CN113022520A - Adaptive braking method, apparatus and storage medium - Google Patents
Adaptive braking method, apparatus and storage medium Download PDFInfo
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- CN113022520A CN113022520A CN202110581076.8A CN202110581076A CN113022520A CN 113022520 A CN113022520 A CN 113022520A CN 202110581076 A CN202110581076 A CN 202110581076A CN 113022520 A CN113022520 A CN 113022520A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T7/00—Brake-action initiating means
- B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T2201/00—Particular use of vehicle brake systems; Special systems using also the brakes; Special software modules within the brake system controller
- B60T2201/02—Active or adaptive cruise control system; Distance control
- B60T2201/022—Collision avoidance systems
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Abstract
The invention relates to the field of vehicle braking, in particular to a self-adaptive braking method, self-adaptive braking equipment and a storage medium. The self-adaptive braking method comprises the following steps: randomly generating a plurality of groups of parameters, wherein each group of parameters comprises brake pressure and collision duration threshold values; in the running process of the vehicle, when the collision duration at one moment is less than or equal to the collision duration threshold value in each group of parameters, applying brake pressure in the same group of parameters to the vehicle; evaluating the braking performance of the vehicle under each group of parameters according to the time sequence of the driving data under each group of parameters in the driving process of the vehicle; optimizing the multiple groups of parameters according to the braking performance under each group of parameters, and returning to the application operation of the braking pressure until the ending condition is met; and determining target brake pressure and a target collision duration threshold according to multiple groups of parameters obtained by final optimization. The embodiment can adaptively learn the optimal braking strategy, is not too aggressive and conservative, and improves the experience of a driver.
Description
Technical Field
The invention relates to the field of vehicle braking, in particular to a self-adaptive braking method, self-adaptive braking equipment and a storage medium.
Background
When the distance between the AEB (automatic emergency braking system) and the front vehicle is less than the safe distance, the AEB can actively brake, so that collision accidents such as rear-end collision and the like are avoided or reduced.
In the actual use process, the braking strategy of the AEB is too conservative, and the braking is started when the distance from a front vehicle is far, so that the experience of a driver is influenced.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The embodiment of the invention provides a self-adaptive braking method, self-adaptive braking equipment and a storage medium, so that an optimal braking strategy can be learned in a self-adaptive mode, excessive acceleration and conservation are avoided, and the experience of a driver is improved.
In a first aspect, an embodiment of the present application provides a self-adaptive braking method, including:
randomly generating a plurality of groups of parameters, wherein each group of parameters comprises brake pressure and collision duration threshold values;
in the running process of the vehicle, when the collision duration at one moment is less than or equal to the collision duration threshold value in each group of parameters, applying brake pressure in the same group of parameters to the vehicle to control the braking of the vehicle;
evaluating the braking performance of the vehicle under each group of parameters according to the time sequence of the driving data under each group of parameters in the driving process of the vehicle; the time series of the travel data includes a time series of deceleration, a time series of relative vehicle speed with the preceding vehicle, and a time series of vehicle distance with the preceding vehicle;
optimizing the multiple groups of parameters according to the braking performance under each group of parameters, and returning to the application operation of the braking pressure until the ending condition is met;
and determining a target braking pressure and a target collision duration threshold according to the multiple groups of parameters obtained by final optimization, so that the vehicle can be applied with the target braking pressure when the collision duration is less than or equal to the target collision duration threshold.
Optionally, in the driving process of the vehicle, when the collision duration at a time is less than or equal to the collision duration threshold in each set of parameters, applying the brake pressure in the same set of parameters to the vehicle includes:
acquiring running data of the vehicle in real time in the running process of the vehicle;
calculating the collision duration of the vehicle in real time according to the distance between vehicles and the relative speed in the driving data, and comparing the collision duration with the threshold value of the collision duration in each group of parameters;
and when the collision duration at a moment is less than or equal to the collision duration threshold value in each group of parameters, applying the brake pressure in the same group of parameters to the vehicle.
Optionally, the evaluating, according to the time series of the driving data of each set of parameters in the driving process of the vehicle, the braking performance of the vehicle under each set of parameters includes:
calculating the brake score b of the vehicle under each group of parameters according to the following formula to serve as the brake performance;
wherein n1, n2 and m are all positive numbers,athe time series of deceleration under a set of parameters, d is the time series minimum value of the vehicle distance under the same set of parameters, and ttc is the collision duration threshold value in the same set of parameters.
Optionally, the optimizing the multiple sets of parameters according to the braking performance under each set of parameters includes:
and optimizing the plurality of groups of parameters according to the brake scores under each group of parameters by adopting a genetic algorithm.
Optionally, after applying the brake pressure in the same set of parameters to the host vehicle to control braking of the host vehicle, the method further includes:
when the collision duration at a moment is greater than the collision duration threshold in the same group of parameters, the brake pressure is cancelled;
the end condition includes: the braking performance is converged, or the plurality of sets of parameters are converged.
Optionally, in the driving process of the host vehicle, when a collision duration at a time is less than or equal to a collision duration threshold in each set of parameters, applying brake pressure in the same set of parameters to the host vehicle to control braking of the host vehicle, including:
in the process that the vehicle runs in each of a plurality of scenes, when the collision duration at a moment is less than or equal to the collision duration threshold value in each group of parameters, applying brake pressure in the same group of parameters to the vehicle to control the vehicle to brake;
the method for evaluating the braking performance of the vehicle under each group of parameters according to the time sequence of the driving data under each group of parameters in the driving process of the vehicle comprises the following steps:
and evaluating the braking performance of the vehicle suitable for all scenes under each group of parameters according to the time sequence of the driving data of each scene and each group of parameters in the driving process of the vehicle.
Optionally, the estimating, according to the time series of the driving data of each scene and each set of parameters in the driving process of the vehicle, the braking performance of the vehicle applicable to all scenes under each set of parameters includes:
calculating the brake score b of the vehicle under each group of parameters in each scene according to the following formula;
wherein n1, n2 and m are all positive numbers,athe time sequence of deceleration under a group of parameters in a scene is shown, d is the minimum value of the time sequence of the vehicle distance under the same scene and the same group of parameters, and ttc is a collision duration threshold value in the same group of parameters;
calculating the brake score of the vehicle suitable for all scenes under each group of parameters according to the following formulaf；
Wherein, mean (b) is the average brake score of each group of parameters in a plurality of scenes, and min (b) is the minimum brake score of each group of parameters in a plurality of scenes.
Optionally, after determining the target braking pressure and the target collision duration threshold according to the multiple sets of parameters obtained by the final optimization, the method further includes:
the time sequence of the real driving data of the driver of the vehicle is collected,
calculating a real braking pressure and a real collision duration threshold according to the time sequence of the real driving data;
and correcting the target braking pressure by adopting the real braking pressure, and correcting the target collision time threshold by adopting the real collision time threshold.
In a second aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the adaptive braking method according to any embodiment.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the adaptive braking method according to any embodiment.
Compared with the prior art, the invention has the beneficial effects that:
the embodiment creatively adopts the brake pressure and the collision time length threshold value as the brake parameter, and does not adopt the distance between the front vehicle and the front vehicle as the brake parameter, so that the optimal brake strategy can be learned in a self-adaptive manner by automatically optimizing the parameters of the brake performance according to different brake force, distance and relative speed; in addition, the braking performance provided by the embodiment considers deceleration, relative speed and vehicle distance, the quality of the braking performance is comprehensively evaluated through the three data, a strategy with better braking performance can be obtained, the braking performance is not excessively accelerated and conservative, and the experience of a driver is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of an adaptive braking method according to an embodiment of the present invention;
FIG. 2 is a graphical illustration of parameters and skid scores for previous iterations provided by an embodiment of the present invention;
FIG. 3 is a flow chart of another adaptive braking method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Fig. 1 is a flowchart of an adaptive braking method according to an embodiment of the present invention, where the embodiment is suitable for a situation where an optimal braking strategy is continuously learned iteratively as a vehicle travels, and an execution subject of the method is an electronic device. The method comprises the following operations:
and S110, randomly generating a plurality of groups of parameters, wherein each group of parameters comprises brake pressure and collision duration threshold values.
The braking strategy comprises a braking pressure and a collision time threshold. The brake pressure is the pressure exerted on a brake pedal of the vehicle, and the collision duration is the distance between the vehicle and the front vehicle divided by the relative speed, i.e. theoretically, the vehicle can collide with the front vehicle after the collision duration. When the collision duration is less than or equal to the collision duration threshold, a force corresponding to the brake pressure needs to be applied to the vehicle.
Illustratively, a plurality of collision time length thresholds are randomly selected within the range of 0.5-3 s, a plurality of brake pressures are randomly selected within the range of 0.1-1 Mpa, and all the selected collision time length thresholds and brake pressures are arranged and combined into a plurality of groups of parameters.
It should be noted that the parameters of each group are partially the same, such as 3s,0.1MPa, and 3s,0.3 MPa. In the method, each group of parameters operates independently, namely only one group of parameters exists in each test, so that the condition that one driving scene simultaneously meets a plurality of groups of threshold conditions does not occur. For example, only (3s,0.1MPa) of these parameters are tested at one time, and only (3s,0.3MPa) is tested at the next time.
And S120, in the driving process of the vehicle, when the collision duration at one moment is less than or equal to the collision duration threshold value in each group of parameters, applying the brake pressure in the same group of parameters to the vehicle so as to control the braking of the vehicle.
And acquiring running data of the vehicle in real time during the running process of the vehicle, wherein the running data comprises the deceleration acquired at each moment, the relative speed with the front vehicle and the distance with the front vehicle. And calculating the collision duration of the vehicle in real time according to the distance and the relative speed in the driving data, and comparing the collision duration with the threshold value of the collision duration in each group of parameters. And when the collision duration at a moment is less than or equal to the collision duration threshold value in each group of parameters, applying the brake pressure in the same group of parameters to the vehicle. For example, if the vehicle distance is 10m and the relative vehicle speed is 2m/s at a time, the collision time period at that time is 5 s. If a group of parameters are 2s and 0.5Mpa, and the parameters are larger than the collision duration threshold value, no operation is performed; and if the other set of parameters is 6s and 0.3Mpa, and the other set of parameters is smaller than the collision duration threshold value, applying the brake pressure of 0.3Mpa to the vehicle.
Assuming that a total of 20 sets of parameters are generated, the braking pressure and the impact duration threshold in each set of parameters are not identical. The operation described in S120 is performed independently for each set of parameters. The vehicle can run in an actual road environment to perform an actual vehicle test, and can also run in a simulation environment to perform a simulation test. Thus, a time series of the travel data for each set of parameters can be obtained, and referring to table 1, at 1.71s, the brake pressure of 0.5Mpa is applied to the vehicle. Referring to Table 2, at 0.57s, a braking pressure of 0.48MPa was applied to the vehicle.
TABLE 1 time series of driving data
| Time | Deceleration rate | Relative speed of a vehicle | Vehicle distance | Brake pressure intensity |
| 1.68 | 0.039128671 | -11.06480229 | 72.44026734 | 0 |
| 1.69 | 0.039494216 | -11.06521459 | 72.32961547 | 0 |
| 1.7 | 0.039850825 | -11.06564432 | 72.21895914 | 0 |
| 1.71 | 0.014079074 | -11.06601427 | 72.10829854 | 0.5 |
| 1.72 | -0.136540746 | -11.06547694 | 71.99763959 | 0.5 |
Note: deceleration is the change in velocity per unit time from the last time to this time. Unit = m/s2. The positive sign represents acceleration, which indicates that the speed is increased; the negative sign represents the negative acceleration, called deceleration,indicating a drop in speed.
Relative vehicle speed: the unit of the vehicle speed of the front vehicle-the vehicle speed of the self vehicle is = km/h, a positive sign represents that the vehicle speed of the front vehicle is greater than the vehicle speed of the self vehicle, and a negative sign represents that the vehicle speed of the front vehicle is less than the vehicle speed of the self vehicle.
Vehicle distance: the unit = m represents the distance between the host vehicle and the leading vehicle.
TABLE 2
| Time | Deceleration rate | Relative speed of a vehicle | Vehicle distance | Brake pressure intensity |
| 0.54 | -0.060065931 | -11.06521813 | 85.04461065 | 0 |
| 0.55 | -0.058829676 | -11.06609339 | 84.9339559 | 0 |
| 0.56 | -0.057592095 | -11.0669488 | 84.82329045 | 0 |
| 0.57 | -0.079197914 | -11.06737216 | 84.71261688 | 0.48 |
| 0.58 | -0.211111407 | -11.06639604 | 84.60194651 | 0.48 |
| 0.59 | -0.386922712 | -11.06313113 | 84.49130411 | 0.48 |
In some embodiments, the purpose of braking is to stop the vehicle, and the braking pressure may be applied until the vehicle stops, ending the collection of the driving data. In other embodiments, the braking is to avoid collision between the host vehicle and the preceding vehicle, and after applying the brake pressure in the same set of parameters to the host vehicle to control braking of the host vehicle, the method further includes: and when the collision duration at a moment is greater than the collision duration threshold in the same group of parameters, the brake pressure is cancelled. That is, when the collision probability is small, the braking pressure is not applied any more, and the vehicle returns to the normal vehicle speed. In this case, a duration, such as 20s, may be specified, and only 20s of travel data may be collected (but it is ensured that the braking pressure is applied to the vehicle within 20s, and then the braking pressure is removed).
S130, evaluating the braking performance of the vehicle under each group of parameters according to the time sequence of the driving data under each group of parameters in the driving process of the vehicle; the time series of the travel data includes a time series of deceleration, a time series of relative vehicle speed with the preceding vehicle, and a time series of vehicle distance with the preceding vehicle.
The braking performance provided by the embodiment considers deceleration, relative speed and vehicle distance, and the quality of the braking performance is comprehensively evaluated through the three data, rather than only considering the vehicle distance as in the prior art.
Optionally, according to formula (1), calculating a braking score b of the vehicle under each set of parameters as the braking performance.
Wherein n1, n2 and m are all positive numbers,athe time series of deceleration under a set of parameters, d is the time series minimum value of the vehicle distance under the same set of parameters, and ttc is the collision duration threshold value in the same set of parameters.
Illustratively, n1=10, n2=3, and m = 6. If the distance between the vehicle and the preceding vehicle is less than or equal to 0, the collision between the vehicle and the preceding vehicle is indicated, and the score is-10. The absolute value of the median of the deceleration is calculated without collision. A larger absolute value indicates a more rapid braking and a lower score. If ttc is greater than 6, indicating that braking is too conservative, and the score should be decreased, then ttc is superimposed on the deceleration sum basis. Referring to equation (1) at equation 3, as ttc increases, the score of n2 is subtracted from the score of equation 2. Therefore, the formula (1) fully considers whether the vehicle collides with the vehicle or brakes are urgent and excessively conservative, and the score is obtained comprehensively.
S140, whether the ending condition is met or not is judged, and if not, the process jumps to S150; if so, it jumps to S160.
In this embodiment, loop iteration is performed on multiple sets of parameters, and the end condition of the iteration includes the convergence of the braking performance, or the convergence of the multiple sets of parameters.
The convergence of the multiple groups of parameters is the convergence among the multiple groups of parameters obtained by one iteration, namely the variance of the multiple groups of brake pressure and the variance of the multiple groups of collision duration threshold values are both smaller than a threshold value, such as 0.3; meanwhile, parameters obtained through iteration of each time are converged. The braking performance convergence is the convergence of a plurality of braking scores under a plurality of groups of parameters obtained by one iteration, namely the variance of the plurality of braking scores is smaller than a threshold value, such as 0.3; meanwhile, the brake score obtained by iteration of each time is also converged.
Fig. 2 is a schematic diagram of parameters and a skid score of previous iterations according to an embodiment of the present invention, and the skid score of each iteration in fig. 2 is the highest value of a plurality of scores obtained by the current iteration. Correspondingly, the parameter of each iteration is the parameter corresponding to the highest score. As can be seen, the parameters and the skid scores converge gradually as the number of iterations increases.
And S150, optimizing the multiple groups of parameters according to the brake performance under each group of parameters, and returning to S120.
Optionally, a genetic algorithm is adopted, and the plurality of sets of parameters are optimized according to the brake score under each set of parameters. Illustratively, after obtaining the braking scores under a plurality of groups of parameters, the first step is deleting the group of parameters with the lowest braking score; selecting a subset from the remaining multiple sets of parameters; thirdly, selecting a group of parameters with the highest brake score in the subset, and copying the parameters so that the number of the parameter groups is unchanged; and fourthly, fine tuning the group of parameters selected in the previous step. Optionally, a small random number, epsilon, is added to the threshold values of the braking pressure and the collision duration respectivelyttc，εp。
And after the multiple groups of parameters are optimized integrally, executing the operation of S120 aiming at each group of optimized parameters, and calculating the brake score under the group of optimized parameters. Then, it is judged whether or not the end condition is satisfied.
If the end condition is not met, the first step to the fourth step are repeatedly executed, so that a plurality of groups of parameters with better overall braking performance can be finally obtained.
S160, determining a target braking pressure and a target collision duration threshold according to the multiple groups of parameters obtained through final optimization, so that the target braking pressure is applied to the vehicle when the collision duration of the vehicle is less than or equal to the target collision duration threshold. And finishing the operation.
Optionally, the parameter with the highest brake score is selected from the multiple sets of parameters obtained through final optimization, and the selected parameter is the final strategy obtained through genetic algorithm iteration. The set of parameters is solidified into a control chip of the vehicle, so that the control chip applies the target braking pressure to the vehicle when judging that the collision time of the vehicle is less than or equal to the target collision time threshold, and the braking performance is good at the moment.
The embodiment creatively adopts the brake pressure and the collision time length threshold value as the brake parameter, and does not adopt the distance between the front vehicle and the front vehicle as the brake parameter, so that the optimal brake strategy can be learned in a self-adaptive manner by automatically optimizing the parameters of the brake performance according to different brake force, distance and relative speed; in addition, the braking performance provided by the embodiment considers deceleration, relative speed and vehicle distance, the quality of the braking performance is comprehensively evaluated through the three data, a strategy with better braking performance can be obtained, the braking performance is not excessively accelerated and conservative, and the experience of a driver is improved.
Fig. 3 is a flowchart of another adaptive braking method according to an embodiment of the present invention, where multiple scenarios are introduced based on the embodiment shown in fig. 1, so as to obtain an optimal braking strategy applicable to multiple scenarios. As shown in fig. 3, the following operations are specifically included:
s210, randomly generating multiple groups of parameters, wherein each group of parameters comprises brake pressure and a collision duration threshold value.
S220, in the process that the vehicle runs in each scene of the multiple scenes, when the collision duration at one moment is less than or equal to the collision duration threshold value in each group of parameters, applying brake pressure in the same group of parameters to the vehicle so as to control the braking of the vehicle.
When the host vehicle runs in the simulation environment, different scene parameters including running parameters (including initial vehicle speed, running time, inter-vehicle distance and the like), road parameters (including gradient, angle, friction coefficient and the like), host vehicle parameters (including host vehicle weight, braking mode, horsepower, size and the like) can be set in the simulation software. Different scene parameters are combined to obtain a plurality of scenes.
When the vehicle runs in the actual road environment, the vehicle can consciously run under different road conditions, and the parameters of the vehicle are different from the running parameters, so that the vehicle can run under different scenes.
Assuming a total of 20 scenes and 20 sets of parameters, the operation described in S220 is performed independently for each scene and each set of parameters.
And S230, according to the time sequence of the driving data of each scene and each group of parameters in the driving process of the vehicle, evaluating the braking performance of the vehicle suitable for all scenes under each group of parameters.
Optionally, according to formula (3), calculating a braking score b of the vehicle under each set of parameters in each scene, and assuming that 400 braking scores are obtained.
Wherein n1, n2 and m are all positive numbers,athe time sequence of the deceleration under a group of parameters in a scene, d is the minimum value of the time sequence of the vehicle distance under the same scene and the same group of parameters, and ttc is the collision duration threshold value in the same group of parameters. According to a formula (4), calculating the brake score of the vehicle suitable for all scenes under each group of parametersf。
Wherein, mean (b) is the average brake score of each group of parameters in a plurality of scenes, and min (b) is the minimum brake score of each group of parameters in a plurality of scenes. That is, 1 brakescore is calculated from 20 brakescores corresponding to 20 scenes under one set of parameters, resulting in a total of 20 brakescores under all sets of parameters.
Brake scoringfThe average and worst performance of each set of parameters across the entire scene is reflected.
S240, whether the ending condition is met or not is judged, and if not, the process jumps to S250; if so, it jumps to S260.
In this embodiment, loop iteration is performed on multiple sets of parameters, and the end condition of the iteration includes the convergence of the braking performance, or the convergence of the multiple sets of parameters. For details, reference is made to the above embodiments, which are not described herein again.
And S250, optimizing the multiple groups of parameters according to the brake performance suitable for all scenes under each group of parameters, and returning to S220.
And optimizing the multiple groups of parameters by adopting a genetic algorithm according to the brake scores suitable for all scenes under each group of parameters. For details, reference is made to the above embodiments, which are not described herein again.
S260, determining a target braking pressure and a target collision duration threshold according to the multiple groups of parameters obtained through final optimization, so that the target braking pressure is applied to the vehicle when the collision duration of the vehicle is less than or equal to the target collision duration threshold.
For details, reference is made to the above embodiments, which are not described herein again.
The embodiment shown in fig. 1 and 3 is parameters adaptively learned according to braking performance, and in order to take account of driving habits of a driver, after determining a target braking pressure and a target collision duration threshold according to a plurality of sets of parameters obtained by final optimization, the embodiment further includes: acquiring a time sequence of real driving data of a driver of the vehicle, and calculating a real braking pressure and a real collision duration threshold according to the time sequence of the real driving data; and correcting the target braking pressure by adopting the real braking pressure, and correcting the target collision time threshold by adopting the real collision time threshold.
Specifically, the real driving data includes the relative speed, the distance, and the braking pressure of the vehicle and the preceding vehicle. When the brake pressure (namely the real brake pressure) is applied, the vehicle distance is divided by the relative vehicle speed to obtain the collision duration threshold. In order to eliminate interference, real driving data under various scenes can be collected, and the collision time length threshold value obtained by each calculation is averaged to obtain the real brake pressure and the real collision time length threshold value.
The modified way is weighted summation. Under the default condition, the real braking pressure and the target braking pressure, and the weight values of the real collision time length threshold and the target collision time length threshold are both 0.5. Preferably, the driver can set the parameters of the two by himself to decide whether the final strategy is inclined to the adaptive result or the actual operation of the driver.
In the embodiment, the target braking pressure is corrected by adopting the real braking pressure, the target collision duration threshold is corrected by adopting the real collision duration threshold, and the operation of a driver is fused in the self-adaptive result, so that the result of machine learning is closer to the operation of a human, and the experience of the driver is further improved.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 4, the electronic device includes a processor 20, a memory 21, an input device 22, and an output device 23; the number of the processors 20 in the device may be one or more, and one processor 20 is taken as an example in fig. 4; the processor 20, the memory 21, the input means 22 and the output means 23 in the device may be connected by a bus or other means, as exemplified by the bus connection in fig. 4.
The memory 21 is a computer readable storage medium, and can be used for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the adaptive braking method in the embodiment of the present invention. The processor 20 executes various functional applications and data processing of the device by executing software programs, instructions and modules stored in the memory 21, so as to implement the above-mentioned adaptive braking method.
The memory 21 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 21 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 21 may further include memory located remotely from processor 20, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 22 may be used to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 23 may include a display device such as a display screen.
The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the computer readable storage medium realizes the adaptive braking method of any embodiment.
The computer storage media of the embodiments of the present application may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (10)
1. An adaptive braking method, comprising:
randomly generating a plurality of groups of parameters, wherein each group of parameters comprises brake pressure and collision duration threshold values;
in the running process of the vehicle, when the collision duration at one moment is less than or equal to the collision duration threshold value in each group of parameters, applying brake pressure in the same group of parameters to the vehicle to control the braking of the vehicle;
evaluating the braking performance of the vehicle under each group of parameters according to the time sequence of the driving data under each group of parameters in the driving process of the vehicle; the time series of the travel data includes a time series of deceleration, a time series of relative vehicle speed with the preceding vehicle, and a time series of vehicle distance with the preceding vehicle;
optimizing the multiple groups of parameters according to the braking performance under each group of parameters, and returning to the application operation of the braking pressure until the ending condition is met;
and determining a target braking pressure and a target collision duration threshold according to the multiple groups of parameters obtained by final optimization, so that the vehicle can be applied with the target braking pressure when the collision duration is less than or equal to the target collision duration threshold.
2. The adaptive braking method according to claim 1, wherein the applying braking pressure in the same set of parameters to the host vehicle when the collision duration at a time is less than or equal to the collision duration threshold in each set of parameters during the driving of the host vehicle comprises:
acquiring running data of the vehicle in real time in the running process of the vehicle;
calculating the collision duration of the vehicle in real time according to the distance between vehicles and the relative speed in the driving data, and comparing the collision duration with the threshold value of the collision duration in each group of parameters;
and when the collision duration at a moment is less than or equal to the collision duration threshold value in each group of parameters, applying the brake pressure in the same group of parameters to the vehicle.
3. The adaptive braking method according to claim 1, wherein the estimating of the braking performance of the host vehicle under each set of parameters according to the time series of the driving data under each set of parameters during the driving of the host vehicle comprises:
calculating the brake score b of the vehicle under each group of parameters according to the following formula to serve as the brake performance;
wherein n1, n2 and m are all positive numbers,athe time series of deceleration under a set of parameters, d is the time series minimum value of the vehicle distance under the same set of parameters, and ttc is the collision duration threshold value in the same set of parameters.
4. The adaptive braking method of claim 3, wherein the optimizing the plurality of sets of parameters based on the braking performance for each set of parameters comprises:
and optimizing the plurality of groups of parameters according to the brake scores under each group of parameters by adopting a genetic algorithm.
5. The adaptive braking method according to any one of claims 1 to 4, further comprising, after applying a braking pressure in the same set of parameters to the host vehicle to control braking of the host vehicle:
when the collision duration at a moment is greater than the collision duration threshold in the same group of parameters, the brake pressure is cancelled;
the end condition includes: the braking performance is converged, or the plurality of sets of parameters are converged.
6. The adaptive braking method according to claim 1, wherein the applying braking pressures in the same set of parameters to the host vehicle to control braking of the host vehicle when a collision duration at a time during driving of the host vehicle is less than or equal to a collision duration threshold in each set of parameters comprises:
in the process that the vehicle runs in each of a plurality of scenes, when the collision duration at a moment is less than or equal to the collision duration threshold value in each group of parameters, applying brake pressure in the same group of parameters to the vehicle to control the vehicle to brake;
the method for evaluating the braking performance of the vehicle under each group of parameters according to the time sequence of the driving data under each group of parameters in the driving process of the vehicle comprises the following steps:
and evaluating the braking performance of the vehicle suitable for all scenes under each group of parameters according to the time sequence of the driving data of each scene and each group of parameters in the driving process of the vehicle.
7. The adaptive braking method according to claim 6, wherein the estimating of the braking performance of the host vehicle under each set of parameters for all scenes according to the time series of the driving data under each scene and each set of parameters during the driving of the host vehicle comprises:
calculating the brake score b of the vehicle under each group of parameters in each scene according to the following formula;
wherein n1, n2 and m are all positive numbers,athe time sequence of deceleration under a group of parameters in a scene is shown, d is the minimum value of the time sequence of the vehicle distance under the same scene and the same group of parameters, and ttc is a collision duration threshold value in the same group of parameters;
calculating the brake score of the vehicle suitable for all scenes under each group of parameters according to the following formulaf；
Wherein, mean (b) is the average brake score of each group of parameters in a plurality of scenes, and min (b) is the minimum brake score of each group of parameters in a plurality of scenes.
8. The adaptive braking method according to claim 1, wherein after determining the target braking pressure and the target collision duration threshold according to the plurality of sets of parameters obtained by the final optimization, the method further comprises:
the time sequence of the real driving data of the driver of the vehicle is collected,
calculating a real braking pressure and a real collision duration threshold according to the time sequence of the real driving data;
and correcting the target braking pressure by adopting the real braking pressure, and correcting the target collision time threshold by adopting the real collision time threshold.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the adaptive braking method of any of claims 1-8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out an adaptive braking method according to any one of claims 1 to 8.
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