Disclosure of Invention
In view of the above, the present invention provides a driving prompting method, apparatus, computer device, storage medium and program product, so as to solve the problem of higher accident probability when entering and exiting a tunnel.
In a first aspect, the present invention provides a driving prompting method, which includes:
Performing image recognition on a target scene in a driving environment of a target vehicle to obtain scene information, wherein the target scene is used for indicating a tunnel entrance scene, and the scene information is used for indicating scene characteristics of the target scene;
calculating distance data between the target vehicle and the target scene based on the scene information;
Performing risk assessment based on the running state parameters of the target vehicle and the distance data to obtain a risk level, wherein the risk level is used for indicating the risk of collision between the target vehicle and the target scene;
and controlling the target vehicle according to the control information corresponding to the risk level so as to early warn the driving object.
In the embodiment of the invention, firstly, image recognition is carried out on a target scene in a driving environment of a target vehicle to obtain scene information, wherein the target scene is used for indicating a tunnel entrance scene, and the scene information is used for indicating scene characteristics of the target scene. Then, distance data between the target vehicle and the target scene can be calculated based on the scene information, and risk assessment is performed based on the running state parameters of the target vehicle and the distance data to obtain a risk level, wherein the risk level is used for indicating the risk of collision of the target vehicle in the target scene. And then, the target vehicle can be controlled according to the control information corresponding to the risk level so as to early warn the driving object, so that the driving object is early warned in advance in the scene that the target vehicle enters and exits the tunnel, and the probability of traffic accidents caused by the fact that the driving object is not focused is reduced.
In an alternative embodiment, the operating state parameters include real-time speed, acceleration;
The risk assessment is performed based on the running state parameters of the target vehicle and the distance data to obtain a risk level, and the risk level comprises:
Calculating the running time of the target vehicle to the target scene based on the real-time speed, the acceleration and the distance data;
And acquiring a risk level matched with the running time based on preset association information, wherein the preset association information is used for indicating the association relationship between the running time and the risk level.
In the embodiment of the invention, the driving time required by the target vehicle to reach the tunnel entrance can be calculated, and the corresponding risk level is determined according to the driving time, so that the driving object is subjected to multi-stage early warning according to the risk level, and the effect of reminding the driving object to concentrate attention is improved.
In an alternative embodiment, the method further comprises:
After a configuration instruction is detected, the preset association information is established based on the association relation between the running time and the risk level indicated by the configuration instruction.
In the embodiment of the invention, the driving time required by the target vehicle to reach the tunnel entrance can be calculated, and the corresponding risk level is determined according to the driving time, so that the driving object is subjected to multi-stage early warning according to the risk level, and the effect of reminding the driving object to concentrate attention is improved.
In an alternative embodiment, the operating state parameters include angular velocity, steering wheel angle;
the risk assessment is performed based on the running state parameters of the target vehicle and the distance data to obtain a risk level, and the method further comprises the following steps:
acquiring the angular velocity based on an angular velocity sensor, and acquiring the steering wheel angle based on a steering wheel angle sensor;
acquiring pedal control data based on a pedal sensor, wherein the pedal control data is used for indicating control data of an accelerator pedal and/or a brake pedal of the target vehicle;
And when the angular speed, the steering wheel angle and the pedal control data are not changed, performing risk assessment based on the distance data to obtain a risk grade.
In the embodiment of the invention, it should be understood that when the angular speed, the steering wheel angle and the pedal control data are not changed, it is indicated that the driving object does not control the driving state of the target vehicle, and there may be a situation of inattention, at this time, since the target network model has detected the tunnel entrance, risk assessment can be performed according to the distance data between the target vehicle and the tunnel entrance, so as to prompt the driving object in time and ensure driving safety.
In an alternative embodiment, the method further comprises:
Respectively detecting change data when the angular speed, steering wheel rotation angle and pedal control data are updated;
And when any of the change data exceeds a corresponding change threshold value, resetting the risk level.
In the embodiment of the invention, whether the driving object controls the vehicle or not can be determined by detecting the angular speed, the steering wheel rotation angle and the change data when the pedal control data are updated, so that whether the driving object is out of the distraction state or not is determined, and the risk level is cleared when the driving object is out of the distraction state, so that the early warning is relieved.
In an alternative embodiment, performing risk assessment based on the distance data to obtain a risk level includes:
acquiring real-time speed and acceleration of the target vehicle;
Calculating a target speed when the target vehicle reaches the target scene based on the distance data, the real-time speed and the acceleration;
And performing risk assessment based on the target speed to obtain a risk grade.
In the embodiment of the invention, it should be understood that when the angular speed, the steering wheel angle and the pedal control data are not changed, it is indicated that the driving object does not control the driving state of the target vehicle, and there may be a situation of inattention, at this time, since the target network model has detected the tunnel entrance, risk assessment can be performed according to the distance data between the target vehicle and the tunnel entrance, so as to prompt the driving object in time and ensure driving safety.
In an alternative embodiment, image recognition is performed on a target scene in a driving environment of a target vehicle to obtain scene information, including:
Based on a camera device, acquiring an image frame of the driving environment;
Based on a target network model, identifying tunnel entrance and exit features and/or tunnel marking features in the image frames to obtain an identification result;
And determining the scene information according to the identification result.
In the embodiment of the invention, the tunnel entrance and exit features and/or the tunnel marking features in the image frame of the driving environment can be identified through the target network model to obtain the identification result, so that the driving object is actively reminded to reach the tunnel entrance and exit when navigation software is not updated timely or the tunnel exit scene is poor in signal and can not be reminded of the driving object in time.
In an alternative embodiment, the scene information includes scene characteristics of the tunnel exit;
The method further comprises the steps of:
After the scene information is obtained, acquiring the accumulated time of the target vehicle entering a tunnel;
and determining the confidence of the identified scene feature based on the accumulated time.
In the embodiment of the invention, under the tunnel exit scene, the recognition result including the scene characteristics of the tunnel exit output by the target network model can be verified in confidence according to the accumulated time of the target vehicle entering the tunnel, so that the false triggering probability of the active reminding operation is reduced.
In an alternative embodiment, the control information comprises safety belt control information, buzzer control information and brake pedal control information;
The method further comprises the steps of:
presetting at least one risk level;
setting corresponding target information for each risk level, wherein the target information comprises at least one control information.
In the embodiment of the invention, the corresponding target information can be set for each risk level, so that the target vehicle can be controlled by utilizing various control modes, the early warning can be better carried out on the driving object, and the driving safety is ensured.
In a second aspect, the present invention provides a driving prompt device, including:
The recognition module is used for carrying out image recognition on a target scene in the driving environment of the target vehicle to obtain scene information, wherein the target scene is used for indicating a tunnel entrance scene, and the scene information is used for indicating scene characteristics of the target scene;
A calculation module for calculating distance data between the target vehicle and the target scene based on the scene information;
The evaluation module is used for performing risk evaluation based on the running state parameters of the target vehicle and the distance data to obtain a risk level, wherein the risk level is used for indicating the collision risk between the target vehicle and the target scene;
And the early warning module is used for carrying out early warning on the driving object of the target vehicle according to the control information corresponding to the risk level.
In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, so as to execute the driving prompting method of the first aspect or any implementation manner corresponding to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the driving prompting method of the first aspect or any one of the embodiments corresponding thereto.
In a fifth aspect, the present invention provides a computer program product, including computer instructions for causing a computer to execute the driving prompting method of the first aspect or any implementation manner corresponding to the first aspect.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The application scenario is described herein in connection with an application scenario on which execution of the driving alert method depends.
At the present time when traffic networks are increasingly abundant, there are very many tunnels in both urban traffic networks and high-speed traffic networks. Driving safety issues for entering and exiting tunnels are also becoming more prominent. In a tunnel entrance scene, a severe change of light needs to be adapted to a driver for a period of time, and the period of time is when an accident is high, and once the accident happens, the accident is a serious accident of collision of a plurality of vehicles.
However, for this scenario, the related driving prompt schemes mostly remind the driver to drive carefully through a warning board outside the tunnel, light, a ground road sign or some third party navigation software in a voice manner. However, for drivers who recognize the road, the navigation prompt is often omitted or even omitted, and for novice drivers, more attention is paid to the situation in the vehicle, and no more attention is paid to external control information, which increases the probability of occurrence of accidents to a certain extent.
Specifically, the related driving prompt scheme mainly comprises two parts, wherein the first part comprises ground marks, warning lamps, prompt guideboards and the like which are added when local road administration or urban construction is carried out at the entrance of a tunnel, in such scheme, the scheme can only play a role in prompting a driver with concentrated attention, most drivers with accidents are not concentrated enough, the probability of not noticing control information is very high, and the cost for constructing maintenance prompt equipment is very high. The third party navigation software in the second part will be voice-prompted before entering the tunnel, which solution requires the driver to use the navigation software, which is certainly quite a matter of course for the driver who knows the route.
However, in these related driving prompt schemes, the driver is required to actively acquire external control information or actively use third party software to get the prompt. In the existing scheme, only the entering tunnel is reminded, control information is almost invalid in the scene of the exiting tunnel, meanwhile, due to the fact that signals in the tunnel are poor, navigation software can be updated in time, and safety is guaranteed by the driving behavior of a driver.
Based on the above, the embodiment of the invention provides a driving prompt method, which comprises the steps of firstly, carrying out image recognition on a target scene in a driving environment of a target vehicle to obtain scene information, wherein the target scene is used for indicating a tunnel entrance scene, and the scene information is used for indicating scene characteristics of the target scene. Then, distance data between the target vehicle and the target scene can be calculated based on the scene information, and risk assessment is performed based on the running state parameters of the target vehicle and the distance data to obtain a risk level, wherein the risk level is used for indicating the risk of collision of the target vehicle in the target scene. And then, the target vehicle can be controlled according to the control information corresponding to the risk level so as to early warn the driving object, so that the driving object is early warned in advance in the scene that the target vehicle enters and exits the tunnel, and the probability of traffic accidents caused by the fact that the driving object is not focused is reduced.
According to an embodiment of the present invention, there is provided a driving prompting method embodiment, it is to be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
In this embodiment, a driving prompting method is provided, which may be used for a vehicle terminal, and fig. 1 is a flowchart of a driving prompting method according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
Step S101, performing image recognition on a target scene in a driving environment of a target vehicle to obtain scene information, wherein the target scene is used for indicating a tunnel entrance scene, and the scene information is used for indicating scene characteristics of the target scene.
In the embodiment of the invention, in the running process of the target vehicle, the road condition image can be obtained in real time through the visual sensor installed in the target vehicle, and the road condition image is identified through the preset network model so as to identify the tunnel entrance scene contained in the road condition image. For example, the preset network model may be a convolutional neural network (Convolutional Neural Networks, i.e. CNN) and its improved architecture, and it should be understood that the preset network model may also be another type of neural network, where the neural network is selected based on the recognition of the tunnel entrance and exit scene contained in the road condition image, which is not specifically limited in the present invention.
After the preset network model identifies the target scene, scene information of the target scene may be obtained, for example, a target road condition image including the target scene acquired by the vision sensor is obtained.
Step S102, calculating distance data between the target vehicle and the target scene based on the scene information.
In the embodiment of the present invention, after the target scene is identified, the preset network model may estimate a distance value between the target vehicle and the target scene based on the scene information, so as to obtain the distance data. For example, the distance value is estimated by the target road condition image using the stereoscopic vision technique.
Specifically, when the target road condition image includes multiple frames of continuous image frames, feature points, such as corner points and edges, of the target scene can be identified and tracked, and the distance value between the target scene and the vehicle can be estimated by comparing the position changes of the feature points in the continuous image frames and combining the motion parameters of the vision sensor and the size information of the target scene.
It should be understood that the distance data between the target vehicle and the target scene may be determined by other manners, for example, determining the distance value between the tunnel entrance and the target vehicle by using a laser radar, and the manner of determining the distance data is not limited in the present invention, and specifically, the present invention is based on the fact that the distance data can be implemented.
Step S103, performing risk assessment based on the running state parameters and the distance data of the target vehicle to obtain a risk level, wherein the risk level is used for indicating the risk of collision of the target vehicle in the target scene.
In the embodiment of the invention, whether the driving object has the inattention condition can be determined by monitoring whether the running state parameter is changed or not. Here, if the running state parameter is not changed, the closer the distance between the target vehicle and the target scene is, the higher the collision risk level is.
For example, the risk level is classified into three levels in total, and when the distance between the target vehicle and the target scene is detected to be 100 meters, it is confirmed that there is a collision risk, the risk level at this time is determined as a first-level risk level, when the distance between the target vehicle and the target scene is detected to be 50 meters, the risk level may be raised to a second-level risk level, and when the distance between the target vehicle and the target scene is detected to be 20 meters, the risk level may be raised to a third-level risk level.
Here, considering that the target scene is a tunnel entrance, it is possible to preset that an obstacle exists at the position of the tunnel entrance to calculate the risk level according to the preset obstacle.
Step S104, the target vehicle is controlled according to the control information corresponding to the risk level, so as to early warn the driving object.
In the embodiment of the invention, corresponding early warning modes can be preset for different risk levels, and control information for controlling the target vehicle can be generated for the early warning modes so as to control the target vehicle to call the attention of a driving object.
For example, the early warning modes can comprise vehicle braking shake, safety belt clamping, audible and visual alarm and the like, wherein each early warning mode can comprise control strategies corresponding to various risk levels. Specifically, the vehicle brake shake can be that a brake deceleration of 5km/h is applied to the vehicle within 200ms, the driver is reminded of safe driving through strong body reaction, meanwhile, the rear vehicle is not influenced, and the driving safety is ensured. The safety belt clamping can be used for controlling the tightening of the safety belt on the driving position, and the early warning of the multi-level risk level is realized by changing the clamping frequency. The audible and visual alarm can provide display alarm for the lamp light of the central control instrument, and the alarm of multi-level risk level is realized by changing the frequency of the buzzer and the color of the lamp light.
It should be understood that the user may customize the control information corresponding to different risk levels according to his own needs, where one or more corresponding control information may be set for each risk level, and control parameters in the control information may be customized.
For example, if the risk level is three levels in total, the early warning mode corresponding to the first level may be the safety belt clamping, the early warning mode corresponding to the second level may be the audible and visual warning, and the early warning mode corresponding to the third level may be the vehicle braking shake. Here, the user can customize the tightening frequency of the safety belt, the light color of the audible and visual alarm, the frequency of the buzzer and the like.
As can be seen from the above description, in the embodiment of the present invention, first, image recognition is performed on a target scene in a driving environment of a target vehicle to obtain scene information, where the target scene is used to indicate a tunnel entrance/exit scene, and the scene information is used to indicate scene characteristics of the target scene. Then, distance data between the target vehicle and the target scene can be calculated based on the scene information, and risk assessment is performed based on the running state parameters of the target vehicle and the distance data to obtain a risk level, wherein the risk level is used for indicating the risk of collision of the target vehicle in the target scene. And then, the target vehicle can be controlled according to the control information corresponding to the risk level so as to early warn the driving object, so that the driving object is early warned in advance in the scene that the target vehicle enters and exits the tunnel, and the probability of traffic accidents caused by the fact that the driving object is not focused is reduced.
In this embodiment, another driving prompting method is provided, which may be used for a vehicle terminal, and fig. 2 is a flowchart of another driving prompting method according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
Step S201, performing image recognition on a target scene in a driving environment of the target vehicle to obtain scene information, where the target scene is used to indicate a tunnel entrance scene and the scene information is used to indicate scene characteristics of the target scene.
Specifically, the step S201 includes:
in step S2011, an image frame of the driving environment is acquired based on the image capturing device.
Step S2012, based on the target network model, identifying the tunnel entrance feature and/or the tunnel mark feature in the image frame, and obtaining the identification result.
Step S2013, determining scene information according to the identification result.
In the embodiment of the invention, the tunnel entrance and exit features may include door opening features of a tunnel, and the tunnel marking features may include ground road marking features, sign board marking features, lamplight marking features and the like for marking entrance and exit of the tunnel.
After determining that any of the features is identified, the target network model may output the feature to obtain an identification result. Here, the confidence level of the recognition result may be determined according to the number of features included in the recognition result, and the scene information may be determined with respect to the image frame corresponding to the recognition result whose confidence level satisfies the confidence condition. For example, the more the number of features and the variety of features contained in the recognition result, the higher the confidence of the recognition result.
Step S202, calculating distance data between the target vehicle and the target scene based on the scene information. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S203, performing risk assessment based on the running state parameters and the distance data of the target vehicle to obtain a risk level, wherein the risk level is used for indicating the risk of collision between the target vehicle and the target scene. Please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S204, the target vehicle is controlled according to the control information corresponding to the risk level, so as to early warn the driving object. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
In the embodiment of the invention, the tunnel entrance and exit features and/or the tunnel marking features in the image frame of the driving environment can be identified through the target network model to obtain the identification result, so that the driving object is actively reminded to reach the tunnel entrance and exit when navigation software is not updated timely or the tunnel exit scene is poor in signal and can not be reminded of the driving object in time.
In some optional embodiments, the scene information includes scene characteristics of the tunnel exit, and the embodiment corresponding to fig. 1 further includes:
Step S11, after scene information is obtained, the accumulated time of the target vehicle entering the tunnel is obtained.
Step S12, determining a confidence level of the identified scene feature based on the accumulated time.
In the embodiment of the invention, under the tunnel exit scene, the confidence verification can be performed on the identification result of the target network model based on the accumulated time of the target vehicle entering the tunnel. Specifically, timing may be started from when the target vehicle is detected to enter the tunnel, and a travel distance of the target vehicle in the tunnel may be calculated according to the accumulated time, so as to verify the confidence of the recognition result according to the travel distance.
The actual length of the tunnel can be obtained according to navigation software before entering the tunnel, the running distance of the target vehicle in the tunnel is calculated in real time according to the accumulated time, and after the recognition result is output by the target network model, the confidence of the recognition result is verified according to the comparison result of the running distance and the actual length. It should be understood that the closer the distance travelled is to the actual length, the higher the confidence of the recognition result.
In the embodiment of the invention, under the tunnel exit scene, the recognition result including the scene characteristics of the tunnel exit output by the target network model can be verified in confidence according to the accumulated time of the target vehicle entering the tunnel, so that the false triggering probability of the active reminding operation is reduced.
In this embodiment, a further driving prompting method is provided, which may be used for a vehicle terminal, and fig. 3 is a flowchart of the further driving prompting method according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
Step S301, performing image recognition on a target scene in a driving environment of the target vehicle to obtain scene information, where the target scene is used to indicate a tunnel entrance scene and the scene information is used to indicate scene characteristics of the target scene. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, calculating distance data between the target vehicle and the target scene based on the scene information. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, performing risk assessment based on the running state parameters and the distance data of the target vehicle to obtain a risk level, wherein the risk level is used for indicating the risk of collision between the target vehicle and the target scene.
Specifically, the operation state parameters include real-time speed and acceleration, and the step S303 includes:
in step S3031, the travel time of the target vehicle to reach the target scene is calculated based on the real-time speed, acceleration and distance data.
Step S3032, acquiring a risk level matched with the driving time based on preset association information, where the preset association information is used to indicate an association relationship between the driving time and the risk level.
In the embodiment of the invention, the real-time speed of the target vehicle can be obtained in real time through the speed sensor arranged in the target vehicleAnd detect acceleration of the target vehicle in real time through the acceleration sensorAnd obtain the calculated distance data. Then, the motion formula can be adoptedCalculating the travel time of target vehicle to target scene。
Next, the travel time may be found based on preset association informationCorresponding risk level, in the preset associated information, driving timeInversely related to risk level, i.e. travel timeThe shorter the risk level. For example, when the risk class is classified into three classes, the risk class is classified into three classesAt the same time, the first risk level can be corresponding toIn this case, a second risk level may be associated,The third risk level may correspond to the time.
It should be understood that, the user may customize the preset association information according to the own use requirement, which specifically includes the following procedures:
After the configuration instruction is detected, the preset association information is established based on the association relation between the running time and the risk level indicated by the configuration instruction.
In the embodiment of the invention, the configurable content of the configuration instruction comprises the following parts of total progression of risk levels, and the association relationship between each risk level and running time.
Specifically, when the total number of risk levels is configured by the configuration instruction, the user may instruct to increase or decrease the total number of risk levels by the configuration instruction. For example, when the total number of levels of the risk level is three, it may be instructed to increase the number of levels within the maximum total number of levels (five levels), or to decrease the total number of levels to the minimum total number of levels (one level).
In addition, when the association relationship between each risk level and the running time is configured through the configuration instruction, the user can instruct to increase or decrease the running time corresponding to each risk level through the configuration instruction. For example, when the travel time corresponding to the first-order risk level is configured, the travel time may be increased to the longest travel time (15 s) corresponding to the first-order risk level, or the travel time may be decreased to the shortest travel time (5 s) corresponding to the first-order risk level.
Step S304, the target vehicle is controlled according to the control information corresponding to the risk level, so as to early warn the driving object. Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
In the embodiment of the invention, the driving time required by the target vehicle to reach the tunnel entrance can be calculated, and the corresponding risk level is determined according to the driving time, so that the driving object is subjected to multi-stage early warning according to the risk level, and the effect of reminding the driving object to concentrate attention is improved.
In some alternative embodiments, the operation state parameters include an angular velocity and a steering wheel angle, and the step S103 further includes:
step S21, an angular velocity is acquired based on the angular velocity sensor, and a steering wheel angle is acquired based on the steering wheel angle sensor.
Step S22, pedal control data is acquired based on a pedal sensor, wherein the pedal control data is used for indicating control data of an accelerator pedal and/or a brake pedal of the target vehicle.
Step S23, when the angular speed, the steering wheel angle and the pedal control data are not changed, performing risk assessment based on the distance data to obtain a risk level.
In the embodiment of the invention, the vehicle sensor may further include the first-stage pedal sensor of the angular velocity sensor, so as to respectively obtain the angular velocity of the target vehicle in the running process, the steering wheel angle and the control data of the driving object when controlling the accelerator pedal and/or the brake pedal in real time.
It should be understood that, when the angular velocity, the steering wheel angle and the pedal control data are not changed, it is indicated that the driving object does not control the driving state of the target vehicle, and there may be a situation of inattention, at this time, since the target network model has detected the tunnel entrance and exit, the real-time velocity can be determined according to the distance data SAcceleration and velocityAnd performing risk assessment to obtain a risk grade.
In some alternative embodiments, the step S23 includes:
step a1, acquiring real-time speed and acceleration of a target vehicle.
And a step a2, calculating the target speed when the target vehicle reaches the target scene based on the distance data, the real-time speed and the acceleration.
And a step a3, performing risk assessment based on the target speed to obtain a risk grade.
In the embodiment of the invention, when risk assessment is carried out, the risk assessment method can be used for carrying out risk assessment according to a kinematic formulaCalculating an arrival speed of a target vehicle at a target sceneAnd assuming that there is a virtual obstacle in the target scene to respond to the arrival speedAnd performing collision risk calculation to obtain a risk grade.
Specifically, the arrival speed is calculatedThereafter, the arrival speed can be obtainedThe associated risk level, or the risk of collision is calculated in real time, that is, the acceleration of the target vehicle when the virtual obstacle collides is calculated, so as to obtain the risk level matched with the acceleration.
In the embodiment of the invention, it should be understood that when the angular speed, the steering wheel angle and the pedal control data are not changed, it is indicated that the driving object does not control the driving state of the target vehicle, and there may be a situation of inattention, at this time, since the target network model has detected the tunnel entrance, risk assessment can be performed according to the distance data between the target vehicle and the tunnel entrance, so as to prompt the driving object in time and ensure driving safety.
In some optional embodiments, the step S103 further includes:
step S31, change data when the angular velocity, steering wheel angle and pedal control data are updated are detected respectively.
And step S32, when any change data exceeds the corresponding change threshold value, resetting the risk level.
In the embodiment of the invention, after the control target vehicle of the driving object is detected, the driving object is not considered to be in a distraction state, so that the risk level can be cleared, and the early warning is released. Here, the determination as to whether the driving object is controlling the target vehicle may be made by the angular velocity of the target vehicle, the steering wheel angle, and the pedal control data.
In a specific implementation, corresponding change thresholds may be set for the angular velocity, the steering wheel rotation angle, and the pedal control data, respectively, and when any change data is detected to exceed the corresponding change thresholds, it may be determined that the driving target is not in the distraction state. For example, if the change thresholds of the steering wheel angle and the angular velocity of the target vehicle are detected to be exceeded simultaneously, the risk level is cleared.
In the embodiment of the invention, whether the driving object controls the vehicle or not can be determined by detecting the angular speed, the steering wheel rotation angle and the change data when the pedal control data are updated, so that whether the driving object is out of the distraction state or not is determined, and the risk level is cleared when the driving object is out of the distraction state, so that the early warning is relieved.
In some alternative embodiments, the control information includes belt control information, buzzer control information, and brake pedal control information, and the corresponding example of fig. 1 further includes:
Step S41, presetting at least one risk level.
Step S42, setting corresponding target information for each risk level, wherein the target information comprises at least one control information.
In the embodiment of the invention, corresponding early warning modes are preset for different risk levels, and control information for controlling the target vehicle is generated for the early warning modes so as to control the target vehicle to call the attention of a driving object.
For example, the early warning mode can comprise vehicle brake shake, safety belt clamping, audible and visual alarm and the like, wherein control information corresponding to the vehicle brake shake can be brake pedal control information, control information corresponding to the safety belt clamping can be safety belt control information, and control information corresponding to the audible and visual alarm can be buzzer control information. The pre-warning mode corresponding to each control information is described in the embodiment corresponding to fig. 1, and is not described herein.
It should be understood that the user may customize the control information corresponding to different risk levels according to his own needs, where one or more corresponding target information may be set for each risk level, and the control parameters in the target information may be customized.
For example, if the risk level is three levels in total, the early warning mode corresponding to the first level may be the safety belt clamping, the early warning mode corresponding to the second level may be the audible and visual warning, and the early warning mode corresponding to the third level may be the vehicle braking shake. Here, the user can customize the tightening frequency of the safety belt, the light color of the audible and visual alarm, the frequency of the buzzer and the like.
In the embodiment of the invention, the corresponding target information can be set for each risk level, so that the target vehicle can be controlled by utilizing various control modes, the early warning can be better carried out on the driving object, and the driving safety is ensured.
In summary, in the embodiment of the present invention, first, image recognition may be performed on a target scene in a driving environment of a target vehicle to obtain scene information, where the target scene is used to indicate a tunnel entrance scene, and the scene information is used to indicate scene features of the target scene. Then, distance data between the target vehicle and the target scene can be calculated based on the scene information, and risk assessment is performed based on the running state parameters of the target vehicle and the distance data to obtain a risk level, wherein the risk level is used for indicating the risk of collision of the target vehicle in the target scene. And then, the target vehicle can be controlled according to the control information corresponding to the risk level so as to early warn the driving object, so that the driving object is early warned in advance in the scene that the target vehicle enters and exits the tunnel, and the probability of traffic accidents caused by the fact that the driving object is not focused is reduced.
The embodiment also provides a driving prompt system, which is used for realizing the embodiment and the preferred implementation, and the description is omitted. As used below, the term "unit" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The embodiment provides a driving prompt system, and as shown in fig. 4, the driving prompt system comprises a visual sensor, a vehicle sensor, a risk assessment unit and a vehicle control unit, wherein the vehicle sensor comprises the speed sensor, an acceleration sensor, an angular velocity sensor, a pedal sensor and a steering wheel angle sensor.
And the visual sensor is used for carrying out image recognition on a target scene in the driving environment of the target vehicle to obtain scene information.
The vehicle sensor is used for acquiring the running state parameters, wherein the running state parameters comprise real-time speed, acceleration, angular speed pedal control data and steering wheel rotation angle.
The risk assessment unit is used for carrying out risk assessment on the running state parameters and the distance data of the target vehicle to obtain risk levels, and acquiring control information corresponding to the risk levels.
And the vehicle control unit is used for controlling the target vehicle according to the control information so as to early warn the driving object.
The embodiment also provides a driving prompting device, which is used for realizing the embodiment and the preferred implementation, and the description is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a driving prompt device, as shown in fig. 5, including:
the recognition module 501 is configured to perform image recognition on a target scene in a driving environment of a target vehicle to obtain scene information, where the target scene is used to indicate a tunnel entrance scene, and the scene information is used to indicate scene features of the target scene;
a calculating module 502, configured to calculate distance data between the target vehicle and the target scene based on the scene information;
The evaluation module 503 is configured to perform risk evaluation based on the running state parameter and the distance data of the target vehicle, to obtain a risk level, where the risk level is used to indicate a collision risk between the target vehicle and the target scene;
And the early warning module 504 is used for early warning the driving object of the target vehicle according to the control information corresponding to the risk level.
In some alternative embodiments, the operating state parameters include real-time speed, acceleration, and the assessment module 503 is further configured to:
Performing risk assessment based on the running state parameters and the distance data of the target vehicle to obtain a risk level, wherein the risk level comprises:
Calculating the running time of the target vehicle to the target scene based on the real-time speed, the acceleration and the distance data;
And acquiring a risk level matched with the running time based on preset association information, wherein the preset association information is used for indicating the association relationship between the running time and the risk level.
In some alternative embodiments, the evaluation module 503 is further configured to:
After the configuration instruction is detected, the preset association information is established based on the association relation between the running time and the risk level indicated by the configuration instruction.
In some alternative embodiments, the operating state parameters include angular velocity, steering wheel angle, and the assessment module 503 is further configured to:
acquiring an angular velocity based on an angular velocity sensor, and acquiring a steering wheel angle based on a steering wheel angle sensor;
Acquiring pedal control data based on a pedal sensor, wherein the pedal control data is used for indicating control data of an accelerator pedal and/or a brake pedal of a target vehicle;
and when the angular speed, the steering wheel angle and the pedal control data are not changed, performing risk assessment based on the distance data to obtain a risk grade.
In some alternative embodiments, the evaluation module 503 is further configured to:
Change data when the angular speed, steering wheel rotation angle and pedal control data are updated are detected respectively;
and when any change data exceeds the corresponding change threshold value, resetting the risk level.
In some alternative embodiments, the evaluation module 503 is further configured to:
Acquiring real-time speed and acceleration of a target vehicle;
calculating a target speed when the target vehicle reaches a target scene based on the distance data, the real-time speed and the acceleration;
and performing risk assessment based on the target speed to obtain a risk grade.
In some alternative embodiments, the identification module 501 is further configured to:
based on the camera device, collecting image frames of the driving environment;
Based on the target network model, identifying tunnel entrance and exit features and/or tunnel marking features in the image frames to obtain an identification result;
and determining scene information according to the identification result.
In some alternative embodiments, the scene information comprises scene characteristics of the tunnel exit, and the device is further used for:
After scene information is obtained, acquiring accumulated time of entering a tunnel by a target vehicle;
based on the accumulated time, a confidence level of the identified scene feature is determined.
In some alternative embodiments, the control information includes a seat belt control information, a buzzer control information, a brake pedal control information, the above device is further configured to:
presetting at least one risk level;
and setting corresponding target information for each risk level, wherein the target information comprises at least one control information.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The traffic prompt device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application SPECIFIC INTEGRATED Circuit) Circuit, a processor and a memory that execute one or more software or firmware programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the driving prompt device shown in the figure 5.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, and as shown in fig. 6, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting components, including a high-speed interface and a low-speed interface. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, application programs required for at least one function, and a storage data area that may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The memory 20 may comprise volatile memory, such as random access memory, or nonvolatile memory, such as flash memory, hard disk or solid state disk, or the memory 20 may comprise a combination of the above types of memory.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random-access memory, a flash memory, a hard disk, a solid state disk, or the like, and further, the storage medium may further include a combination of the above types of memories. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Portions of the present invention may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or aspects in accordance with the present invention by way of operation of the computer. Those skilled in the art will appreciate that the existence of computer program instructions in a computer-readable medium includes, but is not limited to, source files, executable files, installation package files, and the like, and accordingly, the manner in which computer program instructions are executed by a computer includes, but is not limited to, the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled programs, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed programs. Herein, a computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by a computer.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.