CN118269964A - Emergency braking method and device for vehicle - Google Patents
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Abstract
The application relates to the technical field of automatic driving, in particular to an emergency braking method and device for a vehicle, wherein the method comprises the following steps: under the condition that the vehicle is detected to be in a preset emergency braking working condition, acquiring all current state parameters of the vehicle and all current observation parameters of a current environment; based on a target Kalman filtering algorithm, a state estimation value and an observation estimation value of the current moment are obtained according to all current state parameters and all current observation parameters, and an initial data set of a preset emergency braking working condition is compressed by the state estimation value and the observation estimation value, so that an emergency braking control action of the vehicle is generated based on the compressed initial data set. According to the embodiment of the application, in the process of emergency braking control of the vehicle, the Kalman filtering algorithm is utilized to compress the input data of the emergency braking control, so that the data quantity required to be processed by the controller is reduced, the data processing efficiency of the vehicle in the emergency braking process is improved, and the vehicle is safer.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to an emergency braking method and device for a vehicle.
Background
An automatic emergency braking system AEB (Autonomous Emergency Braking) of a vehicle is an electronic system for assisting braking, which is one of important fields in the automatic driving technology, with the purpose of avoiding or reducing collision with a preceding vehicle by active braking or deceleration of the vehicle.
In the related art, aiming at the functions of active obstacle avoidance and the like of emergency braking of a vehicle, parameters such as the vehicle and the surrounding environment can be acquired through sensor technologies such as millimeter wave radar, laser radar, camera vision and the like, so that corresponding braking operation is executed according to the acquired parameters.
However, in the related art, because the sensor in the vehicle is affected by various external factors, accuracy and reliability of a data detection result may be reduced, and the sensor inputs a large amount of data in the emergency braking control process of the vehicle, so that a calculation load is large, timeliness of data processing and real-time response of emergency braking are difficult to ensure, the reliability of emergency braking control is reduced, the safety level of the vehicle is affected, and the problem needs to be solved.
Disclosure of Invention
The application provides an emergency braking method and device for a vehicle, which are used for solving the problems that in the related art, due to the fact that an in-vehicle sensor is influenced by various external factors, the accuracy and reliability of a data detection result are possibly reduced, a large amount of data are input by the sensor in the emergency braking control process of the vehicle, the calculation burden is large, the timeliness of data processing and the real-time response of emergency braking are difficult to ensure, the reliability of the emergency braking control is reduced, the safety level of the vehicle is influenced and the like.
An embodiment of a first aspect of the present application provides an emergency braking method for a vehicle, including the steps of: detecting whether the vehicle is in a preset emergency braking working condition or not; under the condition that the vehicle is detected to be in the preset emergency braking working condition, acquiring all current state parameters of the vehicle and all current observation parameters of a current environment; based on a target Kalman filtering algorithm, a state estimation value and an observation estimation value at the current moment are obtained according to all the current state parameters and all the current observation parameters, and an initial data set of the preset emergency braking working condition is compressed by using the state estimation value and the observation estimation value, so that emergency braking control actions of the vehicle are generated based on the compressed initial data set.
According to the technical scheme, in the process of emergency braking control of the vehicle, the Kalman filtering algorithm is utilized to compress the input data of the emergency braking control, so that the data quantity required to be processed by the controller is reduced, the data processing efficiency of the vehicle in the emergency braking process is improved, and the vehicle is safer.
Optionally, in one embodiment of the present application, the compressing the initial data set of the preset emergency braking condition using the state estimation value and the observed estimation value includes: generating a target vector of the current moment according to the state estimation value and the current observation estimation value; and replacing the initial data set by the target vector to obtain the compressed initial data set.
By the technical scheme, the target vector at the current moment can be generated according to the state estimated value and the current observation estimated value; the initial data set is replaced by the target vector to obtain the compressed initial data set, so that the preliminary estimation of the current emergency braking state of the vehicle is realized, and the operation pressure of emergency braking is further reduced.
Optionally, in one embodiment of the present application, after obtaining the state estimation value and the observation estimation value of the current moment according to the all current state parameters and the all current observation parameters based on the target kalman filtering algorithm, the method further includes: acquiring an actual state value and an actual observed value at the current moment, and acquiring a Kalman gain at the current moment based on the actual state value, the actual observed value, the state estimation value and the observed estimation value; and iterating the target Kalman filtering algorithm by utilizing the Kalman gain so as to obtain a state estimated value and an observation estimated value at the next moment by utilizing the iterated target Kalman filtering algorithm.
According to the technical scheme, the target Kalman filtering algorithm can be iterated based on the actual state value, the actual observed value, the state estimation value and the Kalman gain of the observed estimation value at the current moment, and the target Kalman filtering algorithm is used for calculating the state estimation value and the observed estimation value at the next moment.
Optionally, in one embodiment of the present application, the generating the emergency brake control action of the vehicle based on the compressed initial data set includes: judging whether the compressed initial data set meets a preset error allowance condition or not; and if the compressed initial data set does not meet the preset error allowance condition, updating the initial data set based on all the current state parameters and all the current observation parameters, and generating the emergency braking control action by the updated initial data set.
According to the technical scheme, whether the compressed initial data set meets the preset error permission condition can be judged, if the compressed initial data set does not meet the preset error permission condition, the initial data set is updated based on all current state parameters and all current observation parameters, emergency braking control actions are generated by the updated initial data set, and the accuracy of the emergency braking actions is guaranteed by carrying out error analysis and updating on the compressed data set, and the efficiency requirements of emergency braking control can be met.
Optionally, in one embodiment of the present application, the acquiring all current state parameters of the vehicle and all current observed parameters of the current environment includes: acquiring an actual running scene of the vehicle, and matching a corresponding braking function in the preset emergency braking working condition based on the actual running scene; and extracting all current state parameters and all current observation parameters in the initial data set based on the braking function.
According to the technical scheme, the actual running scene of the vehicle can be obtained, the corresponding braking functions are matched in the preset emergency braking working condition based on the actual running scene, all current state parameters and all current observation parameters are extracted in the initial data set, intelligent response can be made according to the specific situation of the vehicle by matching the corresponding braking functions according to the actual running scene, and the adaptability and safety of the braking system are improved.
An embodiment of a second aspect of the present application provides an emergency brake apparatus for a vehicle, including: the detection module is used for detecting whether the vehicle is in a preset emergency braking working condition or not; the acquisition module is used for acquiring all current state parameters of the vehicle and all current observation parameters of a current environment under the condition that the vehicle is detected to be in the preset emergency braking working condition; the braking module is used for obtaining a state estimation value and an observation estimation value at the current moment according to the current state parameters and the current observation parameters based on a target Kalman filtering algorithm, and compressing an initial data set of the preset emergency braking working condition by utilizing the state estimation value and the observation estimation value so as to generate an emergency braking control action of the vehicle based on the compressed initial data set.
Optionally, in one embodiment of the present application, the braking module includes: the generating unit is used for generating a target vector of the current moment according to the state estimation value and the current observation estimation value; and the replacing unit is used for replacing the initial data set by the target vector to obtain the compressed initial data set.
Optionally, in one embodiment of the present application, the braking module further includes: the acquisition unit is used for acquiring an actual state value and an actual observed value at the current moment after acquiring the state estimated value and the observed estimated value at the current moment according to all the current state parameters and all the current observed parameters based on a target Kalman filtering algorithm, and acquiring Kalman gain at the current moment based on the actual state value, the actual observed value, the state estimated value and the observed estimated value; and the iteration unit is used for iterating the target Kalman filtering algorithm by utilizing the Kalman gain so as to obtain a state estimation value and an observation estimation value at the next moment by utilizing the iterated target Kalman filtering algorithm.
Optionally, in one embodiment of the present application, the braking module includes: the judging unit is used for judging whether the compressed initial data set meets a preset error allowing condition or not; and the updating unit is used for updating the initial data set based on all the current state parameters and all the current observation parameters if the compressed initial data set does not meet the preset error allowance condition, and generating the emergency braking control action by the updated initial data set.
Optionally, in one embodiment of the present application, the acquiring module includes: the matching unit is used for acquiring the actual running scene of the vehicle and matching corresponding braking functions in the preset emergency braking working condition based on the actual running scene; and the extraction unit is used for extracting all the current state parameters and all the current observation parameters in the initial data set based on the braking function.
An embodiment of a third aspect of the present application provides a vehicle including: the emergency braking system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the emergency braking method of the vehicle according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of emergency braking of a vehicle as above.
A fifth aspect embodiment of the application provides a computer program which, when executed, implements the method of emergency braking of a vehicle as above.
According to the embodiment of the application, in the process of emergency braking control of the vehicle, the Kalman filtering algorithm is utilized to compress the input data of the emergency braking control, so that the data quantity required to be processed by the controller is reduced, the data processing efficiency of the vehicle in the emergency braking process is improved, and the vehicle is safer. Therefore, the problems that in the related art, the accuracy and the reliability of a data detection result are possibly reduced due to the fact that the sensor in the vehicle is influenced by various external factors, a large amount of data is input by the sensor in the emergency braking control process of the vehicle, the calculation burden is large, the timeliness of data processing and the real-time response of emergency braking are difficult to ensure, the reliability of the emergency braking control is reduced, the safety level of the vehicle is influenced and the like are solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
Fig. 1 is a flowchart of a method for emergency braking of a vehicle according to an embodiment of the present application;
fig. 2 is a schematic structural view of an emergency brake apparatus of a vehicle according to an embodiment of the present application;
fig. 3 is a schematic structural view of a vehicle according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes an emergency braking method and apparatus for a vehicle according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the problems that in the related art mentioned in the background art, the accuracy and the reliability of a data detection result are possibly reduced due to the fact that an in-vehicle sensor is influenced by various external factors, and the sensor inputs a large amount of data in the emergency braking control process of a vehicle, so that the calculation burden is large, the timeliness of data processing and the real-time response of the emergency braking are difficult to ensure, the reliability of the emergency braking control is reduced, and the safety level of the vehicle is influenced. Therefore, the problems that in the related art, the accuracy and the reliability of a data detection result are possibly reduced due to the fact that the sensor in the vehicle is influenced by various external factors, a large amount of data is input by the sensor in the emergency braking control process of the vehicle, the calculation burden is large, the timeliness of data processing and the real-time response of emergency braking are difficult to ensure, the reliability of the emergency braking control is reduced, the safety level of the vehicle is influenced and the like are solved.
Specifically, fig. 1 is a schematic flow chart of an emergency braking method of a vehicle according to an embodiment of the present application.
As shown in fig. 1, the emergency braking method of the vehicle includes the steps of:
In step S101, it is detected whether the vehicle is in a preset emergency braking condition.
It should be noted that the preset emergency braking condition may be set by a person skilled in the art according to the actual situation, and is not specifically limited herein.
It will be appreciated that in embodiments of the present application, the preset emergency braking condition may be an automatic driving safety condition that is achieved by an automatic emergency braking system of the vehicle by active braking or deceleration of the vehicle to avoid or reduce collisions with the preceding vehicle.
Specifically, the preset emergency braking working condition can be judged by monitoring the power parameters of the vehicle, such as the power parameters of the vehicle including the vehicle speed, the acceleration, the braking pressure and the like, through a sensor installed on the vehicle, when the vehicle speed suddenly decreases or the braking pressure suddenly increases, the emergency braking working condition is judged, if the vehicle is monitored to be in the preset emergency braking working condition, a corresponding emergency braking confirmation signal is generated, and therefore the vehicle is confirmed to be in the preset emergency braking working condition.
In step S102, in the case where it is detected that the vehicle is in the preset emergency braking condition, all current state parameters of the vehicle and all current observation parameters of the current environment are acquired.
It may be appreciated that in the embodiment of the present application, the current state parameters of the vehicle may include a vehicle speed, an acceleration, a brake pressure, a steering angle, an engine speed, and the like, the current observation parameters of the current environment may include a position and a speed of a surrounding vehicle, a road condition, a weather condition, and the like, the observation parameters of the current environment may be obtained through an environment sensor or a vehicle-mounted camera, and all the current state parameters of the vehicle and all the current observation parameters of the current environment may be recorded and stored in real time for analysis and processing in the actual execution process of the subsequent emergency braking condition.
Optionally, in one embodiment of the present application, acquiring all current state parameters of the vehicle and all current observed parameters of the current environment includes: acquiring an actual running scene of a vehicle, and matching a corresponding braking function in a preset emergency braking working condition based on the actual running scene; all current state parameters and all current observed parameters are extracted in the initial dataset based on the braking function.
In the actual execution process, the actual running scene of the vehicle can be obtained through the sensor equipment such as the vehicle-mounted camera, GPS positioning and the like, and the actual running scene comprises information such as the road type, the traffic flow condition, the traffic signal lamp state, the road surface condition and the like of the vehicle. Based on the actual driving scene and the current vehicle state parameters, the method is matched with a preset emergency braking working condition to determine what braking function, such as emergency braking, automatic deceleration and the like, should be adopted in the corresponding driving scene. Based on the determined braking function, all current state parameters and current observation parameters including the actual speed of the vehicle, the braking pressure, the position and speed of surrounding vehicles, the road friction coefficient and the like are extracted from the initial data set so as to further process and store the extracted current state parameters and current observation parameters, thereby facilitating subsequent analysis and decision.
According to the application, the actual running scene of the vehicle can be obtained, the corresponding braking function is matched in the preset emergency braking working condition based on the actual running scene, all the current state parameters and all the current observation parameters are extracted in the initial data set, and intelligent response can be made according to the specific situation of the vehicle by matching the corresponding braking function according to the actual running scene, so that the adaptability and the safety of the braking system are improved.
In step S103, based on the target kalman filtering algorithm, a state estimation value and an observation estimation value at the current moment are obtained according to all the current state parameters and all the current observation parameters, and an initial data set of a preset emergency braking condition is compressed by using the state estimation value and the observation estimation value, so as to generate an emergency braking control action of the vehicle based on the compressed initial data set.
It will be appreciated that in embodiments of the present application, a target Kalman filtering algorithm may be used to reduce the amount of data that needs to be processed in a preset emergency braking condition. The Kalman filtering is a state estimation method, which can estimate the state of a system from measured data and consider the uncertainty and noise of the system, and by using the Kalman filtering, the data volume generated by a sensor can be reduced, so that the data volume required to be processed by a controller is reduced.
Specifically, the state variable can be estimated through a kalman filter algorithm to obtain a state estimated value of each time step, and the observed data is estimated to obtain an observed estimated value of each time step. Establishing a system model comprising a state equation and an observation equation, the state equation describing a change in a state variable, the observation equation describing how observation data is derived from the state variable, the state equation being:
x{k+1}＝Fkxk+Bkuk+Wk，
Wherein x k represents a state vector of the vehicle at time step k, F k represents a state transition matrix of the vehicle, B k represents an input matrix, u k represents an input vector, w k represents a state noise, w k is a white gaussian noise, and w k to N (0, q) are satisfied. The observation equation is:
zk＝Hkxk+vk，
wherein z k represents an observation vector of the vehicle at time step k, H k represents an observation matrix, v k represents observation noise, v k is white gaussian noise, and v k to N (0, r) are satisfied. After the state estimation value and the observation estimation value are obtained based on the equation, the method can be used for compressing an initial data set of a preset emergency braking working condition so as to generate an emergency braking control action of the vehicle based on the compressed initial data set, and reduce the data quantity required to be processed by emergency braking.
Optionally, in one embodiment of the present application, compressing the initial data set of the preset emergency braking condition using the state estimate and the observed estimate includes: generating a target vector at the current moment according to the state estimation value and the current observation estimation value; and replacing the initial data set by the target vector to obtain a compressed initial data set.
In the actual execution process, the state estimation value and the observation estimation value of each time step obtained in the steps can be combined into a target vector under the current time step, the target vector is used as data needing to be collected in emergency braking control, the emergency braking system is set to collect data such as position, speed, acceleration and the like of a vehicle, the sampling rate of each data is 100Hz, namely 100 data points are collected per second, the dimension is n, each data point contains the data such as position, speed, acceleration and the like, therefore, the dimension of the original data is 3n, and the compressed data quantity is smaller than the dimension of an initial database.
The application can generate the target vector at the current moment according to the state estimation value and the current observation estimation value; the initial data set is replaced by the target vector to obtain the compressed initial data set, so that the preliminary estimation of the current emergency braking state of the vehicle is realized, and the operation pressure of emergency braking is further reduced.
Optionally, in one embodiment of the present application, after obtaining the state estimation value and the observation estimation value of the current moment according to all the current state parameters and all the current observation parameters based on the target kalman filtering algorithm, the method further includes: acquiring an actual state value and an actual observed value at the current moment, and obtaining Kalman gain at the current moment based on the actual state value, the actual observed value, the state estimation value and the observed estimation value; and iterating the target Kalman filtering algorithm by using the Kalman gain so as to obtain a state estimated value and an observation estimated value at the next moment by using the iterated target Kalman filtering algorithm.
In the actual implementation, the kalman filter is used as a recursive algorithm for estimating the state of the linear dynamic system, and the formula of the target kalman filter algorithm can be expressed as follows:
x{k+1}＝Fkxk+Bkuk+wk，
yk＝Hkxk+vk，
Where x k represents the state vector of the vehicle at time step k, F k represents the state transition matrix of the vehicle, B k represents the input matrix, u k represents the input vector, w k represents the state noise, y k represents the observation vector, H k represents the observation matrix, and v k represents the observation noise. The update process of the target kalman filter algorithm can be expressed as:
x{k|k}＝Fkx{k-1|k-1}+Bkuk，
P{k|k}＝FkP{k-1|k-1}Fk T+Qk，
Kk＝P{k|k-1}Hk T(HkP{k|k-1}Hk T+Rk)-1,
x{k|k}＝x{k|k}+Kk(yk-Hkx{k|k})，
P{k|k}＝(I-KkHk)P{k|k-1}}，
Where x {k|k} represents the state estimation value of the vehicle at time step K, P {k|k} represents the uncertainty of the state estimation of the vehicle at time step K, Q k represents the covariance matrix of the system noise, R k represents the covariance matrix of the observation noise, K k represents the kalman gain, x {k|k-1} represents the predicted value of the state estimation value at time step K, and P {k|k-1} represents the predicted value of the uncertainty of the state estimation at time step K. As shown in the above formula, the target kalman filtering algorithm may be iterated by using the kalman gain at the current time, so as to obtain the state estimation value and the observation estimation value at the next time by using the iterated target kalman filtering algorithm.
The application can iterate the target Kalman filtering algorithm based on the actual state value, the actual observation value, the state estimation value and the Kalman gain of the observation estimation value at the current moment, and is used for calculating the state estimation value and the observation estimation value at the next moment.
Optionally, in one embodiment of the present application, generating an emergency brake control action of the vehicle based on the compressed initial data set includes: judging whether the compressed initial data set meets a preset error allowance condition or not; if the compressed initial data set does not meet the preset error allowance condition, updating the initial data set based on all current state parameters and all current observation parameters, and generating an emergency braking control action by the updated initial data set.
It should be noted that the preset error allowance condition may be set by those skilled in the art according to the actual situation, and is not specifically limited herein.
In the actual execution process, the compressed initial data set is evaluated, whether the error between the compressed initial data set and the original data set exceeds an error allowable range is judged, if so, the compressed initial data set does not meet a preset error allowable condition, and all current state parameters and all current observation parameters which accord with the current actual working condition are used for updating the initial data set so as to generate an emergency braking action.
The application can judge whether the compressed initial data set meets the preset error allowance condition, if the compressed initial data set does not meet the preset error allowance condition, the initial data set is updated based on all current state parameters and all current observation parameters, the emergency braking control action is generated by the updated initial data set, and the accuracy of the emergency braking action is ensured and the efficiency requirement of the emergency braking control can be met by carrying out error analysis and updating on the compressed data set.
According to the emergency braking method for the vehicle, which is provided by the embodiment of the application, in the process of emergency braking control of the vehicle, the Kalman filtering algorithm is utilized to compress the input data of the emergency braking control, so that the data quantity required to be processed by the controller is reduced, the data processing efficiency of the vehicle in the emergency braking process is improved, and the vehicle is safer. Therefore, the problems that in the related art, the accuracy and the reliability of a data detection result are possibly reduced due to the fact that the sensor in the vehicle is influenced by various external factors, a large amount of data is input by the sensor in the emergency braking control process of the vehicle, the calculation burden is large, the timeliness of data processing and the real-time response of emergency braking are difficult to ensure, the reliability of the emergency braking control is reduced, the safety level of the vehicle is influenced and the like are solved.
An emergency braking apparatus of a vehicle according to an embodiment of the present application will be described next with reference to the accompanying drawings.
Fig. 2 is a schematic structural view of an emergency brake apparatus of a vehicle according to an embodiment of the present application.
As shown in fig. 2, the emergency brake apparatus 10 of the vehicle includes: the device comprises a detection module 100, an acquisition module 200 and a braking module 300.
The detection module 100 is configured to detect whether the vehicle is in a preset emergency braking condition.
The acquiring module 200 is configured to acquire all current state parameters of the vehicle and all current observation parameters of the current environment when it is detected that the vehicle is in a preset emergency braking condition.
The braking module 300 is configured to obtain a state estimation value and an observation estimation value at a current moment according to all current state parameters and all current observation parameters based on a target kalman filtering algorithm, and compress an initial data set of a preset emergency braking condition by using the state estimation value and the observation estimation value, so as to generate an emergency braking control action of the vehicle based on the compressed initial data set.
Alternatively, in one embodiment of the present application, the braking module 300 includes: a generation unit and a replacement unit.
The generating unit is used for generating a target vector at the current moment according to the state estimated value and the current observation estimated value.
And the replacing unit is used for replacing the initial data set by the target vector to obtain a compressed initial data set.
Optionally, in one embodiment of the present application, the braking module 300 further includes: an iteration unit and an acquisition unit.
The acquisition unit is used for acquiring an actual state value and an actual observed value at the current moment after acquiring the state estimated value and the observed estimated value at the current moment according to all the current state parameters and all the current observed parameters based on the target Kalman filtering algorithm, and obtaining Kalman gain at the current moment based on the actual state value, the actual observed value, the state estimated value and the observed estimated value.
The iteration unit is used for iterating the target Kalman filtering algorithm by using the Kalman gain so as to obtain a state estimated value and an observation estimated value at the next moment by using the iterated target Kalman filtering algorithm.
Alternatively, in one embodiment of the present application, the braking module 300 includes: a judging unit and an updating unit.
The judging unit is used for judging whether the compressed initial data set meets preset error permission conditions or not.
And the updating unit is used for updating the initial data set based on all current state parameters and all current observation parameters if the compressed initial data set does not meet the preset error permission condition, and generating an emergency braking control action by the updated initial data set.
Optionally, in one embodiment of the present application, the acquiring module 200 includes: a matching unit and an extracting unit.
The matching unit is used for acquiring the actual running scene of the vehicle and matching the corresponding braking function in the preset emergency braking working condition based on the actual running scene.
And the extraction unit is used for extracting all current state parameters and all current observation parameters in the initial data set based on the braking function.
It should be noted that the foregoing explanation of the embodiment of the emergency braking method of the vehicle is also applicable to the emergency braking apparatus of the vehicle of this embodiment, and will not be repeated here.
According to the emergency braking device for the vehicle, which is provided by the embodiment of the application, in the process of emergency braking control of the vehicle, the Kalman filtering algorithm is utilized to compress the input data of the emergency braking control, so that the data quantity required to be processed by the controller is reduced, the data processing efficiency of the vehicle in the emergency braking process is improved, and the vehicle is safer. Therefore, the problems that in the related art, the accuracy and the reliability of a data detection result are possibly reduced due to the fact that the sensor in the vehicle is influenced by various external factors, a large amount of data is input by the sensor in the emergency braking control process of the vehicle, the calculation burden is large, the timeliness of data processing and the real-time response of emergency braking are difficult to ensure, the reliability of the emergency braking control is reduced, the safety level of the vehicle is influenced and the like are solved.
Fig. 3 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
Memory 301, processor 302, and a computer program stored on memory 301 and executable on processor 302.
The processor 302 implements the emergency braking method of the vehicle provided in the above-described embodiment when executing the program.
Further, the vehicle further includes:
A communication interface 303 for communication between the memory 301 and the processor 302.
A memory 301 for storing a computer program executable on the processor 302.
The memory 301 may comprise a high-speed RAM memory or may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 301, the processor 302, and the communication interface 303 are implemented independently, the communication interface 303, the memory 301, and the processor 302 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (PERIPHERAL COMPONENT INTERCONNECT, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a chip, the memory 301, the processor 302, and the communication interface 303 may communicate with each other through internal interfaces.
Processor 302 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an Application SPECIFIC INTEGRATED Circuit, abbreviated as ASIC, or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the emergency braking method of a vehicle as above.
The present embodiment also provides a computer program which, when executed, implements the emergency braking method of a vehicle as described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. An emergency braking method of a vehicle, comprising the steps of:
Detecting whether the vehicle is in a preset emergency braking working condition or not;
under the condition that the vehicle is detected to be in the preset emergency braking working condition, acquiring all current state parameters of the vehicle and all current observation parameters of a current environment;
based on a target Kalman filtering algorithm, a state estimation value and an observation estimation value at the current moment are obtained according to all the current state parameters and all the current observation parameters, and an initial data set of the preset emergency braking working condition is compressed by using the state estimation value and the observation estimation value, so that emergency braking control actions of the vehicle are generated based on the compressed initial data set.
2. The method of claim 1, wherein said compressing the initial data set of the preset emergency braking condition using the state estimate and the observed estimate comprises:
generating a target vector of the current moment according to the state estimation value and the current observation estimation value;
and replacing the initial data set by the target vector to obtain the compressed initial data set.
3. The method according to claim 1, further comprising, after obtaining the state estimation value and the observation estimation value of the current time according to the all current state parameters and the all current observation parameters based on a target kalman filter algorithm:
acquiring an actual state value and an actual observed value at the current moment, and acquiring a Kalman gain at the current moment based on the actual state value, the actual observed value, the state estimation value and the observed estimation value;
And iterating the target Kalman filtering algorithm by utilizing the Kalman gain so as to obtain a state estimated value and an observation estimated value at the next moment by utilizing the iterated target Kalman filtering algorithm.
4. The method of claim 1, wherein the generating an emergency brake control action of the vehicle based on the compressed initial data set comprises:
judging whether the compressed initial data set meets a preset error allowance condition or not;
and if the compressed initial data set does not meet the preset error allowance condition, updating the initial data set based on all the current state parameters and all the current observation parameters, and generating the emergency braking control action by the updated initial data set.
5. The method of claim 1, wherein the obtaining all current state parameters of the vehicle and all current observed parameters of a current environment comprises:
Acquiring an actual running scene of the vehicle, and matching a corresponding braking function in the preset emergency braking working condition based on the actual running scene;
and extracting all current state parameters and all current observation parameters in the initial data set based on the braking function.
6. An emergency braking apparatus for a vehicle, comprising:
the detection module is used for detecting whether the vehicle is in a preset emergency braking working condition or not;
the acquisition module is used for acquiring all current state parameters of the vehicle and all current observation parameters of a current environment under the condition that the vehicle is detected to be in the preset emergency braking working condition;
the braking module is used for obtaining a state estimation value and an observation estimation value at the current moment according to the current state parameters and the current observation parameters based on a target Kalman filtering algorithm, and compressing an initial data set of the preset emergency braking working condition by utilizing the state estimation value and the observation estimation value so as to generate an emergency braking control action of the vehicle based on the compressed initial data set.
7. The apparatus of claim 6, wherein the braking module comprises:
The generating unit is used for generating a target vector of the current moment according to the state estimation value and the current observation estimation value;
And the replacing unit is used for replacing the initial data set by the target vector to obtain the compressed initial data set.
8. The apparatus of claim 6, wherein the braking module further comprises:
The acquisition unit is used for acquiring an actual state value and an actual observed value at the current moment after acquiring the state estimated value and the observed estimated value at the current moment according to all the current state parameters and all the current observed parameters based on a target Kalman filtering algorithm, and acquiring Kalman gain at the current moment based on the actual state value, the actual observed value, the state estimated value and the observed estimated value;
And the iteration unit is used for iterating the target Kalman filtering algorithm by utilizing the Kalman gain so as to obtain a state estimation value and an observation estimation value at the next moment by utilizing the iterated target Kalman filtering algorithm.
9. A vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of emergency braking of a vehicle as claimed in any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing the emergency braking method of a vehicle according to any one of claims 1-5.
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| CN202410591288.8A CN118269964A (en) | 2024-05-13 | 2024-05-13 | Emergency braking method and device for vehicle |
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| CN202410591288.8A CN118269964A (en) | 2024-05-13 | 2024-05-13 | Emergency braking method and device for vehicle |
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