Collision avoidance method and device based on user driving behavior data set and storage medium

Abstract

The invention relates to a collision avoidance method and device based on a user driving behavior data set and a storage medium, and belongs to the technical field of intelligent driving, and the method comprises the steps: collecting target data in a vehicle driving process in real time; determining a danger coefficient corresponding to the driver based on the driving behavior data set of the driver when determining that the vehicle is about to collide based on the target data; the driving behavior data set is determined based on historical driving data of the driver; when the danger coefficient is greater than or equal to a preset value, prompting the vehicle to decelerate; and when the vehicle is not decelerated to the preset range, executing a braking operation through the ADAS controller to start collision avoidance of the vehicle. According to the collision avoidance method based on the user driving behavior data set, the vehicle information stored in the cloud platform and the driving behavior data set of the driver are combined, pre-judgment is made in advance, the probability of collision is reduced, interference to the driver is reduced, and safety and user experience in the vehicle driving process are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving technology, and in particular to a collision avoidance method, device and storage medium based on a user driving behavior data set. Background Art

[0002] In the field of intelligent driving, the usual emergency collision avoidance method is to collect data through sensors such as radar and cameras, and combine perception algorithms to monitor the condition of the road. When a possible collision risk is detected and the driver fails to take timely measures (such as reducing the accelerator, braking, turning the steering wheel, etc.), the system can automatically implement emergency braking according to the specific situation to help the vehicle reduce or avoid traffic accidents.

[0003] Existing collision avoidance methods mainly decide whether to intervene in collision avoidance based on the relative speed and distance between the vehicle and the vehicle in front, and whether the driver has braked before the collision. It usually includes several sub-functions: collision warning system: identifying potential collision hazards through radar, cameras and other equipment; active emergency braking: implementing emergency braking when necessary to prevent or mitigate the impact of a collision.

[0004] The existing solution takes into account the state of the vehicle, the relative speed and distance between the vehicle and the vehicle in front, the risk of collision, etc., but cannot make collision avoidance measures for individual behavior habits. The lead time may not be enough, resulting in a collision, or the driver may be nervous due to misbraking, etc. That is, the existing solution is a single-vehicle perception and collision avoidance solution, which uses a universal collision avoidance solution for different drivers. Misbraking may occur when avoiding obstacles, resulting in low safety and poor user experience. Summary of the invention

[0005] In view of this, it is necessary to provide a collision avoidance method, device and storage medium based on a user driving behavior data set to solve the problem of how to improve the safety of the collision avoidance process and the user experience.

[0006] In order to solve the above problems, the present invention provides a collision avoidance method based on a user driving behavior data set, comprising: Real-time collection of target data during vehicle driving; the target data includes road data, driver facial data and vehicle data; When it is determined based on the target data that the vehicle will collide, a risk factor corresponding to the driver is determined based on a driving behavior data set of the driver; the driving behavior data set is determined based on the historical driving data of the driver; When the risk factor is greater than or equal to a preset value, the vehicle is prompted to slow down; When the vehicle does not decelerate to the preset range, the ADAS controller performs braking operations to initiate vehicle collision avoidance.

[0007] In a possible implementation, before determining the risk factor corresponding to the driver based on the driving behavior data set of the driver, the method further includes: Based on the driver's facial data and vehicle ID, the driving behavior dataset is extracted on the cloud platform.

[0008] In a possible implementation, determining the risk factor corresponding to the driver based on the driver's driving behavior data set includes: Analyzing the driving behavior data set using a big data analysis method to construct a collision avoidance data analysis system for the driver; Based on the collision avoidance data analysis system, the risk factor is determined.

[0009] In a possible implementation, before determining the risk factor corresponding to the driver based on the driving behavior data set of the driver, the method further includes: Uploading the historical driving data to a cloud platform; The historical driving data is classified and stored based on the driver ID and the vehicle ID to construct the driving behavior data set.

[0010] In a possible implementation, the historical driving data includes: The brake pedal data, throttle data and steering wheel data when the vehicle collides, light brakes, emergency brakes or warnings occur while driving.

[0011] In a possible implementation, the target data is collected by a vehicle-mounted device; The vehicle-mounted equipment includes: a driver monitoring camera, a road monitoring camera and a millimeter wave radar.

[0012] The present invention also provides a collision avoidance device based on a user driving behavior data set, comprising: A collection module, used to collect target data in real time during the vehicle's driving process; the target data includes road data, driver's facial data and vehicle data; A determination module, configured to determine a risk factor corresponding to the driver based on a driving behavior data set of the driver when it is determined based on the target data that the vehicle will collide; the driving behavior data set is determined based on the driver's historical driving data; A prompting module, used for prompting the vehicle to slow down when the risk factor is greater than or equal to a preset value; The collision avoidance module is used to perform braking operations through the ADAS controller to initiate vehicle collision avoidance when the vehicle has not decelerated to a preset range.

[0013] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the collision avoidance method based on the user driving behavior data set described in any of the above implementations.

[0014] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the collision avoidance method based on a user driving behavior data set described in any of the above implementations is implemented.

[0015] On the other hand, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the collision avoidance method based on a user driving behavior data set described in any of the above implementations.

[0016] The beneficial effects of the present invention are as follows: the collision avoidance method, device and storage medium based on the user driving behavior data set provided by the present invention, by real-time collection of target data during vehicle driving, the target may include road data, driver facial data and vehicle data. When it is determined according to the target data that the vehicle will collide, the corresponding risk factor of the driver is obtained in combination with the driver's driving behavior data set, so that the vehicle collision avoidance can be automatically started according to the risk factor. By real-time collection and analysis of data, combined with the driver's driving behavior data set, timely warning and intelligent response to collision risks are achieved. On the basis of existing intelligent assisted driving based on vehicle perception, combined with vehicle information stored in the cloud platform and the driver's driving behavior data set, predictions are made in advance, the probability of collision is reduced, interference to the driver is reduced, and the safety and user experience during vehicle driving are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 One of the method flow charts of an embodiment of the collision avoidance method based on the user driving behavior data set provided by the present invention; Figure 2 A second method flow chart of an embodiment of a collision avoidance method based on a user driving behavior data set provided by the present invention; Figure 3 A schematic structural diagram of an embodiment of a collision avoidance device based on a user driving behavior data set provided by the present invention; Figure 4 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0022] Figure 1 One of the method flow charts of an embodiment of the collision avoidance method based on the user driving behavior data set provided by the present invention is as follows Figure 1 As shown, the collision avoidance method based on the user driving behavior dataset includes: S101, collecting target data in real time during vehicle driving; the target data includes road data, driver facial data and vehicle data; S102, when it is determined based on the target data that the vehicle will collide, determining a risk factor corresponding to the driver based on a driving behavior data set of the driver; the driving behavior data set is determined based on historical driving data of the driver; S103, when the risk factor is greater than or equal to a preset value, prompting the vehicle to decelerate; S104: When the vehicle has not decelerated to a preset range, a braking operation is performed by the ADAS controller to initiate vehicle collision avoidance.

[0023] In S101, during the driving process of the vehicle, road data, driver facial data and vehicle data are collected in real time. Road data may include road type, traffic signs, road surface conditions, weather conditions, etc. Driver facial data may be used to identify the driver's identity information, i.e., driver ID. Vehicle data may include the speed, acceleration, brake pressure, steering angle, distance of obstacles around the vehicle, and may also include the speed of the vehicle ahead, the distance between the vehicle ahead, etc.

[0024] In S102, based on the collected target data, an algorithm is used to analyze whether the vehicle has a potential collision risk. For example, based on the relative speed and relative distance between the front vehicle and the vehicle itself, it is possible to predict whether a collision will occur and calculate the time of the collision.

[0025] When it is predicted that a vehicle will collide, the driver's risk factor can be determined based on the driver's driving behavior data set. The driving behavior data set is obtained by analyzing the driver's historical driving data. The historical driving data may include the driver's reaction pattern, braking habits, acceleration tendency, braking time, etc. under different road and traffic conditions.

[0026] The driving behavior dataset reflects the driver's driving habits, so the driver's driving ability and risk tendency can be determined based on the driving behavior dataset. The driver's driving skills and risk awareness are quantitatively evaluated through the driving behavior dataset to obtain the risk factor.

[0027] In S103, the risk factor is used to reflect the possibility of a collision in the current driving situation. The greater the risk factor, the greater the risk of a collision. When the risk factor is greater than or equal to a preset value, the vehicle is prompted to slow down, and a warning is issued to the driver to remind him to pay attention to the potential risk of a collision.

[0028] In S104, if the driver fails to respond to the warning in time to slow down the vehicle to a preset range, the system can automatically take collision avoidance measures, such as performing automatic emergency braking or adjusting the vehicle trajectory through the Advanced Driver Assistance System (ADAS) controller to avoid a collision.

[0029] Optionally, when the risk factor is greater than or equal to a preset value, it can be determined that the driver has a more aggressive driving style, often taking lane change or emergency braking measures to avoid risks at a late stage before the warning before the danger occurs, and the risk of collision is relatively high. If no measures are taken within the experience interval this time, the ADAS controller will issue an alarm to remind the driver to slow down. If the driver still does not slow down, the ADAS controller will intervene in time to control the vehicle speed within the brakeable range. Avoid the possibility of collision due to being too close to the vehicle in front and not slowing down in time. On the contrary, if the driver has slowed down after being reminded, the brake will not be intervened to avoid interference with the driver.

[0030] Understandably, when the risk factor is less than a preset value, it can be judged that the driver's style is relatively conservative and the vehicle speed meets the requirements for avoiding collisions, and no reminders or braking will be intervened.

[0031] Compared with the prior art, the collision avoidance method based on the user driving behavior data set provided by the embodiment of the present invention collects target data in real time during the vehicle driving process. The target may include road data, driver facial data and vehicle data. When it is determined that the vehicle will collide according to the target data, the corresponding risk factor of the driver is obtained in combination with the driver's driving behavior data set, so that the vehicle collision avoidance can be automatically started according to the risk factor. By collecting and analyzing data in real time and combining the driver's driving behavior data set, timely warning and intelligent response to collision risks are achieved. On the basis of the existing intelligent assisted driving based on the perception of the vehicle, combined with the vehicle information stored in the cloud platform and the driver's driving behavior data set, predictions are made in advance, the probability of collision is reduced, the interference to the driver is reduced, and the safety and user experience during vehicle driving are improved.

[0032] In some embodiments of the present invention, the target data is collected by a vehicle-mounted device; The vehicle-mounted equipment includes: a driver monitoring camera, a road monitoring camera and a millimeter wave radar.

[0033] While the vehicle is driving, target data can be collected through on-board equipment, such as driver facial data through a driver monitoring camera, road data through a road monitoring camera, and vehicle data through millimeter-wave radar.

[0034] The collected target data can be uploaded to the cloud platform by TBOX, and then transmitted to the ADAS controller through the cloud platform. The driver monitoring camera, road monitoring camera, millimeter wave radar, ADAS controller, TBOX and cloud platform constitute a complete vehicle status monitoring and data collection, uploading and processing system.

[0035] In some embodiments of the present invention, the historical driving data includes: The brake pedal data, throttle data and steering wheel data when the vehicle collides, light brakes, emergency brakes or warnings occur while driving.

[0036] In some embodiments of the present invention, before determining the risk factor corresponding to the driver based on the driver's driving behavior data set, the method further includes: Uploading the historical driving data to a cloud platform; The historical driving data is classified and stored based on the driver ID and the vehicle ID to obtain the driving behavior data set.

[0037] For example, when the vehicle is driving, the installed radar and camera sensors collect brake pedal data, throttle data, and steering wheel data of the driver's collision, light braking, emergency braking, or warning during driving. Historical driving data can also include the driver's reaction mode, braking habits, acceleration tendency, braking time, etc. under different road and traffic conditions.

[0038] At the same time, the driver's facial information is collected to identify the driver and uploaded to the cloud platform through TBOX. The camera also synchronously uploads the video data of the road scene outside the vehicle at the above moment to the cloud platform.

[0039] After uploading to the cloud platform, the historical driving data is classified and stored by vehicle ID and driver ID to construct a driving behavior dataset corresponding to the vehicle ID and driver ID.

[0040] In some embodiments of the present invention, before determining the risk factor corresponding to the driver based on the driver's driving behavior data set, the method further includes: Based on the driver's facial data and vehicle ID, the driving behavior dataset is extracted on the cloud platform.

[0041] Based on the driver's facial data, the driver's identity information is confirmed, that is, the driver ID is obtained, so that the driving behavior data set corresponding to the driver ID and vehicle ID can be extracted on the cloud platform.

[0042] In some embodiments of the present invention, determining the risk factor corresponding to the driver based on the driver's driving behavior data set includes: Analyzing the driving behavior data set using a big data analysis method to construct a collision avoidance data analysis system for the driver; Based on the collision avoidance data analysis system, the risk factor is determined.

[0043] For example, the cloud platform establishes a driving behavior data set for statistical analysis according to the vehicle ID and the driver ID, and builds a collision avoidance data analysis system for the driver's behavior habits through big data analysis methods. The collision avoidance data analysis system can output the coefficient of danger (i.e., the risk coefficient) analyzed for the driver's behavior, and the timing and intensity of the braking or deceleration behavior to be taken.

[0044] The results output by the collision avoidance data analysis system can enrich the vehicle's usual method of controlling the vehicle condition by only considering the road surface conditions to a comprehensive method that also considers the driver's behavior habits, which is more comprehensive, safer and more comfortable. It can significantly reduce the occurrence of situations such as emergency braking that interfere with the driver's driving too much and the occurrence of unavoidable collisions.

[0045] After the collision avoidance data analysis system is established, when the driver drives the vehicle on the road, the ADAS controller retrieves the vehicle chassis braking performance and the driver's driving behavior habit data from the cloud platform through TBOX. Based on this, the collision avoidance data analysis system outputs information specific to the driver to the ADAS controller, and the ADAS controller decides whether to intervene in advance based on the system output results.

[0046] Optionally, when analyzing driving behavior data sets using big data analysis methods, machine learning algorithms such as cluster analysis, decision trees, or neural networks can be used to identify driving patterns and potential risk factors. Feature values ​​can be extracted during the analysis process, which represent the driver's specific driving behaviors and habits. Based on the extracted feature values, a driver's collision avoidance data analysis system is constructed, which can predict the driver's reactions and behaviors in different situations based on the driver's historical driving data.

[0047] The collision avoidance data analysis system determines the driver's corresponding risk factor, which reflects the driver's likelihood of a collision under current driving conditions. The calculation of the risk factor can take into account a variety of factors, such as the driver's braking reaction time, steering habits, speed control, and historical accident records.

[0048] The collision avoidance method based on the user driving behavior data set provided by the embodiment of the present invention realizes timely warning and intelligent response to collision risks by combining the driver's driving behavior data set. On the basis of the existing intelligent assisted driving based on the perception of the vehicle, combined with the vehicle information stored in the cloud platform and the driver's driving behavior data set, it makes predictions in advance, reduces the probability of collision, reduces interference to the driver, and improves the safety and user experience during vehicle driving.

[0049] In addition, the present invention also provides a collision avoidance method based on a user driving behavior data set, comprising the following steps: 1. Build a driver behavior data collection and upload system.

[0050] The complete vehicle status monitoring and data collection, uploading and processing system is composed of the vehicle information (including braking distance, throttle characteristics, etc.), driver monitoring camera, road monitoring camera, millimeter wave radar, ADAS controller, TBOX and cloud platform.

[0051] When the vehicle is driving normally, the vehicle collects the brake pedal, accelerator, and steering wheel data of collisions, light braking, emergency braking, warnings, etc. that occur during driving through the installed radar and camera sensors, and simultaneously collects the driver's facial information to identify the driver's identity and uploads it to the cloud platform through TBOX. At the same time, the camera also synchronously uploads the video data of the road scene outside the vehicle at the above moments to the cloud platform.

[0052] After uploading, these data will be input into the dedicated model established for the vehicle ID and the driver ID, and the feature values ​​will be extracted and stored.

[0053] 2. Establish a data analysis system.

[0054] The cloud platform establishes a statistical analysis data set according to the vehicle ID and driver ID, and builds a collision avoidance data analysis system based on the driver's behavior habits through a big data analysis model. The system output includes the coefficient of danger analyzed based on the driver's behavior, and the timing and intensity of braking or deceleration actions to be taken.

[0055] The output of the data analysis system can enrich the vehicle's usual method of controlling vehicle conditions by only considering the road surface conditions to a comprehensive method that also considers the driver's behavior habits, which is more comprehensive, safer and more comfortable. It can significantly reduce the occurrence of situations such as emergency braking that interfere with the driver's driving too much and unavoidable collisions.

[0056] 3. Implementation.

[0057] After the system is established, when the driver drives the vehicle on the road, the ADAS controller retrieves the vehicle chassis braking performance and the driver's driving behavior habit data from the cloud platform through TBOX. Based on this, the system finally outputs information targeting the driver to the ADAS controller, and the ADAS controller decides whether to intervene in advance based on the system output results.

[0058] Figure 2 The second method flow chart of an embodiment of the collision avoidance method based on the user driving behavior data set provided by the present invention is as follows: Figure 2 The collision avoidance method based on the user driving behavior data set provided by the present invention is described in detail with specific embodiments.

[0059] When the vehicle is driving, the controller collects data from millimeter-wave radar, camera, brake and accelerator pedal on the CAN bus, comprehensively judges the speed and distance information of the vehicle in front relative to the vehicle, and calculates the expected time of collision.

[0060] The driver's driving behavior habit data set stored in the cloud and the risk factor given by the data set are called.

[0061] When certain specific scenarios in front of the driver (including the distance to the vehicle in front that is about to reach the warning distance) are highly consistent with the characteristic values ​​of the data set, the system's final judgment result will be closely combined with the vehicle's braking performance and the driver's driving style.

[0062] If the data set shows that the driver has a more aggressive driving style, and often changes lanes or brakes suddenly to avoid risks in the pre-warning stage before danger occurs, the risk of collision is relatively high. If no measures are taken within the experience range this time, the ADAS controller will issue an alarm to remind the driver to slow down. If the driver still does not slow down, the ADAS controller will intervene in time to control the speed within the brakeable range. Avoid the possibility of collision due to being too close to the vehicle in front and not slowing down in time. On the contrary, if the driver has slowed down after being reminded, the brake will not be intervened to avoid interference to the driver.

[0063] If the data set shows that the driver's style is relatively conservative and the vehicle speed meets the requirements for avoiding collisions, no reminders or braking will be intervened.

[0064] The collision avoidance method based on user driving behavior data set provided by the present invention, based on the existing intelligent assisted driving based on vehicle perception, combines the vehicle information stored in the cloud platform and the driver's individual behavior habit data set and judgment decision model, makes predictions in advance, reduces the probability of collision, reduces interference to the driver, protects vehicle driving safety, and reduces casualties.

[0065] In order to better implement the collision avoidance method based on the user driving behavior data set in the embodiment of the present invention, on the basis of the collision avoidance method based on the user driving behavior data set, the embodiment of the present invention also provides a collision avoidance device based on the user driving behavior data set. Figure 3 A schematic diagram of the structure of an embodiment of a collision avoidance device based on a user driving behavior data set provided by the present invention, such as Figure 3 As shown, the collision avoidance device 300 based on the user driving behavior data set includes: The acquisition module 310 is used to collect target data in real time during the vehicle driving process; the target data includes road data, driver face data and vehicle data; A determination module 320, configured to determine a risk factor corresponding to the driver based on a driving behavior data set of the driver when it is determined based on the target data that the vehicle will collide; the driving behavior data set is determined based on the historical driving data of the driver; A prompting module 330, configured to prompt the vehicle to decelerate when the risk factor is greater than or equal to a preset value; The collision avoidance module 340 is used to execute a braking operation through the ADAS controller to initiate vehicle collision avoidance when the vehicle has not decelerated to a preset range.

[0066] The collision avoidance device 300 based on the user driving behavior data set provided in the above embodiment can implement the technical solution described in the above embodiment of the collision avoidance method based on the user driving behavior data set. The specific implementation principles of the above modules or units can refer to the corresponding contents in the embodiment of the collision avoidance method based on the user driving behavior data set, which will not be repeated here.

[0067] like Figure 4 As shown, the present invention also provides an electronic device 400. The electronic device 400 includes a processor 401, a memory 402 and a display 403. Figure 4 Only some components of the electronic device 400 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0068] In some embodiments, the processor 401 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 402, such as the collision avoidance method based on the user driving behavior data set in the present invention.

[0069] In some embodiments, processor 401 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented in a cloud platform. In some embodiments, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0070] In some embodiments, the memory 402 may be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. In other embodiments, the memory 402 may also be an external storage device of the electronic device 400, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 400.

[0071] Furthermore, the memory 402 may include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store application software installed in the electronic device 400 and various data.

[0072] In some embodiments, the display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an organic light-emitting diode (OLED) touch device, etc. The display 403 is used to display information on the electronic device 400 and to display a visual user interface. The components 401-403 of the electronic device 400 communicate with each other via a system bus.

[0073] In one embodiment, when the processor 401 executes the collision avoidance program based on the user driving behavior data set in the memory 402, the following steps may be implemented: Real-time collection of target data during vehicle driving; the target data includes road data, driver facial data and vehicle data; When it is determined based on the target data that the vehicle will collide, a risk factor corresponding to the driver is determined based on a driving behavior data set of the driver; the driving behavior data set is determined based on the historical driving data of the driver; When the risk factor is greater than or equal to a preset value, the vehicle is prompted to slow down; When the vehicle does not decelerate to the preset range, the ADAS controller performs braking operations to initiate vehicle collision avoidance.

[0074] It should be understood that: when the processor 401 executes the collision avoidance program based on the user driving behavior data set in the memory 402, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0075] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 400 mentioned, and the electronic device 400 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 400 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0076] Accordingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions of the collision avoidance method based on the user driving behavior data set provided in the above-mentioned method embodiments.

[0077] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the steps or functions of the collision avoidance method based on the user driving behavior data set provided in the above-mentioned method embodiments.

[0078] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0079] The above is a detailed introduction to the collision avoidance method, device and storage medium based on the user driving behavior data set provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A collision avoidance method based on a user driving behavior dataset, characterized in that: include: Real-time collection of target data during vehicle driving; The target data includes road data, driver face data and vehicle data; When it is determined based on the target data that the vehicle will collide, a risk factor corresponding to the driver is determined based on a driving behavior data set of the driver; the driving behavior data set is determined based on the historical driving data of the driver; When the risk factor is greater than or equal to a preset value, the vehicle is prompted to slow down; When the vehicle does not decelerate to the preset range, the ADAS controller performs braking operations to initiate vehicle collision avoidance.

2. The collision avoidance method based on user driving behavior data set according to claim 1, characterized in that: Before determining the risk factor corresponding to the driver based on the driving behavior data set of the driver, the method further includes: Based on the driver's facial data and vehicle ID, the driving behavior dataset is extracted on the cloud platform.

3. The collision avoidance method based on user driving behavior data set according to claim 1, characterized in that: The determining of the risk factor corresponding to the driver based on the driver's driving behavior data set includes: Analyzing the driving behavior data set using a big data analysis method to construct a collision avoidance data analysis system for the driver; Based on the collision avoidance data analysis system, the risk factor is determined.

4. The collision avoidance method based on user driving behavior data set according to claim 1, characterized in that: Before determining the risk factor corresponding to the driver based on the driving behavior data set of the driver, the method further includes: Uploading the historical driving data to a cloud platform; The historical driving data is classified and stored based on the driver ID and the vehicle ID to construct the driving behavior data set.

5. The collision avoidance method based on user driving behavior data set according to claim 1, characterized in that: The historical driving data includes: The brake pedal data, throttle data and steering wheel data when the vehicle collides, light brakes, emergency brakes or warnings occur while driving.

6. The collision avoidance method based on user driving behavior data set according to claim 1, characterized in that: The target data is collected by vehicle-mounted equipment; The vehicle-mounted equipment includes: a driver monitoring camera, a road monitoring camera and a millimeter wave radar.

7. A collision avoidance device based on a user driving behavior data set, characterized in that: include: The acquisition module is used to collect target data in real time during the vehicle's driving process; The target data includes road data, driver face data and vehicle data; A determination module, configured to determine a risk factor corresponding to the driver based on a driving behavior data set of the driver when it is determined based on the target data that the vehicle will collide; the driving behavior data set is determined based on the driver's historical driving data; A prompting module, used for prompting the vehicle to slow down when the risk factor is greater than or equal to a preset value; The collision avoidance module is used to perform braking operations through the ADAS controller to initiate vehicle collision avoidance when the vehicle has not decelerated to a preset range.

8. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the collision avoidance method based on the user driving behavior data set as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the collision avoidance method based on a user driving behavior data set as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the collision avoidance method based on a user driving behavior data set as described in any one of claims 1 to 6 is implemented.

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