of the target vehicle is not detected, the speed a can be increased according to the last moment u (k-1) o Is linearly interpolated to predict the change in the controlled variable u (k).
In one embodiment of the present application, the measurable input includes real-time speed v of the host vehicle m Acceleration a of the vehicle m And offset angle of bicycle
The output quantity comprises a time to collision TTC, and the establishment of the state prediction model of the target vehicle according to the control quantity u (k) of the target vehicle at the current moment, the measurable input quantity v (k) of the host vehicle at the current moment and the output quantity y (k) of the target vehicle at the current moment comprises the following steps: establishing the target according to the acceleration of the target vehicle at the current moment, the real-time speed of the vehicle, the acceleration of the vehicle, the offset angle of the vehicle and the collision time of the target vehicle at the current momentA state prediction model of the vehicle.
As mentioned above, the state prediction model may be built using u (k), v (k), and y (k), which are the target vehicle system at t k The control quantity of the target vehicle, the measurable input quantity of the self vehicle and the output quantity of the target vehicle in the model prediction control processor at the moment, wherein u (k), v (k) and y (k) are respectively related to the acceleration of the target vehicle at the current moment, the real-time speed, the acceleration and the offset angle of the self vehicle and the collision time of the target vehicle, and the specific expression is as follows:
therefore, in the present embodiment, t can be determined according to k The vehicle running state variable x (k) at the moment predicts the target vehicle at t through the state prediction model k+1 The operating state variable x (k + 1) at that time.
In one embodiment of the present application, the control quantity prediction model of the target vehicle further includes a first calculation factor, a second calculation factor, a third calculation factor, a fourth calculation factor, and a fifth calculation factor required for prediction by the state prediction model, and specific expressions are respectively:
it can be seen that the first calculation factor a d Offset angle from the target vehicle and the own vehicle,The brake light signal information of the target vehicle, the second calculation factor B du The third calculation factor B is related to the maximum braking deceleration which can be provided by the EPS system of the self-vehicle dv The fourth calculation factor C is related to the deceleration output by the ABS system when the vehicle turns d Is a constant value, the fifth calculation factor D dv Associated with the offset angle of the target vehicle.
In one embodiment of the present application, the state prediction model of the target vehicle further includes a sixth calculation factor, a seventh calculation factor, an eighth calculation factor, a ninth calculation factor, and a tenth calculation factor required for prediction of the control quantity prediction model, and specific expressions are respectively:
it is understood that the sixth calculation factor A is related to the acceleration, real-time speed, and offset angle of the vehicle and the target vehicle, and the seventh calculation factor B u The eighth calculation factor B is a first matrix relation established based on the vehicle offset angle v A second matrix relation established based on the vehicle offset angle, the ninth calculation factor C being related to the vehicle offset angle, the vehicle acceleration and the speed of the target vehicle, and the tenth calculation factor D v Related to the vehicle offset angle, the speed of the target vehicle.
In some embodiments of the present application, the vehicle state prediction method further comprises: the method comprises the steps of obtaining real-time state information of a target vehicle and the vehicle, forming a preset system based on the target vehicle and the vehicle, establishing a steady-state prediction model for the preset system, and using the preset system and the steady-state prediction model as a construction basis of the double-level prediction model.
In an embodiment of the present application, there is also provided a vehicle state prediction apparatus 200, as shown in fig. 2, including:
the first model establishing module 210 is configured to establish a control quantity prediction model of a target vehicle according to a control quantity of the target vehicle at a previous time, where the control quantity prediction model is used to predict a control quantity of the target vehicle at a current time.
In one embodiment of the present application, since the current control amount of the target vehicle is not available, in this embodiment, the control amount of the target vehicle at the previous time is obtained by the sensing module of the host vehicle, and a control amount prediction model for the target vehicle is established according to the control amount of the target vehicle at the previous time.
In the control quantity prediction model, t k-1 Indicates the last time, t k Indicates the current time, t k+1 Indicating the next time instant. Preset target vehicle at t k-1 The control amount at the time is u (k-1), and the target vehicle is at t k The control amount at time is u (k), and the target vehicle is at t k+1 The control quantity at the moment is u (k + 1), m (k) represents a relational expression related to the brake light signal information of the target vehicle, n (k) represents a relational expression related to the steering light signal information of the target vehicle, p (k) represents a relational expression related to the lane change probability of the target vehicle, and the control quantity prediction model of the target vehicle is as follows:
wherein u = [ a ] o ]，n＝[ρ t ]，m＝[ρ b ]，p＝[δ l ]；
The meaning of each parameter in the above model is as follows:
a o representing the real-time acceleration, p, of the target vehicle b Representing brake light signal information of the target vehicle, p t Representing target vehicle turn signal information, δ l Representing the lane change probability of the target vehicle;
A d denotes a first calculation factor, B du Representing a second calculation factor, B dv Representing a third calculation factor, C d Denotes a fourth calculation factor, D dv A fifth calculation factor is represented, wherein,
representing the real-time offset angle of the own vehicle,
representing the real-time offset angle, τ, of the target vehicle b Represents the maximum braking deceleration, τ, that an EPS (Electric Power Steering) can provide t Represents the deceleration output by the ABS system (antilock brake system) during steering.
The control amount u (k) of the target vehicle at the present time can be predicted by using the control amount prediction model, and the operation state of the target vehicle at the next time can be predicted by substituting the control amount u (k) into the state prediction model of the target vehicle.
The second model establishing module 220 is configured to establish a state prediction model of the target vehicle according to a control quantity of the target vehicle at the current time, a measurable input quantity of the host vehicle at the current time, and an output quantity of the target vehicle at the current time.
In the state prediction model, t k-1 Indicates the last time, t k Indicates the current time, t k+1 Indicating the next time instant. Preset target vehicle at t k The variable of the vehicle running state at the time is x (k), and the state prediction model of the target vehicle is as follows:
in the formula, the relational expressions relating to x (k), u (k), v (k), and y (k) are as follows:
the meaning of each parameter in the above model is as follows:
x (k + 1) is t k+1 Predicted values of variables of the running state of the target vehicle at the moment, u (k), v (k), and y (k) are respectively the predicted values of the target vehicle system at t k A control amount of the target vehicle, a measurable input amount of the own vehicle, and an output amount of the target vehicle in the model predictive control processor at the time;
d p representing the real-time lateral distance, d, of the target vehicle H Representing real-time longitudinal distance, V, of a target vehicle p Representing the real-time lateral velocity, V, of the target vehicle H Representing the real-time longitudinal speed of the target vehicle,
A real-time offset angle representative of the target vehicle; a is o Representing the real-time acceleration, v, of the target vehicle m Representing the real-time speed of the own vehicle, a m Representing the real-time acceleration of the own vehicle,
representing the real-time offset angle of the own vehicle,
the difference value of the real-time offset angles of the self vehicle and the target vehicle (namely the included angle formed by the heading directions of the self vehicle and the target vehicle) is represented, and the TTC represents the collision time of the target vehicle;
a represents a sixth calculation factor, B u Representing a seventh calculation factor, B v Denotes an eighth calculation factor, C denotes a ninth calculation factor, D v Represents the tenth calculation factor, y (k-1) represents the output quantity of the target vehicle at the last time, and y (k) represents the output quantity of the target vehicle at the present time.
And the prediction module 230 is configured to predict the operation state of the target vehicle at the next time according to the state prediction model of the target vehicle.
In the embodiment of the application, a control quantity prediction model of a target vehicle is established according to the control quantity u (k-1) of the target vehicle at the previous moment, and the control quantity u (k) of the target vehicle at the current moment is calculated; establishing a state prediction model of the target vehicle according to the control quantity u (k) of the target vehicle at the current moment, the measurable input quantity v (k) of the host vehicle at the current moment and the output quantity y (k) of the target vehicle at the current moment; by constructing the two-level model of the control quantity prediction and the state prediction, the prediction of the running state of the target vehicle at the next moment can be realized, namely, the target vehicle at t k+1 Prediction of variable x (k + 1) of the running state at the moment.
The embodiment of the application adopts the double-layer prediction model and combines strategies such as feedback correction, rolling optimization and the like, so that the running state of the target vehicle at the next moment can be efficiently and stably predicted, and the accuracy of a vehicle state prediction result is ensured; meanwhile, an accurate and effective basis is provided for the braking control of the AEB system of the self-vehicle, the self-vehicle can make an emergency braking decision more accurately and efficiently on the premise of ensuring safety, and the driving comfort is effectively improved.
In one embodiment of the present application, in the prediction module 230,
the variables of the running state of the target vehicle at the next moment at least comprise one of the following: the method for predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle comprises the following steps:
and predicting the real-time longitudinal distance, the real-time transverse distance, the real-time longitudinal speed, the real-time transverse speed and the real-time offset angle of the target vehicle at the next moment according to the state prediction model of the target vehicle to obtain a real-time longitudinal distance predicted value, a real-time transverse distance predicted value, a real-time longitudinal speed predicted value, a real-time transverse speed predicted value and a real-time offset angle predicted value of the target vehicle at the next moment.
In one embodiment of the present application, in the first model building module 210,
the control quantity comprises the real-time acceleration of the target vehicle, and the control quantity prediction model of the target vehicle is established according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment and comprises the following steps:
and establishing an acceleration prediction model of the target vehicle according to the acceleration of the target vehicle at the last moment in the control quantity and the brake light signal information of the target vehicle in the measurable input quantity, wherein the acceleration prediction model is used for changing into a nonlinear model when the change of the brake light signal information of the target vehicle is detected, and carrying out linear interpolation according to the change of the acceleration at the last moment when the change of the brake light signal information of the target vehicle is not detected so as to predict the change of the acceleration at the current moment.
In one embodiment of the present application, in the second model building module 220,
the measurable input quantities comprise real-time speed of the own vehicle, acceleration of the own vehicle and bias angle of the own vehicle, the output quantities comprise collision time, and the state prediction model of the target vehicle is established according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the own vehicle at the current moment and the output quantity of the target vehicle at the current moment, and comprises the following steps:
and establishing a state prediction model of the target vehicle according to the acceleration of the target vehicle at the current moment, the real-time speed of the vehicle, the acceleration of the vehicle, the offset angle of the vehicle and the collision time of the target vehicle at the current moment.
In one embodiment of the present application, in the first model building module 210,
the control quantity prediction model of the target vehicle further comprises a first calculation factor, a second calculation factor, a third calculation factor, a fourth calculation factor and a fifth calculation factor which are required by the state prediction model in prediction, wherein the first calculation factor is related to a vehicle offset angle and brake light signal information, the second calculation factor is related to the maximum braking deceleration of the vehicle, the third calculation factor is related to the deceleration output by an ABS system in steering of the vehicle, the fourth calculation factor is a constant, and the fifth calculation factor is related to the vehicle offset angle.
In one embodiment of the present application, in the second model building module 220,
the state prediction model of the target vehicle further includes a sixth calculation factor, a seventh calculation factor, an eighth calculation factor, a ninth calculation factor, and a tenth calculation factor required for prediction by the control amount prediction model,
the sixth calculation factor is related to vehicle acceleration, vehicle real-time speed and vehicle offset angle, the seventh calculation factor is based on a first matrix relation established by the vehicle offset angle, the eighth calculation factor is based on a second matrix relation established by the vehicle offset angle, the ninth calculation factor is related to the vehicle offset angle and the vehicle acceleration, and the tenth calculation factor is related to the vehicle offset angle and the vehicle real-time speed.
In one embodiment of the present application, in the first model building module 210 and the second model building module 220,
further comprising: the method comprises the steps of obtaining real-time state information of a target vehicle and a vehicle, forming a preset system based on the target vehicle and the vehicle, and establishing a steady-state prediction model for the preset system.
It should be noted that, the vehicle state prediction device can implement each step of the vehicle state prediction method provided in the foregoing embodiment, and the explanations regarding the vehicle state prediction method are applicable to the vehicle state prediction device, and are not repeated herein.
To sum up, the technical scheme of this application has reached following technological effect at least: establishing a control quantity prediction model of a target vehicle according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment; establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle. The accuracy of a vehicle state prediction result is ensured by constructing a double-level model of control quantity prediction and state prediction, the running state of a target vehicle at the next moment can be predicted efficiently and stably, meanwhile, an accurate and effective basis can be provided for the braking control of the self-vehicle AEB system, and the safety, the efficiency and the comfort of a driving system are further improved.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best mode of use of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore, may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the vehicle state prediction apparatus according to the embodiments of the present application. The present application may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.
For example, fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 300 comprises a processor 310 and a memory 320 arranged to store computer executable instructions (computer readable program code). The memory 320 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 320 has a storage space 330 storing computer readable program code 331 for performing any of the method steps of the above described method. For example, the memory space 330 for storing the computer readable program code may include respective computer readable program codes 331 respectively for implementing various steps in the above method. The computer readable program code 331 may be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as that shown in fig. 4.
FIG. 4 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application. The computer readable storage medium 400 has stored thereon a computer readable program code 331 for performing the steps of the method according to the application, readable by a processor 310 of an electronic device 300, which computer readable program code 331, when executed by the electronic device 300, causes the electronic device 300 to perform the steps of the method described above, in particular the computer readable program code 331 stored on the computer readable storage medium may perform the method shown in any of the embodiments described above. The computer readable program code 331 may be compressed in a suitable form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A vehicle state prediction method, wherein the method comprises:
establishing a control quantity prediction model of a target vehicle according to the control quantity of the target vehicle at the previous moment, wherein the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment;
establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and
and predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle.
2. The method of claim 1, wherein the variables of the operating state of the target vehicle at the next time comprise at least one of: the method for predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle comprises the following steps:
and predicting the real-time longitudinal distance, the real-time transverse distance, the real-time longitudinal speed, the real-time transverse speed and the real-time offset angle of the target vehicle at the next moment according to the state prediction model of the target vehicle to obtain a real-time longitudinal distance predicted value, a real-time transverse distance predicted value, a real-time longitudinal speed predicted value, a real-time transverse speed predicted value and a real-time offset angle predicted value of the target vehicle at the next moment.
3. The method according to claim 2, wherein the control amount includes a real-time acceleration of the target vehicle, and the establishing of the control amount prediction model of the target vehicle based on the control amount of the target vehicle at a previous time, wherein the control amount prediction model is used for predicting the control amount of the target vehicle at a current time, includes:
and establishing an acceleration prediction model of the target vehicle according to the acceleration of the target vehicle at the last moment in the control quantity and the brake light signal information of the target vehicle in the measurable input quantity, wherein the acceleration prediction model is used for changing into a nonlinear model when the change of the brake light signal information of the target vehicle is detected, and carrying out linear interpolation according to the change of the acceleration at the last moment when the change of the brake light signal information of the target vehicle is not detected so as to predict the change of the acceleration at the current moment.
4. The method as claimed in claim 3, wherein the measurable input quantities include a real-time speed of the own vehicle, an acceleration of the own vehicle, and a bias angle of the own vehicle, and the output quantities include a collision time, and the building of the state prediction model of the target vehicle based on the control quantity of the target vehicle at the present time, the measurable input quantity of the own vehicle at the present time, and the output quantity of the target vehicle at the present time comprises:
and establishing a state prediction model of the target vehicle according to the acceleration of the target vehicle at the current moment, the real-time speed of the vehicle, the acceleration of the vehicle, the offset angle of the vehicle and the collision time of the target vehicle at the current moment.
5. The method according to claim 3, wherein the control amount prediction model of the target vehicle further includes a first calculation factor, a second calculation factor, a third calculation factor, a fourth calculation factor, a fifth calculation factor required for the state prediction model prediction,
the first calculation factor is related to a vehicle offset angle and brake lamp signal information, the second calculation factor is related to the maximum braking deceleration of the vehicle, the third calculation factor is related to the deceleration output by an ABS system when the vehicle steers, the fourth calculation factor is a constant, and the fifth calculation factor is related to the vehicle offset angle.
6. The method according to claim 4, wherein the state prediction model of the target vehicle further includes a sixth calculation factor, a seventh calculation factor, an eighth calculation factor, a ninth calculation factor, a tenth calculation factor required for prediction by the control amount prediction model,
the sixth calculation factor is related to vehicle acceleration, vehicle real-time speed and vehicle offset angle, the seventh calculation factor is based on a first matrix relation established by the vehicle offset angle, the eighth calculation factor is based on a second matrix relation established by the vehicle offset angle, the ninth calculation factor is related to the vehicle offset angle and the vehicle acceleration, and the tenth calculation factor is related to the vehicle offset angle and the vehicle real-time speed.
7. The method of claim 1, wherein the method further comprises: the method comprises the steps of obtaining real-time state information of a target vehicle and a vehicle, forming a preset system based on the target vehicle and the vehicle, and establishing a steady-state prediction model for the preset system.
8. A vehicle state prediction apparatus, wherein the apparatus comprises:
the control quantity prediction method comprises a first model building module, a second model building module and a control quantity prediction module, wherein the first model building module is used for building a control quantity prediction model of a target vehicle according to a control quantity of the target vehicle at a previous moment, and the control quantity prediction model is used for predicting the control quantity of the target vehicle at the current moment;
the second model establishing module is used for establishing a state prediction model of the target vehicle according to the control quantity of the target vehicle at the current moment, the measurable input quantity of the vehicle at the current moment and the output quantity of the target vehicle at the current moment; and
and the prediction module is used for predicting the running state of the target vehicle at the next moment according to the state prediction model of the target vehicle.
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 one 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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| CN117078030A (en) * | 2023-07-12 | 2023-11-17 | 贵州大学 | Fuel cell bus energy management method based on vehicle speed prediction |
| CN117078030B (en) * | 2023-07-12 | 2024-05-03 | 贵州大学 | Fuel cell bus energy management method based on vehicle speed prediction |
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