Vehicle control method for active avoidance of dangerous scene and related equipment

Abstract

The invention discloses a vehicle control method for active avoidance of a dangerous scene and related equipment, and relates to the field of intelligent connected vehicles and auxiliary safe driving, and the method comprises the steps: obtaining first driving scene data of a target vehicle in a preset area, extracting second driving scene data associated with vehicle driving safety from the first driving scene data; the second driving scene data are compared on the basis of a dangerous scene library, the danger level of the first driving scene data is obtained, and preset standard data corresponding to multiple dangerous scenes of vehicle driving are stored in the dangerous scene library; and determining a safety control instruction based on the danger level, and executing a corresponding avoidance measure based on the safety control instruction.

Description

Technical Field

[0001] The present application relates to the field of intelligent connected vehicles and assisted safe driving, and in particular to a vehicle control method and related equipment for actively avoiding dangerous scenarios. Background Art

[0002] At present, the digitalization of automobiles is developing at an astonishing speed, greatly improving the living standards and quality of human society. Cars are no longer just traditional means of transportation, but have gradually evolved into high-tech products that integrate intelligent technology, information interaction and comfortable experience. The application of digital technology has made a qualitative leap in the safety, ease of operation and user experience of automobiles.

[0003] However, road traffic accidents have become an important factor threatening people's lives and property safety. The number of deaths caused by road traffic accidents each year accounts for more than 50% of the total number of accidental deaths. Faced with complex and diverse road traffic scenes, autonomous driving vehicles can only ensure driving safety by accurately understanding and identifying them and making reasonable execution actions. With the improvement of sensor accuracy, the improvement of multi-sensor fusion technology and the advancement of machine recognition algorithms, the ability of autonomous driving vehicles to identify complex traffic scenes is increasing. Some vehicles have been tested on roads for millions of miles, but they still cannot avoid a large number of traffic accidents involving autonomous driving vehicles. In addition, the vehicle's ability to identify and actively avoid dangerous scenes with multiple sources of uncertainty is also lacking. Therefore, it is necessary to propose a vehicle control method and related equipment for active avoidance of dangerous scenes to at least solve some of the above problems. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, an embodiment of the present application provides a vehicle control method for actively avoiding dangerous scenarios, the method comprising:

[0006] Acquire first driving scene data of a target vehicle in a preset area, and extract second driving scene data associated with vehicle driving safety from the first driving scene data;

[0007] Comparing the second driving scene data based on a dangerous scene library to obtain a danger level of the first driving scene data, wherein the dangerous scene library stores preset standard data corresponding to a plurality of dangerous scenes of vehicle driving;

[0008] A safety control instruction is determined based on the risk level, and a corresponding avoidance measure is executed based on the safety control instruction.

[0009] In one embodiment of the present invention, the first driving scene data includes data corresponding to each scene element in the driving scene where the target vehicle is located; the scene elements include pedestrians, the target vehicle, adjacent vehicles, the road where the target vehicle is located, and the climate;

[0010] Extracting second driving scene data associated with vehicle driving safety from the first driving scene data includes:

[0011] Taking the target vehicle as the center, determining the driving range of the target vehicle, determining the positions of pedestrians and adjacent vehicles within the driving range, and obtaining third driving scene data;

[0012] The third driving scene data is analyzed to determine the topological relationship between the various scene elements to obtain the second driving scene data.

[0013] In one embodiment of the present invention, for the preset standard data corresponding to each dangerous scene in the dangerous scene library, the preset standard data includes data corresponding to each scene element respectively;

[0014] The comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data includes:

[0015] Comparing the second driving scene data based on the data corresponding to each scene element in the dangerous scene library to obtain matching target driving scene data;

[0016] Acquire the accident triggering scenario factor in each scenario element of the target driving scenario data, and determine the correlation between the accident triggering scenario factor and the second driving scenario data;

[0017] Based on the correlation between the accident triggering scenario factor and the second driving scenario data, the danger level of the first driving scenario data is determined.

[0018] In one embodiment of the present invention, the determining the danger level of the first driving scene data based on the association relationship between the accident triggering scene factor and the second driving scene data includes:

[0019] The association relationship between the accident triggering scenario factor and the second driving scenario data, and simulating and generating a pre-collision scenario based on the driving data of the target vehicle;

[0020] A danger level of the first driving scenario data is obtained based on the pre-collision scenario.

[0021] In one embodiment of the present invention, if there is danger, the method further includes: sending a safety warning signal.

[0022] In one embodiment of the present invention, it also includes:

[0023] Obtaining avoidance measures corresponding to the pre-collision scenario;

[0024] The determined circumvention measures are executed based on the security control instructions.

[0025] In one embodiment of the present invention, the comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data is triggered based on at least one of the following situations:

[0026] When the speed of the target vehicle is greater than a preset speed, comparing the second driving scene data based on a dangerous scene library;

[0027] When the target vehicle is detected to enter a preset area, comparing the second driving scene data based on a dangerous scene library;

[0028] When it is detected that the pedestrian density is greater than the preset density, the second driving scene data is compared based on the danger scene library.

[0029] In a second aspect, the present application proposes a vehicle control system for active avoidance of dangerous scenarios, the system comprising: an on-board numerical control center module, an intelligent driving control module and an intelligent driving execution module;

[0030] The vehicle-mounted numerical control center module is configured to: obtain first driving scene data of the target vehicle in a preset area, and extract second driving scene data associated with vehicle driving safety from the first driving scene data;

[0031] The intelligent driving control module is configured to: compare the second driving scene data based on a dangerous scene library to obtain a danger level of the first driving scene data, wherein the dangerous scene library stores preset standard data corresponding to a plurality of dangerous scenes of vehicle driving;

[0032] The intelligent driving execution module is configured to: determine a safety control instruction based on the danger level, and execute corresponding avoidance measures based on the safety control instruction.

[0033] In a third aspect, the present application proposes an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of a vehicle control method for actively avoiding dangerous scenarios as described in any one of the first aspects above when executing the computer program stored in the memory.

[0034] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of a vehicle control method for actively avoiding dangerous scenarios according to any one of the first aspects are implemented.

[0035] In summary, a vehicle control method and related equipment for active avoidance of dangerous scenarios in an embodiment of the present application obtains first driving scenario data of a target vehicle in a preset area in real time, analyzes and extracts second driving scenario data related to vehicle driving safety in the first driving scenario data, and compares it with preset standard data corresponding to multiple dangerous scenarios of vehicle driving stored in a dangerous scenario library, determines the danger level of the first driving scenario data, issues corresponding safety control instructions according to different levels of dangerous scenarios, and then controls the target vehicle to actively avoid dangerous scenarios, thereby achieving safe vehicle driving.

[0036] The vehicle control method and related equipment for active avoidance of dangerous scenarios proposed in this application, and other advantages, objectives and features of this application will be reflected in part through the following description, and in part will also be understood by technical personnel in this field through research and practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0038] Figure 1 A schematic diagram of a process flow of a vehicle control method for actively avoiding dangerous scenarios provided in an embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of a vehicle control system for active avoidance of dangerous scenarios provided in an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the structure of a vehicle control electronic device for actively avoiding dangerous scenarios provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to better understand the technical solutions provided by the embodiments of this specification, the technical solutions of the embodiments of this specification are described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0042] In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. The term "more than two" includes two or more than two situations.

[0043] See also Figure 1 , which is a flow chart of a vehicle control method for actively avoiding dangerous scenes provided in an embodiment of the present application, which may specifically include:

[0044] S110, obtaining first driving scene data of a target vehicle in a preset area, and extracting second driving scene data associated with vehicle driving safety from the first driving scene data.

[0045] Exemplarily, first driving scene data of the target vehicle in a preset area is obtained, and data having a direct or indirect impact on the safety of the vehicle during driving is extracted from the first driving scene data. The extracted data is the second driving scene data.

[0046] S120. Compare the second driving scene data based on a dangerous scene library to obtain a danger level of the first driving scene data, wherein the dangerous scene library stores preset standard data corresponding to a plurality of dangerous scenes of vehicle driving.

[0047] Exemplarily, a dangerous scenario library is obtained, which stores preset standard data corresponding to various known dangerous scenarios for vehicle driving. By comparing the second driving scenario data with the preset standard data in the dangerous scenario library, the degree of proximity between the current driving scenario of the target vehicle and the dangerous scenario can be evaluated, thereby determining the danger level of the overall driving condition represented by the first driving scenario data. The danger level can be divided into multiple levels, such as a mildly dangerous scenario, a moderately dangerous scenario, and a severely dangerous scenario. If the second driving scenario data is very close to or completely consistent with the preset standard data of a certain dangerous scenario, then it can be considered that the target vehicle is in a severely dangerous scenario; if only some parameters are close to the dangerous scenario, it can be judged as a moderately dangerous scenario; if there is a certain distance from the dangerous scene, it can be judged as a mildly dangerous scenario. The purpose of this is to monitor the safety of vehicle driving in real time and detect potential dangers in a timely manner.

[0048] S130: Determine a security control instruction based on the risk level, and execute corresponding avoidance measures based on the security control instruction.

[0049] For example, when the danger level is a mild danger scene, a safety warning instruction is issued. This safety warning instruction is mainly used to remind the driver of the main vehicle to make him aware that there are certain safety risks in the current driving scene and that he needs to perform corresponding safe driving operations, such as slowing down and keeping a safe distance. At the same time, the system will also issue reminders to pedestrians involved in the preset area (through sound, lights, etc.) and neighboring cars (through car-to-car communication, etc.), so that they can also pay attention to potential dangers so that they can take corresponding preventive measures.

[0050] When the danger level is a moderately dangerous scenario, not only a safety warning will be issued, but also an intervention-assisted driving instruction. On the one hand, through human-computer interaction, danger reminders are issued to the driver of the main vehicle, pedestrians in the preset area, and neighboring vehicles to increase the vigilance of all parties. On the other hand, assisted driving operations will be performed according to the control instructions, such as automatically adjusting the vehicle speed, correcting the steering wheel angle, etc., to help the driver better deal with dangerous situations. This intervention-assisted operation is intended to reduce the probability of accidents while reducing the burden on the driver.

[0051] When the danger level is a severe danger scene, a direct automatic driving command is issued at the same time as a safety warning. At this time, the system will issue a strong danger reminder to the main vehicle, pedestrians and neighboring vehicles through human-computer interaction. Perform automatic driving operations according to the instructions to actively avoid danger, such as emergency braking, automatic steering to avoid obstacles, etc. In this case, the system completely takes over the control of the vehicle to maximize the safety of the vehicle and surrounding personnel. By accurately judging dangerous scenes with different danger levels and taking corresponding measures, from reminding the driver to assisted driving to automatic driving, the ability to deal with danger is gradually improved to reduce the occurrence of traffic accidents and improve road traffic safety.

[0052] In summary, the vehicle control method for active avoidance of dangerous scenarios proposed in the embodiment of the present application obtains the first driving scenario data of the target vehicle in a preset area in real time, analyzes and extracts the second driving scenario data related to the vehicle driving safety in the first driving scenario data, and compares it with the preset standard data corresponding to multiple dangerous scenarios of vehicle driving stored in the dangerous scenario library, determines the danger level of the first driving scenario data, issues corresponding safety control instructions according to different levels of dangerous scenarios, and then controls the target vehicle to actively avoid dangerous scenarios, thereby achieving safe vehicle driving.

[0053] In some examples, the first driving scene data includes data corresponding to each scene element in the driving scene where the target vehicle is located; the scene elements include pedestrians, the target vehicle, adjacent vehicles, the road where the target vehicle is located, and the climate;

[0054] Extracting second driving scene data associated with vehicle driving safety from the first driving scene data includes:

[0055] Taking the target vehicle as the center, determining the driving range of the target vehicle, determining the positions of pedestrians and adjacent vehicles within the driving range, and obtaining third driving scene data;

[0056] The third driving scene data is analyzed to determine the topological relationship between the various scene elements to obtain the second driving scene data.

[0057] Exemplarily, the first driving scene data includes data corresponding to each scene element in the driving scene where the target vehicle is located; wherein the scene elements include pedestrians, target vehicles, adjacent vehicles, the road where the target vehicle is located, and climate; the real-time data of pedestrians includes the relative posture and position information of vulnerable road users such as pedestrians, cyclists, electric bike riders, and tricycle riders. The real-time data of the target vehicle includes the driving status of the main vehicle. The real-time data of adjacent vehicles includes the driving status information of adjacent vehicles. The real-time data of the road where the target vehicle is located includes basic information such as the road surface status and road infrastructure. The real-time data of climate includes real-time information on weather and climate.

[0058] Taking the target vehicle as the center is to determine a focus area. The driving range can be set according to actual needs, such as an area within a certain radius with the target vehicle as the center. Within this driving range, the specific location of pedestrians and the location information of adjacent vehicles are determined. The third driving scene data obtained mainly focuses on the key dynamic elements around the target vehicle, that is, the location of pedestrians and adjacent vehicles.

[0059] The third driving scene data is analyzed to determine the topological relationship between the various scene elements, where the topological relationship refers to the relative position relationship and connection relationship between different scene elements, such as the distance between the pedestrian and the target vehicle, the position of the adjacent vehicle relative to the target vehicle, the relative position between the pedestrian and the adjacent vehicle, etc.

[0060] By determining these topological relationships, the second driving scene data obtained pays more attention to the correlation and potential impact between scene elements. These data are crucial for evaluating vehicle driving safety because they can reflect safety factors such as the potential collision risk between the target vehicle and surrounding pedestrians and vehicles.

[0061] In some examples, for the preset standard data corresponding to each dangerous scene in the dangerous scene library, the preset standard data includes data corresponding to each scene element respectively;

[0062] The comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data includes:

[0063] Comparing the second driving scene data based on the data corresponding to each scene element in the dangerous scene library to obtain matching target driving scene data;

[0064] Acquire the accident triggering scenario factor in each scenario element of the target driving scenario data, and determine the correlation between the accident triggering scenario factor and the second driving scenario data;

[0065] Based on the correlation between the accident triggering scenario factor and the second driving scenario data, the danger level of the first driving scenario data is determined.

[0066] Exemplarily, each dangerous scene in the dangerous scene library has corresponding preset standard data, which covers different scene elements. For example, for a dangerous scene of high-speed driving on rainy days, the preset standard data includes pedestrians' clothing and walking status (because pedestrians move slowly and their vision is obstructed on rainy days), the speed range of the target vehicle (the speed needs to be reduced when driving at high speed on rainy days), the spacing requirements of adjacent vehicles (the road surface is slippery on rainy days, and the spacing needs to be increased), the road condition information of the road where the target vehicle is located (whether there is water accumulation, road friction coefficient, etc.) and climate conditions (rainfall amount, duration of rainfall, etc.).

[0067] The second driving scene data is compared with the preset standard data of each dangerous scene in the dangerous scene library one by one. By comparing the data of each scene element, the driving scene corresponding to the preset standard data that best matches the current second driving scene data is found, that is, the target driving scene data. For example, if the current second driving scene data shows that the target vehicle is driving in the rain, there are pedestrians around and the adjacent vehicles are close, then it may match the dangerous scene in the dangerous scene library of high-speed driving in the rain and the distance between vehicles is too small.

[0068] For the determined target driving scene data, analyze each scene element therein to find out the key elements that may cause the accident, namely, the accident triggering scene factors. For example, in the above-mentioned scene of high-speed driving in rainy days and too small distance between vehicles, the accident triggering scene factor is the too close distance between adjacent vehicles or the high speed of the target vehicle. At the same time, determine the correlation between the accident triggering scene factor and the second driving scene data. These factors are associated with the specific values ​​in the second driving scene data, reflecting the closeness of the current driving scene to the dangerous scene.

[0069] According to the correlation between the accident triggering scenario factor and the second driving scenario data, the danger level of the overall driving condition represented by the first driving scenario data is comprehensively evaluated. If the accident triggering scenario factor is close to the dangerous scenario, the danger level will be high; conversely, if the accident triggering scenario factor is far away from the dangerous scenario, the danger level will be low. For example, if the current vehicle spacing is very small and the target vehicle is moving very fast, the danger level will be judged as high; if the vehicle spacing is close but still within the safe range and the target vehicle is moving at a moderate speed, the danger level will be low.

[0070] In some examples, determining the danger level of the first driving scene data based on the association between the accident triggering scene factor and the second driving scene data includes:

[0071] The association relationship between the accident triggering scenario factor and the second driving scenario data, and simulating and generating a pre-collision scenario based on the driving data of the target vehicle;

[0072] A danger level of the first driving scenario data is obtained based on the pre-collision scenario.

[0073] Exemplarily, the driving data of the target vehicle includes dynamic information such as the speed, acceleration, and direction of the target vehicle, as well as static information such as the size and performance parameters of the vehicle. Based on these driving data and the correlation between the accident triggering scenario factor and the second driving scenario data, a pre-collision scenario of a collision can be simulated. For example, if the accident triggering scenario factor is that the adjacent vehicles are too close and the target vehicle is moving at a high speed, then combined with the driving data of the target vehicle, a scenario can be simulated in which the target vehicle may collide with the adjacent vehicle within a period of time in the future if the current state does not change.

[0074] The pre-collision scenario is a simulated prediction of a future collision. By analyzing this pre-collision scenario, the degree of danger of the current driving condition can be evaluated, thereby determining the danger level of the first driving scenario data. If the pre-collision scenario shows that the possibility of a collision is high and the consequences are serious, the danger level of the first driving scenario data will be judged as high. For example, in the simulated pre-collision scenario, the target vehicle is about to have a serious collision with an adjacent vehicle or pedestrian, and the danger level may be determined as high. Conversely, if the possibility of a collision in the pre-collision scenario is small or the consequences are relatively minor, the danger level will be low. In this way, appropriate measures can be taken according to the danger level, such as issuing warnings of varying degrees, performing assisted driving intervention, or starting automatic driving to avoid danger.

[0075] In some examples, if there is danger, the method also includes: sending a safety warning signal.

[0076] Exemplarily, when the analysis of the first driving scene data determines that there is a danger, the system will take measures to send a safety warning signal. This safety warning signal can be in many forms, for example: displaying a warning icon or text on the vehicle dashboard to remind the driver of the current dangerous situation, including prompts such as "the vehicle ahead is too close" and "watch out for pedestrians". Or sound alarms, such as beeps, voice prompts, etc., to attract the driver's attention, such as "Danger! Please slow down." If the vehicle is equipped with a function to connect to mobile devices such as mobile phones, push notifications can also be sent to the driver's mobile device to remind the driver that the vehicle is in a dangerous driving state. By sending a safety warning signal, the driver can be reminded in time before the danger occurs, so that the driver has enough time to react and take actions such as slowing down, changing lanes, and braking, thereby reducing the probability of accidents and improving driving safety.

[0077] In some examples, this also includes:

[0078] Obtaining avoidance measures corresponding to the pre-collision scenario;

[0079] The determined circumvention measures are executed based on the security control instructions.

[0080] Exemplarily, after a pre-collision scenario is generated through simulation, if it is determined that there is a potential danger, the system will further look for avoidance measures corresponding to this pre-collision scenario. Different pre-collision scenarios require different avoidance measures to avoid the occurrence of danger. For example, if the pre-collision scenario is that the target vehicle is too close to the vehicle in front and the speed is fast, then the avoidance measures include immediate deceleration, changing lanes, etc. Once the avoidance measures suitable for the current pre-collision scenario are determined, the system will generate safety control instructions to execute these measures. By executing safety control instructions, the vehicle can take corresponding avoidance measures to avoid potential collision hazards and improve driving safety.

[0081] In some examples, the comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data is triggered based on at least one of the following situations:

[0082] When the speed of the target vehicle is greater than a preset speed, comparing the second driving scene data based on a dangerous scene library;

[0083] When the target vehicle is detected to enter a preset area, comparing the second driving scene data based on a dangerous scene library;

[0084] When it is detected that the pedestrian density is greater than the preset density, the second driving scene data is compared based on the danger scene library.

[0085] Exemplarily, comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data is triggered based on at least one of the following situations:

[0086] When the target vehicle's speed exceeds the preset speed, the second driving scenario data is compared based on the dangerous scenario library. The reason is that a higher speed will increase the risk of an accident. For example, when driving at high speed, the braking distance of the vehicle will become longer and the control difficulty will increase. If there are potential dangers in the surrounding driving environment at this time, such as obstacles ahead or other vehicles suddenly changing lanes, it is more likely to cause a serious accident. Therefore, once the speed exceeds the preset speed, it is necessary to promptly assess the danger level of the current scene so that appropriate measures can be taken.

[0087] When the target vehicle enters the preset area, the danger level assessment is triggered. The preset area can be an area with high potential danger, such as accident-prone areas, construction areas, and areas around schools. For example, around schools, there are a large number of students and pedestrians, and vehicles need to be more careful when driving; in construction areas, the road conditions are more complicated, with obstacles and temporary traffic signs, which increase the possibility of accidents. Therefore, when vehicles enter these preset areas, a more rigorous assessment of the driving scene is required to ensure driving safety.

[0088] If it is detected that the pedestrian density in the current area exceeds the preset density, the system will compare the second driving scene data with the dangerous scene library to determine the danger level. A high pedestrian density means an increased risk of collision between vehicles and pedestrians. In areas with dense pedestrians, such as commercial streets and squares, vehicles need to drive more cautiously and pay attention to the movements of pedestrians. By monitoring the pedestrian density and comparing it with the dangerous scene library, potential dangerous situations can be discovered in a timely manner and appropriate measures can be taken, such as slowing down and keeping a safe distance.

[0089] like Figure 2 As shown, the present application proposes a vehicle control system for active avoidance of dangerous scenes, the system comprising: an on-board numerical control center module 210, an intelligent driving control module 220 and an intelligent driving execution module 230;

[0090] The vehicle-mounted numerical control center module 210 is configured to: obtain first driving scene data of the target vehicle in a preset area, and extract second driving scene data associated with vehicle driving safety from the first driving scene data;

[0091] The intelligent driving control module 220 is configured to: compare the second driving scene data based on a dangerous scene library to obtain a danger level of the first driving scene data, wherein the dangerous scene library stores preset standard data corresponding to a plurality of dangerous scenes of vehicle driving;

[0092] The intelligent driving execution module 230 is configured to: determine a safety control instruction based on the danger level, and execute corresponding avoidance measures based on the safety control instruction.

[0093] The effects of the above-mentioned system when applying the above-mentioned method can be found in the description of the above-mentioned method embodiment, which will not be repeated here.

[0094] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor, and when the processor 320 executes the computer program 311, the steps of any of the above-mentioned vehicle control methods for active avoidance of dangerous scenarios are implemented.

[0095] Since the electronic device introduced in this embodiment is a device adopted by a vehicle control device for actively avoiding dangerous scenarios in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment adopted by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0096] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0097] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-readable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the LDPC decoding method of the solid-state hard disk controller.

[0103] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0104] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0105] In the several embodiments provided in the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0109] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0110] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0111] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and variations of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and variations.

Claims

1. A vehicle control method for active avoidance of dangerous scenes, characterized in that: The method comprises: Acquire first driving scene data of a target vehicle in a preset area, and extract second driving scene data associated with vehicle driving safety from the first driving scene data; Comparing the second driving scene data based on a dangerous scene library to obtain a danger level of the first driving scene data, wherein the dangerous scene library stores preset standard data corresponding to a plurality of dangerous scenes of vehicle driving; A safety control instruction is determined based on the risk level, and a corresponding avoidance measure is executed based on the safety control instruction.

2. The vehicle control method for active avoidance of dangerous scenes according to claim 1 is characterized in that: The first driving scene data includes data corresponding to each scene element in the driving scene where the target vehicle is located; the scene elements include pedestrians, the target vehicle, adjacent vehicles, the road where the target vehicle is located, and the climate; Extracting second driving scene data associated with vehicle driving safety from the first driving scene data includes: Taking the target vehicle as the center, determining the driving range of the target vehicle, determining the positions of pedestrians and adjacent vehicles within the driving range, and obtaining third driving scene data; The third driving scene data is analyzed to determine the topological relationship between the various scene elements to obtain the second driving scene data.

3. The vehicle control method for active avoidance of dangerous scenes according to claim 1 is characterized in that: For each dangerous scene in the dangerous scene library, the corresponding preset standard data includes data corresponding to each scene element; The comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data includes: Comparing the second driving scene data based on the data corresponding to each scene element in the dangerous scene library to obtain matching target driving scene data; Acquire the accident triggering scenario factor in each scenario element of the target driving scenario data, and determine the correlation between the accident triggering scenario factor and the second driving scenario data; Based on the correlation between the accident triggering scenario factor and the second driving scenario data, the danger level of the first driving scenario data is determined.

4. The vehicle control method for active avoidance of dangerous scenes according to claim 3 is characterized in that: The determining the danger level of the first driving scene data based on the association relationship between the accident triggering scene factor and the second driving scene data includes: The association relationship between the accident triggering scenario factor and the second driving scenario data, and simulating and generating a pre-collision scenario based on the driving data of the target vehicle; A danger level of the first driving scenario data is obtained based on the pre-collision scenario.

5. The vehicle control method for active avoidance of dangerous scenes according to claim 1 is characterized in that: If there is danger, the method also includes: sending a safety warning signal.

6. The vehicle control method for active avoidance of dangerous scenes according to claim 4 is characterized in that: Also includes: Obtaining avoidance measures corresponding to the pre-collision scenario; The determined circumvention measures are executed based on the security control instructions.

7. The vehicle control method for active avoidance of dangerous scenes according to claim 4 is characterized in that: The comparing the second driving scene data based on the dangerous scene library to obtain the danger level of the first driving scene data is triggered based on at least one of the following situations: When the speed of the target vehicle is greater than a preset speed, comparing the second driving scene data based on a dangerous scene library; When the target vehicle is detected to enter a preset area, comparing the second driving scene data based on a dangerous scene library; When it is detected that the pedestrian density is greater than the preset density, the second driving scene data is compared based on the danger scene library.

8. A vehicle control system for active avoidance of dangerous scenarios, characterized in that: The system includes: an on-board numerical control center module, an intelligent driving control module and an intelligent driving execution module; The vehicle-mounted numerical control center module is configured to: obtain first driving scene data of the target vehicle in a preset area, and extract second driving scene data associated with vehicle driving safety from the first driving scene data; The intelligent driving control module is configured to: compare the second driving scene data based on a dangerous scene library to obtain a danger level of the first driving scene data, wherein the dangerous scene library stores preset standard data corresponding to a plurality of dangerous scenes of vehicle driving; The intelligent driving execution module is configured to: determine a safety control instruction based on the danger level, and execute corresponding avoidance measures based on the safety control instruction.

9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of a vehicle control method for active avoidance of dangerous scenarios as described in any one of claims 1-7 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a vehicle control method for active avoidance of dangerous scenarios as described in any one of claims 1-7 are implemented.

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