CN113147794A

CN113147794A - Method, device and equipment for generating automatic driving early warning information and automatic driving vehicle

Info

Publication number: CN113147794A
Application number: CN202110619786.5A
Authority: CN
Inventors: 王园; 刘桦林
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-06-03
Filing date: 2021-06-03
Publication date: 2021-07-23

Abstract

The invention provides a method, a device and equipment for generating automatic driving early warning information and an automatic driving vehicle, and relates to the field of artificial intelligence such as deep learning, automatic driving and intelligent transportation. One embodiment of the method comprises: acquiring traffic parameters of an automatic driving vehicle and traffic parameters of other traffic participants; inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result; determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree; and generating corresponding early warning information according to the traffic risk level.

Description

Method, device and equipment for generating automatic driving early warning information and automatic driving vehicle

Technical Field

The embodiment of the disclosure relates to the field of computers, in particular to the field of artificial intelligence such as deep learning, automatic driving and intelligent transportation, and particularly relates to a method, a device and equipment for generating automatic driving early warning information and an automatic driving vehicle.

Background

In the automatic driving process, the road condition of the vehicle is complicated, the action directions of other surrounding traffic participants need to be sensed at all times, the action of the other surrounding traffic participants is judged in advance, and warning prompts are given to dangerous behaviors influencing the running of the main vehicle so as to avoid traffic accidents.

Disclosure of Invention

The embodiment of the disclosure provides a method, a device and equipment for generating automatic driving early warning information and an automatic driving vehicle.

In a first aspect, an embodiment of the present disclosure provides a method for generating automatic driving warning information, including: acquiring traffic parameters of an automatic driving vehicle and traffic parameters of other traffic participants; inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result; determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree; and generating corresponding early warning information according to the traffic risk level.

In a second aspect, an embodiment of the present disclosure provides an apparatus for generating automatic driving warning information, including: a parameter acquisition module configured to acquire traffic parameters of the autonomous vehicle and traffic parameters of other traffic participants; the result obtaining module is configured to input the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result; a level determination module configured to determine a traffic risk level for the autonomous vehicle based on the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the traffic impact level; and the information generation module is configured to generate corresponding early warning information according to the traffic risk level.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

In a fourth aspect, the disclosed embodiments propose a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described in the first aspect.

In a fifth aspect, the disclosed embodiments propose a computer program product comprising a computer program that, when executed by a processor, implements the method as described in the first aspect.

In a sixth aspect, the disclosed embodiments propose an autonomous vehicle comprising the electronic device described in the third aspect.

The method, the device and the equipment for generating the automatic driving early warning information and the automatic driving vehicle provided by the embodiment of the disclosure comprise the steps of firstly obtaining traffic parameters of the automatic driving vehicle and traffic parameters of other traffic participants; then inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result; then determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the prediction result; and finally, generating corresponding early warning information according to the traffic risk level. The behavior of other traffic participants can be predicted by using the prediction model to obtain a prediction result; then, determining a traffic risk level aiming at the automatic driving vehicle according to the traffic parameters of the automatic driving vehicle, the traffic parameters of other traffic participants and a prediction result; and finally, whether the early warning information needs to be sent or not can be determined through the traffic risk level, so that the driving safety of the automatic driving vehicle is guaranteed.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

Other features, objects, and advantages of the disclosure will become apparent from a reading of the following detailed description of non-limiting embodiments which proceeds with reference to the accompanying drawings. The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is an exemplary system architecture diagram in which the present disclosure may be applied;

FIG. 2 is a flow diagram of one embodiment of a method of generating autonomous driving warning information according to the present disclosure;

FIG. 3 is a flow diagram of one embodiment of a method of generating autonomous driving warning information according to the present disclosure;

FIG. 4 is a flow diagram of one embodiment of a method of generating autonomous driving warning information according to the present disclosure;

FIG. 5 is a flow diagram of one embodiment of a method of generating autonomous driving warning information according to the present disclosure;

FIG. 6 is a flow diagram of one embodiment of an apparatus for generating automated driving warning information according to the present disclosure;

FIG. 7 is a block diagram of an electronic device used to implement an embodiment of the disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the disclosed method or apparatus for generating autonomous driving warning information may be applied.

As shown in fig. 1, the system architecture 100 may include a terminal device 101 of an autonomous vehicle, terminal devices 102 of other traffic participants, a network 103, and a server 104. The network 103 is used to provide a medium for communication links between the terminal devices 101 of autonomous vehicles, the terminal devices 102 of other traffic participants, and the server 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may interact with the server 104 over the network 103 using the terminal device 101 of the mobile driving vehicle, the terminal devices 102 of the other traffic participants to receive or transmit traffic parameters of the autonomous vehicle and traffic parameters of the other traffic participants, and the like. Various client applications, intelligent interactive applications, such as traffic handling applications, mapping software, etc., may be installed on the terminal device 101 of the autonomous vehicle, the terminal devices 102 of the other traffic participants.

The terminal 101 of the autonomous vehicle and the terminal 102 of the other traffic participants may be hardware or software. When the terminal 101 of the autonomous driving vehicle and the terminal 102 of the other traffic participants are hardware, the terminal may be an electronic product that performs man-machine interaction with a user through one or more modes such as a keyboard, a touch pad, a display screen, a touch screen, a remote controller, voice interaction or handwriting equipment, for example, a PC (Personal Computer), a mobile phone, a smart phone, a PDA (Personal Digital Assistant), a wearable device, a PPC (Pocket PC, palmtop Computer), a tablet Computer, a smart car machine, a smart television, a smart speaker, a tablet Computer, a laptop portable Computer, a desktop Computer, and the like.

The server 104 may provide various services. For example, the server 104 may obtain the traffic parameters of the autonomous vehicle and the traffic parameters of other traffic participants on the terminal device 101 of the autonomous vehicle and the terminal devices 102 of other traffic participants, and input the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result; determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the prediction result; generating corresponding early warning information according to the traffic risk grade; finally, the warning information is sent to the terminal device 101 of the autonomous vehicle and the terminal devices 102 of other traffic participants.

The server 104 may be hardware or software. When the server 104 is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server, for example, a server of a traffic control center. When the server 104 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be noted that the method for generating the automatic driving warning information provided by the embodiment of the present disclosure is generally executed by the server 104, and accordingly, the device for generating the automatic driving warning information is generally disposed in the server 104.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow 200 of one embodiment of a method of generating autonomous driving warning information in accordance with the present disclosure is shown. The method for generating the automatic driving early warning information can comprise the following steps:

step 201, acquiring traffic parameters of the automatic driving vehicle and traffic parameters of other traffic participants.

In this embodiment, the executing entity (e.g., server 104 shown in fig. 1) of the method of generating the autonomous driving warning information may obtain the traffic parameters of the autonomous vehicle and the traffic parameters of the other traffic participants. The other traffic parameters described above are in contrast to autonomous vehicles, which may include, but are not limited to, pedestrians, other motor vehicles, riders, and the like. The traffic parameter of the autonomous vehicle may be a traffic-related parameter generated by the autonomous vehicle during operation, such as a position, a shape, speed information, and the like. The traffic parameters of the other traffic participants may be traffic-related parameters generated by the other traffic participants during operation, such as location, shape, category (e.g., pedestrians, other vehicles, or riders), speed information, and the like.

Correspondingly, in this example, the traffic parameter of the autonomous vehicle may be acquired based on a sensor of the autonomous vehicle, and then the traffic parameter is sent to the execution main body by a terminal of the autonomous vehicle, or acquired by a camera, a speed measuring device, a base station, and the like on the road.

Correspondingly, in this example, the traffic parameters of other traffic participants may be acquired based on a camera, a speed measuring device, a nearby base station, and the like, which are disposed on the road.

In one example, the other traffic participants are pedestrians, and the traffic parameters of the other traffic participants can be acquired based on the terminal devices of the other traffic participants. The other traffic participants are automobiles, and the traffic parameters of the other traffic participants can be acquired based on the terminal devices of the other traffic participants.

When the vehicle is in automatic driving, the traffic parameters of other traffic participants are acquired and obtained based on a plurality of sensors and a map, the other traffic participants can be the traffic participants within a certain range from the automatic driving vehicle, and the certain range can be determined according to whether angles influencing normal driving occur or not.

Specifically, data acquired by various sensors and map information are used as input, and the surrounding environment of the automatic driving vehicle is sensed through a series of calculation and processing. It can provide rich information for the downstream, such as the position, shape, category, speed information, and direction of movement of other traffic participants.

It should be noted that the traffic parameter of the autonomous vehicle and the traffic parameters of other traffic participants may be traffic parameters corresponding to a certain time. However, in order to predict the behaviors of other traffic participants, the traffic parameters of other traffic participants in a preset time period before a certain time may be acquired, so as to predict the behaviors of other traffic participants.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related traffic parameters are all in accordance with the regulations of related laws and regulations, and do not violate the good customs of the public order.

Step 202, inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result.

In this embodiment, the executing entity may input the traffic parameters of the other traffic participants into a pre-trained prediction model to obtain a prediction result corresponding to the traffic participation of the other traffic participants. The prediction result can be used for predicting the traffic state that other traffic participants may be in at the next time after a certain time, for example, predicting the behavior type, predicting the track, and the like.

Here, the prediction model may be trained based on traffic parameters of other traffic participants and corresponding behavior labels.

In this embodiment, it may be accurately determined whether the type, the moving direction, the moving speed, and the like of other traffic participants located outside the autonomous vehicle may affect the normal driving of the autonomous vehicle, and when the behavior of other traffic participants may affect the autonomous vehicle and the relative distance between the autonomous vehicle and the other traffic participants is smaller than a preset distance threshold, it is determined that there is a risk of a traffic accident.

And step 203, determining a traffic risk level aiming at the automatic driving vehicle according to the traffic parameters of the automatic driving vehicle, the traffic parameters of other traffic participants and the prediction result.

In the present embodiment, the execution subject may determine the traffic risk level for the autonomous vehicle according to the traffic parameter of the autonomous vehicle, the traffic parameters of other traffic participants, and the prediction result.

In one example, relative traffic parameters between the autonomous vehicle and other traffic participants may be determined based on traffic parameters of the autonomous vehicle and traffic parameters of the other traffic participants; thereafter, a traffic risk level for the autonomous vehicle is determined based on the relative traffic parameters and the prediction result. The relative traffic parameter may be a relative parameter between the autonomous vehicle and the other traffic participant, such as a distance, a relative speed, etc. between the autonomous vehicle and the other traffic participant.

In this embodiment, the method for generating the automatic driving warning information may further include: and setting a first weight of the relative traffic parameter and a weight corresponding to the prediction result. The first weight and the second weight may be set according to the sensitivity of the warning or may be set by a user.

Correspondingly, in this example, determining a traffic risk level for the autonomous vehicle from the relative traffic parameters and the prediction result may include: and determining a traffic risk level for the autonomous vehicle according to the relative traffic parameters and the corresponding first weights, and the prediction result and the corresponding second weights.

It should be noted that the first weight and the second weight may be adjusted according to actual traffic conditions.

And step 204, generating corresponding early warning information according to the traffic risk level.

In this embodiment, the execution subject may generate the warning information corresponding to the traffic risk level according to the traffic risk level. The early warning information may include early warning types, early warning contents and the like, the early warning types may include ringing, voice reminding, message reminding and the like, and the early warning contents may include possible accidents, suggested change speeds, the suggested change speeds being preset speeds, categories of other traffic participants, prediction results of other traffic participants and the like. The warning information may be used to alert the driver of the autonomous vehicle and/or other traffic participants.

It should be noted that, when the level of the traffic risk level is higher, multiple warning types can be used in combination to realize warning in all directions. Or, the related traffic managers are informed in advance to maintain the traffic conditions in advance so as to reduce the occurrence of accidents.

After generating the warning information, the method of generating the automatic driving warning information may further include: and sending the early warning information to the terminal equipment of the automatic driving vehicle, and reminding the early warning information by the terminal equipment of the automatic driving vehicle, for example, reminding through a display screen on the terminal equipment of the automatic driving vehicle.

In one example, the early warning information is notified to a human-computer interaction device in the autonomous vehicle, and the human-computer interaction device can prompt passengers in forms of characters, voice, whistling and the like, so that the passengers can be well protected from safety and the like.

In addition, the early warning information can be sent to terminal equipment of other traffic parameter persons. Or the execution main body reminds through a loudspeaker on the road without sending early warning information.

It should be noted that after the terminal device of the autonomous vehicle receives the warning information, the speed of the autonomous vehicle may be adjusted to avoid traffic accidents.

The method for generating the automatic driving early warning information provided by the embodiment of the disclosure comprises the steps of firstly obtaining traffic parameters of an automatic driving vehicle and traffic parameters of other traffic participants; then inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result; then determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the prediction result; and finally, generating corresponding early warning information according to the traffic risk level. The behavior of other traffic participants can be predicted by using the prediction model to obtain a prediction result; then, determining a traffic risk level aiming at the automatic driving vehicle according to the traffic parameters of the automatic driving vehicle, the traffic parameters of other traffic participants and a prediction result; and finally, whether the early warning information needs to be sent or not can be determined through the traffic risk level, so that the driving safety of the automatic driving vehicle is guaranteed.

In some optional implementations of this embodiment, the method for generating the automatic driving warning information further includes: determining a traffic influence degree according to the prediction result, wherein the traffic influence degree is the traffic influence degree of the behaviors of other traffic participants on the automatic driving vehicle; determining a traffic risk level for the autonomous vehicle based on the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the prediction, comprising: and determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree in response to the traffic influence degree satisfying a preset traffic influence degree threshold.

In some optional implementations of this embodiment, the method for generating the automatic driving warning information further includes: acquiring traffic environment information; and determining the traffic influence degree according to the prediction result, wherein the method comprises the following steps: and determining the traffic influence degree according to the prediction result and the traffic environment information.

In some optional implementations of the embodiment, determining the traffic risk level for the autonomous vehicle according to the traffic parameter of the autonomous vehicle, the traffic parameters of the other traffic participants, and the traffic influence degree includes: determining relative traffic parameters according to the traffic parameters of the automatic driving vehicle and the traffic parameters of other traffic participants, wherein the relative traffic parameters are the traffic parameters between the automatic driving vehicle and the other traffic participants; and determining a traffic risk level for the autonomous vehicle according to the relative traffic parameters and the traffic influence degree.

In some optional implementations of the embodiment, determining the traffic risk level for the autonomous vehicle with respect to the traffic parameter and the traffic impact degree includes: and determining the traffic risk level aiming at the automatic driving vehicle according to the traffic risk coefficient corresponding to the relative traffic parameter and the traffic risk coefficient corresponding to the traffic influence degree.

In some optional implementations of the embodiment, the prediction result includes a predicted behavior type or a predicted trajectory.

In the implementation mode, the traffic risk grade can be accurately determined based on the accurate prediction of the behaviors of other traffic participants, so that the traffic risk grade can be accurately determined based on the predicted behaviors, the traffic parameters of the automatic driving vehicle and the traffic parameters of other traffic participants.

With further reference to fig. 3, fig. 3 illustrates a flow 300 of one embodiment of a method of generating autonomous driving warning information according to the present disclosure. The method for generating the automatic driving early warning information can comprise the following steps:

step 301, obtaining traffic parameters of the autonomous vehicle and traffic parameters of other traffic participants.

Step 302, inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result.

And step 303, determining a traffic influence degree according to the prediction result, wherein the traffic influence degree is the traffic influence degree of the behaviors of other traffic participants on the automatic driving vehicle.

In the present embodiment, an execution subject (e.g., the server 104 shown in fig. 1) of the method of generating the automatic driving warning information may determine the degree of traffic influence according to the prediction result. The traffic impact level may be used to characterize the impact level of other traffic participants on the normal travel of the autonomous vehicle.

Correspondingly, in this example, determining the traffic influence degree according to the prediction result may include: and determining the traffic influence degree corresponding to the predicted behavior type according to the predicted behavior type included in the prediction result.

In one example, if the predicted behavior type is an accelerated overtaking, the corresponding traffic impact level is determined based on the accelerated overtaking.

It should be noted that the behavior types of other traffic participants in the present embodiment may include normal driving, sudden stop, reverse driving, overtaking, slowing down and slow driving. The corresponding traffic influence degree can be preset according to the possibility of accidents caused by different behavior types.

In one example, the degree of traffic impact corresponding to overtaking is greater than the degree of traffic impact corresponding to slowing down and crawling.

Correspondingly, in this example, determining the traffic influence degree according to the prediction result may include: and determining the traffic influence degree corresponding to the predicted track according to the predicted track included in the prediction result.

In one example, if the predicted trajectory coincides with a preset trajectory, a corresponding traffic influence degree may be determined according to the predicted trajectory.

It should be noted that the preset trajectory is a trajectory that may affect the normal operation of the autonomous vehicle. For example, other traffic participants travel to a preset location in front of the autonomous vehicle, which may be a location where an accident may occur during a period of normal operation of the autonomous vehicle.

And step 304, determining a traffic risk level aiming at the automatic driving vehicle according to the traffic parameters of the automatic driving vehicle, the traffic parameters of other traffic participants and the traffic influence degree.

In this embodiment, the execution subject may determine the traffic risk level for the autonomous vehicle according to the traffic parameter of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree.

Specifically, the execution subject may determine the traffic risk level for the autonomous vehicle according to a traffic accident coefficient corresponding to a traffic parameter of the autonomous vehicle, a traffic parameter of another traffic participant, and a traffic accident coefficient of a traffic influence degree.

In one example, the method of generating the prompt message may further include: the traffic parameters of the automatic driving vehicle, the traffic parameters of other traffic participants and the corresponding weights of the traffic influence degrees are preset, and the traffic risk grade aiming at the automatic driving vehicle is obtained according to the weighted sum of the weights and the traffic accident coefficients.

It should be noted that the weight may be set according to the sensitivity of the warning or set by the user.

And 305, generating corresponding early warning information according to the traffic risk level.

In this embodiment, the specific operations of

steps

301, 302, and 305 have been described in detail in

steps

201, 202, and 204, respectively, in the embodiment shown in fig. 2, and are not described again here.

As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, the method for generating the automatic driving warning information in the embodiment highlights the step of determining the traffic influence degree according to the prediction result. Thus, the solution described in the present embodiment determines the degree of traffic influence from the prediction result. And screening is carried out on the basis of a preset traffic influence degree threshold, and whether early warning is needed or not is further judged only for the traffic influence degree meeting the preset traffic influence degree threshold.

In some optional implementations of this embodiment, the method for generating the automatic driving warning information may further include: and judging whether the traffic influence degree meets a preset traffic influence degree threshold value or not.

Correspondingly, in this example, determining a traffic risk level for the autonomous vehicle from the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the degree of traffic impact may include: and determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree in response to the traffic influence degree satisfying a preset traffic influence degree threshold. The satisfaction may be greater than or equal to a preset traffic influence degree threshold.

In this implementation manner, after the executing step 303, the executing body determines the traffic influence degree, and only when the traffic influence degree meets a preset traffic influence degree threshold, the executing body executes step 304. The preset traffic influence degree threshold is used for measuring whether an accident occurs or not, and can be set by a traffic manager or set according to early warning forest acuity.

It should be noted that the preset traffic influence degree threshold may be set for different other traffic participants; or, setting different running speeds of other traffic participants; or, setting according to different road sections; or, the setting is made according to different weather, environment, and the like.

In this implementation, the condition of small traffic influence degree can be excluded in advance through the preset traffic influence degree threshold value, so as to avoid unnecessary early warning and further save resources.

With further reference to fig. 4, fig. 4 illustrates a flow 400 of one embodiment of a method of generating autonomous driving warning information according to the present disclosure. The method for generating the automatic driving early warning information can comprise the following steps:

step 401, obtaining traffic parameters of the autonomous vehicle and traffic parameters of other traffic participants.

And step 402, inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result.

And step 403, acquiring traffic environment information.

In the present embodiment, an executing entity (e.g., the server 104 shown in fig. 1) of the method for generating the automatic driving warning information may acquire traffic environment information where the automatic driving vehicle is located, such as weather conditions, climate conditions, road segment conditions, traffic jam conditions, and the like.

It should be noted that the training performed in step 403 may be performed simultaneously with step 401, or may be performed at any time before step 404, for example, after step 402.

And step 404, determining the corresponding traffic influence degree according to the prediction result and the traffic environment information.

In this embodiment, the executing entity may determine the traffic influence degree corresponding to the prediction result according to the previous prediction result; then, determining the traffic influence degree corresponding to the traffic environment information according to the traffic environment information; then, determining the final traffic influence degree according to the traffic influence degree corresponding to the prediction result and the traffic influence degree corresponding to the traffic environment information; or determining the final traffic influence degree according to the prediction result and the traffic environment information.

It should be noted that, according to the previous prediction result, the description of determining the traffic influence degree corresponding to the prediction result may refer to the description of 303.

In this embodiment, the execution subject may further preset weights corresponding to the prediction result and the traffic environment information, so as to perform weighted summation according to the weights to obtain the traffic influence degree.

It should be noted that the above-mentioned weight may be adjusted according to the actual traffic environment information. For example, in rainy days, the weight corresponding to the traffic environment information needs to be increased.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related traffic environment information all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

Step 405, determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree.

And 406, generating corresponding early warning information according to the traffic risk level.

In this embodiment, the specific operations of

steps

401, 402, 405, and 406 have been described in detail in

steps

301, 302, 304, and 305, respectively, in the embodiment shown in fig. 3, and are not described again here.

As can be seen from fig. 4, compared with the embodiment corresponding to fig. 3, the method for generating the automatic driving warning information in the present embodiment highlights the step of determining the traffic influence degree. Thus, the scheme described in the embodiment determines the traffic influence degree according to the prediction result and the traffic environment information. The traffic environment information can be further determined based on the actual traffic environment information, so that the traffic influence degree can be more accurately determined, screening is carried out based on a preset traffic influence degree threshold value, and whether early warning needs to be carried out is further judged only on the traffic influence degree meeting the preset traffic influence degree threshold value.

With further reference to fig. 5, fig. 5 illustrates a flow 500 of one embodiment of a method of generating autonomous driving warning information according to the present disclosure. The method for generating the automatic driving early warning information can comprise the following steps:

step 501, obtaining traffic parameters of the automatic driving vehicle and traffic parameters of other traffic participants.

And 502, inputting the traffic parameters of other traffic participants into a pre-trained prediction model to obtain a prediction result.

Step 503, determining a relative traffic parameter according to the traffic parameter of the autonomous vehicle and the traffic parameters of other traffic participants, wherein the relative traffic parameter is the traffic parameter between the autonomous vehicle and the other traffic participants.

In this embodiment, the executing entity (e.g., the server 104 shown in fig. 1) of the method for generating the automatic driving warning information may determine the relative traffic parameters according to the traffic parameters of the automatic driving vehicle and the traffic parameters of other traffic participants. The relative traffic parameter may be a relative parameter between the autonomous vehicle and the other traffic participant, such as a distance, a relative speed, etc. between the autonomous vehicle and the other traffic participant.

And step 504, determining a traffic risk level aiming at the automatic driving vehicle according to the relative traffic parameters and the traffic influence degree.

In this embodiment, the execution subject may determine the traffic risk level for the autonomous vehicle according to the relative traffic parameter and the traffic influence degree.

And 505, generating corresponding early warning information according to the traffic risk level.

In this embodiment, the specific operations of

steps

501, 502, and 505 have been described in detail in

steps

As can be seen from fig. 5, compared with the embodiment corresponding to fig. 2, the method for generating the automatic driving warning information in the present embodiment highlights the step of determining the traffic risk level. Therefore, the scheme described in the embodiment determines the relative traffic parameters according to the traffic parameters of the automatic driving vehicle and the traffic parameters of other traffic participants, wherein the relative traffic parameters are the traffic parameters between the automatic driving vehicle and the other traffic participants; then, a traffic risk level for the autonomous vehicle is determined based on the relative traffic parameters and the degree of traffic impact. Thereby enabling the determination of the traffic risk level.

It should be noted that the traffic risk coefficient can be used to characterize the coefficient for a possible traffic accident. The traffic risk coefficient may be set according to the sensitivity of the warning or by the traffic manager.

In this implementation manner, the execution subject may determine the traffic risk level for the autonomous vehicle according to the traffic risk coefficient corresponding to the relative traffic parameter and the traffic risk coefficient corresponding to the traffic influence degree.

With further reference to fig. 6, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of an apparatus for generating automatic driving warning information, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 6, the apparatus 600 for generating automatic driving warning information according to the present embodiment may include: a parameter obtaining module 601, a result obtaining module 602, a grade determining module 603 and an information generating module 604. The parameter obtaining module 601 is configured to obtain traffic parameters of the autonomous vehicle and traffic parameters of other traffic participants; a result obtaining module 602, configured to input the traffic parameters of the other traffic participants into a pre-trained prediction model to obtain a prediction result; a grade determination module 603 configured to determine a traffic risk grade for the autonomous vehicle based on the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the prediction result; and an information generating module 604 configured to generate corresponding early warning information according to the traffic risk level.

In the present embodiment, the automatic driving warning information generating device 600: the specific processing and the technical effects thereof of the parameter obtaining module 601, the result obtaining module 602, the level determining module 603, and the information generating module 604 can refer to the related descriptions of step 201 and step 204 in the corresponding embodiment of fig. 2, which are not described herein again.

In some optional implementations of this embodiment, the means for generating the automatic driving warning information further includes: a degree determination module configured to determine a traffic influence degree according to the prediction result, wherein the traffic influence degree is a traffic influence degree of behaviors of other traffic participants on the autonomous vehicle;

a rank determination module 603, further configured to: and determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of other traffic participants, and the traffic influence degree in response to the traffic influence degree satisfying a preset traffic influence degree threshold.

In some optional implementations of this embodiment, the means for generating the automatic driving warning information further includes: an information acquisition module configured to acquire traffic environment information; a degree determination module further configured to: and determining the traffic influence degree according to the prediction result and the traffic environment information.

In some optional implementations of this embodiment, the level determining module 603 includes: a parameter determination unit configured to determine a relative traffic parameter according to the traffic parameter of the autonomous vehicle and the traffic parameters of the other traffic participants, wherein the relative traffic parameter is a traffic parameter between the autonomous vehicle and the other traffic participants; a level determination unit configured to determine a traffic risk level for the autonomous vehicle based on the relative traffic parameter and the traffic impact degree.

In some optional implementations of this embodiment, the rank determining unit is further configured to: and determining the traffic risk level aiming at the automatic driving vehicle according to the traffic risk coefficient corresponding to the relative traffic parameter and the traffic risk coefficient corresponding to the traffic influence degree.

The present disclosure also provides an electronic device, a readable storage medium, a computer program product, and an autonomous vehicle according to embodiments of the present disclosure.

FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 701 executes the respective methods and processes described above, such as the automatic driving warning information generation method. For example, in some embodiments, the method of generating autonomous driving warning information may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method of generating autopilot warning information described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of generating the autonomous driving warning information.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Artificial intelligence is the subject of studying computers to simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural voice processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions mentioned in this disclosure can be achieved, and are not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of generating autonomous driving warning information, comprising:

acquiring traffic parameters of an automatic driving vehicle and traffic parameters of other traffic participants;

inputting the traffic parameters of the other traffic participants into a pre-trained prediction model to obtain a prediction result;

determining a traffic risk level for the autonomous vehicle based on the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the prediction result;

and generating corresponding early warning information according to the traffic risk grade.

2. The method of claim 1, further comprising:

determining a traffic influence degree according to the prediction result, wherein the traffic influence degree is the traffic influence degree of the behaviors of the other traffic participants on the automatic driving vehicle;

the determining a traffic risk level for the autonomous vehicle based on the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the prediction, comprises:

and in response to the traffic influence degree meeting a preset traffic influence degree threshold, determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the traffic influence degree.

3. The method of claim 2, further comprising:

acquiring traffic environment information;

the determining the traffic influence degree according to the prediction result comprises the following steps:

and determining the traffic influence degree according to the prediction result and the traffic environment information.

4. The method of claim 2 or 3, wherein the determining a traffic risk level for the autonomous vehicle from the traffic parameter of the autonomous vehicle, the traffic parameter of the other traffic participant, and the degree of traffic impact comprises:

determining relative traffic parameters according to the traffic parameters of the autonomous vehicle and the traffic parameters of the other traffic participants, wherein the relative traffic parameters are the traffic parameters between the autonomous vehicle and the other traffic participants;

determining a traffic risk level for the autonomous vehicle based on the relative traffic parameter and the traffic impact level.

5. The method of claim 4, wherein the relative traffic parameter and the traffic impact level determining a traffic risk level for the autonomous vehicle comprises:

and determining the traffic risk grade aiming at the automatic driving vehicle according to the traffic risk coefficient corresponding to the relative traffic parameter and the traffic risk coefficient corresponding to the traffic influence degree.

6. The method of any of claims 1-5, wherein the prediction comprises a predicted behavior type or a predicted trajectory.

7. An apparatus for generating automatic driving warning information, comprising:

a parameter acquisition module configured to acquire traffic parameters of the autonomous vehicle and traffic parameters of other traffic participants;

the result obtaining module is configured to input the traffic parameters of the other traffic participants into a pre-trained prediction model to obtain a prediction result;

a level determination module configured to determine a traffic risk level for the autonomous vehicle based on the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the prediction result;

and the information generation module is configured to generate corresponding early warning information according to the traffic risk level.

8. The apparatus of claim 7, further comprising:

a degree determination module configured to determine a traffic influence degree according to the prediction result, wherein the traffic influence degree is a traffic influence degree of the behavior of the other traffic participants on the autonomous vehicle;

the rank determination module further configured to: and in response to the traffic influence degree meeting a preset traffic influence degree threshold, determining a traffic risk level for the autonomous vehicle according to the traffic parameters of the autonomous vehicle, the traffic parameters of the other traffic participants, and the traffic influence degree.

9. The apparatus of claim 8, the apparatus further comprising:

an information acquisition module configured to acquire traffic environment information;

the extent determination module further configured to: and determining the traffic influence degree according to the prediction result and the traffic environment information.

10. The apparatus of claim 8 or 9, wherein the rank determination module comprises:

a parameter determination unit configured to determine a relative traffic parameter from the traffic parameter of the autonomous vehicle and the traffic parameters of the other traffic participants, wherein the relative traffic parameter is a traffic parameter between the autonomous vehicle and the other traffic participants;

a level determination unit configured to determine a traffic risk level for the autonomous vehicle based on the relative traffic parameter and the traffic impact degree.

11. The apparatus of claim 10, wherein the rank determination unit is further configured to:

12. The apparatus of any of claims 7-11, wherein the prediction comprises a predicted behavior type or a predicted trajectory.

13. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

16. An autonomous vehicle comprising the electronic device of claim 13.