An assisted driving adaptive cruise following method and system

Abstract

The present invention is applicable to the technical field of following distance adjustment, and particularly relates to an assisted driving adaptive cruise following method and system. The method includes: reading the speed data of road vehicles, calculating the acceleration, and configuring the changes of road vehicles, where the changes include acceleration and deceleration; collecting the license plate information of road vehicles, establishing the corresponding relationship between the changes and the license plate information, constructing a data query platform, and uploading the corresponding relationship to the data query platform; selecting a test vehicle from the road vehicles, and from the adaptive cruise system of the test vehicle. By increasing or deleting the lead value, the present invention can stably maintain the vehicle distance, improve driving comfort while ensuring safety and traffic efficiency, and by setting the prediction value, it can adjust the following distance according to the lane change situation of road vehicles, optimize traffic flow, further improve driving comfort, and greatly enhance the user's riding experience.

Description

Technical Field

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

[0002] An adaptive cruise following system is a driving assistance system that utilizes sensors and algorithms, which can automatically adjust the speed of a vehicle, maintain a constant following distance from the vehicle ahead, and does not require the driver to manually accelerate or brake.

[0003] When the vehicle ahead suddenly decelerates or brakes, the adaptive cruise system will also brake suddenly. However, different from manual driving, the driver may not anticipate the braking action and thus is not mentally prepared, which may lead to a sudden sense of discomfort and greatly affect the driving and riding experience.

[0004] Therefore, "how to utilize the vehicle ahead to perform smooth braking in advance" is the technical problem to be solved by the present invention. 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 utilize the vehicle ahead to perform smooth braking in advance" proposed in the above background technique.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An assisted driving adaptive cruise following method, the method includes:

[0008] Read out the speed data of the road vehicles, calculate the acceleration, configure the changes of the road vehicles, where the changes include: acceleration and deceleration, collect the license plate information of the road vehicles, establish the corresponding relationship between the changes and the license plate information, construct a data query platform, and upload the corresponding relationship to the data query platform;

[0009] Select a test vehicle from the road vehicles, read out the following distance set by the user from the adaptive cruise system of the test vehicle, use the camera pre-integrated in the test vehicle to collect video monitoring data containing the vehicle ahead, identify the license plate information of the vehicle ahead, determine the leading vehicle, traverse the data query platform, find out the changes corresponding to the leading vehicle, judge whether the leading vehicle brakes, if so, insert an advance value into the following distance, continue to read the changes, judge whether the leading vehicle accelerates, if so, delete the advance value from the following distance;

[0010] Create several blocks corresponding to the road vehicles one by one, input the license plate information and changes of the road vehicles into the blocks, calculate the average speed of each section of the road, find out the sections where the average speed is less than the threshold value, and define them as target sections, integrate the blocks corresponding to the target sections to generate a sequence, identify the push and pop of the blocks, set a predicted value, and correct the following distance;

[0011] Generate a control command using the following distance and write it into the adaptive cruise control system.

[0012] Further, the steps of reading the speed data of the road vehicles, calculating the acceleration, and configuring the changes of the road vehicles include:

[0013] Judge whether the acceleration is greater than the threshold value. If so, generate a voice warning message and push it to the in-vehicle terminal;

[0014] Obtain the control authority of the adaptive cruise control system, and dynamically adjust the following distance based on the speed data and the average speed.

[0015] Further, the steps of constructing the data query platform include:

[0016] Find out the registrants of the data query platform, and send a request to fill in personal information to the registrants, where the personal information at least includes: contact person, vehicle model, vehicle color, and license plate information;

[0017] Use the data query platform to encrypt and store the personal information.

[0018] Further, the steps of uploading the corresponding relationship to the data query platform include:

[0019] Locate the real-time position of the road vehicle, based on the real-time position, cluster the blocks into several groups, and create an indexing mechanism with the license plate information as the primary key;

[0020] Insert the label generated by the real-time position into the group.

[0021] Further, the steps of determining the leading vehicle include:

[0022] Collect the video surveillance data of the preceding vehicle, select the leading vehicle, and establish a mapping between the leading vehicle and the test vehicle;

[0023] Based on the real-time position, find out the edge device, and store the mapping and the sequence into the edge device.

[0024] Further, the steps of judging whether the leading vehicle brakes and inserting an advance value into the following distance if so include:

[0025] Identify the lighting characteristics from the video surveillance data, and based on a preset triggering mechanism, initiate the judgment of the leading vehicle;

[0026] Push a selection pop-up window integrated with driving modes to the test vehicle, where each driving model corresponds to an advance value.

[0027] Further, the step of integrating the blocks corresponding to the target section to generate a sequence includes:

[0028] Construct a convolutional neural network, input the video surveillance data into the convolutional neural network, output the distance between the test vehicle and the vehicle in front, and convert the distance into blocks;

[0029] Judge whether the distance between two adjacent blocks is less than a preset threshold. If so, integrate the blocks to obtain a sequence.

[0030] Further, the system includes:

[0031] An upload module for reading the speed data of the road vehicles, calculating the acceleration, configuring the changes of the road vehicles, where the changes include acceleration and deceleration, collecting the license plate information of the road vehicles, establishing the corresponding relationship between the changes and the license plate information, constructing a data query platform, and uploading the corresponding relationship to the data query platform;

[0032] A judgment module for selecting a test vehicle from the road vehicles, reading the set following distance from the adaptive cruise system of the test vehicle, using a camera pre-integrated in the test vehicle to collect video surveillance data containing the vehicle in front, identifying the license plate information of the vehicle in front, determining the leading vehicle, traversing the data query platform, finding the changes corresponding to the leading vehicle, judging whether the leading vehicle brakes. If so, insert an advance value into the following distance, continue to read the changes, and judge whether the leading vehicle accelerates. If so, delete the advance value from the following distance;

[0033] A calibration module for creating blocks corresponding one-to-one to the road vehicles, inputting the license plate information and changes of the road vehicles into the blocks, calculating the average vehicle speed of each section, finding the sections where the average vehicle speed is less than the threshold, and defining them as target sections, integrating the blocks corresponding to the target sections to generate a sequence, identifying the pushing and popping of the blocks, setting a prediction value, and calibrating the following distance;

[0034] A writing module for generating a control command using the following distance and writing it into the adaptive cruise system.

[0035] Further, the upload module includes:

[0036] A push unit, configured to determine whether the acceleration is greater than a threshold value. If so, generate a voice warning message and push it to the in-vehicle terminal.

[0037] An adjustment unit, configured to obtain the control authority of the adaptive cruise control system, and dynamically adjust the following distance based on the speed data and the average vehicle speed.

[0038] A sending-down unit, configured to find out the registrant of the data query platform and send a filling request for personal information to the registrant, where the personal information at least includes: contact person, vehicle model, vehicle color, and license plate information.

[0039] A storage unit, configured to encrypt and store the personal information by using the data query platform.

[0040] A creating unit, configured to locate the real-time position of the road vehicles, cluster the several blocks into several groups based on the real-time position, and create an index mechanism with the license plate information as the primary key.

[0041] An inserting unit, configured to insert a label generated from the real-time position into the group.

[0042] Further, the judgment module includes:

[0043] A establishing unit, configured to collect the video monitoring data of the preceding vehicle, select the leading vehicle, and establish a mapping between the leading vehicle and the test vehicle.

[0044] A searching unit, configured to find out the edge device based on the real-time position, and store the mapping and the sequence into the edge device.

[0045] A starting unit, configured to identify the light characteristics from the video monitoring data, and start the judgment of the leading vehicle based on a preset triggering mechanism.

[0046] An integrating unit, configured to push a selection pop-up window integrating driving modes to the test vehicle, where each driving model corresponds to an advance value.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] By calculating speed data and acceleration, the traffic conditions ahead can be visually displayed, providing a data basis for adjusting the following distance. By constructing a data query platform, the changes of the leading vehicle can be obtained, and the following distance can be adjusted in advance to reduce sudden braking, providing a more comfortable following experience for users. By increasing or deleting the advance value, the vehicle distance can be stably maintained, improving driving comfort while ensuring safety and traffic efficiency. By setting a prediction value, the following distance can be adjusted according to the lane-changing situation of the vehicles on the road surface, optimizing traffic flow and further improving driving comfort, greatly enhancing the user's riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of a sequence provided by an embodiment of the present invention;

[0050] Figure 2 It is a flowchart of an assisted driving adaptive cruise following method provided by an embodiment of the present invention;

[0051] Figure 3 It is a first sub-flowchart of an assisted driving adaptive cruise following method provided by an embodiment of the present invention;

[0052] Figure 4 It is a second sub-flowchart of an assisted driving adaptive cruise following method provided by an embodiment of the present invention;

[0053] Figure 5 It is a third sub-flowchart of an assisted driving adaptive cruise following method provided by an embodiment of the present invention;

[0054] Figure 6 It is a block diagram of the composition of an assisted driving adaptive cruise following system provided by an embodiment of the present invention;

[0055] Figure 7 It is a block diagram of the composition of an upload module in an assisted driving adaptive cruise following system provided by an embodiment of the present invention;

[0056] Figure 8 It is 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;

[0057] Figure 9 It is a block diagram of the composition of a calibration module in an assisted driving adaptive cruise following system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0059] In Embodiment 1, Figure 1 and Figure 2 show the implementation process of the assisted driving adaptive cruise following method provided by the embodiments of the present invention, which will be described in detail as follows:

[0060] S100: Read the speed data of the road vehicles, calculate the acceleration, configure the changes of the road vehicles, where the changes include acceleration and deceleration, collect the license plate information of the road vehicles, establish the corresponding relationship between the changes and the license plate information, construct a data query platform, and upload the corresponding relationship to the data query platform.

[0061] Define the road vehicles, where the road vehicles refer to all vehicles driving on the road. Read the speed data of the road vehicles from the vehicle control system, and calculate the acceleration of each vehicle based on the speed difference and time difference at adjacent moments. In addition, the acceleration can also be determined according to the throttle or brake opening (the degree to which the throttle or brake pedal is depressed); determine the changes of each road vehicle, where the changes can be understood as the driving characteristics of the road vehicles, 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 the threshold, it is defined as a change, specifically an acceleration change. Conversely, if the deceleration is greater than the threshold, it is defined as a deceleration change; construct a data query platform, where the data query platform is mainly used to store the changes of the road vehicles; establish a communication link between the in-vehicle terminal and the data query platform, and upload the changes to the data query platform via this communication link, and establish the corresponding relationship between the license plate information and the changes according to the personal information uploaded by the user in the data query platform; where the user refers to the driver of the road vehicle.

[0062] S200: Select a test vehicle from the road vehicles, read the following distance set by the user from the adaptive cruise system of the test vehicle, use the camera pre-integrated in the test vehicle to collect video monitoring data including the vehicle in front, identify the license plate information of the vehicle in front, determine the leading vehicle, traverse the data query platform, find the changes corresponding to the leading vehicle, determine whether the leading vehicle brakes, if so, insert an advance value into the following distance, continue to read the changes, and determine whether the leading vehicle accelerates, if so, delete the advance value from the following distance.

[0063] Select a test vehicle from the road vehicles, where the test vehicle is the vehicle that is using the adaptive cruise control system, and read out the following distance set by the user from the adaptive cruise control system; specifically, the adaptive cruise control system maintains a safe distance from the vehicle ahead through on-vehicle sensors (such as radar or camera), and this safe distance is set by the user before driving; use the camera pre-integrated in the test vehicle to collect the video monitoring data in front of the test vehicle; during actual driving, the camera has an image recognition function and can identify obstacles or other vehicles ahead. The video monitoring data in this embodiment should include at least one vehicle ahead.

[0064] Meanwhile, the vehicle ahead also collects the video monitoring data of the road vehicles and determines the leading vehicle from it; for example, if the vehicle ahead of A is B and the vehicle ahead of B is C, then C is the leading vehicle of A.

[0065] Use the license plate information of the leading vehicle to traverse the data query platform and find out the corresponding changes of the leading vehicle. If the change is braking, insert an advance value into the following distance. The insertion of the advance value is to increase safety, so that in the case of the leading vehicle braking, the test vehicle can react in advance and reduce the risk of hard braking of the test vehicle; the advance value is determined by the driving style; during the driving process according to 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.

[0066] In this embodiment, use the changes of the leading vehicle to flexibly adjust the following distance, so as to make a response in advance, avoid hard braking, and improve user comfort.

[0067] S300: Create blocks corresponding to the road vehicles one by one, input the license plate information and changes of the road vehicles into the blocks, calculate the average speed of each section of the road, find out the sections where the average speed is less than the threshold value, and define them as target sections, integrate the blocks corresponding to the target sections to generate a sequence, identify the push and pop of the blocks, set a prediction value, and correct the following distance.

[0068] Create blocks corresponding to the road vehicles one by one. The block is the basic structure for data storage, similar to a cache table. The block is mainly used to represent the road vehicle and store the corresponding license plate information and changes; use the speed data of the road vehicle to calculate the average speed of the section of the road. The average speed can be an estimated value. In addition, the average speed can also be obtained by querying public data; according to the average speed of each section of the road, find out the sections where the average speed is less than the threshold value and define them as target sections; integrate the blocks located in the target sections to obtain a sequence. The sequence is a set composed of blocks, which is mainly used to represent the arrangement of road vehicles in the target section. Further, the blocks are arranged in the sequence according to the actual vehicle order.

[0069] By analyzing video surveillance data, when it is found that a vehicle changes lanes (it should be noted that lane changes here include: a vehicle in the current lane changing to another lane, and also a vehicle in another lane entering the current lane), the following-distance is adjusted in advance using the predicted value; among them, a vehicle in the current lane changing to another lane is defined as popping out, and a vehicle in another lane entering the current lane is defined as pushing in.

[0070] Specifically, by analyzing the video surveillance data of vehicles on a certain road surface, it is found that the vehicle in front changes to another lane, that is, it pops out, and at this time, there is no need to adjust the following-distance; when a vehicle on the road surface finds that a vehicle enters the current lane, that is, it pushes in, a mark is inserted into the corresponding block of the road vehicle, and the predicted value needs to be added to the following-distances of all vehicles behind this mark.

[0071] S400: Generate a control command using the following-distance and write it into the adaptive cruise control system.

[0072] When the following-distance changes, the adaptive cruise control system calculates the ideal braking or accelerating force according to the set following-distance, generates the corresponding acceleration or deceleration command, and sends it to the adaptive cruise control system to adjust the vehicle.

[0073] For example, there are five road vehicles A - E on a certain section of the road. Among them, C is the leading vehicle of A, D is the leading vehicle of B, and E is the leading vehicle of C; accordingly, five blocks corresponding to the road vehicles are created, and these five blocks are integrated to obtain the sequence shown in the appendix Figure 1 During driving, when vehicle C brakes suddenly, that is, C changes, the change is uploaded to the corresponding block. At this time, an advance value is inserted into the following-distance of A, increasing the distance between A and B; when B decelerates, A only needs to brake slowly to ensure the safety and smoothness of A's driving.

[0074] In Embodiment 2, Figure 3 The implementation process of the assisted driving adaptive cruise following method provided by the embodiment of the present invention is shown. The steps of reading the speed data of the road vehicle, calculating the acceleration, and configuring the changes of the road vehicle are described in detail as follows:

[0075] S101: Determine whether the acceleration is greater than the threshold value. If so, generate a voice warning message and push it to the in-vehicle terminal.

[0076] Determine whether the acceleration of the vehicle is greater than the threshold value. If so, it means that the vehicle is undergoing rapid acceleration or abnormal acceleration, and there may be a safety risk, which requires special attention from the user. At this time, generate a voice warning message and send it to the in-vehicle terminal to remind the user by voice.

[0077] S102: Obtain the control authority of the adaptive cruise control system, and dynamically adjust the following distance based on the speed data and the average vehicle speed.

[0078] After obtaining the control authority of the adaptive cruise control system, dynamically adjust the following distance according to the speed data of the vehicle and in combination with the average vehicle speed of the current road section; specifically, if the vehicle speed is higher than the average vehicle speed and approaching the vehicle in front, appropriately increase the following distance to ensure a safe distance and avoid collisions due to too large a speed difference; if the vehicle speed is lower than the average vehicle speed and the following distance is large, appropriately reduce the distance to improve the driving efficiency and road traffic capacity.

[0079] In Embodiment 3, Figure 3 The implementation process of the assisted driving adaptive cruise following method provided by the embodiment of the present invention is shown. The steps of constructing the data query platform are described in detail as follows:

[0080] S103: Locate the registrants of the data query platform, and send a request to fill in personal information to the registrants, where the personal information at least includes: contact person, vehicle model, vehicle color, and license plate information.

[0081] Locate all the registered user information, determine whether the registrant needs to complete personal information. If so, send a request to fill in personal information to the registrant. This request can be sent to the registrant by means such as text message, email, mobile application push, and in-vehicle terminal notification. The personal information at least includes contact information (name, mobile phone number or email), the vehicle model (brand, model) owned, vehicle color, and license plate information; the advantage of doing this is that the specific vehicle can be quickly located according to the license plate information.

[0082] S104: Use the data query platform to encrypt and store the personal information.

[0083] Use the built-in encryption algorithm (such as AES, RSA, or hash function, etc.) in the data query platform to encrypt the personal information, and after the encryption is completed, store it in the data query platform.

[0084] In Embodiment 4, Figure 3 The implementation process of the assisted driving adaptive cruise following method provided by the 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:

[0085] S105: Locate the real-time position of the road surface vehicle, based on the real-time position, cluster the data blocks into several groups, and create an index mechanism with the license plate information as the primary key.

[0086] Using GPS, vehicle-mounted sensors, etc., locate the vehicles on the road surface, obtain the real-time positions, and in the data query platform, divide the vehicles on the road surface into several groups, and create an indexing mechanism with the license plate information as the primary key. The indexing mechanism with the license plate information as the primary key is: index the vehicles on the road surface according to the license plate information; the advantage of this is that when the license plate information of the vehicle in front is obtained, the corresponding changes of the vehicle in front can be quickly determined.

[0087] S106: Insert the tag generated from the real-time position into the group.

[0088] After dividing the license plate information into several groups for storage, insert the tag generated from the real-time position into each group; for example, classify the vehicles on the road surface according to the road section, generate multiple groups such as Group A, Group B, and Group C, generate tags using the names of the corresponding road sections, and insert the tags into groups such as Group A, Group B, and Group C.

[0089] In Embodiment 5, Figure 4 The implementation process of the assisted driving adaptive cruise following method provided by the embodiment of the present invention is shown. The following details the step of determining the leading vehicle, as follows:

[0090] S201: Collect the video monitoring data of the vehicle in front, select the leading vehicle, and establish a mapping between the leading vehicle and the test vehicle.

[0091] After determining the vehicle in front, collect the video monitoring data of the vehicle in front, define the vehicle in front of the vehicle in front as the leading vehicle, and establish a mapping between the leading vehicle and the test vehicle; through this mapping, the leading vehicle can be quickly determined.

[0092] S202: Based on the real-time position, find the edge device and store the mapping and the sequence number into the edge device.

[0093] According to the real-time position of the vehicle on the road surface, select the edge device from the roadside. The edge device can be an edge computing server or a 5G base station, etc., and store the mapping and the sequence number into the edge device.

[0094] In Embodiment 6, Figure 4 The implementation process of the assisted driving adaptive cruise following method provided by the embodiment of the present invention is shown. The following details the step of determining whether the leading vehicle brakes. If so, insert an advance value into the following distance, as follows:

[0095] S203: Identify the light characteristics from the video monitoring data, and based on the preset trigger mechanism, start the judgment of the leading vehicle.

[0096] Extract the image information of the vehicle in front from the video surveillance data, use computer vision algorithms to detect the brake light area of the vehicle, identify the lighting characteristics of the brake lights. If the brake lights are detected to be on, it indicates that the vehicle in front starts to brake, and continue to judge whether the vehicle in front has changed according to the acceleration of the vehicle in front; the triggering mechanism is that when it is recognized that the vehicle in front starts to brake, trigger the judgment on whether the vehicle in front has changed.

[0097] S204: Push a selection pop-up window integrated with driving models to the test vehicle, where each driving model corresponds to an advance value.

[0098] Before driving, push a selection pop-up window to each test vehicle. The selection pop-up window is integrated with several driving models, and each driving model corresponds to an advance value, so as to customize and adjust the following distance and optimize the driving experience and safety.

[0099] In Embodiment 7, Figure 5 The implementation process of the assisted driving adaptive cruise following method provided by the embodiment of the present invention is shown. The steps of integrating the several blocks corresponding to the target section to generate a sequence are described in detail as follows:

[0100] 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 vehicle in front, and convert the distance into several blocks.

[0101] Create a convolutional neural network composed of several convolutional layers, where the convolutional neural network is mainly used to calculate the distance between vehicles and map this distance into a sequence.

[0102] S302: Judge whether the distance between two adjacent said several blocks is less than a preset threshold. If so, integrate the several blocks to obtain a sequence.

[0103] If the distance between two adjacent several blocks is less than the threshold, classify these two several blocks into the same sequence; otherwise, reorganize the sequence.

[0104] Figure 6 The composition structure block diagram of the assisted driving adaptive cruise following system provided by the embodiment of the present invention is shown. The assisted driving adaptive cruise following system 1 includes:

[0105] An upload module 11, configured to read the speed data of the road vehicles, calculate the acceleration, configure the changes of the road vehicles, where the changes include: acceleration and deceleration, collect the license plate information of the road vehicles, establish the corresponding relationship between the changes and the license plate information, construct a data query platform, and upload the corresponding relationship to the data query platform;

[0106] A judgment module 12 is configured to select a test vehicle from the road vehicles, read out the set following distance of the user from the adaptive cruise control system of the test vehicle, collect video monitoring data including the preceding vehicle by using a camera pre-integrated in the test vehicle, identify the license plate information of the preceding vehicle, determine the leading vehicle, traverse the data query platform, find out the changes corresponding to the leading vehicle, judge whether the leading vehicle brakes, if so, insert an advance value into the following distance, continue to read the changes, and judge whether the leading vehicle accelerates, if so, delete the advance value from the following distance;

[0107] A calibration module 13 is configured to create blocks corresponding one by one to the road vehicles, input the license plates and changes of the road vehicles into the blocks, calculate the average vehicle speed of each section, find out the sections where the average vehicle speed is less than the threshold value, and define them as target sections, integrate the blocks corresponding to the target sections to generate a sequence, identify the pushing and popping of the blocks, set a prediction value, and calibrate the following distance;

[0108] A writing module 14 is configured to generate a control command by using the following distance and write it into the adaptive cruise control system.

[0109] Figure 7 The composition structure block diagram of the assisted driving adaptive cruise following vehicle system provided by the embodiment of the present invention is shown. The uploading module 11 includes:

[0110] A pushing unit 111 is configured to judge whether the acceleration is greater than the threshold value. If so, generate a voice warning message and push it to the in-vehicle terminal;

[0111] An adjusting unit 112 is configured to obtain the control authority of the adaptive cruise control system and dynamically adjust the following distance based on the speed data and the average vehicle speed;

[0112] A sending-down unit 113 is configured to find out the registrants of the data query platform and send a filling request for personal information to the registrants, where the personal information at least includes: contact person, vehicle model, vehicle color, and license plate information;

[0113] A storage unit 114 is configured to encrypt and store the personal information by using the data query platform;

[0114] A creating unit 115 is configured to locate the real-time position of the road vehicle, cluster the blocks into several groups based on the real-time position, and create an index mechanism with the license plate information as the primary key;

[0115] An inserting unit 116 is configured to insert a label generated from the license plate information into the group.

[0116] Figure 8 The composition structure block diagram of the assisted driving adaptive cruise following vehicle system provided by the embodiment of the present invention is shown. The judgment module 12 includes:

[0117] A establishing unit 121, configured to collect video monitoring data of the vehicle ahead, select a leading vehicle, and establish a mapping between the leading vehicle and the test vehicle;

[0118] A searching unit 122, configured to search for an edge device based on the real-time position, and store the mapping and a number sequence into the edge device;

[0119] A starting unit 123, configured to identify a light feature from the video monitoring data, and start the judgment of the leading vehicle based on a preset triggering mechanism;

[0120] An integrating unit 124, configured to push a selection pop-up window integrating a driving mode to the test vehicle, where each driving model corresponds to an advance value.

[0121] Figure 9 The composition structure block diagram of the assisted driving adaptive cruise following vehicle system provided by the embodiment of the present invention is shown. The correction module 13 includes:

[0122] A conversion unit 131, configured 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 vehicle ahead, and convert the distance into data blocks;

[0123] A obtaining unit 132, configured to judge whether the distance between two adjacent data blocks is less than a preset threshold. If so, integrate the data blocks to obtain a number sequence.

[0124] Wherein, the uploading module 11 is mainly used to complete step S100, the judgment module 12 is mainly used to complete step S200, the correction module 13 is mainly used to complete step S300, and the writing module 14 is mainly used to complete step S400;

[0125] The pushing unit 111 is mainly used to complete step S101, the adjusting unit 112 is mainly used to complete step S102, the sending-down unit 113 is mainly used to complete step S103, the storing unit 114 is mainly used to complete step S104, the creating unit 115 is mainly used to complete step S105, and the inserting unit 116 is mainly used to complete step S106;

[0126] 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;

[0127] The conversion unit 131 is mainly used to complete step S301, and the resulting unit 132 is mainly used to complete step S302.

[0128] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0129] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be subject to the appended claims.

[0130] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An assistive driving adaptive cruise following method, characterized in that, The method includes: Reading the speed data of road vehicles, calculating the acceleration, and configuring the changes of road vehicles, where the changes include acceleration and deceleration, collecting the license plate information of road vehicles, establishing the corresponding relationship between the changes and the license plate information, constructing a data query platform, and uploading the corresponding relationship to the data query platform; Selecting test vehicles from the road vehicles, reading the set following distance from the adaptive cruise system of the test vehicles, using the cameras pre-integrated in the test vehicles to collect video monitoring data containing the leading vehicle, identifying the license plate information of the leading vehicle, determining the leading vehicle, traversing the data query platform, finding the changes corresponding to the leading vehicle, judging whether the leading vehicle brakes, and if so, inserting an advance value into the following distance, continuing to read the changes, and judging whether the leading vehicle accelerates, and if so, deleting the advance value from the following distance; Creating blocks corresponding one by one to the road vehicles, inputting the license plate information and changes of the road vehicles into the blocks, calculating the average vehicle speed of each section, finding the sections where the average vehicle speed is less than the threshold, and defining them as target sections, integrating the blocks corresponding to the target sections to generate a sequence, identifying the pushing and popping of the blocks, setting a prediction value based on the pushing, and correcting the following distance; Generating a control command using the following distance and writing it into the adaptive cruise system; The step of judging whether the leading vehicle brakes and if so, inserting an advance value into the following distance includes: Identifying the light characteristics from the video monitoring data and starting the judgment of the leading vehicle based on a preset triggering mechanism; Pushing a selection pop-up window integrating driving modes to the test vehicle, where each driving model corresponds to an advance value; The step of integrating the blocks corresponding to the target sections to generate a sequence includes: Constructing a convolutional neural network, inputting the video monitoring data into the convolutional neural network, outputting the distance between the test vehicle and the leading vehicle, and converting the distance into the blocks; Judging whether the distance between two adjacent blocks is less than a preset threshold, and if so, integrating the blocks to obtain a sequence.

2. The assisted driving adaptive cruise following method according to claim 1, characterized in that The step of reading the speed data of road vehicles, calculating the acceleration, and configuring the changes of road vehicles includes: Judging whether the acceleration is greater than the threshold, and if so, generating a voice warning message and pushing it to the in-vehicle terminal; Obtaining the control authority of the adaptive cruise system and dynamically adjusting the following distance 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 step of constructing the data query platform includes: Finding the registrants of the data query platform and sending a request for filling in personal information to the registrants, where the personal information at least includes: contact person, vehicle model, vehicle color, and license plate information; Encrypting and storing the personal information using the data query platform.

4. The method for an assisted driving adaptive cruise following vehicle 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 several groups based on the real-time position, and create an indexing mechanism with the license plate information as the primary key; Insert the tags generated by the real-time position into the group.

5. The method for assisted driving adaptive cruise following according to claim 4, wherein, The step of determining the leading vehicle includes: Collect the video monitoring data of the vehicle in front, select the leading vehicle, and establish a mapping between the leading vehicle and the test vehicle; Based on the real-time position, find the edge device, and store the mapping and the sequence into the edge device.

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