CN114394109A

CN114394109A - Driving assistance method, device, equipment, medium and program product

Info

Publication number: CN114394109A
Application number: CN202210139903.2A
Authority: CN
Inventors: 高航; 石远; 杜军红; 葛振纲
Original assignee: Shanghai Haocheng Information Technology Co ltd
Current assignee: Shanghai Haocheng Information Technology Co ltd
Priority date: 2022-02-16
Filing date: 2022-02-16
Publication date: 2022-04-26

Abstract

The application provides a driving assistance method, a driving assistance device, a driving assistance medium and a driving assistance program product, wherein when a danger factor is detected to appear in a preset monitoring range, a recognition model is used for determining characteristic information of the danger factor according to monitoring information, and the preset monitoring range and a motion state of a carrier and/or a running environment have a preset corresponding relation; then determining control parameters of the wearable equipment by using a preset prompt model according to the characteristic information, wherein a plurality of vibration units are arranged on the wearable equipment; and controlling the vibration unit to generate a corresponding vibration field according to the control parameters so that a driver can track the dynamic information of the risk factors in real time through the vibration field and adjust the driving mode of the carrier in time. The technical problem of how to enable a driver to drive a vehicle more safely in a more and more complex traffic environment is solved. The technical effect that a driver can sense surrounding danger factors influencing safe driving of the carrier without twisting the head is achieved, and therefore accidents are avoided actively.

Description

Driving assistance method, device, equipment, medium and program product

Technical Field

The present application relates to the field of intelligent transportation, and in particular, to a driving assistance method, apparatus, device, medium, and program product.

Background

With the development of social productivity, people can not leave various vehicles in production and life, vehicles, airplanes and ships can greatly shorten the transportation time of people and goods, and even a traffic system is blood and power for the operation of the whole human society.

At present, most of vehicles still depend on drivers to drive, and unmanned driving in the true sense cannot be achieved. Also, manual driving does not disappear even if real unmanned driving is realized in the future due to factors such as production cost, individual demand, and the like.

Therefore, how to enable the driver to drive the vehicle more safely in the more and more complicated traffic environment becomes a technical problem to be solved urgently.

Disclosure of Invention

The application provides a driving assisting method, a driving assisting device, driving assisting equipment, a driving assisting medium and a program product, and aims to solve the technical problem of how to enable a driver to drive a vehicle more safely in a more and more complex traffic environment.

In a first aspect, the present application provides a driving assistance method including:

when the danger factors are detected to appear in the preset monitoring range, determining the characteristic information of the danger factors according to the monitoring information by using the identification model, wherein the preset monitoring range has a preset corresponding relation with the motion state of the carrier and/or the running environment;

determining control parameters of the wearable equipment by using a preset prompt model according to the characteristic information, wherein the wearable equipment is provided with a plurality of vibration units;

and controlling the vibration unit to generate a corresponding vibration field according to the control parameter, wherein the vibration field is used for representing the change condition of the relative position between the danger factor and the carrier, so that a driver can track the dynamic information of the danger factor in real time through the vibration field and adjust the driving mode of the carrier in time.

In one possible design, when the vehicle is any one of an electric vehicle, a bicycle and a motorcycle, the wearable device includes a plurality of directional sound sensors for detecting the risk factors, and the detection of the occurrence of the risk factors within the preset monitoring range includes:

receiving a detection signal sent by a directional sound sensor;

the detection signal is resolved using a predetermined directional discriminant algorithm to identify one or more risk factors.

In one possible design, when the vehicle is any one of an automobile, a truck, and a bus, the vehicle includes a plurality of directional sound sensors for detecting the risk factors, the preset monitoring range includes a blind area of the vehicle, and the occurrence of the risk factors in the preset monitoring range is detected, including:

receiving a detection signal sent by a directional sound sensor;

Optionally, the feature information includes position information, motion state information, and classification information of the risk factor.

Optionally, the control parameters include: at least one of intensity, frequency, duration, number, interval of vibration.

In one possible design, the vehicle includes a motor vehicle and a non-motor vehicle, and the classification information includes: first type vehicle and second type vehicle, the running noise of first type vehicle is less than preset volume threshold, and the running noise of second type vehicle is greater than or equal to preset volume threshold, and what correspond utilizes preset suggestion model, according to characteristic information, confirms wearable device's control parameter, includes:

when the danger factor is the first type of vehicle, determining the intensity as a first intensity and/or the duration as a first time;

when the danger factor is the second type of vehicle, determining the intensity as a second intensity and/or the duration as a second time;

wherein the first intensity is greater than the second intensity, and the first time is greater than or equal to the second time.

In one possible design, determining the control parameter of the wearable device according to the characteristic information by using a preset prompt model, further includes:

determining the relative acceleration of the danger factor and the carrier according to the motion state information;

when the vehicle is located in a pointing range corresponding to the relative acceleration and the magnitude of the acceleration is larger than a preset acceleration threshold, determining that the intensity is a third intensity and/or the duration is a third time;

wherein the third intensity increases as the relative position of the risk factor and the vehicle decreases.

In one possible design, controlling the vibration unit to generate the corresponding vibration field according to the control parameter includes:

and controlling the plurality of vibration units to vibrate alternately according to the control parameters so as to form a time-varying vibration field through vibration superposition, so that the driver perceives the approaching process and/or the leaving process of the risk factor.

In one possible design, the reference origin of the predetermined monitoring range is on the vehicle and varies with the motion of the vehicle.

In one possible design, the reference origin of the preset monitoring range corresponds to a characteristic attribute of the driver, and the characteristic attribute includes: at least one of the location, the physiological structural characteristics, and the driving habits.

In one possible design, a modular adjustment interface is provided on the wearable device, and the modular adjustment interface is used for enabling a driver to perform customized adjustment on the position and/or the number of the vibration units within a preset adjustment range.

In one possible design, the driving assistance method further includes:

acquiring traffic flow density within a preset range, wherein the traffic flow density is used for representing the total amount of traffic participation objects within a preset unit area;

and if the traffic flow density is greater than or equal to the preset density threshold value, closing the vibration prompt function of the wearable device.

In a second aspect, the present application provides a driving assistance apparatus comprising:

the monitoring module is used for determining the characteristic information of the danger factor according to the monitoring information by utilizing the identification model when the danger factor is detected to appear in the preset monitoring range, and the preset monitoring range has a preset corresponding relation with the motion state of the carrier and/or the running environment;

the processing module is used for determining control parameters of the wearable equipment according to the characteristic information by using a preset prompt model, and the wearable equipment is provided with a plurality of vibration units;

and the control module is used for controlling the vibration unit to generate a corresponding vibration field according to the control parameters, and the vibration field is used for representing the change condition of the relative position between the danger factor and the carrier, so that a driver can track the dynamic information of the danger factor in real time through the vibration field and adjust the driving mode of the carrier in time.

In one possible design, when the vehicle is any one of an electric vehicle, a bicycle and a motorcycle, the wearable device includes a plurality of directional sound sensors for detecting the risk factors, and the monitoring module is used for:

receiving a detection signal sent by a directional sound sensor;

In one possible design, when the vehicle is any one of an automobile, a truck and a bus, the vehicle includes a plurality of directional sound sensors for detecting risk factors, the preset monitoring range includes a visual blind area of the vehicle, and the monitoring module is used for:

receiving a detection signal sent by a directional sound sensor;

In one possible design, the vehicle includes a motor vehicle and a non-motor vehicle, and the classification information includes: first type vehicle and second type vehicle, the running noise of first type vehicle is less than preset volume threshold, and the running noise of second type vehicle is greater than or equal to preset volume threshold, and is corresponding, processing module is used for:

In one possible design, the processing module is further configured to:

In one possible design, a control module is configured to:

In one possible design, the monitoring module is further configured to obtain a traffic flow density within a preset range, where the traffic flow density is used to represent a total amount of traffic participation objects within a preset unit area;

and the processing module is further used for closing the vibration prompt function of the wearable device if the traffic flow density is greater than or equal to a preset density threshold value.

In a third aspect, the present application provides an electronic device, comprising:

a memory for storing program instructions;

and the processor is used for calling and executing the program instructions in the memory to execute any one of the possible driving assistance methods provided by the first aspect.

In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, the computer program being configured to execute any one of the possible driving assistance methods provided by the first aspect.

In a fifth aspect, the present application further provides a computer program product comprising a computer program, which when executed by a processor, implements any one of the possible driving assistance systems provided by the first aspect.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

FIG. 1 is a schematic view of a driving assistance scenario provided herein;

fig. 2 is a schematic flow chart of a driving assistance method according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of a wearable device according to an embodiment of the present disclosure;

FIG. 4 is a schematic flow chart of another driving assistance method provided in the practice of the present application;

FIG. 5 is a schematic view of a blind spot in a truck according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of a driving assistance device according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of an electronic device provided in the present application.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, including but not limited to combinations of embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any inventive step are within the scope of the present application.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Due to the physiological configuration of the human eye, 360 degree environmental monitoring is not possible with the human eye. And various blind areas of vision may be generated at different positions due to different configurations of the carriers.

In addition, in different environments, the visual range may also change, for example, at night, in a road with poor lighting, in the case of heavy fog, heavy rain, flying dust, etc., the visual field is easily limited. Furthermore, as the vehicle speed increases, the clear boundary range in the driver's field of vision becomes narrower and narrower, i.e., the blind area of vision expands as the vehicle speed increases.

In addition, the inventor of the present application has also found that when the driver needs to turn around or twist his head to observe the blind area, the driver may pay attention to the front, and when the traffic situation is complicated, the driver may not find the emergency in time, which may result in traffic accidents.

For manual driving, how to make the driver drive the vehicle more safely in more and more complicated traffic environments becomes a technical problem to be solved.

In order to solve the technical problems, the invention idea of the application is as follows:

the environmental monitoring information in each direction is transmitted to the driver by means of other means besides vision, namely, tactile means. A time-varying vibration field is established through the wearable device, and risk factors which may threaten the safety of a currently driven vehicle are simulated and tracked. So that the driver establishes a perception system of mutual position and motion relation with the risk factors through the vibration field. Thus, the perception range of the driver is extended, head twisting is not needed, and all directions are not needed to be paid attention to constantly. Through the vibration field established by the wearable equipment, the driving fatigue and the driving pressure are reduced, so that the occurrence probability of traffic accidents is reduced.

Fig. 1 is a scene schematic diagram of driving assistance provided in the present application. As shown in fig. 1, a driver 100 wears a wearable device, such as a headband, a helmet, a waistcoat, etc., to drive a motorcycle, an electric vehicle, or a bicycle to travel on the road, and the wearable device uses a vehicle as a reference origin to divide a preset monitoring range into several different areas, namely, a highest risk area 101, a secondary risk area 103, a rear early warning area 102, and a front early warning area 104.

When the vehicle gets into different regions, wearable equipment will send different forms of vibration field, give the driver transmission early warning information that corresponds to make the driver need not to turn round the head and can discover being close to of danger factor.

For example, when the vehicle 24 enters the rear precaution area 102, the wearable device generates a slight vibration at the corresponding location of the vehicle 24, indicating that the vehicle is approaching behind. When vehicle 23 touches the boundary of highest risk area 101 from secondary risk area 103, the wearable device is in the corresponding position, and generates strong vibration. For vehicle 22, the wearable device does not make a vibration output if it remains in secondary hazard zone 103 or if it is back-ended from secondary hazard zone 103 to rear precaution zone 102. Similarly, the wearable device does not need to generate a vibration prompt message when the vehicle 21 enters the front early warning area 104 from the secondary danger area 103, because the driver can easily find the trace by naked eyes.

In order to facilitate understanding of the driving assistance method provided in the present application, the following detailed description is provided for the detailed flow of the method:

fig. 2 is a schematic flow chart of a driving assistance method according to an embodiment of the present application. As shown in fig. 2, the driving assistance method includes the following specific steps:

s201, when the danger factor is detected to appear in the preset monitoring range, the characteristic information of the danger factor is determined according to the monitoring information by using the recognition model.

In this step, the preset monitoring range and the motion state of the vehicle and/or the operation environment have a preset corresponding relationship. For example, when the traveling speed of the vehicle is low, such as in the speed range of 0-30km/h, the preset monitoring range may be a circular area with the vehicle as the center, and the radius of the circular area may be within 2 to 5 meters, and further, if there are vehicles in more than two directions, such as the front side and the rear side, or the front side and the right side, in the front, rear, left, right, and left sides of the vehicle in the current running environment, it is determined that the vehicle is a congestion environment, and at this time, the preset monitoring range may be narrowed, such as the radius of the circular area is narrowed to a range of 1 to 2 meters, or only the range where the vehicle exists is narrowed, and the monitoring radius in the other directions is not changed. Otherwise, the preset monitoring range can be expanded, for example, the radius of the circular area is expanded to 5 to 8 meters.

For another example, when the vehicle is traveling at a high speed, such as at a speed of 30km/h or more, the circular area corresponding to the preset monitoring range may be set to be 8-10 meters, and when a vehicle appears in any direction, the monitoring radius in the corresponding direction is reduced to a distance corresponding to the position of the vehicle, and the rest of the monitoring radius is kept unchanged.

In this embodiment, when the vehicle driven by the driver is any one of an electric vehicle, a bicycle, and a motorcycle, the sensor on the wearable device includes: multiple directional sound sensors, radar, cameras, etc. for detecting risk factors.

Detecting the occurrence of a risk factor within a preset monitoring range, including:

receiving a detection signal sent by a directional sound sensor;

Specifically, a preset directional discriminant algorithm filters a detection signal to eliminate noise interference in a preset retrieval range;

then, by positioning analysis, it is identified whether the detection signal contains a plurality of risk factors, for example, the directional sound sensor detects audio sources in different directions, so that the detection signal includes the position of the risk factor relative to the vehicle, and the type of the risk factor can be known by monitoring the change of the position in a period of time, and the type includes: potentially dangerous, low risk, high risk. The potential danger determination principle is that the position is not changed within a period of time, the low-risk determination principle is that the change situation of the position within a period of time is far away from the vehicle, and the high-risk determination principle is that the change situation of the position within a period of time is close to the vehicle. It should be noted that, by the difference of the audio frequency, it is possible to identify how many danger factors are specifically included. It should be noted that the risk factor does not necessarily cause a risk, but rather it has a possibility of collision with the own vehicle, which may be present or future.

In addition, when a plurality of vehicles are close to each other, due to different danger factors, the sound frequencies of the vehicles are different, and even if the sounds are mixed and superposed, the sensors can perform decoupling separation through filtering of the sound frequency, so that the different danger factors can be identified.

Optionally, the feature information includes: location information, motion state information, and classification information of the risk factors.

Specifically, the motion state of the vehicle at least includes: a starting state and a running state. The operating environment comprises: the natural environment and the motion of other traffic participants on the road.

For example, in a forward starting state, the preset monitoring range mainly detects a range 180 degrees ahead of the vehicle. Similarly, for a backward start state, the preset monitoring range mainly detects a range of 180 degrees behind the vehicle.

For another example, after the vehicle speed is increased, the view range of the driver is reduced, and at this time, the wearable device changes the sub-interval division of the preset monitoring area, as shown in fig. 1, a partial area of the front precaution area 104 is converted into the secondary danger area 103.

In addition, the determination of the risk factor can be performed according to the following steps:

determining category information of each influence factor in a preset monitoring range according to a monitoring image in the monitoring information by using an image recognition algorithm;

determining the relative position and the relative movement speed of each influence factor and the carrier according to the relative distance information in the monitoring information;

judging whether the influence factor is a risk factor according to the category information, the relative position and the relative movement speed, specifically, identifying the category of the influence factor as a car, a bicycle, a pedestrian, a truck and the like through the image, wherein the relative position is the distance between the influence factor and the vehicle, and the relative movement speed comprises two aspects, namely a movement direction and a movement speed, for example, identifying the category of the influence factor as the truck through the image identification, wherein the relative position is located in a secondary risk area 103 shown in fig. 1, and the relative movement speed is towards the vehicle and is advanced at the speed of 2m/s, so that the truck is defined as the risk factor;

if yes, the position information in the feature information comprises a relative position, the motion state information in the feature information comprises a relative motion speed, and the classification information in the feature information comprises category information.

It should be noted that the monitoring information includes image information collected by a camera, and distance information collected by an infrared or laser sensor or an electromagnetic wave radar. The motion state information of each influence can be obtained by detecting the variation of the distance.

It should be noted that the influence factors include static obstacles and dynamic obstacles.

The static obstacle includes: mountains, rocks, trees, fences, railings, pillars, road teeth, pits, bumps, large stones in the road, large goods, roadblocks, etc.

The dynamic barrier includes: pedestrians, animals, other vehicles, etc.

It should be noted that the risk factor is an obstacle in the influence factor that moves relative to the vehicle driven by the driver. When the vehicle driven by the driver is in a motion state, the static obstacle and the vehicle directly have relative motion speed.

Optionally, according to the difference of the relative movement speeds and the difference of the types of the risk factors, the risk factors may be classified into risk classes, and the high-class risk factor gives a prompt preferentially, or the high-class risk factor may interrupt the prompt information of the low-class risk factor and give an insertion prompt. Therefore, the phenomenon that prompting is disordered due to multiple danger factors can be avoided.

For example, when a driver remotely drives a vehicle or performs virtual driving, the driver is not located on the vehicle, and thus the reference origin of the preset monitoring range is not located on the driver or the wearable device but on the vehicle.

For example, the driver's seat is relatively tall for a truck driver, and thus is easily hidden by the vehicle body for a short obstacle. In this case, the preset monitoring range is a three-dimensional stereo region. And because different drivers have different heights, namely different physiological structure characteristics, the reference origin of the three-dimensional stereo area is also different. Furthermore, the frequency of viewing the surrounding environment by each driver is different, some drivers can turn their heads greatly to view the surrounding environment, and some drivers pay little attention to the surroundings, so that the required monitoring range is different, that is, the driving habits of the drivers can also influence the preset monitoring range. Such as making the preset monitoring area left-right asymmetric, i.e., offset from the reference origin.

S202, determining control parameters of the wearable device according to the characteristic information by using a preset prompt model.

In this step, a plurality of vibration units are arranged on the wearable device, and the control parameters are used for controlling the plurality of vibration units to vibrate alternately so as to obtain a vibration field corresponding to the parameters.

Fig. 3 is a schematic structural diagram of a wearable device provided in an embodiment of the present application. As shown in fig. 3, the smart headring 300 is provided with a plurality of vibration units 31, and the distribution of the vibration units 31 is distributed according to the maximum horizontal visual angle of 188 degrees of human eyes. There are fewer vibration units 31 in the front area and more densely distributed vibration units 31 in the rear area.

In one possible design, the vibration unit 31 may be integrated with a data acquisition module, as shown in an enlarged view of the vibration unit 31 in fig. 3, and the vibration unit 31 includes: a vibration sounder 311, a camera 312, a sensor 313 and a sensor 314. The camera 312 is used to collect image data of a surrounding preset monitoring area, and the

sensors

313 and 314 are used to detect the relative distance between the risk factor and the driver, and/or whether the wearing state of the smart head ring 301 meets preset use requirements.

It should also be noted that, in one possible design, a modular adjustment interface is provided on the wearable device, and the modular adjustment interface is used to enable the driver to perform customized adjustment on the position and/or number of the vibration units within a preset adjustment range.

As shown in fig. 3, the vibration unit 31 on the smart headband 30 is an independent module, which can be clipped to a corresponding adjustment interface, and a user can increase or decrease the number of the vibration units 31 or adjust the position of the vibration unit 31 according to his or her own needs, so as to achieve personalized customization and improve the user experience and flexibility.

For example, a driver rides a bicycle or an electric vehicle to run on a road, and because the bicycle or the electric vehicle generally does not have a rearview mirror, the driver needs to twist his head to observe the situation behind, and the driver can wear a wearable device with a vibration unit, such as an intelligent helmet or an intelligent head ring. When the intelligent helmet or the intelligent head ring detects that the vehicle comes from the rear, the type of the vehicle is identified through the detected image information.

For example, a rear-coming vehicle is a small vehicle such as: electric motor car, bicycle, motorcycle etc. because the sound that sends when bicycle, electric motor car travel is very little, consequently the vibration unit of predetermineeing the position (like anterior or correspond the position with the vehicle) on intelligent helmet or the intelligent head ring can send strong short-time (like 2 s) vibration, reminds the driver, and there is the small-size vehicle in rear.

When a vehicle coming from the rear is a sedan type vehicle, people basically cannot hear the coming vehicle from the rear in consideration of the fact that the existing electric vehicle basically has no sound when running, so that the intelligent helmet or the vibration unit at the preset position (such as the front part or the position corresponding to the vehicle) on the intelligent head ring can send out slight short-time (such as 5 s) vibration to remind a driver that a small vehicle is arranged at the rear.

When the fact that a vehicle coming from the rear is a large vehicle is detected, strong sound can be emitted in the driving process of the large vehicle, so that the vibration unit at a preset position (such as the front part or a position corresponding to the vehicle) on the intelligent helmet or the intelligent head ring can emit slight transient (such as 2 s) vibration to remind a driver, and the large vehicle is driven from the rear.

When the speed of a vehicle coming from the rear is suddenly increased, the vibration unit at the position corresponding to the vehicle on the intelligent helmet or the intelligent head ring gives a short-time (such as 2 s) strong vibration sense to remind a driver of accelerating the vehicle at the rear to approach and pay attention to avoiding.

It should be noted that the intensity of the vibration represented strongly is greater than a preset threshold, and the intensity of the vibration represented slightly is less than or equal to the preset threshold.

When detecting that a coming vehicle at the rear is closer to a driver, the vibration sense is changed from weak to strong and continuously vibrates until the farther the distance is, the lower the danger level is, the vibration of the headband is gradually weakened until the headband stops.

For each of the above cases, in this step, the control parameters corresponding to each vibration unit are determined.

And S203, controlling the vibration unit to generate a corresponding vibration field according to the control parameter.

In this step, the vibration field is used to represent the change of the relative position between the risk factor and the vehicle, so that the driver can track the dynamic information of the risk factor in real time through the vibration field and adjust the driving mode of the vehicle in time.

It should be noted that, the carrier mentioned in the present application includes: various types of vehicles, aircraft, watercraft, submersibles, spacecraft, etc.

The embodiment provides a driving assistance method, which is characterized in that when a danger factor is detected to appear in a preset monitoring range, a recognition model is used for determining characteristic information of the danger factor according to monitoring information, and the preset monitoring range and a motion state of a carrier and/or a running environment have a preset corresponding relation; then determining control parameters of the wearable equipment by using a preset prompt model according to the characteristic information, wherein a plurality of vibration units are arranged on the wearable equipment; and controlling the vibration unit to generate a corresponding vibration field according to the control parameters so that a driver can track the dynamic information of the risk factors in real time through the vibration field and adjust the driving mode of the carrier in time. The technical problem of how to enable a driver to drive a vehicle more safely in a more and more complex traffic environment is solved. The technical effect that a driver can sense surrounding danger factors influencing safe driving of the carrier without twisting the head is achieved, and therefore accidents are avoided actively.

In order to deepen understanding of the driving assistance method provided by the present application, a scenario in which a driver of a large truck drives by using the driving assistance method will be described below as an example.

Fig. 4 is a schematic flow chart of another driving assistance method provided in the present application. As shown in fig. 4, the driving assistance method includes the specific steps of:

s401, scanning a preset monitoring range to acquire monitoring information.

In this embodiment, the vehicle is a truck, and a plurality of sensors are provided on the truck to monitor the environment around the truck.

The driver wears a wearable device with a vibration unit, such as a smart headband, and then clicks a connection button, so that the smart headband is initially connected to a controller of the vehicle, i.e., the truck.

And the intelligent head ring identifies that the type of the carrier is a truck, and enters a driving auxiliary program corresponding to the carrier.

A plurality of sensors are arranged at different positions on the truck, the sensors comprise a directional sound sensor, a radar, a camera and the like, and the wearable device can acquire detection signals of the sensors in a wireless or wired connection mode.

A sensor on the truck scans a preset monitoring range such as a visual blind area of the truck in real time to obtain image information in the preset monitoring range and running state information of each traffic participant, namely, relevant monitoring information.

In addition, the wireless device on the truck can also receive traffic condition information sent by other vehicles, pedestrians or cloud-end controllers, that is, the monitoring information includes traffic condition information.

FIG. 5 is a schematic view of a blind spot in a truck vision provided by an embodiment of the present application. As shown in fig. 5, the field of view blind area of the truck 500 is a light gray area 501, the light gray area 501 is an irregular geometric shape, and the field of view blind area at the rear of the truck 500 is not contiguous with other field of view blind areas. And the area of the light grey zone 501 will expand further as truck speed increases.

In order to avoid traffic accidents caused by the influence of blind vision areas, a plurality of sensors are arranged at different positions on the truck, the sensors comprise directional sound sensors, and the wearable device can acquire detection signals of the sensors in a wireless or wired connection mode. So as to achieve the effect of reducing the frequency of the driver looking at the rearview mirror through the wearable device, i.e. the smart head ring in this embodiment. Because the number of the rearview mirrors on the truck is large, drivers can easily feel driving fatigue by paying attention to the rearview mirrors, and the driving pressure of the drivers can be reduced and the driving fatigue can be relieved after the auxiliary driving method is adopted.

S402, when the danger factor is detected to appear in the preset monitoring range, the characteristic information of the danger factor is determined according to the monitoring information by using the recognition model.

In this step, the preset monitoring range has a preset corresponding relationship with the motion state of the vehicle and/or the operation environment.

receiving a detection signal sent by a directional sound sensor;

then, by means of localization analysis, it is identified whether the detection signal contains a plurality of risk factors, for example, the directional sound sensor detects audio sources in different directions, so that the position of the risk factors relative to the truck is included in the detection signal.

In the present embodiment, the feature information includes position information, motion state information, and classification information of the risk factors. Wherein the classification information includes: the device comprises pedestrians, first-class vehicles and second-class vehicles, wherein the running noise of the first-class vehicles is smaller than a preset volume threshold value, and the running noise of the second-class vehicles is larger than or equal to the preset volume threshold value.

Specifically, images collected by a plurality of cameras mounted on a truck are identified through an image identification model, whether the risk factors are vehicles or pedestrians and the motion states of the risk factors are identified, and corresponding risk levels are set for the risk factors.

And S403, determining control parameters of the wearable device according to the characteristic information by using a preset prompt model.

In this step, a plurality of vibration units are provided on the wearable device.

Specifically, when the risk factor is a first type of vehicle, determining the intensity as a first intensity and/or the duration as a first time;

the first intensity is greater than the second intensity, the first time is greater than or equal to the second time, the running noise of the first type of vehicle is smaller than a preset volume threshold, and the running noise of the second type of vehicle is greater than or equal to the preset volume threshold.

For example, when the risk factor is a small vehicle such as: electric motor car, bicycle, motorcycle etc. because the sound that sends when bicycle, electric motor car travel is very little, consequently wearable equipment goes the vibration unit of presetting the position (like anterior or with the vehicle corresponding position), can send strong short-time (like 2 s) vibration, reminds the driver, and there is the small-size vehicle in rear.

When the danger factor is a sedan type vehicle, people basically cannot hear a coming vehicle behind in consideration of the fact that the existing electric vehicle basically has no sound when running, and therefore the vibration unit at a preset position (such as the front part or the position corresponding to the vehicle) on the wearable device can emit slight transient (such as 5 s) vibration to remind a driver that a small vehicle is behind.

When the danger factor is detected to be a large vehicle, a very strong sound is emitted in the driving process of the large vehicle, a vibration unit at a preset position (such as the front part or a position corresponding to the vehicle) on the wearable device can emit slight transient (such as 2 s) vibration to remind a driver, and the large vehicle drives behind the wearable device.

When the speed of the danger factor is suddenly accelerated, the vibration unit at the position corresponding to the vehicle on the wearable device sends out transient (such as 2 s) strong vibration sense to remind a driver of accelerating the vehicle behind to approach and pay attention to avoiding.

When the danger factors are detected to be closer to the truck, the vibration sense is continuously vibrated from weak to strong until the farther the distance is, the lower the danger level is, the headband vibration is gradually weakened until the headband stops.

In this embodiment, when the truck driver wears intelligent bandeau can wearing equipment, because the visual angle of intelligent bandeau is higher, and has some visual angle blind areas when driver tired easily or traveles fast when driving night, consequently the increase visual angle that the bandeau of wearing can be great.

For example, at a crossroad or when driving fatigue, the intelligent head band can be used for always polling, when a person or a small vehicle in front is monitored, the position of the head band corresponding to the person can continuously vibrate twice, a driver is reminded that the person or the small vehicle is in a certain position in front, and safety can be improved.

And S404, controlling the plurality of vibration units to vibrate alternately according to the control parameters so as to form a time-varying vibration field through vibration superposition, so that the driver can perceive the approaching process and/or the leaving process of the risk factor.

In this step, the vibration field is a time-varying vibration signal, which is present for a limited time. Generally speaking, if no danger factor with a higher danger level needs to be prompted, the vibration field corresponding to the current danger factor will exist all the time until the danger factor is far away or the controller judges that the danger level of the vibration field is reduced, and the vibration field will stop.

It will be appreciated that if the risk factors of higher risk level are close, the vibration field of lower risk level will pause. The plurality of vibration units firstly send out pause signals (for example, firstly continuously vibrate twice), and then generate new vibration fields.

The strongest vibration region in the vibration field represents the direction of the risk factor with respect to the vehicle, the magnitude of the vibration represents the distance between the risk factor and the vehicle, and the intensity increases as the distance decreases.

Alternatively, when the risk factor is also a large vehicle, since the vibration of its operation is very noisy, the driver can feel the approach of the risk factor even without the vibration of the wearable device. Therefore, the magnitude of the vibration intensity of the wearable device can be set inversely, i.e. the further the distance, the greater the vibration intensity.

S405, obtaining the traffic flow density in a preset range.

In this step, the traffic flow density is used to represent the total amount of traffic-participating objects within a preset unit area.

In this embodiment, when the truck is stopped in front of the sidewalk, if there are a large number of pedestrians to cross the road, the use experience of the driver is not affected by the vibration generated by each pedestrian when passing. Therefore, the traffic flow density needs to be acquired.

Optionally, the number of vehicles and pedestrians in the area where the truck is located may be obtained by connecting the internet of things with a server of a traffic control center, and the traffic flow density is calculated according to a preset density model by combining image data shot by a vehicle-mounted camera.

S406, if the traffic flow density is larger than or equal to the preset density threshold value, the vibration prompt function of the wearable device is closed.

In this embodiment, if the traffic flow density is greater than or equal to the preset density threshold, the driving alertness of the driver is relatively high, the vehicle speed is generally not very high, the driver has enough time to react to handle the emergency, and therefore, the vibration prompting function can be turned off.

It can be understood that, at this moment, the voice prompt function can be turned on, and voice prompt is used instead, so that the influence of a large amount of vibration on the use experience of a driver is avoided.

Fig. 6 is a schematic structural diagram of a driving assistance device according to an embodiment of the present application. The driving assistance means may be implemented by software, hardware, or a combination of both.

As shown in fig. 6, the driving assistance apparatus 600 includes:

the monitoring module 601 is configured to, when it is detected that a risk factor occurs within a preset monitoring range, determine, by using the identification model, feature information of the risk factor according to the monitoring information, where a preset corresponding relationship exists between the preset monitoring range and a motion state and/or an operating environment of the vehicle;

the processing module 602 is configured to determine, by using a preset prompt model, a control parameter of the wearable device according to the characteristic information, where the wearable device is provided with a plurality of vibration units;

and the control module 603 is configured to control the vibration unit to generate a corresponding vibration field according to the control parameter, where the vibration field is used to represent a change situation of a relative position between the risk factor and the vehicle, so that a driver can track dynamic information of the risk factor in real time through the vibration field and adjust a driving mode of the vehicle in time.

In one possible design, when the vehicle is any one of an electric vehicle, a bicycle, and a motorcycle, the wearable device includes a plurality of directional sound sensors thereon for detecting the risk factors, and the monitoring module 601 is configured to:

receiving a detection signal sent by a directional sound sensor;

In a possible design, when the vehicle is any one of an automobile, a truck, and a bus, the vehicle includes a plurality of directional sound sensors for detecting risk factors, the preset monitoring range includes a blind vision area of the vehicle, and the monitoring module 601 is configured to:

receiving a detection signal sent by a directional sound sensor;

In one possible design, the vehicle includes a motor vehicle and a non-motor vehicle, and the classification information includes: first type vehicle and second type vehicle, the running noise of first type vehicle is less than preset volume threshold, and the running noise of second type vehicle is greater than or equal to preset volume threshold, and correspondingly, processing module 602 is used for:

In one possible design, the processing module 602 is further configured to:

In one possible design, the control module 603 is configured to:

In a possible design, the monitoring module 601 is further configured to obtain a traffic flow density within a preset range, where the traffic flow density is used to represent a total amount of traffic participants within a preset unit area;

the processing module 602 is further configured to close the vibration prompting function of the wearable device if the traffic flow density is greater than or equal to a preset density threshold.

It should be noted that the apparatus provided in the embodiment shown in fig. 6 can execute the method provided in any of the above method embodiments, and the specific implementation principle, technical features, term explanation and technical effects thereof are similar and will not be described herein again.

Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic device 700 may include: at least one processor 701 and a memory 702. Fig. 7 shows an electronic device as an example of a processor.

And a memory 702 for storing programs. In particular, the program may include program code including computer operating instructions.

The memory 702 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The processor 701 is configured to execute computer-executable instructions stored by the memory 702 to implement the methods described in the method embodiments above.

The processor 701 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

Alternatively, the memory 702 may be separate or integrated with the processor 701. When the memory 702 is a device independent from the processor 701, the electronic device 700 may further include:

a bus 703 for connecting the processor 701 and the memory 702. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. Buses may be classified as address buses, data buses, control buses, etc., but do not represent only one bus or type of bus.

Alternatively, in a specific implementation, if the memory 702 and the processor 701 are implemented in a single chip, the memory 702 and the processor 701 may communicate via an internal interface.

An embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium may include: various media that can store program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and in particular, the computer-readable storage medium stores program instructions for the methods in the above method embodiments.

An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the method in the foregoing method embodiments.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A driving assist method characterized by comprising:

when a danger factor is detected to appear in a preset monitoring range, determining characteristic information of the danger factor according to monitoring information by using an identification model, wherein the preset monitoring range has a preset corresponding relation with the motion state of a carrier and/or the running environment, and the danger factor is an obstacle moving relative to the carrier driven by a driver;

determining control parameters of the wearable equipment according to the characteristic information by using a preset prompt model, wherein the wearable equipment is provided with a plurality of vibration units;

and controlling the vibration unit to generate a corresponding vibration field according to the control parameter, wherein the vibration field is used for representing the change situation of the relative position between the danger factor and the carrier, so that a driver can track the dynamic information of the danger factor in real time through the vibration field and adjust the driving mode of the carrier in time.

2. The driving assistance method according to claim 1, wherein when the vehicle is any one of an electric vehicle, a bicycle, and a motorcycle, the wearable device includes a plurality of directional sound sensors thereon for detecting the risk factor, and the detecting that the risk factor is present within a preset monitoring range includes:

receiving a detection signal sent by the directional sound sensor;

and analyzing the detection signal by using a preset directional discriminant algorithm to identify one or more danger factors.

3. The driving assistance method according to claim 1, wherein when the vehicle is any one of an automobile, a truck, and a bus, the vehicle includes a plurality of directional sound sensors thereon for detecting the risk factor, the preset monitoring range includes a blind visual area of the vehicle, and the detecting of the occurrence of the risk factor within the preset monitoring range includes:

receiving a detection signal sent by the directional sound sensor;

4. The driving assist method according to claim 1, characterized in that the characteristic information includes position information, motion state information, and classification information of the risk factor.

5. The driving assist method according to claim 4, characterized in that the control parameter includes: at least one of intensity, frequency, duration, number, interval of vibration.

6. The driving assist method according to claim 5, wherein the vehicle includes a motor vehicle and a non-motor vehicle, and the classification information includes: the method comprises the following steps that a first type of vehicle and a second type of vehicle are adopted, the running noise of the first type of vehicle is smaller than a preset volume threshold, the running noise of the second type of vehicle is larger than or equal to the preset volume threshold, correspondingly, a preset prompt model is utilized, and control parameters of the wearable device are determined according to the characteristic information, and the method comprises the following steps:

determining the intensity as a first intensity and/or the duration as a first time when the risk factor is the first type of vehicle;

when the risk factor is the second type of vehicle, determining the intensity as a second intensity and/or the duration as a second time;

the first intensity is greater than the second intensity, and the first time is greater than or equal to the second time.

7. The driving assistance method according to claim 6, wherein the determining, by using a preset prompt model, the control parameter of the wearable device according to the characteristic information further comprises:

when the vehicle is located in a pointing range corresponding to the relative acceleration and the acceleration is greater than a preset acceleration threshold, determining that the intensity is a third intensity and/or the duration is a third time;

8. The driving assist method according to claim 7, wherein the controlling the vibration unit to generate the corresponding vibration field according to the control parameter includes:

according to the control parameters, a plurality of vibration units are controlled to vibrate alternately to form the time-varying vibration field through vibration superposition, so that the driver perceives the approaching process and/or the leaving process of the danger factor.

9. The driving assistance method according to any one of claims 1 to 8, wherein a reference origin of the preset monitoring range is on the vehicle and varies with a movement of the vehicle.

10. The driving assist method according to claim 9, wherein the reference origin of the monitoring range corresponds to a characteristic attribute of the driver, the characteristic attribute including: at least one of the location, the physiological structural characteristics, and the driving habits.

11. The driving assistance method according to claim 10, wherein a modular adjustment interface is provided on the wearable device, and the modular adjustment interface is used for enabling the driver to customize and adjust the position and/or number of the vibration units within a preset adjustment range.

12. The driving assist method according to claim 11, characterized by further comprising:

and if the traffic flow density is greater than or equal to a preset density threshold value, closing the vibration prompt function of the wearable equipment.

13. A driving assist apparatus, characterized by comprising:

the monitoring module is used for determining the characteristic information of the danger factor according to monitoring information by utilizing an identification model when the danger factor is detected to appear in a preset monitoring range, wherein the preset monitoring range has a preset corresponding relation with the motion state of a carrier and/or the running environment, and the danger factor is an obstacle which moves relative to the carrier driven by a driver;

14. An electronic device, comprising:

a processor; and the number of the first and second groups,

a memory for storing a computer program for the processor;

wherein the processor is configured to perform the driving assistance method of any one of claims 1 to 12 via execution of the computer program.

15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the driving assistance method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program, characterized in that the computer program realizes the driving assistance method of any one of claims 1 to 12 when executed by a processor.