Intelligent electric vehicle self-adaptive cruise system capable of memorizing driver style

Abstract

The invention relates to the technical field of intelligent automobiles, in particular to an intelligent electric automobile self-adaptive cruise system capable of memorizing driver styles. According to the technical scheme, the system comprises an information acquisition module, a driver style recognition module, a car following distance matching module and a self-adaptive cruise control module. According to the method, driver styles are divided into five types through a fuzzy algorithm according to jerk, acceleration standard deviation and average acceleration in the driving process of a driver; and the optimal vehicle following distance of the driver is adaptively obtained according to the style of the driver, and adaptive cruise is realized by controlling acceleration and deceleration of the vehicle, so that the driving requirements of different drivers are met, and the user satisfaction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent automobiles, and particularly relates to a memory driver style intelligent electric automobile adaptive cruise system. BACKGROUND

[0002] With the development of automatic driving technology, the adaptive cruise control module (ACC) of intelligent electric automobiles gradually becomes an important technology for improving driving safety and comfort. The traditional ACC system relies on sensor data to adjust the vehicle speed and the distance from the front vehicle, but it lacks the ability to learn and adapt to the driving style of the driver. Therefore, an intelligent ACC system capable of memorizing the driving style of the driver is of great significance. Such a system can learn and remember the acceleration, deceleration patterns and responses to traffic conditions of the driver, thereby providing a more personalized driving experience and improving driving satisfaction under the premise of ensuring safety, laying a foundation for the development of future intelligent transportation systems and automatic driving technology.

[0003] Comparative document CN107757621A discloses an adaptive cruise method for memorizing the driving habits of a driver, including the storage of driver driving data, the filtering processing of driving data and the control steps of the vehicle. However, the above-mentioned application does not provide a specific determination method for driving habits, and lacks specific implementation steps. SUMMARY

[0004] To solve the problems raised in the background art, the present application provides an intelligent electric automobile adaptive cruise system for memorizing the driving style of a driver, which can intelligently adjust according to the specific driving habits of the driver, provide a more personalized driving experience, and not only improve driving comfort, but also improve driving satisfaction.

[0005] The technical solution of the present application is as follows: The present application provides an intelligent electric automobile adaptive cruise system for memorizing the driving style of a driver, which includes an information acquisition module, a driver style recognition module, a following distance matching module and an adaptive cruise control module. The information acquisition module includes a camera, a vehicle speed sensor and a vehicle-mounted radar, the camera is used for face recognition of the driver to identify driver information, the vehicle speed sensor is used for measuring the vehicle speed , acceleration and jerk , and the vehicle-mounted radar is used for measuring the actual following distance . The driver style recognition module comprises a fuzzy classification algorithm unit and a driver information and style recording unit, the fuzzy classification algorithm unit is used for recognizing the style of an unknown driver, the driver information and style recording unit is used for recording a known driver and the corresponding driving style of the known driver, and the driver style is classified; The following distance matching module comprises a minimum following distance calculation unit, a maximum following distance calculation unit, and an optimal following distance calculation and matching unit, the minimum following distance calculation unit is used for calculating the minimum following distance according to the vehicle speed of the host vehicle , the maximum following distance calculation unit is used for calculating the maximum following distance according to the vehicle speed of the host vehicle , and the optimal following distance calculation and matching unit is used for calculating and matching the optimal following distance according to the minimum following distance, the maximum following distance, and the driver style .

[0006] Preferably, the following steps are included: S1: setting a vehicle adaptive cruise speed and activating; S2: an information acquisition module acquires driver information, and simultaneously recognizes the vehicle speed , the acceleration , the jerk , and the actual following distance ; S3: based on the acquired driver information, a driving style recognition system is used to classify the driver style into five types, i.e., aggressive type, relatively aggressive type, regular type, relatively conservative type, and conservative type, and a driver and the driving style of the driver are recorded; S4: based on the recognized vehicle speed , the minimum following distance and the maximum following distance of the host vehicle are calculated, and the corresponding optimal following distance is calculated by a following distance matching module and based on the five types of driving styles; S5: based on the recognized actual following distance , the actual following distance and the optimal following distance are compared in size, and the host vehicle is controlled to accelerate or decelerate by an adaptive cruise control module to realize adaptive cruise.

[0007] Preferably, the S3 comprises the following steps: S31: based on the acquired driver information, if the driver is an unknown driver, a driver style recognition module uses a fuzzy classification algorithm to recognize the driver style and record; S32: if the driver is a known driver, the driver style is directly queried.

[0008] Preferably, the fuzzy classification algorithm in S31 comprises the following steps: S311: based on the identified acceleration , calculate the average acceleration during the driving process of the driver and the acceleration standard deviation , and calculate the intermediate variable , wherein ; S312: based on the identified jerk and the calculated intermediate variable , take the jerk and the intermediate variable as the input variables of the fuzzy model, and take the driving style as the output, which is divided into five categories: aggressive type (A), relatively aggressive type (RA), normal type (N), relatively conservative type (RC), and conservative type (C); S313: set the domain of the fuzzy model input variable as [-15, 15], the fuzzy domain of the input variable as [-10, 10], and the quantization factor as 1, and the output of the fuzzy model as the class label of the driving style {A, RA, N, RC, and C}; S314: divide the domain of the fuzzy model input variable into 3 language variable levels, and define the fuzzy set as {B, M, S}, representing {large, medium, small} respectively; divide the domain of the fuzzy model input variable into 7 language variable levels, and define the fuzzy set as {NB, NM, NS, Z0, PS, PM, PB}, representing {negative large, negative medium, negative small, zero, positive small, positive medium, and positive large} respectively; S315: the fuzzy model control rule is: when the input variable is B and the input variable is PB, the driving style of the driver is A; when the input variable is M and the input variable is Z0, the driving style of the driver is N; when the input variable is S and the input variable is NB, the driving style of the driver is C.

[0009] Preferably, the S4 comprises the following steps: S41: based on the identified vehicle speed , set the minimum following distance of the vehicle as ; S42: based on the identified vehicle speed , set the maximum following distance of the vehicle as ; S43: based on the minimum following distance and the maximum following distance , calculate the optimal following distance wherein is a driver style coefficient, when the driver is respectively five types of aggressive type, relatively aggressive type, normal type, relatively conservative type and conservative type, respectively 0.2, 0.4, 0.6, 0.8 and 1; S44: the car-following distance matching module will automatically match the optimal car-following distance according to the identified driver style.

[0010] Preferably, the step S5 is specifically: when the actual car-following distance is greater than the optimal car-following distance , the adaptive cruise control module controls the vehicle to decelerate to make the actual car-following distance approach the optimal car-following distance . ; When the actual car-following distance is less than the optimal car-following distance , the adaptive cruise control module controls the vehicle to accelerate to make the actual car-following distance approach the optimal car-following distance .

[0011] Compared with the prior art, the beneficial effects of the present application are: the present application divides the driver style into five types by using the jerk, the standard deviation and the average acceleration of the driver during driving, and uses the fuzzy algorithm; and the optimal car-following distance of the driver is obtained according to the driver style, and the adaptive cruise is realized by controlling the acceleration and deceleration of the vehicle, so as to meet the driving needs of different drivers and improve the user satisfaction. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 is a structure block diagram of the intelligent electric vehicle adaptive cruise system in the embodiment of the present application; Figure 2 is a flow chart of the intelligent electric vehicle adaptive cruise system in the embodiment of the present application; Figure 3 is a schematic diagram of the optimal car-following distance corresponding to different driver styles in the embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to more clearly understand the purpose, technical scheme and advantages of the present application, the present application is described and explained in combination with the drawings and embodiments.

[0014] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the same meaning as those commonly understood by a person skilled in the art to which the present application belongs. The terms "one", "a", "an", "the", "these", and similar terms in the present application do not represent the number of quantity limitation, and they can be singular or plural. The terms "include", "contain", "have", and any variants thereof in the present application are intended to cover non-exclusive inclusion; for example, a process, method, and system, product or device containing a series of steps or modules (units) are not limited to the listed steps or modules (units), but can include steps or modules (units) not listed, or can include other steps or modules (units) inherent to the process, method, product or device. The terms "connected", "connected", "coupled" and the like in the present application are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The term "multiple" in the present application refers to two or more. The term "and / or" describes the association between the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that A exists alone, A and B exist together, and B exists alone. Generally, the character " / " represents an "or" relationship between the front and rear associated objects. The terms "first", "second", "third", and the like in the present application are only used to distinguish similar objects, and do not represent a specific order for the objects.

[0015] As shown in Figure 1 , an intelligent electric vehicle adaptive cruise system for remembering driver style includes an information acquisition module, a driver style recognition module, a following distance matching module, and an adaptive cruise control module. The information acquisition module includes a camera, a vehicle speed sensor, and a vehicle-mounted radar. The camera is used for face recognition of the driver to identify the driver information. The vehicle speed sensor is used to measure the vehicle speed , acceleration , and jerk . The vehicle-mounted radar is used to measure the actual following distance . The driver style recognition module includes a fuzzy classification algorithm unit and a driver information and style recording unit. The fuzzy classification algorithm unit is used to identify the style of unknown drivers. The driver information and style recording unit is used to record known drivers and their corresponding driving styles, and to classify the driving styles of the drivers. The following distance matching module includes a minimum following distance calculation unit, a maximum following distance calculation unit, and an optimal following distance calculation and matching unit. The minimum following distance calculation unit is used to calculate the minimum following distance according to the vehicle speed. The maximum following distance calculation unit is used to calculate the maximum following distance The optimal following distance calculation and matching unit is configured to calculate and match the optimal following distance based on the minimum following distance, the maximum following distance and the driver style The adaptive cruise control module includes a speed-up and speed-down control unit configured to control the speed-up and speed-down of the vehicle based on the comparison result of the actual following distance and the optimal following distance . It is to be noted that the adaptive cruise control module is a common function of the intelligent electric vehicle, and thus will not be described in detail.

[0016] With reference to the accompanying drawings Figure 2 and 3 , the intelligent electric vehicle adaptive cruise system for memorizing the driver style includes the following steps: S1: setting the adaptive cruise speed of the vehicle and activating; S2: the information acquisition module acquires the driver information and simultaneously identifies the vehicle speed , the acceleration , the jerk and the actual following distance ; S3: based on the acquired driver information, the driver style is classified into five types, i.e., the aggressive type, the relatively aggressive type, the regular type, the relatively conservative type and the conservative type, through the driving style recognition system, and the driver and the driving style are recorded; S4: based on the identified vehicle speed , the minimum following distance and the maximum following distance of the vehicle are calculated, and the corresponding optimal following distance is calculated through the following distance matching module and based on the five types of driving styles; S5: based on the identified actual following distance , the actual following distance and the optimal following distance are compared, and the adaptive cruise is realized through the adaptive cruise control module to control the speed-up or speed-down of the vehicle.

[0017] Specifically, the above step S2 includes: S21: the driver information is identified through the camera to recognize the face of the driver; S22: the vehicle speed , the acceleration and the jerk are measured through the vehicle speed sensor of the vehicle, and the actual following distance is measured through the vehicle-mounted radar of the vehicle.

[0018] ​Furthermore, the above step S3 includes the following steps: S31: Based on the acquired driver information, if the driver is an unknown driver, the driver style recognition module uses a fuzzy classification algorithm to identify the driver style and record it; S32: If the driver is a known driver, directly query the driver's style.

[0019] It should also be noted that the fuzzy classification algorithm in S31 includes the following steps: S311: Acceleration based on recognition , calculate the average acceleration of the driver during driving and acceleration standard deviation , and calculate the intermediate variables ,in ; S312: Identification-based jerk , and the calculated intermediate variables As the input of the fuzzy model, driving style is the output. Driving styles are divided into five categories: aggressive (A), relatively aggressive (RA), conventional (N), relatively conservative (RC) and conservative (C); S313: Setting fuzzy model input variables The domain of discourse is [-15,15], and the input variable The fuzzy domain is [-10, 10], the quantization factors are all 1, and the output of the fuzzy model is the class label of the driving style {A, RA, N, RC and C}; S314: Inputting fuzzy model into variables The domain of discourse is divided into three levels of linguistic variables, and the fuzzy set is defined as {B, M, S}, representing {large, medium, small} respectively; the fuzzy model is input into the variable The domain of discourse is divided into 7 levels of linguistic variables, and the fuzzy set is defined as {NB, NM, NS, Z0, PS, PM, PB}, which represent {negative large, negative medium, negative small, zero, positive small, positive medium, positive large} respectively; S315: The fuzzy model control rule is: when the input is B and When the input is PB, the driver style is type A; when the input is is M and When the input is Z0, the driver style is N; is S and When it is NB, the driver style is C.

[0020] In step S315, the fuzzy classification is defined as 21 fuzzy rules according to fuzzy logic. The 21 fuzzy rules are listed as follows: And, in the present embodiment, it needs to be explained that, in the fuzzy classification algorithm of the driver style recognition module, the triangular function is selected as the membership function when calculating the membership of the fuzzy set of the input variable.

[0021] Specifically, first, the membership function is used to describe the degree of the input variable belonging to a certain fuzzy set, with the value range being between [0, 1], and the value closer to 1 indicates that the degree of the variable belonging to the corresponding fuzzy set is higher.

[0022] Secondly, the shape of the triangular membership function is a triangle, and its function image is determined by three characteristic points: the starting point, the vertex, and the end point. For a fuzzy set of an input variable, such as the “big (B)” “medium (M)” “small (S)” of the jerk, or the “negative big (NB)” “negative medium (NM)” and the like, by setting the three characteristic points, the triangular membership function corresponding to the fuzzy set can be determined.

[0023] For example, if the triangular membership function of the “big (B)” fuzzy set of the jerk has a starting point of 5, a vertex of 15, and an end point of 15 (combined with the domain of the jerk [-15, 15]), when the jerk is 15, the membership of “big (B)” is 1; when the jerk is 5, the membership is 0; and when the jerk is between 5 and 15, the membership increases linearly with the value, which conforms to the change rule of the triangle.

[0024] Further, the above step S4 includes the following steps: S41: based on the recognized vehicle speed of the host vehicle , setting the minimum following distance of the host vehicle as ; S42: based on the recognized vehicle speed of the host vehicle , setting the maximum following distance of the host vehicle as ; S43: based on the minimum following distance and the maximum following distance , calculating the optimal following distance , wherein is the driver style coefficient, and when the driver is of the five types of aggressive, relatively aggressive, conventional, relatively conservative, and conservative, 0.2, 0.4, 0.6, 0.8, and 1, respectively; S44: the following distance matching module will automatically match the optimal following distance according to the recognized driver style.

[0025] In addition, the above step S5 is specifically: when the recognized actual following distance , and the actual following distance ​greater than the optimal following distance When the actual following distance is greater than the optimal following distance, the adaptive cruise control module causes the actual following distance to approach the optimal following distance by controlling the vehicle to slow down ; When the actual following distance is less than the optimal following distance, the adaptive cruise control module causes the actual following distance to approach the optimal following distance by controlling the vehicle to speed up When the actual following distance is less than the optimal following distance, the adaptive cruise control module causes the actual following distance to approach the optimal following distance by controlling the vehicle to speed up .

[0026] In the description of the present application, it should be understood that the above-mentioned embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above-mentioned embodiments can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0027] In this application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0028] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of the processes should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0029] ​​​Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0030] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0031] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0032] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0033] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0034] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0035] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent electric vehicle adaptive cruise control system that memorizes the driver's style, characterized in that: Including information acquisition module, driver style recognition module, following distance matching module and adaptive cruise control module; The information acquisition module includes a camera, a vehicle speed sensor and a vehicle-mounted radar. The camera is used to perform facial recognition on the driver to identify the driver's information. The vehicle speed sensor is used to measure the vehicle speed. , acceleration and jerk , the vehicle's onboard radar is used to measure the actual following distance ; The driver style recognition module includes a fuzzy classification algorithm unit and a driver information and style recording unit. The fuzzy classification algorithm unit is used to identify the style of unknown drivers, and the driver information and style recording unit is used to record known drivers and their corresponding driving styles and classify the driver styles. The following distance matching module includes a minimum following distance calculation unit, a maximum following distance calculation unit and an optimal following distance calculation and matching unit. The minimum following distance calculation unit is used to calculate the minimum following distance according to the vehicle speed. The maximum following distance calculation unit is used to calculate the maximum following distance according to the vehicle speed. The optimal following distance calculation and matching unit is used to calculate and match the optimal following distance based on the minimum following distance, maximum following distance and driver style. .

2. The intelligent electric vehicle adaptive cruise control system that memorizes the driver's style according to claim 1 is characterized in that: The adaptive cruise control method for an intelligent electric vehicle using the system includes the following steps: S1: Set the vehicle's adaptive cruise speed and activate it; S2: The information acquisition module obtains the driver's information and identifies the vehicle's speed at the same time , acceleration , acceleration and actual following distance ; S3: Based on the acquired driver information, the driving style recognition system classifies the driver's style into five categories: aggressive, relatively aggressive, conventional, relatively conservative, and conservative. The driver and his / her driving style are then recorded. S4: Vehicle speed based on recognition , calculate the minimum following distance of this vehicle and maximum following distance , the following distance matching module calculates the corresponding optimal following distance based on five types of driving styles ; S5: Actual following distance based on recognition , compared with the actual following distance and optimal following distance The size of the vehicle is controlled by the adaptive cruise control module to accelerate or decelerate to achieve adaptive cruise.

3. The intelligent electric vehicle adaptive cruise control system that memorizes the driver's style according to claim 2 is characterized in that: The S3 comprises the following steps: S31: Based on the acquired driver information, if the driver is an unknown driver, the driver style recognition module uses a fuzzy classification algorithm to identify the driver style and record it; S32: If the driver is a known driver, directly query the driver's style.

4. The intelligent electric vehicle adaptive cruise control system that memorizes the driver's style according to claim 3 is characterized in that: The fuzzy classification algorithm in S31 comprises the following steps: S311: Acceleration based on recognition , calculate the average acceleration of the driver during driving and acceleration standard deviation , and calculate the intermediate variables ,in ; S312: Identification-based jerk , and the calculated intermediate variables As the input of the fuzzy model, driving style is the output. Driving styles are divided into five categories: aggressive (A), relatively aggressive (RA), conventional (N), relatively conservative (RC) and conservative (C); S313: Setting fuzzy model input variables The domain of discourse is [-15,15], and the input variable The fuzzy domain is [-10, 10], the quantization factors are all 1, and the output of the fuzzy model is the class label of the driving style {A, RA, N, RC and C}; S314: Inputting fuzzy model into variables The domain of discourse is divided into three levels of linguistic variables, and the fuzzy set is defined as {B, M, S}, representing {large, medium, small} respectively; the fuzzy model is input into the variable The domain of discourse is divided into 7 levels of linguistic variables, and the fuzzy set is defined as {NB, NM, NS, Z0, PS, PM, PB}, which represent {negative large, negative medium, negative small, zero, positive small, positive medium, positive large} respectively; S315: The fuzzy model control rule is: when the input is B and When the input is PB, the driver style is type A; when the input is is M and When the input is Z0, the driver style is N; is S and When it is NB, the driver style is C.

5. The intelligent electric vehicle adaptive cruise control system that memorizes the driver's style according to claim 2 is characterized in that: The S4 comprises the following steps: S41: Vehicle speed based on recognition , set the minimum following distance of the vehicle to ; S42: Vehicle speed based on recognition , set the maximum following distance of the vehicle to ; S43: Based on minimum following distance and maximum following distance , calculate the optimal following distance ,in is the driver style coefficient. When the drivers are divided into five categories: aggressive, relatively aggressive, conventional, relatively conservative and conservative, They are 0.2, 0.4, 0.6, 0.8 and 1 respectively; S44: The following distance matching module will automatically match the optimal following distance based on the identified driver style.

6. The intelligent electric vehicle adaptive cruise control system that memorizes the driver's style according to claim 2, characterized in that: The step S5 is specifically as follows: when the actual following distance is identified , when the actual following distance Greater than the optimal following distance When the adaptive cruise control module controls the vehicle to slow down, the actual following distance Approaching the optimal following distance ; When the actual following distance Less than the optimal following distance When the adaptive cruise control module controls the vehicle acceleration to make the actual following distance Approaching the optimal following distance .

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