CN107360204B - Method and device for predicting generation time of Internet of vehicles cooperative early warning message - Google Patents
Method and device for predicting generation time of Internet of vehicles cooperative early warning message Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting generation time of a vehicle networking cooperative early warning message, wherein the method comprises the following steps: determining a fitting function of target parameters with respect to time according to vehicle history information, wherein the target parameters comprise a vehicle direction, a vehicle position and a vehicle speed; determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameters and the constraint condition corresponding to each parameter; and determining the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message. The invention solves the problem that a semi-static transmission scheme cannot be used due to the dynamic change of the generation period of the cooperative early warning message, and is beneficial to other vehicles to acquire the resource occupation condition so as to reduce the collision among the messages.
Description
Technical Field
The invention relates to the technical field of Internet of vehicles, in particular to a method and a device for predicting generation time of a collaborative early warning message of the Internet of vehicles.
Background
In the Internet of vehicles system, vehicles declare resources to be used in next transmission while transmitting messages each time, other vehicles are facilitated to acquire resource occupation conditions so as to reduce collision among messages, and the message transmission mode is semi-static transmission. The periodic messages defined by the third Generation Partnership Project (3 GPP) have a fixed Generation period and thus the semi-static transmission described above can be used.
The European Telecommunications Standardization Institute (ETSI) defines a generation mechanism of a Collaborative Awareness Message (CAM) as follows: the minimum time interval for generating CAM messages is 100ms and the maximum time interval is 1 s. Generating a CAM message when one of the following conditions is satisfied:
1. the time from the last CAM message generation is greater than or equal to the minimum time interval, and one of the following conditions is satisfied:
a) the absolute value of the difference between the current vehicle direction and the vehicle direction indicated in the last CAM message is greater than 4 °;
b) the difference between the current vehicle position and the vehicle position indicated in the last CAM message is greater than 4 m;
c) the absolute value of the difference between the current vehicle speed and the vehicle speed indicated in the last CAM message is greater than 0.5 m/s.
2. The time since the last CAM message generation is greater than or equal to the maximum time interval.
As can be seen from the above CAM message generation mechanism, the CAM message generation period defined by ETSI is related to the speed, direction and position of the vehicle, and dynamically changes according to the actual situation, so the generation time of the CAM message at each time is uncertain, and therefore, the CAM message cannot be transmitted in a semi-static transmission manner, resulting in increased collisions between messages transmitted by different vehicles.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for predicting generation time of a collaborative early warning message in an internet of vehicles, so as to implement semi-static transmission of the collaborative early warning message in the internet of vehicles.
In order to achieve the purpose, the invention adopts the following technical scheme:
on one hand, the embodiment of the invention provides a method for predicting generation time of a vehicle networking cooperative early warning message, which comprises the following steps:
determining a fitting function of target parameters with respect to time according to vehicle history information, wherein the target parameters comprise a vehicle direction, a vehicle position and a vehicle speed;
determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameters and the constraint condition corresponding to each parameter;
and predicting the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message.
On the other hand, the embodiment of the invention provides a device for predicting the generation time of a vehicle networking cooperative early warning message, which comprises the following steps:
the fitting function determining module is used for determining a fitting function of target parameters with respect to time according to vehicle historical information, wherein the target parameters comprise a vehicle direction, a vehicle position and a vehicle speed;
the minimum generation time determining module is used for determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameters and the constraint condition corresponding to each parameter;
and the generation time prediction module is used for predicting the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message.
The invention has the beneficial effects that: according to the method and the device for predicting the generation time of the vehicle networking cooperative early warning message, fitting functions of the vehicle direction, the vehicle position and the vehicle speed with respect to time are respectively obtained according to known vehicle history information, the minimum generation time of the next cooperative early warning message meeting constraint conditions is determined according to the fitting functions based on a generation mechanism of the cooperative early warning message in the prior art, and the generation time of the next cooperative early warning message is predicted by combining the constraint time in the existing generation mechanism. The method and the device can accurately predict the generation time of the next cooperative early warning message, further can determine the transmission time of the next cooperative early warning message, realize semi-static transmission of the cooperative early warning message, and are beneficial to other vehicles to acquire the resource occupation condition so as to reduce the collision among the messages.
Drawings
The above and other features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail exemplary embodiments thereof with reference to the attached drawings, in which:
fig. 1 is a schematic flowchart of a method for predicting generation time of a collaborative early warning message in the internet of vehicles according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for predicting generation time of a collaborative early warning message in the internet of vehicles according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of a collaborative early warning message triggered by a simulated trajectory of a vehicle movement and a change of each target parameter according to a third embodiment of the present invention;
FIG. 4 is a partially enlarged view of the vehicle movement trace of FIG. 3 at the peak thereof according to a third embodiment of the present invention;
FIG. 5 is a partially enlarged view of a trough of the vehicle movement track in FIG. 3 according to a third embodiment of the present invention;
fig. 6 is a time interval distribution diagram of two adjacent cooperative early warning messages according to a third embodiment of the present invention;
fig. 7 is a simulation diagram of the maximum values of the mean square error and the absolute value of the error between the predicted generation time and the actual generation time of the next cooperative early warning message provided by the third embodiment of the present invention;
fig. 8 is a block diagram of a device for predicting generation time of a collaborative early warning message in the internet of vehicles according to a fourth embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flowchart of a method for predicting generation time of a car networking collaborative early warning message according to an embodiment of the present invention. The method is suitable for the condition that the vehicles transmit the cooperative early warning messages in a semi-static transmission mode in the Internet of vehicles system, and can be executed by a prediction device of the generation time of the Internet of vehicles cooperative early warning messages. The means may be implemented by means of software and/or hardware. As shown in fig. 1, the method includes:
The target parameters include a vehicle direction, a vehicle position, and a vehicle speed.
Specifically, determining a fitting function of the target parameter with respect to time according to the vehicle history information includes:
A. and acquiring the generation time of the latest preset number of the cooperative early warning messages and the value of the target parameter corresponding to each generation time according to the historical cooperative early warning messages of the vehicle.
B. And determining discrete sample points of the fitting function according to the generation time of the latest preset number of the cooperative early warning messages and the numerical value of the target parameter corresponding to each generation time.
C. And performing curve fitting on the discrete sample points to obtain a fitting function of the target parameters with respect to time.
For example, the vehicle direction may be represented as an included angle a (t) between the vehicle head direction and a preset reference direction (e.g., due north), and the vehicle position is mapped onto the planar coordinate system according to the GPS information, so the vehicle position may be represented as coordinates (x (t), y (t)), the vehicle speed may be represented as v (t), and the next cooperative early warning message may be denoted as the ith cooperative early warning message.
Therefore, the generation time t of the latest n pieces of collaboration early warning messages is obtainedi-1，ti-2，……，ti-nAnd the value of the target parameter corresponding to each generation time, namely A (t)i-1)、A(ti-2)、……、A(ti-n)，(X(ti-1),Y(ti-1))、(X(ti-2),Y(ti-2))、……、(X(ti-n),Y(ti-n) And V (t)i-1)、V(ti-2)、……、V(ti-n). The values of the target parameters with respect to the generation time are used as discrete sample points of the target parameters, and curve fitting is performed on the discrete sample points of the target parameters to obtain a fitting function a (t) of the vehicle direction, a fitting function (x (t), y (t)) of the vehicle position, and a fitting function v (t) of the vehicle speed.
And 102, determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameter and the constraint condition corresponding to each parameter.
The constraint condition is a generation condition in an existing generation mechanism of the collaborative early warning message.
Illustratively, the target parameter isWhen the vehicle is in the direction, the fitting function of the vehicle direction with respect to time is A (t), and the constraint condition corresponding to the vehicle direction is | A (t)i)-A(ti-1) | ≧ a, where a (t) denotes an included angle between the vehicle head direction and a preset reference direction at each time, a is a preset angle, tiRepresents the constraint | A (t)i)-A(ti-1) The generation time of the next cooperative early warning message under | ≧ a, ti-1And indicating the generation time of the latest collaborative early warning message in the vehicle history information.
Correspondingly, according to the fitting function of the target parameter and the constraint condition corresponding to the target parameter, determining the minimum generation time of the next cooperative early warning message under the constraint condition, including:
according to | A (t)i)-A(ti-1) | ≧ a, calculate ti≥ti-1Minimum t of timeiThe minimum t is setiAs a constraint | A (t)i)-A(ti-1) Minimum generation time of next cooperative early warning message under | ≧ a
When the target parameter is the vehicle position, the fitting function of the vehicle position with respect to time is (X (t), Y (t)), and the constraint condition corresponding to the vehicle position is
Wherein (X (t), Y (t)) represent coordinates in which the vehicle position at each time is mapped on a planar coordinate system, and b is a predetermined length, t'iRepresenting constraints
Time of generation, t ', of next collaboration warning message'i-1And indicating the generation time of the latest collaborative early warning message in the vehicle history information.
Correspondingly, according to the fitting function of the target parameter and the constraint condition corresponding to the target parameter, determining the minimum generation time of the next cooperative early warning message under the constraint condition, including:
according to
Calculating t'i≥t'i-1Of (c)'iOf said minimum t'iAs a constraint
Minimum generation time of next cooperative early warning message
When the target parameter is the vehicle speed, the fitting function of the vehicle speed with respect to time is V (t), and the constraint condition corresponding to the vehicle speed is | V (t'i)-V(t”i-1) | not less than c, where V (t) represents the vehicle speed at each moment, c is a preset speed, t "iRepresenting constraint | V (t "i)-V(t”i-1) Generation time, t', of the next collaborative early warning message under | ≧ c "i-1And indicating the generation time of the latest collaborative early warning message in the vehicle history information.
Correspondingly, according to the fitting function of the target parameter and the constraint condition corresponding to the target parameter, determining the minimum generation time of the next cooperative early warning message under the constraint condition, including:
according to | V (t "i)-V(t”i-1) | not less than c, calculate t "i≥t”i-1Minimum t of time "iWill minimize t "iAs a constraint | V (t "i)-V(t”i-1) Minimum generation time of next cooperative early warning message under | ≧ c
In the above constraints, the values of a, b, and c may be values satisfying the generation conditions in the existing generation mechanism, for example, a is 4a, b is 4m, and c is 0.5m/s, and the values of a, b, and c may also be modified according to the actual situation or different protocol standards.
And 103, predicting the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message.
Wherein the constraint time ranges from [100ms, 1000ms ].
Based on step 102, predicting the generation time of the next collaborative early warning message according to the minimum generation time of the next collaborative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the collaborative early warning message, including:
will be provided with
And determining the generation time of the next cooperative early warning message.
For example, in the prediction method of this embodiment, after the previous cooperative early warning message is generated, the constraint condition of each target parameter is determined in real time, and the determined time interval is consistent with the minimum time granularity scheduled by the communication system, that is, the precision of the time t is consistent with the minimum time granularity scheduled by the communication system, if the minimum time granularity scheduled by the LTE system is 1ms, the precision of the time t is 1ms, that is, the constraint condition is determined once every 1 ms. In this embodiment, the generation time of the latest historical collaboration early warning message is recorded as 0, and if the latest historical collaboration early warning message is generated, the generation time is recorded as 0
Or
If the time is less than 100ms, determining 100ms as the generation time of the next cooperative early warning message; in addition, depending on the accuracy of the time t,
and
may be greater than 1000ms, if
And
and if the time intervals are larger than 1000ms, determining the 1000ms as the generation time of the next cooperative early warning message.
According to the method for predicting the generation time of the vehicle networking cooperative early warning message, fitting functions of the vehicle direction, the vehicle position and the vehicle speed with respect to time are respectively obtained according to known vehicle history information, the minimum generation time of the next cooperative early warning message meeting constraint conditions is determined according to the fitting functions based on a cooperative early warning message generation mechanism in the prior art, and the generation time of the next cooperative early warning message is predicted by combining the constraint time in the prior generation mechanism. The method and the device can accurately predict the generation time of the next cooperative early warning message, further can determine the transmission time of the next cooperative early warning message, realize semi-static transmission of the cooperative early warning message, and are beneficial to other vehicles to acquire the resource occupation condition so as to reduce the collision among the messages.
Further, curve fitting the discrete sample points includes:
A. and selecting a preset curve type according to the environment parameters.
The environmental parameters may include scene types, road congestion degrees, vehicle speeds and/or base station configuration states, and the preset curve types may include polynomial curves, exponential curves and logarithmic curves.
The scene type can be a town or a highway; the road types can be straight lines, curves, intersections and the like and can be obtained by GPS information; the road congestion degree can be obtained by the received cooperative early warning message of other vehicles; the base station configuration state represents a preset curve type configured for the vehicle by the base station to which the vehicle belongs. The embodiment can select any environmental parameter to determine the curve type, and can also determine the curve type by synthesizing multiple environmental parameters.
Further, when the preset curve type is a polynomial curve, after selecting the preset curve type according to the environmental parameter, the method further includes:
and determining a polynomial parameter corresponding to the current road congestion degree according to a pre-established corresponding relation between the road congestion degree and the polynomial parameter, wherein the polynomial parameter comprises a polynomial sample number and a fitting order.
Preferably, the pre-establishing the corresponding relationship between the road congestion degree and the polynomial parameter includes:
a. and determining the value range of the polynomial parameters according to the road congestion degree.
For example, when the road is clear, the change rate of the direction, the position and the speed of the vehicle is close to linear, and a small number of multi-term samples and a low fitting order can be used; when the road is congested, the direction, the position and the speed of the vehicle are changed more complexly, and more polynomial sample numbers and higher fitting orders can be used.
b. And selecting different polynomial parameters from the value ranges of the polynomial parameters, and predicting the generation time of the next cooperative early warning message according to the historical cooperative early warning message of the vehicle and the selected different polynomial parameters.
c. And respectively comparing each prediction result with an actual result, and establishing the most accurate corresponding relation between the polynomial parameters of the prediction results and the road congestion degree according to the comparison results.
B. And performing curve fitting on the discrete sample points by using the selected preset curve type.
When the preset curve type is a polynomial curve, performing curve fitting on the discrete sample points by using the selected preset curve type, wherein the curve fitting comprises the following steps:
and performing curve fitting on the discrete sample points by using the selected preset curve type and the determined polynomial parameters.
Example two
Fig. 2 is a schematic flowchart of a method for predicting generation time of a car networking collaborative early warning message according to a second embodiment of the present invention. In this embodiment, optimization is performed based on the first embodiment, and after the generation time of the next collaborative early warning message is predicted, the following steps are added: selecting a time offset value according to the prediction accuracy; and determining the sum of the predicted generation time of the next cooperative early warning message and the time deviation value as the transmission time of the next cooperative early warning message.
Correspondingly, the method of the embodiment includes:
Step 202, determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameter and the constraint condition corresponding to each parameter.
And 203, predicting the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message.
And step 204, selecting a time offset value according to the prediction precision.
Specifically, in actual operation, a larger time offset value may be selected at the initial stage of prediction, or semi-static transmission is not performed at the initial stage of prediction, the prediction error in a period of time thereafter is counted, and the time offset value is adjusted accordingly according to the statistical result.
And step 205, determining the sum of the predicted generation time of the next cooperative early warning message and the time offset value as the transmission time of the next cooperative early warning message.
Considering that there may be a certain error between the actual generation time and the predicted generation time of the cooperative early warning message, when the semi-static transmission scheme is used, the declared transmission time of the next cooperative early warning message should be later than the predicted generation time, so that the sum of the predicted generation time of the next cooperative early warning message and the time offset value adjusted according to the predicted error can be determined as the transmission time of the next cooperative early warning message.
According to the method for predicting the generation time of the car networking cooperative early warning message, provided by the embodiment of the invention, the transmission time of the next cooperative early warning message is adjusted according to the statistical result of the prediction error, so that the transmission time of the cooperative early warning message can better meet the condition of a semi-static transmission scheme.
EXAMPLE III
In order to facilitate understanding of the method for predicting the generation time of the vehicle networking cooperative early warning message, the present embodiment simulates a vehicle motion trajectory by using a sine curve, and the equation of the sine curve is that y is 40sin (x/20). As shown in fig. 3, a distribution of the collaborative early warning message triggered by the change of each target parameter is represented, and fig. 3 shows a position and a trigger type when the collaborative early warning message is generated in the process of the vehicle moving for 50 s.
Illustratively, the vehicle is at position (0,0) at time 0, has a speed of 0, and then follows the trajectory in FIG. 3, with an acceleration of 0.1m/s in the positive x-axis direction2. From the above conditions, it can be found that the functions of the vehicle position, speed, direction (clockwise angle with respect to the positive y-axis) with respect to time t are:
(X(t),Y(t))＝(0.1t2/2,40sin(0.1t2/40)) (1)
A(t)＝90-arctan(2cos(0.1t2/40)) (3)
if the first cooperative early warning message is generated at the time 0, the actual generation information (time granularity is 1ms) of all the cooperative early warning messages can be obtained according to the generation mechanism of the cooperative early warning message and the formulas (1) - (3). As can be seen from fig. 3 in conjunction with fig. 4 and 5, at the peak and trough of the vehicle motion trajectory, i.e., at the vehicle corner, the vehicle direction changes relatively quickly, and the generation cycle of the cooperative early warning message is short.
Fig. 6 shows the time interval distribution of two adjacent cooperative early warning messages. For example, in fig. 6, the abscissa 10 represents the 10 th and 11 th collaborative early warning messages, the corresponding ordinate is 1000ms, and the time interval between the 10 th and 11 th collaborative early warning messages is 1000 ms.
As can be seen from fig. 3 and 6, as the position, speed and direction of the vehicle change, the cooperative warning message may be triggered by different conditions, and the time interval between adjacent cooperative warning messages also changes. In an actual scene, the changes are irregular, so that the generation time of the future collaborative early warning message needs to be predicted according to historical information.
For example, the embodiment uses the same environmental parameters as those in fig. 3, and if the related information of the previous 10 collaborative early warning messages is known, the prediction method of the present invention is used to predict the future generation time of the collaborative early warning message. Specifically, polynomial fitting is used in prediction, the number of the historical polynomial samples is n, the fitting order is k, and fig. 7 shows the Mean Square Error (MSE) of the predicted generation time and the actual generation time when n and k take different values, and the maximum value (ME) of the absolute values of the errors between the predicted generation time and the actual generation time when each n value MSE is the minimum.
As can be seen from fig. 7, the selection of the number n of polynomial samples and the fitting order k has a large influence on the prediction error when the polynomial fitting method is used; when n and k match, that is, the value of k and the value of n corresponding to the minimum value of n MSE are selected, the prediction accuracy increases as n increases, and for example, when n is 6 and k is 5, the maximum value of the absolute value of the error between the predicted generation time and the actual generation time is 1 ms. Therefore, the method for predicting the generation time of the vehicle networking cooperative early warning message can effectively predict the generation time of the cooperative early warning message.
Example four
Fig. 8 is a block diagram illustrating a device for predicting generation time of a collaborative early warning message in an internet of vehicles according to a fourth embodiment of the present invention, where the device may be installed in a vehicle. As shown in fig. 8, the apparatus includes a fitting function determination module 10, a minimum generation time determination module 20, and a generation time prediction module 30.
The fitting function determining module 10 is configured to determine a fitting function of target parameters with respect to time according to vehicle history information, where the target parameters include a vehicle direction, a vehicle position, and a vehicle speed;
the minimum generation time determining module 20 is configured to determine, according to the fitting function of each parameter in the target parameter and the constraint condition corresponding to each parameter, the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter;
and the generation time prediction module 30 is configured to predict the generation time of the next collaborative early warning message according to the minimum generation time of the next collaborative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the collaborative early warning message.
Further, the fitting function determining module 10 may include:
the historical data acquisition unit is used for acquiring the generation time of the latest preset number of cooperative early warning messages and the value of the target parameter corresponding to each generation time according to the historical cooperative early warning messages of the vehicle;
the discrete sample point determining unit is used for determining discrete sample points of a fitting function according to the generation time of the latest preset number of the cooperative early warning messages and the numerical value of the target parameter corresponding to each generation time;
and the fitting function obtaining unit is used for performing curve fitting on the discrete sample points to obtain a fitting function of the target parameters with respect to time.
Preferably, in the above aspect, when the target parameter is a vehicle direction, the fitting function of the vehicle direction with respect to time is a (t), and the constraint condition corresponding to the vehicle direction is | a (t)i)-A(ti-1) | ≧ a, where a (t) denotes an included angle between the vehicle head direction and a preset reference direction at each time, a is a preset angle, tiRepresents the constraint | A (t)i)-A(ti-1) The generation time of the next cooperative early warning message under | ≧ a, ti-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, the minimum generation time determination module 20 is specifically configured to:
according to | A (t)i)-A(ti-1) | ≧ a, calculate ti≥ti-1Minimum t of timeiWill minimize tiAs a constraint | A (t)i)-A(ti-1) Minimum generation time of next cooperative early warning message under | ≧ a
When the target parameter is the vehicle position, the fitting function of the vehicle position with respect to time is (X (t), Y (t)), and the constraint condition corresponding to the vehicle position is
Wherein (X (t), Y (t)) represent coordinates in which the vehicle position at each time is mapped on a planar coordinate system, and b is a predetermined length, t'iRepresenting constraints
Time of generation, t ', of next collaboration warning message'i-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, the minimum generation time determination module 20 is specifically configured to:
according to
Calculating t'i≥t'i-1Of (c)'iWill be minimum t'iAs a constraint
Minimum generation time of next cooperative early warning message
When the target parameter is the vehicle speed, the fitting function of the vehicle speed with respect to time is V (t), and the constraint condition corresponding to the vehicle speed is | V (t'i)-V(t”i-1) | not less than c, where V (t) represents the vehicle speed at each moment, c is a preset speed, t "iRepresenting constraint | V (t "i)-V(t”i-1) Generation time, t', of the next collaborative early warning message under | ≧ c "i-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, the minimum generation time determination module 20 is specifically configured to:
according to | V (t "i)-V(t”i-1) | not less than c, calculate t "i≥t”i-1Minimum t of time "iWill minimize t "iAs a constraint | V (t "i)-V(t”i-1) Minimum generation time of next cooperative early warning message under | ≧ c
Further, the constraint time is in a range of [100ms, 1000ms ];
accordingly, the generation time prediction module 30 is specifically configured to:
will be provided with
And determining the generation time of the next cooperative early warning message.
Further, in the foregoing solution, the fitting function obtaining unit may include:
the system comprises a preset curve type selection subunit, a base station configuration unit and a control unit, wherein the preset curve type selection subunit is used for selecting a preset curve type according to environmental parameters, the environmental parameters can comprise a scene type, a road congestion degree, a vehicle speed and/or a base station configuration state, and the preset curve type can comprise a polynomial curve, an exponential curve and a logarithmic curve;
and the curve fitting subunit is used for performing curve fitting on the discrete sample points by using the selected preset curve type.
Preferably, when the preset curve type is a polynomial curve, the method further includes:
the polynomial parameter determining subunit is used for determining a polynomial parameter corresponding to the current road congestion degree according to a pre-established corresponding relationship between the road congestion degree and the polynomial parameter, and the polynomial parameter comprises a polynomial pattern cost number and a fitting order number;
accordingly, the curve fitting subunit is specifically configured to:
and performing curve fitting on the discrete sample points by using the selected preset curve type and the determined polynomial parameters.
Further, the prediction apparatus further includes a correspondence relationship pre-establishing module, where the correspondence relationship pre-establishing module includes:
the polynomial parameter value range determining unit is used for determining the value range of the polynomial parameter according to the road congestion degree;
the generating time prediction unit is used for selecting different polynomial parameters from the value ranges of the polynomial parameters and predicting the generating time of the next cooperative early warning message according to the historical cooperative early warning message of the vehicle and the selected different polynomial parameters;
and the corresponding relation establishing unit is used for respectively comparing each prediction result with the actual result and establishing the most accurate corresponding relation between the polynomial parameters of the prediction results and the road congestion degree according to the comparison results.
Preferably, the prediction apparatus of the present embodiment may further include:
the time deviation value selection module is used for selecting a time deviation value according to the prediction precision after predicting the generation time of the next cooperative early warning message;
and the transmission time determining module is used for determining the sum of the predicted generation time of the next cooperative early warning message and the time deviation value as the transmission time of the next cooperative early warning message.
The device for predicting the generation time of the vehicle networking collaborative early warning message provided by the embodiment of the invention and the method for predicting the generation time of the vehicle networking collaborative early warning message provided by any embodiment of the invention belong to the same inventive concept, can execute the method for predicting the generation time of the vehicle networking collaborative early warning message provided by any embodiment of the invention, and has corresponding functions and beneficial effects. For technical details that are not described in detail in this embodiment, reference may be made to a method for predicting generation time of a collaborative early warning message in internet of vehicles according to any embodiment of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (16)
1. A method for predicting generation time of a vehicle networking cooperative early warning message is characterized by comprising the following steps:
determining a fitting function of target parameters with respect to time according to vehicle history information, wherein the target parameters comprise a vehicle direction, a vehicle position and a vehicle speed;
determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameters and the constraint condition corresponding to each parameter;
and predicting the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message.
2. The method of claim 1, wherein determining a fit function of the target quantity over time based on the vehicle history information comprises:
acquiring generation time of a preset number of latest cooperative early warning messages and a value of a target parameter corresponding to each generation time according to historical cooperative early warning messages of the vehicle;
determining discrete sample points of a fitting function according to the generation time of the latest preset number of the cooperative early warning messages and the numerical value of the target parameter corresponding to each generation time;
and performing curve fitting on the discrete sample points to obtain a fitting function of the target parameters with respect to time.
3. The method of claim 1, wherein the target parameter is vehicle directionThe fitting function of the vehicle direction with respect to time is A (t), and the constraint condition corresponding to the vehicle direction is | A (t)i)-A(ti-1) | ≧ a, where a (t) denotes an included angle between the vehicle head direction and a preset reference direction at each time, a is a preset angle, tiRepresents the constraint | A (t)i)-A(ti-1) The generation time of the next cooperative early warning message under | ≧ a, ti-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, determining the minimum generation time of the next cooperative early warning message under the constraint condition according to the fitting function of the target parameter and the constraint condition corresponding to the target parameter, including:
according to | A (t)i)-A(ti-1) | ≧ a, calculate ti≥ti-1Minimum t of timeiThe minimum t is setiAs a constraint | A (t)i)-A(ti-1) Minimum generation time t of next cooperative early warning message under | ≧ ai A；
When the target parameter is the vehicle position, the fitting function of the vehicle position with respect to time is (X), (t), Y (t)), and the constraint condition corresponding to the vehicle position is
Wherein (X (t), Y (t)) represent coordinates in which the vehicle position at each time is mapped on a planar coordinate system, and b is a predetermined length, t'iRepresenting constraints
Time of generation, t ', of next collaboration warning message'i-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, determining the minimum generation time of the next cooperative early warning message under the constraint condition according to the fitting function of the target parameter and the constraint condition corresponding to the target parameter, including:
according to
Calculating t'i≥t′i-1Of (c)'iOf said minimum t'iAs a constraint
Minimum generation time of next cooperative early warning message
When the target parameter is the vehicle speed, the fitting function of the vehicle speed with respect to time is V (t), and the constraint condition corresponding to the vehicle speed is | V (t ″)i)-V(t″i-1) C, wherein V (t) represents the vehicle speed at each moment, c is a preset speed, t ″iDenotes the constraint | V (t ″)i)-V(t″i-1) The generation time, t ≧ c, of the next cooperative early warning messagei-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, determining the minimum generation time of the next cooperative early warning message under the constraint condition according to the fitting function of the target parameter and the constraint condition corresponding to the target parameter, including:
according to | V (t ″)i)-V(t″i-1) C, calculating t | ", andi≥t″i-1minimum t of time ″)iThe minimum t ″, will beiAs a constraint | V (t ″)i)-V(t″i-1) Minimum generation time of next cooperative early warning message under | ≧ c
4. The method of claim 3, wherein the constraint time is in a range of [100ms, 1000ms ];
correspondingly, predicting the generation time of the next collaborative early warning message according to the minimum generation time of the next collaborative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the collaborative early warning message, including:
will be provided with
And determining the generation time of the next cooperative early warning message.
5. The method of claim 2, wherein said curve fitting the discrete sample points comprises:
selecting a preset curve type according to environment parameters, wherein the environment parameters comprise a scene type, a road congestion degree, a vehicle speed and/or a base station configuration state, and the preset curve type comprises a polynomial curve, an exponential curve and a logarithmic curve;
and performing curve fitting on the discrete sample points by using the selected preset curve type.
6. The method of claim 5, wherein when the predetermined curve type is a polynomial curve, after selecting the predetermined curve type according to the environmental parameter, further comprising:
determining a polynomial parameter corresponding to the current road congestion degree according to a pre-established corresponding relation between the road congestion degree and the polynomial parameter, wherein the polynomial parameter comprises a polynomial sample number and a fitting order;
correspondingly, the curve fitting is performed on the discrete sample points by using the selected preset curve type, and the method comprises the following steps:
and performing curve fitting on the discrete sample points by using the selected preset curve type and the determined polynomial parameters.
7. The method according to claim 6, wherein the pre-establishing the corresponding relationship between the road congestion degree and the polynomial parameters comprises:
determining the value range of the polynomial parameters according to the road congestion degree;
selecting different polynomial parameters from the value ranges of the polynomial parameters, and predicting the generation time of the next cooperative early warning message according to the historical cooperative early warning message of the vehicle and the selected different polynomial parameters;
and respectively comparing each prediction result with an actual result, and establishing the most accurate corresponding relation between the polynomial parameters of the prediction results and the road congestion degree according to the comparison results.
8. The method of claim 1, further comprising, after predicting the time of generation of the next collaborative early warning message:
selecting a time offset value according to the prediction accuracy;
and determining the sum of the predicted generation time of the next cooperative early warning message and the time offset value as the transmission time of the next cooperative early warning message.
9. A device for predicting generation time of a vehicle networking cooperative early warning message is characterized by comprising:
the fitting function determining module is used for determining a fitting function of target parameters with respect to time according to vehicle historical information, wherein the target parameters comprise a vehicle direction, a vehicle position and a vehicle speed;
the minimum generation time determining module is used for determining the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter according to the fitting function of each parameter in the target parameters and the constraint condition corresponding to each parameter;
and the generation time prediction module is used for predicting the generation time of the next cooperative early warning message according to the minimum generation time of the next cooperative early warning message under the constraint condition corresponding to each parameter and the constraint time for generating the cooperative early warning message.
10. The apparatus of claim 9, wherein the fitting function determination module comprises:
the historical data acquisition unit is used for acquiring the generation time of the latest preset number of cooperative early warning messages and the value of the target parameter corresponding to each generation time according to the historical cooperative early warning messages of the vehicle;
the discrete sample point determining unit is used for determining discrete sample points of a fitting function according to the generation time of the latest preset number of the cooperative early warning messages and the numerical value of the target parameter corresponding to each generation time;
and the fitting function obtaining unit is used for performing curve fitting on the discrete sample points to obtain a fitting function of the target parameter with respect to time.
11. The apparatus according to claim 9, wherein when the target parameter is a vehicle direction, the fitting function of the vehicle direction with respect to time is a (t), and the constraint condition corresponding to the vehicle direction is | a (t |)i)-A(ti-1) | ≧ a, where a (t) denotes an included angle between the vehicle head direction and a preset reference direction at each time, a is a preset angle, tiRepresents the constraint | A (t)i)-A(ti-1) The generation time of the next cooperative early warning message under | ≧ a, ti-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, the minimum generation time determination module is specifically configured to:
according to | A (t)i)-A(ti-1) | ≧ a, calculate ti≥ti-1Minimum t of timeiThe minimum t is setiAs a constraint | A (t)i)-A(ti-1) The minimum generation time tiA of the next collaborative early warning message under | ≧ a;
when the target parameter is the vehicle position, the fitting function of the vehicle position with respect to time is (X), (t), Y (t)), and the constraint condition corresponding to the vehicle position is
Wherein (X), (t), Y (t)) represent coordinates in which the vehicle position at each time is mapped on a plane coordinate system, and b is a reference valueLength, t'iRepresenting constraints
Time of generation, t ', of next collaboration warning message'i-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, the minimum generation time determination module is specifically configured to:
according to
Calculating t'i≥t′i-1Of (c)'iOf said minimum t'iAs a constraint
Minimum generation time of next cooperative early warning message
When the target parameter is the vehicle speed, the fitting function of the vehicle speed with respect to time is V (t), and the constraint condition corresponding to the vehicle speed is | V (t ″)i)-V(t″i-1) C, wherein V (t) represents the vehicle speed at each moment, c is a preset speed, t ″iDenotes the constraint | V (t ″)i)-V(t″i-1) The generation time, t ≧ c, of the next cooperative early warning messagei-1Representing the generation time of the latest collaborative early warning message in the vehicle historical information;
correspondingly, the minimum generation time determination module is specifically configured to:
according to | V (t ″)i)-V(t″i-1) C, calculating t | ", andi≥t″i-1minimum t of time ″)iThe minimum t ″, will beiAs a constraint | V (t ″)i)-V(t″i-1) Minimum generation time of next cooperative early warning message under | ≧ c
12. The apparatus of claim 11, wherein the constraint time is in a range of [100ms, 1000ms ];
correspondingly, the generation time prediction module is specifically configured to:
will be provided with
And determining the generation time of the next cooperative early warning message.
13. The apparatus of claim 10, wherein the fitting function obtaining unit comprises:
the system comprises a preset curve type selection subunit, a base station configuration unit and a control unit, wherein the preset curve type selection subunit is used for selecting a preset curve type according to environment parameters, the environment parameters comprise a scene type, a road congestion degree, a vehicle speed and/or a base station configuration state, and the preset curve type comprises a polynomial curve, an exponential curve and a logarithmic curve;
and the curve fitting subunit is used for performing curve fitting on the discrete sample points by using the selected preset curve type.
14. The apparatus of claim 13, wherein when the predetermined curve type is a polynomial curve, further comprising:
the polynomial parameter determining subunit is used for determining a polynomial parameter corresponding to the current road congestion degree according to a pre-established corresponding relation between the road congestion degree and the polynomial parameter after a preset curve type is selected according to the environment parameter, wherein the polynomial parameter comprises a polynomial sample number and a fitting order number;
accordingly, the curve fitting subunit is specifically configured to:
and performing curve fitting on the discrete sample points by using the selected preset curve type and the determined polynomial parameters.
15. The apparatus according to claim 14, further comprising a correspondence relationship pre-establishing module, wherein the correspondence relationship pre-establishing module comprises:
the polynomial parameter value range determining unit is used for determining the value range of the polynomial parameter according to the road congestion degree;
the generating time prediction unit is used for selecting different polynomial parameters from the value ranges of the polynomial parameters and predicting the generating time of the next cooperative early warning message according to the historical cooperative early warning message of the vehicle and the selected different polynomial parameters;
and the corresponding relation establishing unit is used for respectively comparing each prediction result with the actual result and establishing the corresponding relation between the polynomial parameter with the most accurate prediction result and the road congestion degree according to the comparison result.
16. The apparatus of claim 9, further comprising:
the time deviation value selection module is used for selecting a time deviation value according to the prediction precision after predicting the generation time of the next cooperative early warning message;
and the transmission time determining module is used for determining the sum of the predicted generation time of the next cooperative early warning message and the time deviation value as the transmission time of the next cooperative early warning message.
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