CN112863244B - Method and device for promoting safe driving of vehicle - Google Patents

Abstract

The present invention relates to a method and device for promoting safe driving of a vehicle, the method comprising: determining the current driving scene of the vehicle; obtaining at least one vehicle accident scene from a database; judging whether the current driving scene of the vehicle is consistent with the at least one vehicle accident scenario matches; and if the vehicle's current driving scenario matches the at least one vehicle accident scenario, automatically performing an action to avoid the vehicle from entering the at least one vehicle accident scenario.

Description

Method and apparatus for promoting safe driving of a vehicle

technical field

The present invention relates to a method and a device for promoting safe driving of a vehicle.

Background technique

In the field of automotive autonomous driving, vehicle safety is the key to be considered. The current self-driving car drives on the road with various sensors and controllers, and continuously optimizes the self-driving algorithm through the safe driving data collected during the natural driving process of the vehicle. Data collected in this way is safe driving data for normal or non-extreme situations. This kind of safe driving data trains the smart vehicle to know what is a good and safe driving way, so that the smart vehicle can only learn the driving way in non-accident situations. However, in a real driving process, bad situations usually occur, that is, an accident occurs. Because it is often very difficult and costly to collect driving data in extreme situations (such as accidents) through the natural driving conditions of the vehicle itself, it is usually not possible to train current self-driving cars on how to deal with accident scenarios based on extreme situation data.

Therefore, in order to promote the safe driving of self-driving vehicles, it is hoped that the combination of safe driving data and vehicle accident data can teach the vehicle's automatic driving system to know what is a safe driving method and what is a dangerous driving method, thereby improving the vehicle's intelligent learning level and efficiency.

Contents of the invention

This Summary is provided to introduce a set of concepts that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

The object of the present invention is to provide a method and a device for promoting safe driving of a vehicle to at least partly overcome the disadvantages of the prior art.

An embodiment of the present invention provides a method for promoting safe driving of a vehicle, including: determining the current driving scene of the vehicle; obtaining at least one vehicle accident scene from a database; judging whether the current driving scene of the vehicle is consistent with the at least one A vehicle accident scenario matches; and if the current driving scenario of the vehicle matches the at least one vehicle accident scenario, automatically performing an action to avoid the vehicle from entering the at least one vehicle accident scenario.

An embodiment of the present invention also provides a device for promoting safe driving of a vehicle, including: a determination module, used to determine the current driving scene of the vehicle; an obtaining module, used to obtain at least one vehicle accident scene from a database; a judging module, for judging whether the current driving scene of the vehicle matches the at least one vehicle accident scene; and an execution module for automatically executing if the current driving scene of the vehicle matches the at least one vehicle accident scene an action to avoid entry of the vehicle into the at least one vehicle accident scenario.

An apparatus for promoting safe driving of a vehicle according to an embodiment of the present invention includes: a processor; and a memory for storing executable instructions, wherein the executable instructions, when executed, cause the processor to perform the aforementioned method.

According to a machine-readable medium according to an embodiment of the present invention, executable instructions are stored thereon, wherein, when executed, the executable instructions cause a machine to execute the aforementioned method.

A system for promoting safe driving of a vehicle according to an embodiment of the present invention includes: a sensor for sensing driving condition information of the vehicle and surrounding environment information of the vehicle; and a control device for executing the aforementioned method.

Wherein, the system further includes an execution device, configured to perform operations on the vehicle based on the output of the control device.

As can be seen from the above description, the solution of the embodiment of the present invention uses the training model to conduct punitive training for some erroneous or dangerous driving behaviors of the self-driving vehicle based on the accident data in the natural driving data, so that the self-driving vehicle Ability to avoid entering accident scenarios where accidents could occur.

It should be noted that one or more aspects above include the features described in the following detailed description as well as those particularly pointed out in the claims. Certain illustrative features of the one or more aspects are set forth in the following description and accompanying drawings. These features are merely indicative of the various ways in which the principles of various aspects can be implemented and this disclosure is intended to include all such aspects and their equivalents.

Description of drawings

The disclosed aspects will be described below with reference to the accompanying drawings, which are provided to illustrate but not limit the disclosed aspects.

Fig. 1 shows a schematic architecture diagram of a system for promoting safe driving of a vehicle according to an embodiment of the present invention;

Fig. 2 shows a schematic flowchart of a method for promoting safe driving of a vehicle according to an embodiment of the present invention;

Fig. 3 shows a schematic flowchart of a method for promoting safe driving of a vehicle according to another embodiment of the present invention;

FIG. 4 shows an example diagram of a method for generating a vehicle accident scene according to an embodiment of the present invention;

Fig. 5 shows a schematic diagram of a device for promoting safe driving of a vehicle according to an embodiment of the present invention;

FIG. 6 shows a schematic diagram of a device for promoting safe driving of a vehicle according to an embodiment of the present invention.

Detailed ways

The present disclosure will now be discussed with reference to various exemplary embodiments. It should be understood that the discussion of these embodiments is only for the purpose of enabling those skilled in the art to better understand and thus implement the embodiments of the present disclosure, rather than teaching any limitation to the scope of the present disclosure.

Various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

FIG. 1 shows a schematic structural diagram of a system 100 for promoting safe driving of a vehicle according to an embodiment of the present invention. As shown in FIG. 1 , an exemplary system 100 for facilitating safe travel of a vehicle may include a sensor 102 , a processor 104 , a control device 106 , a database 108 , and an execution device 110 .

The sensor 102 may include various types of sensors for sensing the driving condition information of the vehicle and the surrounding environment information of the vehicle, for example, a speed sensor for detecting the speed and/or acceleration of the vehicle itself, and a speed sensor for detecting the size of objects near the vehicle. Radar sensors for , distance and moving speed, etc., temperature sensors for detecting engine temperature, image sensors for capturing the surrounding environment of the vehicle, brightness sensors, sound sensors, etc. For the sake of brevity, only some examples of sensors applied to vehicles are listed here. In practical applications, there may be any available sensors applicable to vehicles. Sensor 102 may communicate detected data or information to processor 104 and/or control device 106 .

The processor 104 can analyze the data detected by the sensor 102 to determine the current driving scene of the vehicle. In some embodiments, the current driving scene of the vehicle can be represented by a feature set, wherein the features in the feature set include but are not limited to at least one of the following: inherent information of the vehicle, current speed, current time, location, surrounding environment information, the vehicle's automatic driving system or basic information of the driver, etc. In some examples, the inherent information of the vehicle includes but is not limited to the model, brand, manufacturer, displacement, transmission, engine information, factory date, total driving distance and time, wear and tear, driving mode, etc.; the vehicle's automatic driving system The basic information of the vehicle includes but not limited to the development company of the system, the version of the system, the software and hardware parameters of the system, etc.; the basic information of the driver of the vehicle includes but not limited to gender, age, driving experience, physical condition, driving style (such as like to drive Fast trains, like to drive slowly, like to merge and overtake, etc.) etc. In some examples, the surrounding environment information includes but is not limited to the temperature, brightness, sound, weather, size of other surrounding objects, distance to the vehicle, speed, minimum and maximum speed limit of the road section, road conditions, road surface quality etc. For example, the current driving scene of the vehicle can be represented by the following exemplary feature set F1: [8 o'clock in the evening, county road, rainy day, smooth road conditions, poor road surface quality, maximum speed limit 100, vehicle speed 100, other vehicles within 100 meters ahead Vehicle, there are other vehicles 100 meters behind, model A, has been driving for 10 years, the tires are aging, and the automatic driving system version 1]. It should be understood that, for ease of description, only some exemplary features are listed here; in practice, the feature set representing the current driving scene of the vehicle may include more, less, or different features than the above examples.

The processor 104 provides the analyzed data to the control device 106 so that the control device 106 can compare the current driving scenario of the vehicle determined at the processor 104 with the vehicle accident scenario obtained from the database 108 . In this paper, the vehicle accident scene can be represented by a feature set, wherein the features in the feature set can include but not limited to at least one of the following: type, time, place, reason, surrounding environment, vehicle speed, Vehicle damage, casualties, and optional inherent information of the vehicle. In some examples, causes of accidents include, but are not limited to: vehicle speed exceeding or below a predetermined speed, distance to nearby vehicles or objects below a predetermined distance, steering wheel rotation angle above or below a predetermined angle, steering wheel rotation in the wrong direction , the braking force or degree exceeds or falls below the predetermined value, vehicle failure, etc.; the inherent information of the vehicle includes but is not limited to the vehicle model, brand, manufacturer, displacement, transmission, engine information, factory date, total driving distance and Time, loss, driving mode, etc.; the surrounding environment includes but is not limited to the temperature, brightness, sound, weather, size of other surrounding objects, distance to the vehicle, speed, minimum and maximum of the road section at the time of the accident. Limit speed, road conditions, road surface quality, etc. For example, an exemplary vehicle accident scene can be represented by the following feature set F': [rear-end collision, 8 o'clock in the evening, county road, rainy day, smooth road conditions, poor road surface quality, maximum speed limit 100, vehicle speed 120, exceeding the predetermined Vehicle speed 20%, car model A, has been driving for 10 years, tires are aging, autopilot system version 1, front bumper is seriously damaged, and people in the car are slightly injured]. It should be understood that, for ease of description, only some exemplary features are listed here; in practice, the feature set representing the vehicle accident scene may include more, less, or different features than the above examples.

In some embodiments, the control device 106 can match the current driving scene of the vehicle with the vehicle accident scene by way of feature comparison. If the two match or the matching degree reaches a threshold, the control device 106 may instruct the execution device 110 to automatically perform an action to prevent the vehicle from entering an accident scene. It should be understood that any appropriate matching or comparison method may be used to perform the matching operation between the current driving scene of the vehicle and the vehicle accident scene.

In some examples, automatically performing an action may include adjusting the current operating state of the vehicle by the vehicle's automatic driving system, for example, adjusting from the vehicle's constant speed driving operation to performing at least one action including: braking, parking, turning, accelerating, Slow down, merge, etc. In other examples, the automatic execution operation may include sending a prompt signal to the user of the vehicle, for example, an audio signal such as "accelerate and merge, the current driving situation is consistent with a rear-end collision scenario", displayed on the screen of the vehicle display Warning messages, projected warning messages on the glass in front of the user, etc. Herein, the user of the vehicle includes at least one of the vehicle's automated driving system or a human driver. In some optional examples, the control device 106 may also provide indication signals to other vehicles near the own vehicle, so as to instruct users of other vehicles to perform actions to avoid entering an accident scene. In some examples, the indication signal may include a sound signal, a light signal, an image signal, and the like.

For example, when the above-mentioned features in the feature set of the current driving scene F1 of the vehicle are matched with the corresponding features in the feature set F' of the vehicle accident scene, it can be judged that the two match or the matching degree reaches a threshold, so as to control The device can judge that the vehicle may enter a rear-end collision accident scene, and instruct the execution device to adjust the current driving state of the vehicle, for example, to slow down the vehicle and/or turn to another lane. Optionally, the control device may also provide an indication signal to the vehicle behind, such as a voice signal, an image signal, etc. indicating that the vehicle behind slows down. To give another example, suppose the current driving scene F2 of the vehicle is [2:00 p.m., highway, sunny day, smooth road conditions, good road surface quality, maximum speed limit 120, vehicle speed 100, no other objects within 200 meters around, model A, driving 0.5 years, new tires, automatic driving system version 10], when the control device matches the current driving scene F2 with the accident scene F', it can be judged that the two do not match or the matching degree does not reach the threshold, then the control device does not instruct the execution device The current driving state of the vehicle is adjusted, and optionally, a new current driving scene is continuously obtained from the processor 104 .

It should be understood that although processor 104 is shown as being separate from control device 106 in FIG. 1 , processor 104 may also be incorporated into control device 106 in some embodiments. In addition, although the database 108 is shown as being included in the system 100 in FIG. 1 , in some examples, the database 108 can also be located outside the system 100 and can transmit data with the system 100 through wired or wireless communication. In addition, various components included in the system 100 may be connected in any wireless or wired manner.

FIG. 2 shows a schematic flowchart of a method 200 for promoting safe driving of a vehicle according to an exemplary embodiment of the present invention. The method 200 shown in FIG. 2 can be implemented by the system 100, for example.

As shown in FIG. 2 , at block 202 , a current driving scene is determined, for example, a current driving scene of a vehicle in motion.

At block 204, vehicle accident scenarios are obtained from a database. In some examples, the database may be a local database placed in the vehicle, or an external database, cloud database, etc. that can communicate with the automatic driving system of the vehicle.

In block 206, it is possible to determine the matching between the current driving scene of the vehicle and the vehicle accident scene, such as complete matching, partial matching, matching degree reaching a threshold, and the like.

In block 208, if the current driving scene of the vehicle matches or matches with one or some accident scenes obtained from the database, or the degree of matching reaches a threshold, an action is automatically performed to prevent the vehicle from entering the vehicle accident scene.

FIG. 3 shows a schematic diagram of a method 300 for promoting safe driving of a vehicle according to another exemplary embodiment of the present invention. The method 300 shown in FIG. 3 may be implemented by the system 100, for example.

As shown in FIG. 3 , the method 300 may include, at block 302 , sensing driving conditions and surrounding environment information, for example, this may be performed by the sensor 102 in the system 100 .

In block 304 , the current driving scene may be determined, for example, the current driving scene of the vehicle may be determined according to the driving conditions and surrounding environment information sensed at 302 . This can be performed by processor 104 in system 100, for example.

At block 306, vehicle accident scenarios may be obtained from a database. In some examples, vehicle accident scenarios may be generated based on real accident data by training a model and pre-stored in a database. For example, this may be performed by control device 106 in system 100 .

At block 308 , it may be determined whether the current driving scenario of the vehicle determined at block 304 matches at least one of the vehicle accident scenarios obtained at block 306 . If it is no, that is, it does not match or the degree of matching is lower than the threshold, then optionally, the system may continue to determine the current driving scene of the vehicle. If yes, ie they match or the degree of match is above a threshold, the method may proceed to block 310 where actions are automatically performed to avoid the vehicle from entering a vehicle accident scenario.

Optionally, the method 300 may further include, at block 312 , providing indication signals to other vehicles near the vehicle to instruct users of other vehicles to perform actions to avoid entering a vehicle accident scene.

FIG. 4 illustrates an example process 400 for obtaining vehicle accident scenarios according to one embodiment of the present invention. For example, the process may be an example for the operations in block 204 of method 200 and block 306 of method 300 . In this exemplary process, vehicle accident scenarios may be generated based on collected real vehicle accident data by training a model.

At block 402, vehicle accident data may be collected. In some examples, the vehicle accident data is data about actual accidents that have occurred. In this paper, the vehicle accident data can be collected from one or more vehicle accidents that have occurred by a third party or a cloud server. For example, the third party may include at least one of the following: a transportation authority, an insurance company, a driving recorder data collection company, and the like.

At block 404, the collected vehicle accident data is analyzed to classify the accident data into valid accident data and invalid accident data based at least on the cause of the accident. For example, accidents caused by the driver's actions and avoidable in the case of automatic driving can generally be considered invalid accidents, such as accidents caused by the driver dozing off or looking at the mobile phone, while accidents caused by non-driver actions, And accidents that are unavoidable in autonomous driving situations can usually be considered valid accidents, such as accidents caused by rear vehicle collisions.

For example, when the vehicle is running normally, the data involved in the accident caused by the sudden appearance of other objects (such as pedestrians, other vehicles, animals, thrown objects, etc.) near the vehicle can be regarded as is valid accident data. Under such accident reasons, such accidents may not be completely avoidable, regardless of whether the vehicle is driven manually by a human or by an automatic driving system. Therefore, by collecting such accident data and training the model to generate accident scenarios based on these valid accident data, and providing the accident scenarios to the vehicle's automatic driving system, the automatic driving system can prompt the vehicle to avoid such accidents during subsequent driving. ACCIDENT.

In other examples, a vehicle operating autonomously can avoid accidents caused by a driver dozing off or not paying attention to their surroundings. Therefore, data related to such accidents can be considered invalid accident data.

The above examples are only exemplary, and in practice, the classification of accident data can be performed in any known suitable manner.

At block 406, valid accident data is extracted from the classified accident data.

In block 408, vehicle accident scenarios may be generated according to the extracted valid accident data, for comparison or matching with current driving scenarios during vehicle driving.

Optionally, as the collected vehicle accident data changes, the generated accident scenarios may be updated. In some instances, valid accident data in vehicle accident data collected from the most recent accident or accidents may also will be updated accordingly. For example, some valid accident data may be updated to become invalid accident data as time goes by and after the automatic driving system is trained using the technique of the present invention. For example, initially, a rear-end collision accident can be considered as an unavoidable effective accident caused by non-driver's operation; after adopting the technology of the present invention, the automatic driving system can judge the current driving scene of the vehicle and When the accident scene matches (for example, the distance between the two vehicles in front and behind is less than 50 meters), the acceleration and/or merging actions are automatically performed to avoid rear-end collision accidents. Therefore, in the accident data collected after adopting the technology of the present invention, such rear-end collision accidents can be updated as invalid accidents.

Those skilled in the art should understand that the above examples are only illustrative, and in other embodiments, vehicle accident data can be classified in any other manner and accident scenarios can be generated accordingly.

FIG. 5 shows a schematic diagram of a device 500 for promoting safe driving of a vehicle according to an exemplary embodiment of the present invention. The device 500 shown in FIG. 5 can be implemented by using software, hardware, or a combination of software and hardware.

As shown in FIG. 5 , the apparatus 500 may include a determining module 502 , an obtaining module 504 , a judging module 506 and an executing module 508 .

The determination module 502 can be used to determine the current driving scene of the vehicle.

The obtaining module 504 may be used to obtain at least one vehicle accident scenario from the database, wherein the at least one vehicle accident scenario is generated based on the collected vehicle accident data by training a model. In some examples, the vehicle accident data is classified into valid accident data and invalid accident data based at least on a cause of the accident, and wherein the at least one vehicle accident scenario is generated by training a model based on the valid accident data. In some implementations, the vehicle accident data is collected by a third party or by a cloud server from one or more vehicle accidents that have occurred. Herein, the vehicle accident scene is represented by a feature set including at least one of the following features: type, time, place, reason, surrounding environment, vehicle speed, vehicle damage, casualties, and vehicle intrinsic information, etc.

The judging module 506 may be used to judge whether the current driving scene of the vehicle matches the at least one vehicle accident scene.

The execution module 508 may be configured to automatically execute an action to prevent the vehicle from entering the at least one vehicle accident scene if the current driving scene of the vehicle matches the at least one vehicle accident scene, for example, the matching degree reaches a threshold.

Further, the obtaining module 504 is also configured to: obtain the updated at least one vehicle accident scene. In some embodiments, the updated at least one vehicle accident scenario is further generated based on valid accident data in the updated vehicle accident data, and wherein the updated vehicle accident data is based on the third party or the cloud server It is obtained from vehicle accident data collected from the latest one or more vehicle accidents.

In addition, optionally, the device 500 may further include a sending module, which is configured to send an indication signal to other vehicles near the vehicle, wherein the indication signal is used to instruct users of the other vehicles to perform actions to avoid entering the vehicle. describe at least one vehicle accident scenario.

FIG. 6 shows a schematic diagram of an apparatus 600 for promoting safe driving of a vehicle according to an embodiment of the present invention.

As shown in FIG. 6, the device 600 may include a processor 602 and a memory 604, wherein the memory 604 is used to store executable instructions that, when executed, cause the processor 602 to perform the methods 200 and 200 shown in FIG. /or the method 300 shown in FIG. 3 .

The embodiment of the present invention also provides a machine-readable medium on which executable instructions are stored, and when the executable instructions are executed, the machine executes the method 200 shown in FIG. 2 and/or the method shown in FIG. 3 300.

It should be understood that all operations in the method described above are exemplary only, and the present disclosure is not limited to any operation in the method or the order of these operations, but should cover all other equivalent transformations under the same or similar concept .

It should also be understood that all modules in the apparatus described above may be implemented in various ways. These modules may be implemented as hardware, software, or a combination thereof. Furthermore, any of these modules may be functionally further divided into sub-modules or grouped together.

Processors have been described in connection with various apparatus and methods. These processors may be implemented using electronic hardware, computer software, or any combination thereof. Whether such processors are implemented as hardware or software will depend upon the particular application and overall design constraints imposed on the system. As examples, a processor, any portion of a processor, or any combination of processors presented in this disclosure may be implemented as a microprocessor, microcontroller, digital signal processor (DSP), field programmable gate array (FPGA) ), programmable logic devices (PLDs), state machines, gate logic, discrete hardware circuits, and other suitable processing components configured to perform the various functions described in this disclosure. The functionality of a processor, any portion of a processor, or any combination of processors given in this disclosure may be implemented as software executed by a microprocessor, microcontroller, DSP, or other suitable platform.

The above description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Accordingly, the claims are not intended to be limited to the aspects shown herein. All structural and functional equivalents to the elements of the various aspects described in this disclosure that are known or come to be known to those skilled in the art are expressly incorporated herein by reference and are intended to be covered by the claims .

Those skilled in the art should understand that various modifications and variations can be made to the embodiments disclosed above without departing from the essence of the invention, and these modifications and variations should fall within the protection scope of the present invention, and the present invention The scope of protection should be defined by the claims.

Claims (18)

1. A method for promoting safe driving of a vehicle comprising: Determine the current driving scene of the vehicle; obtaining at least one vehicle accident scenario from a database; judging whether the current driving scene of the vehicle matches the at least one vehicle accident scene; and If the current driving scenario of the vehicle matches the at least one vehicle accident scenario, automatically perform an action to avoid the vehicle from entering the at least one vehicle accident scenario, wherein the at least one vehicle accident scenario is obtained by training the model Generated based on valid accident data in the collected vehicle accident data, and wherein the valid accident data refers to data related to accidents caused by non-driver operations and unavoidable under automatic driving conditions. 2. The method of claim 1, wherein the vehicle accident data is classified into valid accident data and invalid accident data based at least on a cause of occurrence of the accident. 3. The method according to claim 1, wherein the vehicle accident data is collected from one or more vehicle accidents that have occurred by a third party or by a cloud server. 4. The method according to claim 3, wherein obtaining the at least one vehicle accident scene further comprises: obtaining an updated at least one vehicle accident scene, Wherein, the updated at least one vehicle accident scene is further generated based on valid accident data in the updated vehicle accident data, and wherein the updated vehicle accident data is based on the third party or the cloud server It is obtained from vehicle accident data collected from the latest one or more vehicle accidents. 5. The method according to claim 1, wherein the vehicle accident scene is represented by a set comprising at least one of the following: type, time, place, cause, surrounding environment, vehicle speed, vehicle damage conditions, casualties, and inherent information about the vehicle. 6. The method of claim 1, further comprising: Sending an indication signal to other vehicles in the vicinity of the vehicle, wherein the indication signal is used to instruct a user of the other vehicle to perform an action to avoid entering the at least one vehicle accident scenario. 7. The method of claim 1, wherein automatically performing an action includes at least one of: adjusting current operation of the vehicle, sending an alert signal to a user of the vehicle. 8. An apparatus for promoting safe travel of a vehicle comprising: A determining module, configured to determine the current driving scene of the vehicle; Obtaining a module for obtaining at least one vehicle accident scene from the database; A judging module, configured to judge whether the current driving scene of the vehicle matches the at least one vehicle accident scene; and An execution module, configured to automatically execute actions to prevent the vehicle from entering the at least one vehicle accident scene if the current driving scene of the vehicle matches the at least one vehicle accident scene, wherein the at least one vehicle accident scene The scenario is generated by training the model based on valid accident data in the collected vehicle accident data, and wherein the valid accident data refers to an accident caused by an operation by a non-driver and unavoidable in an autonomous driving situation relevant data. 9. The apparatus of claim 8, wherein the vehicle accident data is classified into valid accident data and invalid accident data based on at least a cause of occurrence of the accident. 10. The device according to claim 8, wherein the vehicle accident data is collected by a third party or by a cloud server from one or more vehicle accidents that have occurred. 11. The apparatus according to claim 10, wherein the obtaining module is further configured to: obtain the updated at least one vehicle accident scene, Wherein, the updated at least one vehicle accident scene is further generated based on valid accident data in the updated vehicle accident data, and wherein the updated vehicle accident data is based on the third party or the cloud server It is obtained from vehicle accident data collected from the latest one or more vehicle accidents. 12. The device according to claim 8, wherein the vehicle accident scene is represented by a set comprising at least one of the following: type, time, place, cause, surrounding environment, vehicle speed, vehicle damage conditions, casualties, and inherent information about the vehicle. 13. The apparatus of claim 8, further comprising: A sending module, configured to send an indication signal to other vehicles near the vehicle, wherein the indication signal is used to instruct users of the other vehicles to perform actions to avoid entering the at least one vehicle accident scene. 14. The apparatus of claim 8, wherein automatically performing an action includes at least one of: adjusting the current operation of the vehicle, sending an alert signal to a user of the vehicle. 15. An apparatus for promoting safe travel of a vehicle comprising: processor; and A memory for storing executable instructions, wherein the executable instructions, when executed, cause the processor to perform the method of any one of claims 1-7. 16. A machine-readable medium having executable instructions stored thereon, wherein the executable instructions, when executed, cause a machine to perform the method of any one of claims 1-7. 17. A system for promoting safe travel of a vehicle comprising: The sensor is used to sense the driving condition information of the vehicle and the surrounding environment information of the vehicle; A control device configured to execute the method according to any one of claims 1-7. 18. The system of claim 17, further comprising: an execution device for performing an operation on the vehicle based on the output of the control device.

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