Auxiliary driving self-adaptive cruise car following method and system

Abstract

The invention is suitable for the technical field of vehicle following distance adjustment, and particularly relates to an auxiliary driving adaptive cruise vehicle following method and system, and the method comprises the steps: reading the speed data of a road vehicle, calculating the acceleration, and configuring the changes of the road vehicle, and the changes comprise the acceleration and deceleration, and the collection of the license plate information of the road vehicle. Establishing a corresponding relationship between the change and the license plate information, constructing a data query platform, and uploading the corresponding relationship to the data query platform; and selecting a test vehicle from the road surface vehicles, and selecting the test vehicle from the adaptive cruise system of the test vehicle. According to the method, the vehicle distance can be stably kept by increasing or deleting the advance value, the driving comfort is improved while the safety and the passing efficiency are ensured, the vehicle following distance can be adjusted and the traffic smoothness can be optimized by setting the predicted value according to the lane changing condition of the vehicle on the road surface, the driving comfort is further improved, and the riding experience of a user is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of following distance adjustment, and in particular to an assisted driving adaptive cruise following method and system. Background Art

[0002] The adaptive cruise control system is a driving assistance system that uses sensors and algorithms to automatically adjust the vehicle's speed and maintain a constant following distance from the vehicle in front without the driver having to manually accelerate or brake.

[0003] When the vehicle in front suddenly slows down or brakes, the adaptive cruise system will also brake suddenly. However, unlike manual driving, the driver may not anticipate the braking action and is not mentally prepared, which may cause a sudden feeling of discomfort and greatly affect the driving and riding experience.

[0004] Therefore, “how to utilize the preceding vehicle to perform smooth braking in advance” is the technical problem that the present invention needs to solve. Summary of the invention

[0005] The purpose of the present invention is to provide an assisted driving adaptive cruise following method and system to solve the problem of "how to use the leading vehicle to perform smooth braking in advance" raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: An assisted driving adaptive cruise following method, the method comprising: Read out the speed data of the road vehicle, calculate the acceleration, configure the change of the road vehicle, wherein the change includes: acceleration and deceleration, collect the license plate information of the road vehicle, establish the corresponding relationship between the change and the license plate information, build a data query platform, and upload the corresponding relationship to the data query platform; A test vehicle is selected from the vehicles on the road, and a following distance set by a user is read from the adaptive cruise control system of the test vehicle, and a camera pre-integrated in the test vehicle is used to collect video surveillance data including a preceding vehicle, and the license plate information of the preceding vehicle is identified to determine a preceding vehicle, and the data query platform is traversed to find out the change corresponding to the preceding vehicle, and it is determined whether the preceding vehicle brakes, and if so, an advance value is inserted into the following distance, and the change is continued to be read to determine whether the preceding vehicle accelerates, and if so, the advance value is deleted from the following distance; Create a number block corresponding to the road vehicle one by one, input the license plate information and changes of the road vehicle into the number block, calculate the average speed of each road section, find the road section where the average speed is less than a threshold, and define it as a target road section, integrate the number blocks corresponding to the target road section, generate a number series, identify the push and pop of the number block, set the prediction value, and correct the following vehicle distance; A control command is generated using the following vehicle distance and written into the adaptive cruise control system.

[0007] Furthermore, the steps of reading out the speed data of the road vehicle, calculating the acceleration, and configuring the change of the road vehicle include: Determine whether the acceleration is greater than a threshold value, and if so, generate a voice warning message and push it to the vehicle terminal; Obtain control authority for the adaptive cruise control system and dynamically adjust the following vehicle distance based on the speed data and the average vehicle speed.

[0008] Furthermore, the step of building a data query platform includes: Find out the registrants of the data query platform, and send a request to the registrants to fill in personal information, wherein the personal information at least includes: contact person, vehicle model, vehicle color and license plate information; The data query platform is used to encrypt and store the personal information.

[0009] Furthermore, the step of uploading the corresponding relationship to the data query platform includes: Locate the real-time position of the road vehicle, cluster the blocks into a number of groups based on the real-time position, and create an index mechanism with the license plate information as the primary key; Insert a tag generated by the real-time location into the group.

[0010] Furthermore, the step of determining the leading vehicle includes: Collecting video surveillance data of the preceding vehicle, selecting a preceding vehicle, and establishing a mapping between the preceding vehicle and the test vehicle; Based on the real-time location, an edge device is found, and the mapping and sequence are stored in the edge device.

[0011] Furthermore, the step of determining whether the leading vehicle brakes, and if so, inserting the advance value into the following vehicle distance comprises: Identify lighting features from the video surveillance data and initiate a determination of the leading vehicle based on a preset trigger mechanism; A selection pop-up window integrating driving modes is pushed to the test vehicle, wherein each driving mode corresponds to an advance value.

[0012] Furthermore, the step of integrating the number blocks corresponding to the target road section to generate a number sequence includes: Constructing a convolutional neural network, inputting the video surveillance data into the convolutional neural network, outputting the distance between the test vehicle and the preceding vehicle, and converting the distance into a number block; It is determined whether the distance between two adjacent number blocks is less than a preset threshold value. If yes, the number blocks are integrated to obtain a number sequence.

[0013] Furthermore, the system comprises: The upload module is used to read the speed data of the road vehicle, calculate the acceleration, configure the change of the road vehicle, wherein the change includes: acceleration and deceleration, collect the license plate information of the road vehicle, establish the corresponding relationship between the change and the license plate information, build a data query platform, and upload the corresponding relationship to the data query platform; A judgment module is used to select a test vehicle from the vehicles on the road, and read the following distance set by the user from the adaptive cruise control system of the test vehicle, collect video surveillance data including the preceding vehicle using a camera pre-integrated in the test vehicle, and identify the license plate information of the preceding vehicle, determine the preceding vehicle, traverse the data query platform, find out the change corresponding to the preceding vehicle, judge whether the preceding vehicle brakes, and if so, insert an advance value into the following distance, continue to read the change, judge whether the preceding vehicle accelerates, and if so, delete the advance value from the following distance; A correction module is used to create a number block corresponding to the road vehicle one by one, input the license plate information and changes of the road vehicle into the number block, calculate the average speed of each road section, find the road section where the average speed is less than a threshold value and define it as a target road section, integrate the number blocks corresponding to the target road section, generate a number series, identify the push and pop of the number block, set the prediction value, and correct the following vehicle distance; A writing module is used to generate a control command using the following vehicle distance and write the control command into the adaptive cruise control system.

[0014] Furthermore, the upload module includes: A push unit, used to determine whether the acceleration is greater than a threshold value, and if so, generate a voice warning message and push it to the vehicle terminal; an adjustment unit, configured to obtain control authority of the adaptive cruise control system and dynamically adjust the following vehicle distance based on the speed data and the average vehicle speed; A sending unit, used for finding out the registrants of the data query platform, and sending a request for filling in personal information to the registrants, wherein the personal information at least includes: contact person, vehicle model, vehicle color and license plate information; A storage unit, used to encrypt and store the personal information using the data query platform; A creation unit, used to locate the real-time position of the road vehicle, cluster the blocks into a plurality of groups based on the real-time position, and create an index mechanism with the license plate information as the primary key; The inserting unit is used to insert the label generated by the real-time position into the group.

[0015] Furthermore, the judging module includes: An establishing unit is used to collect video surveillance data of the preceding vehicle, select a preceding vehicle, and establish a mapping between the preceding vehicle and the test vehicle; A search unit, configured to search for an edge device based on the real-time location, and store the mapping and the sequence in the edge device; A starting unit, used to identify light features from the video surveillance data and start the judgment of the leading vehicle based on a preset trigger mechanism; The integrated unit is used to push a selection pop-up window integrating driving modes to the test vehicle, wherein each driving mode corresponds to an advance value.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By calculating speed data and acceleration, it can intuitively display the traffic conditions ahead and provide a data basis for adjusting the following distance. By building a data query platform, it can obtain changes in the leading vehicle and adjust the following distance in advance to reduce emergency braking, providing users with a more comfortable following experience. By adding or deleting the advance value, the vehicle distance can be maintained smoothly, while ensuring safety and traffic efficiency, while improving driving comfort. By setting the prediction value, the following distance can be adjusted according to the lane change of vehicles on the road, optimizing traffic smoothness, further improving driving comfort, and greatly improving the user's riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a number series provided by an embodiment of the present invention; Figure 2 A flowchart of the assisted driving adaptive cruise following method provided by an embodiment of the present invention; Figure 3 A first sub-flow chart of the assisted driving adaptive cruise following method provided by an embodiment of the present invention; Figure 4 A second sub-flow chart of the assisted driving adaptive cruise following method provided by an embodiment of the present invention; Figure 5 A third sub-flow chart of the assisted driving adaptive cruise following method provided by an embodiment of the present invention; Figure 6 A block diagram of the components of the assisted driving adaptive cruise following system provided by an embodiment of the present invention; Figure 7 A block diagram of the composition of an upload module in the assisted driving adaptive cruise following system provided by an embodiment of the present invention; Figure 8 A block diagram of the composition of a judgment module in an assisted driving adaptive cruise following system provided by an embodiment of the present invention; Fig. 9 This is a block diagram of the composition of the correction module in the assisted driving adaptive cruise following system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In Example 1, Figure 1 and Figure 2 The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown, and is described in detail below: S100: Read out the speed data of road vehicles, calculate the acceleration, configure the changes of road vehicles, wherein the changes include: acceleration and deceleration, collect the license plate information of road vehicles, establish the correspondence between the changes and the license plate information, build a data query platform, and upload the correspondence to the data query platform.

[0020] Road vehicles are delineated, where road vehicles refer to all vehicles traveling on the road. The speed data of road vehicles are read from the vehicle control system, and the acceleration of each vehicle is calculated based on the speed difference and time difference at adjacent moments. In addition, the acceleration can also be determined based on the throttle or brake opening (the degree to which the throttle or brake pedal is depressed); the change of each road vehicle is determined, where the change can be understood as the driving characteristics of the road vehicle, that is, whether the road vehicle is in an accelerating state or a decelerating state; if the acceleration of the road vehicle is greater than a threshold, it is defined as a change, specifically an acceleration change, otherwise, if the deceleration is greater than a threshold, it is defined as a deceleration change; a data query platform is constructed, where the data query platform is mainly used to store the changes of road vehicles; a communication link is established between the vehicle-mounted terminal and the data query platform, and the changes are uploaded to the data query platform via the communication link, and a corresponding relationship between the license plate information and the changes is established based on the personal information uploaded by the user in the data query platform; where the user refers to the driver of the road vehicle.

[0021] S200: A test vehicle is selected from the vehicles on the road, and a following distance set by a user is read from the adaptive cruise control system of the test vehicle. The camera pre-integrated in the test vehicle is used to collect video surveillance data of the leading vehicle, and the license plate information of the leading vehicle is identified to determine the leading vehicle. The data query platform is traversed to find the changes corresponding to the leading vehicle, and it is determined whether the leading vehicle brakes. If so, an advance value is inserted into the following distance, and the changes are continued to be read to determine whether the leading vehicle accelerates. If so, the advance value is deleted from the following distance.

[0022] A test vehicle is selected from the vehicles on the road, wherein the test vehicle is a vehicle that is using an adaptive cruise system, and the following distance set by the user is read from the adaptive cruise system; specifically, the adaptive cruise system maintains a safe distance from the vehicle in front through on-board sensors (such as radar or camera), and this safe distance is set by the user before driving; a camera pre-integrated in the test vehicle is used to collect video surveillance data in front of the test vehicle; in actual driving, the camera has an image recognition function and can identify obstacles or other vehicles in front. The video surveillance data in this embodiment should include at least one vehicle in front.

[0023] At the same time, the preceding vehicle will also collect video surveillance data of vehicles on the road and determine the preceding vehicle from it; for example, if A's preceding vehicle is B, and B's preceding vehicle is C, then C is A's preceding vehicle.

[0024] Using the license plate information of the leading vehicle, traverse the data query platform to find out the corresponding changes of the leading vehicle. If the change is braking, insert an advance value into the following distance. The advance value is inserted to increase safety, so that when the leading vehicle brakes, the test vehicle can react in advance to reduce the risk of sudden braking of the test vehicle. The advance value is determined by the driving style. While driving at the new following distance, continue to monitor the changes of the leading vehicle. If the leading vehicle accelerates, delete the advance value from the following distance.

[0025] In this embodiment, the change of the leading vehicle is utilized to flexibly adjust the following vehicle distance, so as to respond in advance, avoid sudden braking, and improve user comfort.

[0026] S300: Create digital blocks corresponding to the vehicles on the road one by one, input the license plate information and changes of the vehicles on the road into the digital blocks, calculate the average speed of each road section, find out the road section where the average speed is less than a threshold, and define it as a target road section, integrate the digital blocks corresponding to the target road section, generate a sequence, identify the push and pop of the digital blocks, set the prediction value, and correct the following vehicle distance.

[0027] Create blocks corresponding to the vehicles on the road one by one, where blocks are the basic structure of data storage, similar to cache tables. Blocks are mainly used to characterize vehicles on the road and store corresponding license plate information and changes; use the speed data of vehicles on the road to calculate the average speed of the road section. The average speed can be an estimated value. In addition, the average speed can also be obtained from public data; according to the average speed of each road section, find the road section with an average speed less than a threshold and define it as the target road section; integrate the blocks in the target road section to obtain a sequence, where the sequence is a collection of blocks, which is mainly used to characterize the arrangement of vehicles on the road in the target road section. Furthermore, the blocks are arranged in the sequence according to the actual vehicle order.

[0028] By analyzing the video surveillance data, when a vehicle is found to be changing lanes (it should be noted that the lane change here includes: vehicles in the current lane changing to other lanes, and vehicles in other lanes entering the current lane), the predicted value is used to adjust the following vehicle distance in advance; among which, changing the vehicle in the current lane to other lanes is defined as pop-up, and vehicles in other lanes entering the current lane is defined as push-in.

[0029] Specifically, by analyzing the video surveillance data of a certain vehicle on the road, it is found that the vehicle in front changes to other lanes, that is, several blocks pop up, and there is no need to adjust the following distance at this time; when the vehicle on the road finds that a vehicle enters this lane, that is, several blocks are pushed in, then a mark is inserted into the block corresponding to the vehicle on the road, and the predicted value needs to be added to the following distance of all vehicles behind this mark.

[0030] S400: Generate a control command using the following vehicle distance, and write the control command into the adaptive cruise control system.

[0031] When the following distance changes, the adaptive cruise system calculates the ideal braking or acceleration force based on the set following distance, generates corresponding acceleration or deceleration commands, and sends them to the adaptive cruise system to adjust the vehicle.

[0032] For example, there are five vehicles AE on a road section, among which C is the preceding vehicle of A, D is the preceding vehicle of B, and E is the preceding vehicle of C. Based on this, five blocks corresponding to the vehicles on the road are created, and the five blocks are integrated to obtain the following: Figure 1 The number series shown; during driving, when vehicle C brakes suddenly, that is, C changes, the change is uploaded to the corresponding number block. At this time, the advance value is inserted into the following distance of A to increase the distance between A and B; when B decelerates, A only needs to brake slowly, thereby ensuring the safety and stability of A's driving.

[0033] In Example 2, Figure 3The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown. The steps of reading the speed data of the road vehicle, calculating the acceleration, and configuring the change of the road vehicle are described in detail as follows: S101: Determine whether the acceleration is greater than a threshold value. If so, generate a voice warning message and push it to the vehicle terminal.

[0034] Determine whether the vehicle's acceleration is greater than a threshold. If so, it means that the vehicle is experiencing sudden acceleration or abnormal acceleration, which may pose a safety risk and requires the user's special attention. At this time, a voice alarm message is generated and sent to the vehicle terminal to remind the user by voice.

[0035] S102: Obtaining control authority of the adaptive cruise control system, and dynamically adjusting the following vehicle distance based on the speed data and the average vehicle speed.

[0036] After obtaining control authority of the adaptive cruise control system, the following distance is dynamically adjusted based on the vehicle's speed data and the average speed of the current road section; specifically, if the vehicle speed is higher than the average speed and close to the vehicle in front, the following distance is appropriately increased to ensure a safe distance and avoid collision due to excessive speed difference; if the vehicle speed is lower than the average speed and the following distance is large, the distance is appropriately reduced to improve driving efficiency and road capacity.

[0037] In Example 3, Figure 3 The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown, and the steps of building the data query platform are described in detail as follows: S103: Find out the registrants of the data query platform, and send a request for filling in personal information to the registrants, wherein the personal information at least includes: contact person, vehicle model, vehicle color and license plate information.

[0038] Find out all registered user information and determine whether the registrant needs to complete the personal information. If so, send a request to the registrant to complete the personal information. The request can be sent to the registrant via SMS, email, mobile application push and vehicle terminal notification, etc. The personal information includes at least contact information (name, mobile phone number or email), vehicle model (brand, style), vehicle color and license plate information. The advantage of this is that the specific vehicle can be quickly found based on the license plate information.

[0039] S104: Utilizing the data query platform to encrypt and store the personal information.

[0040] The personal data is encrypted using the built-in encryption algorithm (such as AES, RSA or hash function, etc.) in the data query platform, and after encryption, it is stored in the data query platform.

[0041] In Example 4, Figure 3 The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown. The steps of uploading the corresponding relationship to the data query platform are described in detail as follows: S105: Locate the real-time position of the road vehicle, cluster the blocks into a plurality of groups based on the real-time position, and create an index mechanism with the license plate information as the primary key.

[0042] Use GPS or vehicle-mounted sensors to locate vehicles on the road and obtain real-time locations. In the data query platform, divide the vehicles on the road into several groups and create an indexing mechanism with license plate information as the primary key. The indexing mechanism with license plate information as the primary key is: index the vehicles on the road according to the license plate information. The advantage of this is that after obtaining the license plate information of the preceding vehicle, the corresponding changes of the preceding vehicle can be quickly determined.

[0043] S106: Inserting a tag generated by the real-time location into the group.

[0044] After the license plate information is divided into several groups for storage, the label generated by the real-time location is inserted into each group; for example, the vehicles on the road are classified according to the road sections to generate multiple groups such as A, B and C, and the labels are generated using the names of the corresponding road sections and inserted into the groups such as A, B and C.

[0045] In Example 5, Figure 4 The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown, and the steps of determining the leading vehicle are described in detail as follows: S201: Collecting video surveillance data of the preceding vehicle, selecting a preceding vehicle, and establishing a mapping between the preceding vehicle and the test vehicle.

[0046] After the preceding vehicle is determined, video surveillance data of the preceding vehicle is collected, the preceding vehicle of the preceding vehicle is defined as a leading vehicle, and a mapping between the leading vehicle and the test vehicle is established; through this mapping, the preceding vehicle can be quickly determined.

[0047] S202: Find an edge device based on the real-time location, and store the mapping and sequence in the edge device.

[0048] According to the real-time position of the vehicle on the road, an edge device is selected from the roadside, where the edge device can be an edge computing server or a 5G base station, etc., and the mapping and series are stored in the edge device.

[0049] In Example 6, Figure 4 The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown. The following is a detailed description of the steps of determining whether the leading vehicle brakes and, if so, inserting an advance value into the following vehicle interval, as follows: S203: Identify lighting features from the video surveillance data, and initiate a determination of a leading vehicle based on a preset trigger mechanism.

[0050] The image information of the vehicle in front is extracted from the video surveillance data, and the brake light area of ​​the vehicle is detected using a computer vision algorithm to identify the lighting characteristics of the brake lights. If the brake lights are detected to be on, it indicates that the vehicle in front has started to brake, and the system continues to judge whether the vehicle in front has changed based on its acceleration. The trigger mechanism is to trigger the judgment of whether the vehicle in front has changed when it is identified that the vehicle in front has started to brake.

[0051] S204: Pushing a selection pop-up window integrating driving modes to the test vehicle, wherein each driving mode corresponds to an advance value.

[0052] Before driving, a selection pop-up window is pushed to each test vehicle. The selection pop-up window integrates several driving modes, and each driving mode corresponds to an advance value, so as to customize the following vehicle distance and optimize the driving experience and safety.

[0053] In Example 7, Figure 5 The implementation process of the assisted driving adaptive cruise following method provided by an embodiment of the present invention is shown. The steps of integrating the blocks corresponding to the target road section and generating a sequence are described in detail as follows: S301: Construct a convolutional neural network, input the video surveillance data into the convolutional neural network, output the distance between the test vehicle and the preceding vehicle, and convert the distance into a number block.

[0054] Create a convolutional neural network consisting of several convolutional layers, where the convolutional neural network is mainly used to calculate the distance between vehicles and map this distance into a series.

[0055] S302: Determine whether the distance between two adjacent number blocks is less than a preset threshold; if so, integrate the number blocks to obtain a number sequence.

[0056] If the distance between two adjacent number blocks is less than the threshold, the two number blocks are included in the same number sequence; otherwise, the number sequence is reconstructed.

[0057] Figure 6 The structure block diagram of the assisted driving adaptive cruise following system provided by an embodiment of the present invention is shown. The assisted driving adaptive cruise following system 1 includes: The upload module 11 is used to read the speed data of the road vehicle, calculate the acceleration, configure the change of the road vehicle, wherein the change includes: acceleration and deceleration, collect the license plate information of the road vehicle, establish the corresponding relationship between the change and the license plate information, build a data query platform, and upload the corresponding relationship to the data query platform; The judgment module 12 is used to select a test vehicle from the vehicles on the road, and read the following distance set by the user from the adaptive cruise control system of the test vehicle, collect video surveillance data including the preceding vehicle using a camera pre-integrated in the test vehicle, and identify the license plate information of the preceding vehicle, determine the preceding vehicle, traverse the data query platform, find out the change corresponding to the preceding vehicle, determine whether the preceding vehicle brakes, and if so, insert an advance value into the following distance, continue to read the change, determine whether the preceding vehicle accelerates, and if so, delete the advance value from the following distance; The correction module 13 is used to create a number block corresponding to the road vehicle one by one, input the license plate and change of the road vehicle into the number block, calculate the average speed of each road section, find the road section where the average speed is less than a threshold value and define it as a target road section, integrate the number blocks corresponding to the target road section, generate a number series, identify the push and pop of the number block, set the prediction value, and correct the following vehicle distance; The writing module 14 is used to generate a control command using the following vehicle distance and write the control command into the adaptive cruise control system.

[0058] Figure 7 The structure block diagram of the assisted driving adaptive cruise following system provided by an embodiment of the present invention is shown, and the uploading module 11 includes: The push unit 111 is used to determine whether the acceleration is greater than a threshold value, and if so, generate a voice warning message and push it to the vehicle terminal; An adjustment unit 112, configured to obtain control authority of the adaptive cruise control system and dynamically adjust the following vehicle distance based on the speed data and the average vehicle speed; The sending unit 113 is used to find out the registrants of the data query platform and send a request for filling in personal information to the registrants, wherein the personal information at least includes: contact person, vehicle model, vehicle color and license plate information; The storage unit 114 is used to encrypt and store the personal information using the data query platform; A creation unit 115 is used to locate the real-time position of the road vehicle, cluster the blocks into a plurality of groups based on the real-time position, and create an index mechanism with the license plate information as the primary key; The inserting unit 116 is used to insert a label generated from the license plate information into the group.

[0059] Figure 8 The structure block diagram of the assisted driving adaptive cruise following system provided by an embodiment of the present invention is shown, and the judgment module 12 includes: An establishing unit 121 is used to collect video surveillance data of the preceding vehicle, select a preceding vehicle, and establish a mapping between the preceding vehicle and the test vehicle; A search unit 122, configured to search for an edge device based on the real-time location, and store the mapping and the sequence in the edge device; A starting unit 123, used to identify light features from the video surveillance data and start the judgment of the leading vehicle based on a preset trigger mechanism; The integration unit 124 is used to push a selection pop-up window integrating driving modes to the test vehicle, wherein each driving mode corresponds to an advance value.

[0060] Fig. 9 The structure block diagram of the assisted driving adaptive cruise following system provided by an embodiment of the present invention is shown, and the correction module 13 includes: A conversion unit 131 is used to construct a convolutional neural network, input the video monitoring data into the convolutional neural network, output the distance between the test vehicle and the preceding vehicle, and convert the distance into a number block; The obtaining unit 132 is used to determine whether the distance between two adjacent number blocks is less than a preset threshold. If yes, the number blocks are integrated to obtain a number series.

[0061] The uploading module 11 is mainly used to complete step S100, the judging module 12 is mainly used to complete step S200, the correcting module 13 is mainly used to complete step S300, and the writing module 14 is mainly used to complete step S400; The push unit 111 is mainly used to complete step S101, the adjustment unit 112 is mainly used to complete step S102, the issuing unit 113 is mainly used to complete step S103, the storage unit 114 is mainly used to complete step S104, the creation unit 115 is mainly used to complete step S105, and the insertion unit 116 is mainly used to complete step S106; The establishing unit 121 is mainly used to complete step S201, the searching unit 122 is mainly used to complete step S202, the starting unit 123 is mainly used to complete step S203, and the integrating unit 124 is mainly used to complete step S204; The converting unit 131 is mainly used to complete step S301, and the obtaining unit 132 is mainly used to complete step S302.

[0062] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An assisted driving adaptive cruise following method, characterized in that: The method comprises: Read out the speed data of the road vehicle, calculate the acceleration, configure the change of the road vehicle, wherein the change includes: acceleration and deceleration, collect the license plate information of the road vehicle, establish the corresponding relationship between the change and the license plate information, build a data query platform, and upload the corresponding relationship to the data query platform; A test vehicle is selected from the vehicles on the road, and a following distance set by a user is read from the adaptive cruise control system of the test vehicle, and a camera pre-integrated in the test vehicle is used to collect video surveillance data including a preceding vehicle, and the license plate information of the preceding vehicle is identified to determine a preceding vehicle, and the data query platform is traversed to find out the change corresponding to the preceding vehicle, and it is determined whether the preceding vehicle brakes, and if so, an advance value is inserted into the following distance, and the change is continued to be read to determine whether the preceding vehicle accelerates, and if so, the advance value is deleted from the following distance; Create a number block corresponding to the road vehicle one by one, input the license plate information and changes of the road vehicle into the number block, calculate the average speed of each road section, find the road section where the average speed is less than a threshold, and define it as a target road section, integrate the number blocks corresponding to the target road section, generate a number series, identify the push and pop of the number block, set the prediction value based on the push, and correct the following vehicle distance; A control command is generated using the following vehicle distance and written into the adaptive cruise control system.

2. The assisted driving adaptive cruise following method according to claim 1, characterized in that: The steps of reading out the speed data of the road vehicle, calculating the acceleration, and configuring the change of the road vehicle include: Determine whether the acceleration is greater than a threshold value, and if so, generate a voice warning message and push it to the vehicle terminal; The control authority of the adaptive cruise control system is obtained, and the following vehicle distance is dynamically adjusted based on the speed data and the average vehicle speed.

3. The assisted driving adaptive cruise following method according to claim 1, characterized in that: The steps of constructing the data query platform include: Find out the registrants of the data query platform, and send a request to the registrants to fill in personal information, wherein the personal information at least includes: contact person, vehicle model, vehicle color and license plate information; The data query platform is used to encrypt and store the personal information.

4. The assisted driving adaptive cruise following method according to claim 3, characterized in that: The step of uploading the corresponding relationship to the data query platform includes: Locate the real-time position of the road vehicle, cluster the blocks into a number of groups based on the real-time position, and create an index mechanism with the license plate information as the primary key; Insert a tag generated by the real-time location into the group.

5. The assisted driving adaptive cruise following method according to claim 4, characterized in that: The step of determining the leading vehicle comprises: Collecting video surveillance data of the preceding vehicle, selecting a preceding vehicle, and establishing a mapping between the preceding vehicle and the test vehicle; Based on the real-time location, an edge device is found, and the mapping and sequence are stored in the edge device.

6. The assisted driving adaptive cruise following method according to claim 1, characterized in that: The step of determining whether the leading vehicle brakes and, if so, inserting the advance value into the following vehicle distance comprises: Identify lighting features from the video surveillance data and initiate a determination of the leading vehicle based on a preset trigger mechanism; A selection pop-up window integrating driving modes is pushed to the test vehicle, wherein each driving mode corresponds to an advance value.

7. The assisted driving adaptive cruise following method according to claim 6, characterized in that: The step of integrating the blocks corresponding to the target road section to generate a sequence includes: Constructing a convolutional neural network, inputting the video surveillance data into the convolutional neural network, outputting the distance between the test vehicle and the preceding vehicle, and converting the distance into a number block; It is determined whether the distance between two adjacent number blocks is less than a preset threshold value. If yes, the number blocks are integrated to obtain a number sequence.

8. An assisted driving adaptive cruise following system, characterized in that: The system comprises: The upload module is used to read the speed data of the road vehicle, calculate the acceleration, configure the change of the road vehicle, wherein the change includes: acceleration and deceleration, collect the license plate information of the road vehicle, establish the corresponding relationship between the change and the license plate information, build a data query platform, and upload the corresponding relationship to the data query platform; A judgment module is used to select a test vehicle from the vehicles on the road, and read the following distance set by the user from the adaptive cruise control system of the test vehicle, collect video surveillance data including the preceding vehicle using a camera pre-integrated in the test vehicle, and identify the license plate information of the preceding vehicle, determine the preceding vehicle, traverse the data query platform, find out the change corresponding to the preceding vehicle, judge whether the preceding vehicle brakes, and if so, insert an advance value into the following distance, continue to read the change, judge whether the preceding vehicle accelerates, and if so, delete the advance value from the following distance; A correction module is used to create a number block corresponding to the road vehicle one by one, input the license plate information and changes of the road vehicle into the number block, calculate the average speed of each road section, find the road section where the average speed is less than a threshold value and define it as a target road section, integrate the number blocks corresponding to the target road section, generate a number series, identify the push and pop of the number block, set the prediction value, and correct the following vehicle distance; A writing module is used to generate a control command using the following vehicle distance and write the control command into the adaptive cruise control system.

9. The assisted driving adaptive cruise following system according to claim 8, characterized in that: The upload module includes: A push unit, used to determine whether the acceleration is greater than a threshold value, and if so, generate a voice warning message and push it to the vehicle terminal; an adjustment unit, configured to obtain control authority of the adaptive cruise control system and dynamically adjust the following vehicle distance based on the speed data and the average vehicle speed; A sending unit, used for finding out the registrants of the data query platform, and sending a request for filling in personal information to the registrants, wherein the personal information at least includes: contact person, vehicle model, vehicle color and license plate information; A storage unit, used to encrypt and store the personal information using the data query platform; A creation unit, used to locate the real-time position of the road vehicle, cluster the blocks into a plurality of groups based on the real-time position, and create an index mechanism with the license plate information as the primary key; The inserting unit is used to insert the label generated by the real-time position into the group.

10. The assisted driving adaptive cruise following system according to claim 9, characterized in that: The judging module comprises: An establishing unit is used to collect video surveillance data of the preceding vehicle, select a preceding vehicle, and establish a mapping between the preceding vehicle and the test vehicle; A search unit, configured to search for an edge device based on the real-time location, and store the mapping and the sequence in the edge device; A starting unit, used to identify light features from the video surveillance data and start the judgment of the leading vehicle based on a preset trigger mechanism; The integrated unit is used to push a selection pop-up window integrating driving modes to the test vehicle, wherein each driving mode corresponds to an advance value.

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