Automobile driving safety early warning system

Abstract

The invention provides an automobile driving safety early warning system which comprises a vehicle-mounted system and a background server, the vehicle-mounted system is in communication connection with the background server, and the vehicle-mounted system comprises a data acquisition module, a data analysis module, a safety early warning module and a data sharing module. The automobile driving safety early warning system is used for executing the automobile driving safety early warning method and can solve the problem that when automobile advertisement design of an equal-scale model on a duct or the automobile advertisement design of the equal-scale model on the duct replaces other objects, an automobile can make wrong judgment in the driving process and then brake is adopted. According to the system, real-time monitoring and early warning of automobile driving safety are achieved through close cooperation of the vehicle-mounted system and the background server, the early warning accuracy and response speed of the system can be continuously improved through continuous data collection, analysis, early warning, sharing and optimization, and safer and more reliable driving guarantee is provided for a driver.

Description

Technical Field

[0001] The invention relates to the technical field of automobile driving safety warning technology, and in particular to an automobile driving safety warning system. Background Art

[0002] With the rapid development of the automobile industry and the sharp increase in road traffic, the issue of driving safety has become increasingly prominent. In order to meet this challenge, automobile safety technology has received widespread attention and development. Among them, the driving safety warning system, as one of the important means to improve driving safety, has made significant progress in recent years. Most of the existing driving safety warning systems are based on single sensor technology, such as cameras, radars, etc. However, these systems still have some shortcomings in the practical application of single sensor technology, especially some proportional models of automobile advertising designs on highways or ducts on roads. The existing cameras will have errors in recognition, or when some proportional models of automobile advertising designs on highways or ducts on roads are replaced with other objects, the car will make a wrong judgment during driving and take braking actions, thereby affecting driving safety, and the existing warning methods are single: some systems only warn through sound or visual methods, lack of diversified warning methods, and it is difficult to adapt to the needs under different driving conditions. In view of the above problems, the present invention proposes a new type of automobile driving safety warning system to solve the problems existing in the prior art, improve driving safety, and reduce the occurrence of traffic accidents. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a vehicle driving safety warning system to at least solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A vehicle driving safety warning system, the system includes a vehicle-mounted system, a background server, the vehicle-mounted system is in communication with the background server, the vehicle-mounted system includes a data acquisition module, a data analysis module, a safety warning module and a data sharing module, the vehicle driving safety warning system is used to execute a vehicle driving safety warning method, the method includes the following steps:

[0006] S1, the data acquisition module collects vehicle driving data through the sensors of its own vehicle system;

[0007] S2, the data analysis module analyzes the collected vehicle driving data and obtains the analysis results;

[0008] S3, the security warning module takes security warning measures based on the analysis results;

[0009] S4, the data sharing module shares data to the backend server based on the security warning measures taken;

[0010] S5. The backend server determines whether to optimize the shared security warning measures and obtains the determination result;

[0011] S6. The backend server iterates the vehicle system version based on the judgment result.

[0012] Furthermore, in step S1, the vehicle driving data includes vehicle driving environment, vehicle positioning and vehicle speed data.

[0013] Furthermore, in step S2, the following sub-steps are included:

[0014] S21, the data analysis module obtains lane object model recognition data based on the collected vehicle driving environment data, and performs movement judgment on the recognized lane object model. If it is judged that the lane object model is in a stationary state, the position distance between the vehicle and the lane object model is obtained, and a first analysis result is obtained;

[0015] S22. The data analysis module obtains lane object model recognition data based on the collected automobile driving environment data, and makes a movement judgment on the recognized lane object model. If the lane object model is judged to be in a stationary state, when the stationary state is obtained, the data analysis module obtains whether there is a culvert in the automobile driving environment. If it is judged that there is a culvert, the data analysis module associates the stationary lane object model with the position information of the culvert to see whether it is consistent. If the position information is consistent, a second analysis result is obtained.

[0016] Furthermore, in step S3, the following steps are included:

[0017] S31, the safety warning module sets a distance threshold. When obtaining the first analysis result, the safety warning module obtains the position distance between the vehicle and the lane object model. When the position distance is less than the distance threshold, the safety warning module obtains the vehicle speed data to make a safety braking judgment and takes a speed reduction safety warning measure.

[0018] S32. Based on the second analysis result, the safety warning module obtains the vehicle speed data and takes safety warning measures of using the obtained vehicle speed data as the speed of passing through the duct.

[0019] Further, in S4, the data sharing module shares data with the background server according to the security warning measures taken:

[0020] The data sharing module shares the acquired vehicle speed data to the background server based on the safety warning measures of speed reduction and the safety warning measures of passing through the duct.

[0021] Furthermore, for the speed reduction safety warning measures data shared to the background server, the background server obtains whether the vehicle speed is in a state of 0 and records the duration of the speed being 0. When the vehicle speed is in a state of 0, the background server determines that there is a road accident and shares the road accident with other vehicle systems;

[0022] When the backend server obtains that the vehicle speed data has interval changes between 0 and other speeds, the backend server determines that the road accident is resolved and shares the resolved road accident with other vehicle systems.

[0023] Further, in step S5, the background server determines whether to optimize the shared security warning measures, and obtains the following specific determination results:

[0024] If the backend server receives a warning to take safety measures to reduce speed, it is judged as an accident-prone area;

[0025] If the backend server receives a safety warning measure of taking the acquired vehicle speed data as the speed of passing through the duct, it is determined as a duct advertising judgment result.

[0026] Furthermore, in step S6, the backend server performs the vehicle system version iteration according to the judgment result as follows:

[0027] The backend server iteratively updates the accident-prone locations in the vehicle system based on the accident-prone location judgment results;

[0028] The backend server iteratively updates the culvert advertisement locations in the vehicle-mounted system based on the culvert advertisement judgment results.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention proposes an automobile driving safety warning system. The system can solve the problem that when the proportional model of the automobile advertising design on the duct is replaced by other objects, the automobile will make wrong judgments and take braking actions while driving. The system realizes real-time monitoring and early warning of automobile driving safety through close cooperation between the on-board system and the background server. Through continuous data collection, analysis, early warning, sharing and optimization, the system can continuously improve its own early warning accuracy and response speed, and provide drivers with safer and more reliable driving protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 The figure is a schematic diagram of the overall structure of a vehicle driving safety warning system provided by an embodiment of the present invention.

[0033] Figure 2 It is a schematic diagram of the overall flow of a vehicle driving safety warning method executed in a vehicle driving safety warning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0035] Reference Figure 1 and Figure 2 The present invention provides a vehicle driving safety warning system, the system includes a vehicle-mounted system, a background server, the vehicle-mounted system is communicatively connected with the background server, the vehicle-mounted system includes a data acquisition module, a data analysis module, a safety warning module and a data sharing module, the vehicle driving safety warning system is used to execute a vehicle driving safety warning method, the method includes the following steps:

[0036] S1, the data acquisition module collects vehicle driving data through the sensors of its own vehicle system;

[0037] S2, the data analysis module analyzes the collected vehicle driving data and obtains the analysis results;

[0038] S3, the security warning module takes security warning measures based on the analysis results;

[0039] S4, the data sharing module shares data to the backend server based on the security warning measures taken;

[0040] S5. The backend server determines whether to optimize the shared security warning measures and obtains the determination result;

[0041] S6. The backend server iterates the vehicle system version based on the judgment result.

[0042] Exemplarily, in order to continuously improve its own warning accuracy and response speed, and provide drivers with safer and more reliable driving protection, the vehicle-mounted system can be closely coordinated with the background server, and the execution method can be used to execute S1. The data acquisition module collects the car driving data through the sensors of its own vehicle-mounted system. In this step, the data acquisition module of the vehicle-mounted system uses a variety of sensors, such as GPS, accelerometers, gyroscopes, cameras, radars, etc. to collect the car's driving environment, car positioning and car speed data in real time; S2. The data analysis module analyzes the collected car driving data and obtains the analysis results. In this step, the data analysis module analyzes and processes the data after receiving the data from the data acquisition module, so as to use the analysis results as the basis for subsequent safety warnings. S3. The safety warning module takes safety warning measures based on the analysis results. In this step, the safety warning module determines whether it is necessary to take warning measures based on the analysis results provided by the data analysis module, so as to remind the driver to pay attention to potential dangers, or take warning measures when necessary. directly avoid accidents when accidents occur; S4. The data sharing module shares data to the background server based on the safety warning measures taken. In this step, in order to ensure the continuous optimization and updating of the system, the data sharing module will share the safety warning measures taken and related driving data to the background server, and the background server will analyze them to help other vehicle systems to more accurately and effectively implement safety warning measures; S5. The background server determines whether the shared safety warning measures should be optimized and obtains the judgment result. In this step, after receiving the data from the vehicle system, the background server can determine whether the current warning strategy needs to be optimized and obtain the corresponding judgment result. S6. The background server iterates the vehicle system version based on the judgment result. In this step, based on the judgment result, the background server will formulate a corresponding optimization plan and iterate the vehicle system version. Through continuous version iteration, the performance and safety of the vehicle system will be continuously improved, providing drivers with more comprehensive and reliable driving protection.

[0043] In step S1, the vehicle driving data includes vehicle driving environment, vehicle positioning and vehicle speed data.

[0044] Exemplarily, the vehicle driving environment data can be obtained through on-board cameras, radars, LiDAR and other sensors, which can capture images, videos and distance and speed information of objects around the vehicle, thereby helping the system understand the current driving environment. For example, the camera can identify road signs, pedestrians, other vehicles, etc.; the radar LiDAR can provide accurate distance and speed information of obstacles ahead, and the vehicle positioning data can be provided by the GPS sensor to determine the precise location of the vehicle worldwide. In addition to longitude and latitude information, GPS can also provide auxiliary data such as speed and direction. The vehicle speed data can be directly measured by the vehicle speed sensor, reflecting the current driving speed of the vehicle, so as to assess whether the vehicle is speeding, whether it needs to slow down to avoid collisions, and other scenarios.

[0045] In step S2, the following sub-steps are included:

[0046] S21, the data analysis module obtains lane object model recognition data based on the collected vehicle driving environment data, and performs movement judgment on the recognized lane object model. If it is judged that the lane object model is in a stationary state, the position distance between the vehicle and the lane object model is obtained, and a first analysis result is obtained;

[0047] S22. The data analysis module obtains lane object model recognition data based on the collected automobile driving environment data, and makes a movement judgment on the recognized lane object model. If the lane object model is judged to be in a stationary state, when the stationary state is obtained, the data analysis module obtains whether there is a culvert in the automobile driving environment. If it is judged that there is a culvert, the data analysis module associates the stationary lane object model with the position information of the culvert to see whether it is consistent. If the position information is consistent, a second analysis result is obtained.

[0048] For example, in order to evaluate the potential impact of stationary objects on vehicle driving safety, lane object model recognition can be performed through a data analysis module, that is, the collected vehicle driving environment data is processed to identify the object model in the lane. These objects may include pedestrians, vehicles, roadblocks, trees, etc. For each identified lane object model, the data analysis module will make a movement judgment. If it is determined that the position of the lane object model has not changed within a period of time, it is considered to be in a stationary state. When the lane object model is confirmed to be in a stationary state, the data analysis module will calculate and obtain the position distance between the car and the object in order to evaluate the collision risk, and the data analysis module generates a first analysis result. In order to evaluate the special impact of the stationary object on the driving safety of the car when it is an advertisement designed on the culvert, the lane object model recognition and movement judgment are the same as S21. The data analysis module first identifies the object model in the lane and makes a movement judgment to determine which objects are in a stationary state. After confirming that the lane object model is in a stationary state, the data analysis module will further analyze whether there is a culvert in the car driving environment. If it is determined that there is a culvert, the data analysis module will associate the stationary lane object model with the position information of the culvert. If the stationary lane object model is consistent with the position information of the culvert, that is, the object is located on the culvert, the data analysis module generates a second analysis result.

[0049] In step S3, the following steps are included:

[0050] S31, the safety warning module sets a distance threshold. When obtaining the first analysis result, the safety warning module obtains the position distance between the vehicle and the lane object model. When the position distance is less than the distance threshold, the safety warning module obtains the vehicle speed data to make a safety braking judgment and takes a speed reduction safety warning measure.

[0051] S32. Based on the second analysis result, the safety warning module obtains the vehicle speed data and takes safety warning measures of using the obtained vehicle speed data as the speed of passing through the duct.

[0052] Exemplarily, in order to prevent a potential collision by reducing speed when it is detected that the distance between the car and the stationary object in the lane is too close, a distance threshold may be preset in the safety warning module to determine whether the distance between the car and the stationary object in the lane is safe. When the safety warning module obtains the first analysis result, i.e., the stationary object in the lane has been identified and the position distance between the car and the object has been calculated, the real-time position distance between the current car and the lane object model is obtained again. If the real-time position distance is less than the preset distance threshold, the safety warning module will consider that there is a collision risk, and further determine whether braking measures need to be taken. Based on the comparison result of the car speed data and the distance threshold, if it is determined that braking measures need to be taken to reduce the collision risk, the safety warning module will trigger a speed reduction safety warning measure, including issuing a sound, visual or tactile warning to the driver, requiring the driver to slow down. In the automatic driving or assisted driving system, the vehicle is directly controlled to perform a deceleration operation. In order to reduce the error judgment of taking speed reduction measures when it is detected that the car is about to enter the duct and there is a stationary object on the duct, the safety warning module obtains the car speed data based on the second analysis result, and takes the safety warning measure of using the acquired car speed data as the speed of passing the duct.

[0053] In S4, the data sharing module shares data with the backend server according to the security warning measures taken:

[0054] The data sharing module shares the acquired vehicle speed data to the background server based on the safety warning measures of speed reduction and the safety warning measures of passing through the duct.

[0055] For example, in order to reduce the misjudgment of other vehicle systems, the data sharing module can share the obtained vehicle speed data as a safety warning measure of the speed through the duct to the background server, so that the background server can make subsequent judgments.

[0056] For the speed reduction safety warning measures data shared to the backend server, the backend server obtains whether the vehicle speed is 0 and records the duration of the speed being 0. When the vehicle speed is 0, the backend server determines that a road accident has occurred and shares the road accident with other vehicle systems.

[0057] When the backend server obtains that the vehicle speed data has interval changes between 0 and other speeds, the backend server determines that the road accident is resolved and shares the resolved road accident with other vehicle systems.

[0058] Exemplarily, in order to accurately identify and share road accidents, the backend server continuously monitors the vehicle speed data received from the data sharing module. When the backend server detects that the speed data of a certain vehicle is 0 for a long time or for a continuous period of time, it believes that the vehicle may be stationary and may stop driving due to a road accident, traffic congestion or other reasons. The backend server will record the length of time the vehicle's speed is 0 to further confirm whether it is a road accident. Usually, there will be a preset threshold such as a few seconds to a few minutes. If the threshold is exceeded, it is considered that there is a potential road accident. If the vehicle speed is 0 for a long time and meets other possible judgment conditions such as multiple vehicles reporting a speed reduction or stop at the same location during the same period, the backend server will determine that there is a road accident. When it is determined to be a road accident, the backend server will immediately share the accident information including location, time, accident type, etc. to other vehicle systems so that other drivers can be informed in advance and take corresponding avoidance measures. The background server continues to monitor the speed data of the vehicle and surrounding vehicles. When the background server detects that the speed of the vehicle that was previously 0 begins to recover and there is an interval change with other speeds, that is, the speed gradually increases from 0 or returns to the normal driving speed, it is considered that the vehicle may have restarted or left the accident scene. Combined with the change in speed data and other possible factors such as the recovery of traffic flow and the cleanup of the accident scene, the background server will determine that the road accident has been resolved. When it is determined that the road accident has been resolved, the background server will immediately share this information with other vehicle systems so that the driver can understand that the road conditions have returned to normal and can drive safely.

[0059] In step S5, the background server determines whether to optimize the shared security warning measures, and obtains the following specific judgment results:

[0060] If the backend server receives a warning to take safety measures to reduce speed, it is judged as an accident-prone area;

[0061] If the backend server receives a safety warning measure of taking the acquired vehicle speed data as the speed of passing through the duct, it is determined as a duct advertising judgment result.

[0062] For example, in order to ensure the safety of automobile driving, if the background server receives a safety warning measure of speed reduction, it is judged as a judgment result of accident-prone areas; if the background server receives a safety warning measure of using the acquired automobile speed data as the speed of passing through a culvert, it is judged as a culvert advertisement judgment result, so that the vehicle system can update the road safety map, send warning information to vehicles about to enter the area, optimize road design or traffic management strategies, etc.

[0063] In step S6, the backend server performs vehicle system version iteration according to the judgment result as follows:

[0064] The backend server iteratively updates the accident-prone locations in the vehicle system based on the accident-prone location judgment results;

[0065] The backend server iteratively updates the culvert advertisement locations in the vehicle-mounted system based on the culvert advertisement judgment results.

[0066] Exemplarily, the background server continuously collects safety warning data from various vehicle systems and analyzes them to determine which places are accident-prone. Based on the results of the data analysis, the background server generates a judgment result of accident-prone places, which may include specific location information, accident type, frequency of occurrence, etc. The background server sends the information of these accident-prone places to the vehicle system. The vehicle system updates its built-in map or navigation system based on the received information to mark the accident-prone places at the corresponding location and provide warnings or suggestions to the driver. This process is carried out in real time or regularly to ensure that the accident-prone place information in the vehicle system is up to date and can reflect the current traffic conditions and safety risks. For culvert advertising design, the background server can iteratively update the culvert advertising location in the vehicle system so that the vehicle system can better identify the advertising design in the culvert area, thereby reducing misjudgment and enhancing driving safety.

[0067] 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, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle driving safety warning system, characterized in that: The system includes a vehicle-mounted system and a background server, the vehicle-mounted system is in communication with the background server, the vehicle-mounted system includes a data acquisition module, a data analysis module, a safety warning module and a data sharing module, the automobile driving safety warning system is used to execute the automobile driving safety warning method, and the method includes the following steps: S1, the data acquisition module collects vehicle driving data through the sensors of its own vehicle system; S2, the data analysis module analyzes the collected vehicle driving data and obtains the analysis results; S3, the security warning module takes security warning measures based on the analysis results; S4, the data sharing module shares data to the backend server based on the security warning measures taken; S5. The backend server determines whether to optimize the shared security warning measures and obtains the determination result; S6. The backend server iterates the vehicle system version based on the judgment result.

2. The vehicle driving safety warning system according to claim 1, characterized in that: In step S1, the vehicle driving data includes vehicle driving environment, vehicle positioning and vehicle speed data.

3. The vehicle driving safety warning system according to claim 2, characterized in that: In step S2, the following sub-steps are included: S21, the data analysis module obtains lane object model recognition data based on the collected vehicle driving environment data, and performs movement judgment on the recognized lane object model. If it is judged that the lane object model is in a stationary state, the position distance between the vehicle and the lane object model is obtained, and a first analysis result is obtained; S22. The data analysis module obtains lane object model recognition data based on the collected automobile driving environment data, and makes a movement judgment on the recognized lane object model. If the lane object model is judged to be in a stationary state, when the stationary state is obtained, the data analysis module obtains whether there is a culvert in the automobile driving environment. If it is judged that there is a culvert, the data analysis module associates the stationary lane object model with the position information of the culvert to see whether it is consistent. If the position information is consistent, a second analysis result is obtained.

4. The vehicle driving safety warning system according to claim 3, characterized in that: In step S3, the following steps are included: S31, the safety warning module sets a distance threshold. When obtaining the first analysis result, the safety warning module obtains the position distance between the vehicle and the lane object model. When the position distance is less than the distance threshold, the safety warning module obtains the vehicle speed data to make a safety braking judgment and takes a speed reduction safety warning measure. S32. Based on the second analysis result, the safety warning module obtains the vehicle speed data and takes safety warning measures of using the obtained vehicle speed data as the speed of passing through the duct.

5. The vehicle driving safety warning system according to claim 4, characterized in that: In S4, the data sharing module shares data with the backend server according to the security warning measures taken: The data sharing module shares the acquired vehicle speed data to the background server based on the safety warning measures of speed reduction and the safety warning measures of passing through the duct.

6. The vehicle driving safety warning system according to claim 5, characterized in that: For the speed reduction safety warning measures data shared to the backend server, the backend server obtains whether the vehicle speed is 0 and records the duration of the speed being 0. When the vehicle speed is 0, the backend server determines that a road accident has occurred and shares the road accident with other vehicle systems. When the backend server obtains that the vehicle speed data has interval changes between 0 and other speeds, the backend server determines that the road accident is resolved and shares the resolved road accident with other vehicle systems.

7. The vehicle driving safety warning system according to claim 5, characterized in that: In step S5, the background server determines whether to optimize the shared security warning measures, and obtains the following specific judgment results: If the backend server receives a warning to take safety measures to reduce speed, it is judged as an accident-prone area; If the backend server receives a safety warning measure of taking the acquired vehicle speed data as the speed of passing through the duct, it is determined as a duct advertising judgment result.

8. The vehicle driving safety warning system according to claim 7, characterized in that: In step S6, the backend server performs vehicle system version iteration according to the judgment result as follows: The backend server iteratively updates the accident-prone locations in the vehicle system based on the accident-prone location judgment results; The backend server iteratively updates the culvert advertisement locations in the vehicle-mounted system based on the culvert advertisement judgment results.

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