CN116897380A - Road condition detection method, readable medium and electronic equipment - Google Patents

Abstract

A road condition detection method, a readable medium and an electronic device. The road condition detection method is used for electronic equipment and comprises the following steps: and acquiring a video stream acquired by a camera on the target road section. According to the video stream, current characteristic information of the target object on the target road section is determined, wherein the characteristic information comprises motion state information and/or position state information. And determining the current road condition information of the target road section based on the current characteristic information of the target object and/or the historical characteristic information of the target object. And sending the current road condition information of the target road section to the vehicle within the preset range of the target road section. Therefore, the vehicle in the preset range of the target road section can acquire the road condition information of the target road section in real time, the accuracy of the road condition information of the target road section received by the automatic driving vehicle is ensured, the accuracy of the automatic driving vehicle control is improved, and the safety and the user experience of the automatic driving vehicle are further improved.

Description

Road condition detection method, readable medium and electronic device Technical field

This application relates to the field of autonomous driving, and in particular, to a road condition detection method, a readable medium, and an electronic device.

Background technique

Traffic safety has always been a hot issue in the transportation field. There are many studies on road traffic safety at home and abroad. Currently, for accident-prone areas that are prone to road accidents, traffic warning signs are mainly set up on accident-prone road sections or marked in the navigation map of the vehicle to remind drivers of the road section. It is an accident-prone section, so drive with caution. However, the implementation of both methods requires a period of time after a traffic accident occurs on the road before the driver can learn about it. This will result in self-driving vehicles being unable to obtain information about traffic accident-prone areas in the first time, thus affecting the safety and user experience of self-driving vehicles.

For example, the information about traffic accident-prone areas published by navigation maps is mainly based on the information about traffic accidents on road sections collected by third-party data in the past period. Through manual analysis, we can determine whether it is a traffic accident-prone area, and then put it into the Information about areas prone to traffic accidents is posted on the navigation map. However, there is a long time interval between the collection and analysis of the above-mentioned traffic accident-related information and its release to the navigation map. The traffic accidents that occurred during this time interval were not collected and analyzed. Therefore, the release cannot be guaranteed. The timeliness of information on areas prone to traffic accidents.

Contents of the invention

Embodiments of the present application provide a road condition detection method, a readable medium, and an electronic device.

In a first aspect, embodiments of the present application provide a road condition detection method for electronic devices, including: acquiring a video stream collected by a camera on a target road section. According to the video stream, current feature information of the target object on the target road section is determined, where the feature information includes motion state information and/or location state information. Based on the current characteristic information of the target object and/or the historical characteristic information of the target object, the current road condition information of the target road section is determined.

For example, the road test equipment obtains the video stream collected in real time by the camera installed on the target road section, performs target detection on each frame of image in the video stream, determines each target object and the position of each target object in each frame of image, and based on the continuous multiple The location of each target object in the frame image is determined to determine the characteristic information corresponding to each target object. The characteristic information includes motion status information and/or position status information, etc. Among them, the motion status information can be information such as speed, acceleration, etc., and the position status information can be position coordinates in the world coordinate system, etc. Based on the characteristic information of each current target object in the road segment and the characteristic information of each target object in the history of the road segment, the current traffic condition information of the target road segment is determined, and then the current traffic condition information of the target road segment is sent to the vehicles within the preset range of the target road segment. . The current traffic condition information of the target road section may be the road risk level of the target road section.

It can be understood that according to the road condition detection method of the present application, the electronic device can update the road condition information of the target road section in real time, and can send the road condition information of the target road section in real time to the vehicles within the preset range of the target road section. This enables vehicles within the preset range of the target road section to obtain the road condition information of the target road section in real time, ensuring the accuracy of the acquired road condition information of the target road section, thus improving the accuracy of autonomous vehicle control and further improving the safety of autonomous vehicles. sex and user experience.

In a possible implementation of the above first aspect, the above method further includes: sending the current road condition information of the target road section to vehicles within a preset range of the target road section.

In a possible implementation of the above first aspect, the above method further includes: the target object includes at least one of the following: a vehicle, a person, and an obstacle.

In a possible implementation of the above first aspect, the above method further includes: the current road condition information of the target road section includes a road risk level. Furthermore, determining the current traffic condition information of the target road section based on the current feature information of the target object and/or the historical feature information of the target object includes: determining the current traffic volume of the target road section based on the current feature information of the target object and the historical feature information of the target object. and collision information, as well as historical traffic flow and collision information. Based on the current traffic volume and collision accident information of the target road section, as well as the historical traffic flow and collision accident information, the current road risk level of the target road section is determined.

In a possible implementation of the above first aspect, the above method further includes: the collision accident information includes at least one of the following: the number of collision accidents, the average relative acceleration of the collision vehicles, and the average relative speed of the collision vehicles.

In a possible implementation of the above first aspect, the above method further includes: based on the current traffic volume and collision accident information of the target road section and the historical traffic flow and collision accident information, determining the current road risk level of the target road section includes:

Based on the current traffic volume and collision accident information of the target road section, as well as the historical traffic volume and collision accident information, determine the collision risk indicator parameters of the target road section, where the collision risk indicator parameters include at least one of the following: the severity of the collision accident, the severity of the collision accident Exposure and controllability of collision accidents.

Based on the collision risk indicator parameters of the target road section, the current road risk level of the target road section is determined.

In a possible implementation of the above first aspect, the above method further includes: the current road risk level R _dynamic is calculated by the following formula:

R _dynamic =S*E*C

Among them, S represents the severity of the collision. C represents the controllability of the collision, and E represents the exposure to the collision.

In a possible implementation of the above first aspect, the above method further includes: based on the current traffic flow and collision accident information of the target road section and the historical traffic flow and collision accident information, determining the collision risk indicator parameters of the target road section includes:

Based on the number of collision accidents in the current and historical target road section, the average relative acceleration of the collision vehicle, and the average relative speed of the collision vehicle, the severity of the collision accident in the target road section is determined.

In a possible implementation of the above first aspect, the above method further includes: the number of collision accidents includes: the number of collisions between vehicles, the number of collisions between vehicles and people, and the number of collisions between vehicles and obstacles.

The severity S of a collision is calculated by the following formula:

S＝α ₀ v _r exp(β ₀ a _r )*(α ₁ *N _CC +α ₂ *N _CO +α ₃ *N _CP )*exp(β ₁ N _death )

Among them, v _r represents the average relative speed of the colliding vehicle, a _r represents the average relative acceleration of the colliding vehicle, N _CC represents the number of collisions between vehicles, N _CO represents the number of collisions between vehicles and obstacles, and N _CP represents the collision between vehicles and people. The number of α ₀ , α ₁ , α ₂ , α ₃ , β ₀ , and β ₁ respectively represent the weight parameters, N _death represents the number of deaths in collision accidents in the current and historical target road sections, and exp represents the exponential function.

In a possible implementation of the above first aspect, the above method further includes: based on the current traffic flow and collision accident information of the target road section and the historical traffic flow and collision accident information, determining the collision risk indicator parameters of the target road section includes:

Based on the average relative acceleration of the collision vehicles and the average relative speed of the collision vehicles in the current and historical target road sections, the controllability of the collision accident in the target road section is determined.

In a possible implementation of the above first aspect, the above method further includes: the controllability C of the collision accident on the target road section is calculated by the following formula:

Among them, a _r represents the average relative acceleration of the collision vehicle, v _r represents the average relative speed of the collision vehicle, β ₂ represents the weight parameter, N _death represents the number of deaths in collision accidents in the current and historical target road sections, and exp represents the exponential function.

In a possible implementation of the above first aspect, the above method further includes: based on the current traffic flow and collision accident information of the target road section and the historical traffic flow and collision accident information, determining the collision risk indicator parameters of the target road section includes:

Based on the current and historical traffic volume of the target road section and the number of collision accidents, the exposure to collision accidents of the current target road section is determined.

In a possible implementation of the above first aspect, the above method further includes: the exposure E of the collision accident is calculated by the following formula:

Among them, N _accident represents the number of collision accidents, and N _total represents the traffic volume.

In a possible implementation of the above first aspect, the above method further includes: the current traffic condition information of the target road section includes characteristic information of sensitive traffic participants.

Based on the current feature information of the target object and/or the historical feature information of the target object, determining the current traffic condition information of the target road section includes:

Based on the current characteristic information of the target object, sensitive traffic participants are screened out from the target objects and the current characteristic information of the sensitive traffic participants is determined, where the sensitive traffic participants include at least one of the following: pedestrians, heavy trucks, bicycles, and vans truck.

In the second aspect, embodiments of the present application provide a readable medium. Instructions are stored on the readable medium. When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the above-mentioned first aspect and various possible implementations of the first aspect. Any kind of road condition detection method.

In a third aspect, embodiments of the present application provide an electronic device, including:

a memory for storing instructions for execution by one or more processors of the electronic device, and a processor, being one of the processors of the electronic device, for performing the above-described first aspect and various possible implementations of the first aspect. Any kind of road condition detection method.

In the fourth aspect, embodiments of the present application provide a computer program product, including a computer program/instruction, which when executed by a processor implements the above-mentioned first aspect and any one of the various possible implementations of the first aspect. Road condition detection method.

Description of the drawings

Figure 1 shows a road condition detection scene diagram according to an embodiment of the present application;

Figure 2 shows another road condition detection scene diagram according to an embodiment of the present application;

Figure 3 is a flow chart showing a road condition detection according to an embodiment of the present application;

Figure 4 is a flow chart showing another road condition detection according to an embodiment of the present application;

Figure 5 is a flow chart showing another road condition detection according to an embodiment of the present application;

FIG. 6 is a structural block diagram of a consistent electronic device according to an embodiment of the present application.

Detailed ways

Illustrative embodiments of the present application include, but are not limited to, road condition detection methods, readable media, and electronic devices.

In order to solve the above problems, this application proposes a road condition detection method, which is applied to electronic equipment. The method includes: obtaining the video stream collected in real time by the camera set on the target road section, performing target detection on each frame of image in the video stream, determining each target object and the position of each target object in each frame of image, and based on the consecutive multiple frames of images. The location of each target object in the target object is determined, and the characteristic information corresponding to each target object is determined. The characteristic information includes motion status information and/or position status information, etc. Among them, the motion status information can be information such as speed, acceleration, etc., and the position status information can be position coordinates in the world coordinate system, etc. Based on the characteristic information of each current target object in the road segment and the characteristic information of each target object in the history of the road segment, the current traffic condition information of the target road segment is determined, and then the current traffic condition information of the target road segment is sent to the vehicles within the preset range of the target road segment. . The current traffic condition information of the target road section may be the road risk level of the target road section.

It can be understood that the above target objects can be various vehicles, such as bicycles, cars, heavy trucks, etc. The vehicles within the preset range of the target road segment may be vehicles on the target road segment, or may be vehicles that are within a preset distance from the target road segment.

Among them, based on the characteristic information of each target object (each vehicle) of the current road section and the characteristic information of each target object in the history of the road section, the specific method of determining the current road condition information of the target road section (taking the road risk level as an example) can be:

Based on the current characteristic information of each vehicle in the road section and the historical location of each vehicle in the road section, the traffic volume and collision accident information of the current and historical target road section can be determined, where the collision accident information can include the number of collision accidents. It can be understood that, for example, when the positions of two vehicles are within a certain range, it can be determined that the two vehicles have collided. The road risk level of the target road section can be determined based on the traffic volume and the number of vehicle collisions at the current moment and in the past period. For example, when the traffic flow and the number of vehicle collisions at the current moment and in the past period are relatively large, the road risk level is relatively high; when the traffic flow and the number of vehicle collisions at the current moment and in the past period are relatively small, the road risk level relatively low.

It can be understood that according to the road condition detection method of the present application, the electronic device can update the road condition information of the target road section in real time, and can send the road condition information of the target road section in real time to the vehicles within the preset range of the target road section. This enables vehicles within the preset range of the target road section to obtain the road condition information of the target road section in real time, ensuring the accuracy of the acquired road condition information of the target road section, thus improving the accuracy of autonomous vehicle control and further improving the safety of autonomous vehicles. sex and user experience.

It can be understood that the electronic device in the embodiment of the present application may be a roadside device, a server, or a vehicle or other equipment. This application does not specifically limit the type of electronic device according to the actual application.

It can be understood that the road condition detection method of this application can be applied to automatic driving scenarios, manual driving scenarios, unmanned driving scenarios, etc. It can be understood that according to actual applications, this application does not apply to actual application scenarios. No specific restrictions are made.

To facilitate understanding, terms related to road risk levels in this article, for example, the term "severity" can refer to the degree of damage to relevant people and property once the risk materializes. "Exposure rate" can refer to the probability that people or property may be affected when a risk occurs. "Controllability" can refer to the extent to which drivers and others can take proactive measures to avoid damage when risks occur.

In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below with reference to FIGS. 1 to 6 .

Figure 1 shows a road condition detection scene diagram according to an embodiment of the present application. The scene in Figure 1 includes: section A, section B, section C, and section D. As shown in Figure 1, electronic device 100 may be a roadside device. Among them, the roadside equipment 100 is installed on the roadside of section A.

As shown in Figure 1, taking road section A as an example, road section A is provided with a camera 200-1, a camera 200-2, and a camera 200-3. Road section A also includes multiple target objects. The multiple target objects are vehicle 300-1, collision vehicle 300-2, collision vehicle 300-3, pedestrian 300-4, pedestrian 300-5, and pedestrian 300-6.

As shown in Figure 1, camera 200-1, camera 200-2, and camera 200-3 are used to collect the video stream on road section A in real time, and send the video stream to the roadside device 100.

As shown in Figure 1, the roadside device 100 is used to receive the video stream on road section A collected by cameras 200-1 to 200-3 in real time, and determine the target of road section A based on the video stream on road section A collected by camera 100. Object information, where the target object information of section A is used to describe the positions of vehicle 300-1, collision vehicle 300-2, collision vehicle 300-3, pedestrian 300-4, pedestrian 300-5, and pedestrian 300-6 in section A. and/or movement status. The roadside device 100 is also used to determine the road condition information of the A road segment based on the characteristic information of the target object of the A road segment, and send the road condition information of the A road segment to the vehicles within the preset range of the A road segment. Among them, the vehicle that receives the road condition information can adjust the vehicle's driving speed in section A based on the road condition information.

For example, as shown in Figure 1, vehicle 300-1 receives the road risk level of section A sent by the electronic device 100 in real time when entering section A. When receiving that the road risk level is a high risk level, vehicle 300-1 According to the road risk level, the driving speed of vehicle 300-1 in section A can be adjusted from 100Km/h to 30Km/h.

It can be understood that the road condition information of section A can be used to assist the driver or the self-driving vehicle in adjusting the driving speed of the vehicle in section A. After vehicles within the preset range of section A receive the road condition information of section A, they can predetermine whether to adjust the driving of the vehicle in section A based on the road condition information of section A before the self-driving vehicle is in section A or enters section A. speed. The accuracy of autonomous vehicle control is improved, further improving the safety and driving experience of autonomous vehicles.

It can be understood that, as shown in Figure 1, the roadside device 10 can also obtain the video streams collected by the cameras of road section B, road section C, and road section D, and generate the video streams of road section B, road section C, and road section D based on the collected video streams of road section B, road section C, and road section D. Traffic information of section C and section D.

As shown in FIG. 1 , the roadside device 100 is installed on the roadside of road section A. In other embodiments, other roadside equipment can also be provided on the roadside of road section B, road section C, and road section D respectively. Other roadside devices can also obtain the video streams collected by cameras in Section B, Section C, and Section D respectively, and generate traffic information of Section B, Section C, and Section D based on the video streams collected in Section B, Section C, and Section D. . It can be understood that the scenario ratio of this application is not limited to the scenario shown in Figure 1. According to the actual application, the specific location and number of roadside equipment installed on the roadside are not specifically limited in this application.

The roadside equipment 100 may be infrastructure equipment or fixed equipment or a Road Side Unit (RSU) set on the roadside, or it may be a vehicle-to-everything (V2X) support set on the roadside. ) application equipment. It can be understood that, according to actual applications, this application does not impose restrictions on the roadside equipment 100.

Figure 2 shows another road condition detection scene diagram according to an embodiment of the present application. Compared with the scenario of FIG. 1 , in the scenario of FIG. 2 , the electronic device 100 may be a remote server.

As shown in Figure 2, taking road section A as an example, the server 100 is used to receive the video stream on road section A collected by cameras 200-1 to 200-3 in real time, and based on the video stream of road section A collected by cameras 200-1 to 200-3. The video stream on the road section determines the target object information of road section A, where the target object information of road section A is used to describe the position and/or movement state of the target object in the target road section. The server 100 is also configured to determine the road condition information of road section A based on the target object information of road section A, where the road condition information of road section A is used to adjust the driving speed of the vehicle in road section A. The server 100 is also used to send the traffic condition information of road section A to the vehicles within the preset range of road section A.

It can be understood that the server 100 is also used to receive the video streams collected by the cameras of road sections B, C, and D, and generate the video streams of road sections B, C, and D based on the video streams collected from road sections B, C, and D. Traffic information. For specific content, please refer to the description of section A and will not be repeated here.

It can be understood that the server 100 can be a hardware server. The server 100 can be an independent physical server, or a server cluster composed of multiple physical servers, or a server that provides basic cloud computing services such as cloud database, cloud storage, and CDN. According to actual applications, the embodiments of this application do not limit this.

It can be understood that in the scene of FIG. 1 or FIG. 2 , the camera 200-1, the camera 200-2, the camera 200-3 and the electronic device 100 can be connected for communication through one or more networks. The network can be a wired network or a wireless network. For example, the wireless network can be a mobile cellular network (such as 5G, 4G, 3G or GPRS), or it can be a Wireless-Fidelity (WIFI) network. Of course There may also be other possible networks, which are not limited in the embodiments of this application. For example, the cameras 200-1 to 200-3 send the video stream on road section A collected in real time to the electronic device 100 through the wired network.

It can be understood that in the scenario of FIG. 1 or 2 , the electronic device 100 and the vehicles within the preset range of road section A can communicate through one or more wireless networks. For example, the wireless network may be a mobile cellular network (such as 5G, 4G, 3G or GPRS), or it may be a Wireless-Fidelity (WIFI) network, and of course it may also be other possible networks. This is not the case in the embodiments of this application. Make restrictions. For example, the electronic device 100 sends the traffic condition information of road section A to vehicles within a preset range of road section A through the wireless network.

It can be understood that the camera used in this application to collect the video stream of the road section can be a 360-degree rotating camera (i.e., the camera 200-1), or a long-range camera, a zoom camera, a close-range camera, a flash camera, a buckle camera, a speed measuring camera, etc.

It can be understood that the electronic device 100 to which the road condition detection method of the present application is applied can be a roadside device in the scene of Figure 1, a server in the scene of Figure 2, or a car machine, a laptop computer, a desktop computer, or a tablet computer. , cell phones, wearable devices, head-mounted displays, mobile email devices, portable game consoles, portable music players, reader devices, or other electronic devices capable of accessing the network. In some implementations, embodiments of the present application may also be applied to wearable devices worn by users. For example, smart watches, bracelets, jewelry (eg, devices made into decorative items such as earrings, bracelets, etc.) or glasses, etc., or as part of a watch, bracelet, jewelry, or glasses, etc. According to actual applications, the embodiment of the present application does not limit the electronic device 100.

Based on the above scenario, Figure 3 shows a flow chart of road condition detection. The execution subject in Figure 3 is the electronic device 100. As shown in Figure 3, it specifically includes:

S301: Obtain the target object of the target road section in real time.

In some embodiments, the electronic device 100 acquires a video stream shot on a target road section, performs target detection on each frame of image in the video stream, and determines the target object in each frame of image.

In some embodiments, the target object may include at least one of the following: a collision vehicle, a bicycle, a car, a heavy truck, etc. In some other embodiments, the target objects may also include static objects such as standing water, roadblocks, etc. It can be understood that according to actual applications, this application does not limit the type of target objects in the target road section.

For example, as shown in Figure 1, taking road section A as an example, the electronic device 100 can obtain real-time videos of road section A taken by the cameras 200-1 to 200-3 set on road section A, and detect the video stream according to the target detection algorithm. Perform target object detection on each frame of the image to obtain the target object appearing in each frame of the video stream.

S302: Based on the real-time acquisition of the target object of the target road section, determine the characteristic information corresponding to the target object.

In some embodiments, the electronic device 100 determines the characteristic information corresponding to the target object based on the target object of the target road section obtained in real time. The characteristic information corresponding to the target object may include: target object motion status information and position status information. The position status information of the target object on the target road segment may be the position coordinates of the target object on the target road segment. The movement status information of the target object on the target road section can be speed, acceleration and other information.

In some embodiments, after the electronic device 100 detects the target object in each frame of image, it can also determine the motion status information of the target object through a speed detection algorithm. For example, the speed detection algorithm is used to determine the running time Δt of the target object based on the frame rate of the camera that collects the video stream. By obtaining the coordinates of the three-dimensional space for the position of the target object, the actual running distance ΔS of the target object is obtained, and then calculated Get the instantaneous speed or average speed V or acceleration a of the target object, etc. The information for determining the target object is described in detail below and will not be described in detail here.

In some embodiments, the target object in each frame of the image may also include static objects such as water, roadblocks, etc., then the position of the target object in each frame of the image is determined based on the position of the target object in the consecutive multiple frames of images. The characteristic information of the target object and the location status information of the target object can be the location of water accumulation in the target road section, the location of roadblocks in the target road section, etc. The status information of the target object can be speed 0 or acceleration 0, etc.

S303: Based on the characteristic information of the target object of the target road section, determine the traffic condition information of the target road section. The traffic condition information of the target road section is used to describe the traffic conditions of the target road section.

In some embodiments, in addition to the road risk level of section A, the road condition information of section A can also include sensitive traffic participants of section A, the traffic flow of section A, the depth of water accumulation in section A, and the location of roadblocks in section A. wait.

In some embodiments, the electronic device 100 determines the traffic condition information of the target road section based on the target object information of the target road section, and the traffic condition information of the target road section is used to describe the traffic conditions of the target road section. Based on the road condition information of the target road section, the vehicle can promptly adjust the operating status (for example, deceleration, parking, etc.) or operating trajectory (for example, the operating route of the self-driving vehicle) of the self-driving vehicle before entering the target road section or while on the target road section. This ensures the driving safety of autonomous vehicles.

In some embodiments, the traffic condition information of the target road section may be the road risk level of the target road section. For example, the electronic device 100 can determine the collision vehicle on the target road section at the current moment and the past period of time based on the relative position of the vehicle at the current moment and the past period of time. According to the speed of the vehicle when the vehicle collides with the vehicle at the current moment and the past period of time, Acceleration, etc. determine the road risk level of the target road section.

Specifically, taking the target object as a vehicle, the electronic device 100 determines the collision accident information and the cumulative number of vehicle traffic on the target road section at the current moment and within a preset time period in the past based on the vehicle information of the target road section obtained in real time. Among them, the collision accident information includes at least one of the following: the cumulative number of vehicle collision accidents, the average relative speed at the time of vehicle collision, the average relative acceleration at the time of vehicle collision, etc., wherein the cumulative number of vehicle collision accidents includes: the number of vehicle-to-vehicle collision accidents , the number of collisions between vehicles and obstacles, the number of collisions between vehicles and pedestrians, etc.

In some embodiments, the electronic device 100 determines the characteristic information of the collision vehicle on the target road section at the current moment and within the past preset time period based on the target object information of the target road section obtained in real time and the target object information of the target road section in the past preset time period. , based on the characteristic information of the collision vehicle on the target road section at the current time and the past preset time period, determine the collision accident information of the target road section at the current time and the past preset time period. Taking the target object as the vehicle in Figure 1 as an example, as shown in Figure 1, based on the position status of vehicle 300-2 and vehicle 300-3, it is determined that vehicle 300-2 collides with vehicle 300-3. Thus, the relative speed and relative acceleration of the vehicle 300-2 and the vehicle 300-3 at the time of collision are obtained respectively.

Further, the electronic device 100 determines the collision risk indicator parameters of the target road section based on the collision accident information of the target road section in the past preset time period, and determines the road risk level of the target road section based on the collision risk indicator parameters of the target road section. Among them, the collision risk index parameters of the target road section include at least one of the following: severity of the collision accident, exposure to the collision accident, and controllability of the collision accident. The following describes in detail how the electronic device 100 determines the road risk level of the target road section based on the target object information of the target road section, which will not be described in detail here.

In some embodiments, the traffic condition information of the target road section may be sensitive traffic participants of the target road section. Specifically, the electronic device 100 screens out the sensitive traffic participants in the target road section based on the target object information of the target road section, where the characteristic information of the sensitive traffic participants may include at least one of the following: the location of the sensitive traffic participant in the target road section, the driving speed and driving acceleration.

In some embodiments, in urban road traffic, sensitive traffic participants may include but are not limited to: pedestrians, bicycles, strollers, people in wheelchairs, etc. In highway traffic, sensitive traffic participants include but are not limited to large trucks, vans, etc.

In some other embodiments, the traffic condition information of the target road section may also include the traffic volume of the target road section, the depth of water accumulation in the target road section, the location of roadblocks in the target road section, etc. It can be understood that the road condition information of the target road section can be used by the self-driving vehicle to adjust the vehicle's operating status (for example, deceleration, parking, etc.) or operating trajectory (for example, the operating route of the self-driving vehicle) on the target road section, thereby ensuring that the self-driving vehicle driving safety.

S304: Send traffic information to vehicles within the preset range of the target road segment.

In some embodiments, the electronic device 100 sends auxiliary driving information to vehicles within a preset range of the target road section. The vehicle can predetermine the driving assistance information based on the auxiliary driving information sent by the electronic device 100 before the vehicle is within the target road section or enters the target road section. Whether to adjust the vehicle's driving speed on the target road section. The accuracy of autonomous vehicle control is improved, further improving the safety and driving experience of autonomous vehicles.

According to the road condition detection method described in FIG. 3 above, the electronic device 100 determines the road condition information of the target road section based on the target object information of the target road section obtained in real time, and sends the road condition information of the target road section in real time to the targets within the preset range of the target road section. vehicle. Among them, the road condition information of the target road section can include road risk level, sensitive traffic participants, traffic flow, water depth, roadblock location and other information.

It is not difficult to see that according to the road condition detection method of the present application, the electronic device 100 can update the road condition information of the target road section in real time, and the road condition information of the target road section can be used to assist the driver or the autonomous vehicle to adjust the driving speed of the vehicle on the target road section. Vehicles within the preset range of the target road section obtain the road condition information of the target road section in real time. The driver or self-driving vehicle can learn the road section information of the target road section in real time based on the road condition information of the target road section, so that when the vehicle is within the target road section or enters the target Before starting the road segment, determine in advance whether to adjust the driving speed of the autonomous vehicle on the target road segment. Improving the accuracy of autonomous vehicle control further improves the safety and driving experience of autonomous vehicles.

In some embodiments, the road condition information of the target road section may be a road risk level. The electronic device 100 determines the road risk level of the target road section based on the target object information of the target road section obtained in real time, and combines the road risk with the road risk level. Levels are sent to vehicles within a preset range of the target road segment.

Based on the road condition detection scenario of Figure 1 or Figure 2, Figure 4 shows a flow chart of another road condition detection method. The execution subject of the process of Figure 4 is the electronic device 100, as shown in Figure 4, which specifically includes:

S401: Obtain the video stream collected by at least one camera on section A in real time.

For example, as shown in Figure 1, the electronic device 100 obtains the video stream of road section A collected by the cameras 200-1 to 200-3 on road section A, and can also obtain the video stream of road section B collected by the three cameras on road section B. Video stream, you can also get the video stream about road section C collected by the two cameras on road section C, and you can also get the video stream about road section C collected by the two cameras on road section D. The following takes road section A as the target road section as an example to describe the road condition detection process in Figure 4 in detail.

S402: Based on the video stream of road section A obtained in real time, determine the real-time target object of road section A and the characteristic information corresponding to the target object.

In some embodiments, the electronic device 100 determines the characteristic information corresponding to the target object based on the target object of road section A obtained in real time. The characteristic information corresponding to the target object may include: target object motion status information and position status information. The position status information of the target object in road section A may be the position coordinates of the target object in road section A. The movement status information of the target object in section A may be speed, acceleration and other information.

In some embodiments, the electronic device 100 performs target object detection on each frame of the video stream based on the video stream collected by the camera set on the road segment A, and obtains all targets appearing in the video stream. object. Specifically, target object detection can be performed on each frame of the video stream through a target detection algorithm to obtain all target objects appearing in the video stream. For example, in the scene in Figure 1, if the target object is set to a car, it is detected through the target detection algorithm that there are three target objects on road section A, namely vehicle 300-1, vehicle 300-2, and vehicle 300-3. . If the target objects are set to cars and people, the target detection algorithm detects that there are a total of six target objects on road section A, namely vehicle 300-1 and vehicle 300-2, vehicle 300-3, pedestrian 300-4, and pedestrian 300-5, pedestrian 300-6. In some other embodiments, the target object can also be set as a collision vehicle, a heavy truck, a box truck, or a water block on a road section, a roadblock, etc. It can be understood that according to actual applications, this application does not limit the types of target objects on the road.

In some embodiments, the target detection algorithm is mainly used to traverse each frame of the input video stream, classify the target objects and non-target objects in each frame of the image, and determine the position coordinates of the target object in each frame of the image. In some embodiments, the faster-region convolutional neural networks (Faster RCNN) algorithm can be used through the cascade-region convolutional neural networks (cascade RCNN) algorithm. algorithm, a target detection (Single-Shot MultiBox Detector, SSD) algorithm, a real-time fast target detection (You Only Look Once, YOLO) algorithm, any target detection algorithm for each frame of the video stream. Detect,obtain all target objects appearing in the video stream.

In some embodiments, after the electronic device 100 detects the target object in each frame of image, it can also determine the speed, acceleration, etc. of the target object through a speed detection algorithm. For example, the speed detection algorithm is used to determine the running time Δt of the target object based on the frame rate of the camera that collects the video stream, and obtains the coordinates of the three-dimensional space by positioning the target object to obtain the actual running distance ΔS of the target object, and then calculate Get the instantaneous speed or average speed V or acceleration a of the target object, etc.

S403: Determine the collision accident information of road section A in the past preset time period based on the characteristic information corresponding to the real-time target object of road section A and the characteristic information corresponding to the target object of road section A in the past preset time period.

In some embodiments, the electronic device 100 determines the characteristics of road section A in the past preset time period based on the characteristic information corresponding to the real-time target object of road section A and the characteristic information corresponding to the target object of road section A in the past preset time period. The cumulative number of vehicle traffic (i.e., traffic volume) and collision accident information. The collision accident information of the A road section may include at least one of the following: the cumulative number of vehicle collision accidents, the average relative speed of the vehicle collision, and the average relative speed of the vehicle collision. acceleration. Among them, the cumulative number of vehicle collisions includes: the number of vehicle-to-vehicle collisions, the number of vehicle-to-obstacle collisions, and the number of vehicle-to-pedestrian collisions.

Taking section A as an example, the preset time period can be within the past year. For example, if the current time is 13:00 on December 24, 2021, then the past year will be from 13:00 on December 24, 2020 to December 2021. At 13:00 on the 24th of the month.

Specifically, the electronic device 100 determines the collision accident information of road section A in the past preset time period based on the real-time characteristic information corresponding to the target object of road section A and the characteristic information corresponding to the target object of road section A in the past preset time period. . For example, it is determined that in the past year, the number of collisions that occurred on section A totaled 10, of which the number of vehicle-to-vehicle collisions was 5, the number of vehicle-to-obstacle collisions was 3, and the number of vehicle-to-pedestrian collisions was Starting from 2. Then the average relative speed of vehicles in collision is: the average relative speed of vehicles in 10 collision accidents. The average relative acceleration of vehicles in collisions is: the average relative acceleration of vehicles in 10 collision accidents. In the collision accident between vehicle E and vehicle F, the relative speed at the time of vehicle collision is the speed of vehicle E relative to vehicle F. Similarly, the relative acceleration at the time of vehicle collision is the acceleration of vehicle E relative to vehicle F. In the collision accident between vehicle X and obstacle Y, the relative speed of the vehicle at the time of collision is the speed of vehicle In a collision accident between a vehicle M and a pedestrian N, the relative speed of the vehicle during the collision is the speed of the vehicle M relative to the pedestrian N. Similarly, the relative acceleration of the vehicle during the collision is the acceleration of the vehicle M relative to the pedestrian N.

S404: Based on the collision accident information of road section A in the past preset time period, determine the collision risk indicator parameters of road section A. The collision risk indicator parameters of road section A include at least one of the following: the severity of the collision accident, the exposure to the collision accident, Controllability of collision accidents.

For example, in the past preset time period, the collision accident information of road section A includes the average relative speed of vehicles when they collide, the average relative acceleration of vehicles when they collide, the number of vehicle-to-vehicle collision accidents, the number of vehicle-to-obstacle collision accidents, Number of vehicle-pedestrian collisions. The electronic device 100 can determine the number of collision accidents in section A based on the average relative speed when the vehicle collides, the average relative acceleration when the vehicle collides, the number of vehicle-to-vehicle collision accidents, the number of vehicle-to-obstacle collision accidents, and the number of vehicle-to-pedestrian collision accidents. Severity.

In some embodiments, the electronic device 100 is based on the collision accident information of road section A in the past preset time period, and the severity S of the collision accident of road section A in the past preset time period can be calculated by formula (1). :

S＝α ₀ v _r exp(β ₀ a _r )*(α ₁ *N _CC +α ₂ *N _CO +α ₃ *N _CP )*exp(β ₁ N _death ) (1)

In formula (1), v _r represents the average relative speed of vehicles in section A during the collision within the preset time period, a _r represents the average relative acceleration of vehicles in section A during the preset time period, and N _CC represents the preset time period. Assume the number of vehicle-to-vehicle collision accidents in section A within a time period, N _CO represents the number of vehicle-to-obstacle collision accidents in section A in a preset time period, α ₀ , α ₁ , α ₂ , α ₃ , β ₀ , β ₁ represents the weight parameter. N _death represents the number of deaths in collision accidents on section A within the preset time period. exp represents an exponential function. * in this article means multiply.

It is not difficult to understand that in the past preset time period, the higher the average relative speed of the collision of vehicles in section A, the higher the severity of the collision in section A. In the past preset time period, the higher the average relative acceleration of vehicles in section A when they collided, the higher the severity of the collision in section A. In the past preset time period, the higher the cumulative number of vehicle collisions in section A, the higher the severity of the collisions in section A. In the past preset time, the higher the number of fatalities in vehicle collisions on Section A, the higher the severity of the collisions on Section A.

For example, in the past preset time period, the collision accident information of road section A includes the average relative speed of the vehicle when it collides and the average relative acceleration of the vehicle when it collides. The electronic device 100 can determine the controllability of the collision accident in section A in the past preset time period based on the average relative speed and the average relative acceleration of the vehicle collision in section A in the past preset time period. Spend. Specifically, within the past preset time period, the controllability C of the collision accident on section A can be calculated by the following formula (2):

In formula (2), a _r represents the relative acceleration when the collision accident on section A occurred in the past preset time period, v _r represents the relative speed when the collision accident on section A occurred during the preset time period in the past, β ₂ Represents the weight parameter. N _death represents the number of deaths in collision accidents. exp represents an exponential function.

It is not difficult to understand that in the past preset time period, the higher the average relative speed of the vehicle collision in section A, the lower the controllability of the collision in section A in the past preset time period. In the past preset time period, the higher the average relative acceleration of the vehicle collision in section A, the lower the controllability of the collision in section A in the past preset time period. In the past preset time period, the higher the number of fatalities in vehicle collisions in section A, the lower the controllability of collisions in section A in the past preset time period.

For example, in the past preset time period, the collision accident information of road section A includes the cumulative number of vehicle traffic and the cumulative number of vehicle collision accidents. The electronic device 100 can determine the exposure to collision accidents in section A in the past preset time period based on the cumulative number of vehicle traffic in section A and the cumulative number of vehicle collision accidents in the past preset time period. Specifically, during the past preset time period, the exposure E of the collision accident on road section A can be calculated by the following formula (3):

In formula (3), N _accident represents the cumulative number of vehicle collision accidents in section A in the past preset time period, and N _total represents the cumulative number of vehicle traffic in section A in the past preset time period.

It is not difficult to understand that in the past preset time period, the higher the cumulative number of vehicle collisions in section A accounted for in the cumulative number of vehicle traffic, the higher the exposure to collision accidents in section A in the past preset time period. The higher.

S405: Determine the road risk level of section A based on the collision risk indicator parameters of section A within the past preset time.

In some embodiments, the electronic device 100 determines the road risk indicator of the target road section based on the collision risk indicator parameters of the target road section within a preset time in the past. The electronic device 100 may determine the road risk level of section A according to the numerical range of the road risk indicator corresponding to the pre-divided road risk level.

For example, the road risk level of section A includes four levels, namely 1, 2, 3, and 4, where 1 is the lowest level and 4 is the highest level. For example, the numerical range of the road risk indicator corresponding to the pre-divided road risk level 1 is [a, b], the numerical range of the road risk indicator corresponding to the pre-divided road risk level 2 is [c, d], and the numerical range of the road risk indicator corresponding to the pre-divided road risk level 2 is [c, d]. The numerical range of the road risk index corresponding to risk level 3 is [e, f], and the numerical range of the road risk index corresponding to the pre-divided road risk level 4 is [g, h].

In some embodiments, the electronic device 100 determines the road risk indicator of the target road section based on the collision risk indicator parameters of the target road section within a preset time in the past. Specifically, the road risk index R _dynamic of the target road section can be calculated through formula (4):

R _dynamic =S*E*C (4)

Among them, S represents the severity of the collision accident on section A in the past preset time period. C represents the controllability of the collision accident on section A in the past preset time period. E represents the exposure to collision accidents on section A in the past preset time period.

It is not difficult to see that the higher the severity of the collision in Section A, the higher the road risk level in Section A. The higher the controllability of the collision accident in section A, the higher the road risk level in section A. The higher the exposure to collision accidents in section A, the higher the road risk level in section A.

S406: Send the road risk level of section A to the vehicles within the preset range of section A.

In some embodiments, the vehicles within the preset range of road segment A may be vehicles on road segment A, or may be vehicles that are no more than a preset distance away from road segment A. For example, the preset distance may be 1 km, and vehicles within the preset range of section A can adjust their speeds based on the received road risk level of section A. For example, in an autonomous driving scenario, when the vehicle receives a road risk level of 3 on section A, the vehicle can automatically reduce the speed of the vehicle when it is a preset distance from section A or enters section A. When the road risk level of section A received by the vehicle is 1, the vehicle can pass section A without adjusting the speed, that is, maintaining the original speed.

It can be seen from the above description that the electronic device 100 can obtain the video stream collected by at least one camera on the road section A in real time, analyze the video stream collected in real time, and obtain the target object information of the road section A in real time. Based on the target object information of the road section A obtained in real time, Object information to determine the road risk level of section A. And the electronic device 100 can also update the road risk level of section A in real time, so that vehicles within the preset range of section A can understand the road risk level of section A in real time, so that when the self-driving vehicle is in section A or enters the target Before the road section, based on the real-time understanding of the road risk level of section A, it is determined in advance whether to adjust the driving speed of the autonomous vehicle on the target road section. Improving the accuracy of autonomous vehicle control further improves the safety and driving experience of autonomous vehicles.

In some embodiments, the traffic condition information of the target road section may be sensitive traffic participants. The electronic device 100 determines the sensitive traffic of the target road section based on the target object of the target road section and the characteristic information of the target object obtained in real time. Participants and send sensitive traffic participants to vehicles within the preset range of the target road segment.

Based on the road condition detection scenario of Figure 1 or Figure 2, Figure 5 shows a flow chart of another road condition detection method. The execution subject of the process of Figure 5 is the electronic device 100, as shown in Figure 5, which specifically includes:

S501: Obtain the video stream collected by at least one camera on the target road section in real time. For specific content, please refer to step S401, which will not be described in detail here.

S502: Based on the video stream of the target road section obtained in real time, determine the target object of the target road section and the characteristic information corresponding to the target object. For details, please refer to step S402, which will not be described in detail here.

S503: Screen out the sensitive traffic participants of the target road section from the target objects of the target road section according to the type of the target object.

As described in step S402 in Figure 4 , the target object of the target road section may be a vehicle traveling on the target road section, or it may be a pedestrian, etc. Specifically, it can be cars, trucks, vans, bicycles, pedestrians, strollers, etc.

In some embodiments, sensitive traffic participants are target objects that may affect the speed of vehicles on the road section. In urban road traffic, sensitive traffic participants include but are not limited to: pedestrians, bicycles, strollers, people in wheelchairs, etc. In highway traffic, sensitive traffic participants include but are not limited to large trucks, vans, etc.

In some embodiments, the electronic device 100 filters out the sensitive traffic participants of the target road section from the target objects of the target road section according to the type of the target object, and obtains the characteristic information corresponding to the sensitive traffic participants. Among them, the characteristic information of the sensitive traffic participants in the target road section includes the location, driving speed and driving acceleration of the sensitive traffic participants in the target road section.

For example, at the current moment, the sensitive traffic participant p _n on road section A can be expressed as (x _n ,y _n ,v _n ,an ₎ , where (x _n ,y _n ) represents the sensitive traffic participant p _n on road section A The position coordinates on the road segment, v _n represents the speed of the sensitive traffic participant p _n at the current moment, and a _n represents the acceleration of the sensitive traffic participant p _n at the current moment. Specifically, as shown in Figure 1, section A is an urban road, and the sensitive traffic participants in section A may include pedestrians 300-4, pedestrians 300-5, and pedestrians 300-6. Then the position information of pedestrian 300-4 can be expressed as (x ₁ , y ₁ , v ₁ , a ₁ ), the position information of pedestrian 300-4 can be expressed as (x ₂ , y ₂ , v ₂ , a ₂ ), and the pedestrian The position information of 300-4 can be expressed as (x ₃ , y ₃ , v ₃ , a ₃ ).

Since in step S502, the electronic device 100 determines the position state corresponding to the target object of the target road segment based on the video stream of the target road segment obtained in real time, if the position state corresponding to the target object is the position coordinate generated based on its own coordinate system, then in step S502 In S503, among the sensitive traffic participants in the target road section filtered out from the position status of the target object in the target road section, the position coordinates of the sensitive traffic participants included in the sensitive traffic participants are also position coordinates generated based on the own coordinate system. Therefore, the electronic device 100 needs to convert the location coordinates of sensitive traffic participants generated based on its own coordinate system into the location coordinates of sensitive traffic participants generated based on the world coordinate system, and then convert the location coordinates containing the sensitive traffic participants generated based on the world coordinate system. The location coordinates of the sensitive traffic participants are sent to the vehicles on the target road segment.

In some embodiments, the electronic device 100 can generate the generated data based on the own coordinate system based on the conversion relationship matrix between the own coordinate system and the world coordinate system, the reference coordinates of the own coordinate system, and the position coordinates of sensitive traffic participants generated based on the own coordinate system. The position coordinates of the sensitive traffic participants are converted into the position coordinates of the sensitive traffic participants generated based on the world coordinate system. Specifically, the position coordinate L of the sensitive traffic participant generated based on the world coordinate system can be expressed by the following formula (5):

L＝M*P*N (5)

In formula (5), M represents the transformation relationship matrix between the own coordinate system and the world coordinate system, N represents the reference coordinate of the own coordinate system, and P represents the position coordinates of sensitive traffic participants generated based on the own coordinate system.

S504: Send the sensitive traffic participants of the target road section and the characteristic information corresponding to the sensitive traffic participants to the vehicles within the preset range of the target road section.

In some embodiments, the vehicle may adjust the speed of the vehicle based on the received positions and speeds of sensitive traffic participants in the target road segment before entering the target road segment. For example, as shown in Figure 1, in the autonomous driving scenario, when the vehicle receives that road section A contains three sensitive traffic participants (i.e., pedestrian 300-4, pedestrian 300-5, and pedestrian 300-6), the autonomous driving vehicle It can automatically reduce the speed of the self-driving vehicle before entering section A or when entering section A.

It can be understood that according to the road condition detection process described in FIG. 5 , the electronic device 100 can update the sensitive traffic participants of the target road section in real time, and can send the sensitive traffic participants of the target road section to vehicles within the preset range of the target road section in real time. This enables vehicles within the preset range of the target road section to learn the sensitive traffic participants of the target road section in real time. According to the sensitive traffic participants of the target road section, the road section information of the target road section can be understood in real time, so that when the self-driving vehicle is within or enters the target road section, Before entering the target road section, determine in advance whether to adjust the driving speed of the autonomous vehicle on the target road section. Improving the accuracy of autonomous vehicle control further improves the safety and driving experience of autonomous vehicles.

FIG. 6 shows a structural block diagram of an electronic device 100 according to an embodiment of the present application. FIG. 6 schematically shows an example electronic device 100 according to multiple embodiments. In one embodiment, electronic device 100 may include one or more processors 1404 , system control logic 1408 coupled to at least one of the processors 1404 , system memory 1412 coupled to system control logic 1408 , and system control logic 1408 A non-volatile memory (NVM) 1416 is connected, and a network interface 1420 is connected to the system control logic 1408 .

In some embodiments, processor 1404 may include one or more single-core or multi-core processors. In some embodiments, processor 1404 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, applications processors, baseband processors, etc.). In an embodiment in which the electronic device 100 adopts an eNB (Evolved Node B, enhanced base station) or RAN (Radio Access Network, radio access network) controller, the processor 1404 may be configured to perform various conforming embodiments, such as , one or more of the multiple embodiments shown in Figure 3 or Figure 4 or Figure 5 . For example, processing 1404 can be used to perform the above-mentioned detection method based on traffic conditions. For example, processing 1404 can be used to obtain the video stream collected by at least one camera on road section A and determine the target object information. It can also be used to obtain the target object information based on the target road section. Determine the traffic information of the target road section, etc.

In some embodiments, system control logic 1408 may include any suitable interface controller to provide any suitable interface to at least one of processors 1404 and/or any suitable device or component in communication with system control logic 1408 .

In some embodiments, system control logic 1408 may include one or more memory controllers to provide an interface to system memory 1412 . System memory 1412 may be used to load and store data and/or instructions. Memory 1412 of system 1400 may include any suitable volatile memory in some embodiments, such as suitable dynamic random access memory (DRAM).

NVM/memory 1416 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. In some embodiments, the NVM/memory 1416 may include any suitable non-volatile memory such as flash memory and/or any suitable non-volatile storage device, such as HDD (Hard Disk Drive), CD (Compact Disc). , Compact Disc) drive, and at least one of a DVD (Digital Versatile Disc, Digital Versatile Disc) drive.

NVM/memory 1416 may comprise a portion of storage resources on the device on which electronic device 100 is installed, or it may be accessed by the device but is not necessarily part of the device. For example, NVM/storage 1416 may be accessed over the network via network interface 1420.

In particular, system memory 1412 and NVM/storage 1416 may include temporary and permanent copies of instructions 1424, respectively. Instructions 1424 may include instructions that, when executed by at least one of processors 1404, cause electronic device 100 to implement the method illustrated in FIG. 1 . In some embodiments, instructions 1424, hardware, firmware, and/or software components thereof may additionally/alternatively be placed in system control logic 1408, network interface 1420, and/or processor 1404.

Network interface 1420 may include a transceiver for providing a radio interface for electronic device 100 to communicate with any other suitable device (eg, front-end module, antenna, etc.) over one or more networks. In some embodiments, network interface 1420 may be integrated with other components of electronic device 100 . For example, network interface 1420 may be integrated with at least one of processor 1404, system memory 1412, NVM/storage 1416, and a firmware device (not shown) with instructions that when at least one of processor 1404 executes said When instructed, the electronic device 100 implements the methods shown in FIGS. 3 to 5 .

Network interface 1420 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface or a wired electrical interface. For example, network interface 1420 may be a network adapter, a wireless network adapter, a telephone modem, and/or a wireless modem.

In one embodiment, at least one of the processors 1404 may be packaged with logic for one or more controllers of the system control logic 1408 to form a system in package (SiP). In one embodiment, at least one of the processors 1404 may be integrated on the same die with logic for one or more controllers of the system control logic 1408 to form a system on a chip (SoC).

Electronic device 100 may further include an input/output (I/O) device 1432 . The I/O device 1432 may include a user interface that enables a user to interact with the electronic device 100; the peripheral component interface is designed to enable peripheral components to also interact with the electronic device 100. In some embodiments, the electronic device 100 further includes a sensor for determining at least one of environmental conditions and location information related to the electronic device 100 .

In some embodiments, the user interface may include, but is not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., a still image camera and/or video camera), a flashlight (e.g., LED flash) and keyboard.

In some embodiments, peripheral component interfaces may include, but are not limited to, non-volatile memory ports, audio jacks, and power interfaces.

In some embodiments, sensors may include, but are not limited to, gyroscope sensors, accelerometers, proximity sensors, ambient light sensors, and positioning units. The positioning unit may also be part of or interact with the network interface 1420 to communicate with components of the positioning network (eg, Global Positioning System (GPS) satellites).

It can be understood that the structure illustrated in FIG. 6 does not constitute a specific limitation on the electronic device 100 . In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown in the figures, or combine some components, or separate some components, or arrange different components. The components illustrated may be implemented in hardware, software, or a combination of software and hardware.

Various embodiments of the mechanisms disclosed in this application may be implemented in hardware, software, firmware, or a combination of these implementation methods. Embodiments of the present application may be implemented as a computer program or program code executing on a programmable system including at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements) , at least one input device and at least one output device.

Program code may be applied to input instructions to perform the functions described herein and to generate output information. Output information can be applied to one or more output devices in a known manner. For the purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

Program code may be implemented in a high-level procedural language or an object-oriented programming language to communicate with the processing system. When necessary, assembly language or machine language can also be used to implement program code. In fact, the mechanisms described in this application are not limited to the scope of any particular programming language. In either case, the language may be a compiled or interpreted language.

In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried on or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be operated by one or more processors Read and execute. For example, instructions may be distributed over a network or through other computer-readable media. Thus, machine-readable media may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy disks, optical disks, optical disks, read-only memories (CD-ROMs), magnetic Optical disk, read-only memory (ROM), random-access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical card, flash memory, or Tangible machine-readable storage used to transmit information (e.g., carrier waves, infrared signals, digital signals, etc.) using electrical, optical, acoustic, or other forms of propagated signals over the Internet. Thus, machine-readable media includes any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (eg, computer).

In the drawings, some structural or methodological features may be shown in specific arrangements and/or orders. However, it should be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of structural or methodological features in a particular figure is not meant to imply that such features are required in all embodiments, and in some embodiments these features may not be included or may be combined with other features.

It should be noted that each unit/module mentioned in each device embodiment of this application is a logical unit/module. Physically, a logical unit/module can be a physical unit/module, or it can be a physical unit/module. Part of the module can also be implemented as a combination of multiple physical units/modules. The physical implementation of these logical units/modules is not the most important. The combination of functions implemented by these logical units/modules is what solves the problem of this application. Key technical issues raised. In addition, in order to highlight the innovative part of this application, the above-mentioned equipment embodiments of this application do not introduce units/modules that are not closely related to solving the technical problems raised by this application. This does not mean that the above-mentioned equipment embodiments do not exist. Other units/modules.

It should be noted that in the examples and descriptions of this patent, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply There is no such actual relationship or sequence between these entities or operations. Furthermore, the terms "comprises," "comprises," or any other variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also those not expressly listed other elements, or elements inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a" does not exclude the presence of additional identical elements in a process, method, article, or device that includes the stated element.

Although the present application has been illustrated and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes may be made in form and detail without departing from the present invention. The spirit and scope of the application.

Claims (16)

A road condition detection method for electronic equipment, characterized by: Obtain the video stream collected by the camera on the target road section; According to the video stream, determine the current characteristic information of the target object on the target road section, where the characteristic information includes motion state information and/or location state information; Based on the current characteristic information of the target object and/or the historical characteristic information of the target object, the current traffic condition information of the target road section is determined. The method according to claim 1, further comprising: The current road condition information of the target road section is sent to vehicles within a preset range of the target road section. The method according to claim 1, wherein the target object includes at least one of the following: vehicles, people, and obstacles. The method according to claim 3, characterized in that the current traffic condition information of the target road section includes a road risk level; and Determining the current traffic condition information of the target road section based on the current feature information of the target object and/or the historical feature information of the target object includes: Based on the current characteristic information of the target object and the historical characteristic information of the target object, determine the current traffic flow and collision accident information of the target road section, as well as the historical traffic flow and collision accident information; The current road risk level of the target road section is determined based on the current traffic volume and collision accident information of the target road section and historical traffic volume and collision accident information. The method according to claim 4, wherein the collision accident information includes at least one of the following: the number of collision accidents, the average relative acceleration of the collision vehicles, and the average relative speed of the collision vehicles. The method according to claim 5, characterized in that the current road risk level of the target road section is determined based on the current traffic volume and collision accident information of the target road section and historical traffic volume and collision accident information. include: Based on the current traffic volume and collision accident information of the target road section, as well as the historical traffic volume and collision accident information, the collision risk indicator parameters of the target road section are determined, wherein the collision risk indicator parameters include at least one of the following: the severity of the collision accident , exposure to collision accidents, controllability of collision accidents; The current road risk level of the target road section is determined based on the collision risk indicator parameter of the target road section. The method according to claim 6, characterized in that the current road risk level R _dynamic is calculated by the following formula: R _dynamic =S*E*C Among them, S represents the severity of the collision. C represents the controllability of the collision, and E represents the exposure to the collision. The method according to claim 6 or claim 7, characterized in that the collision risk indicator parameters of the target road section are determined based on the current traffic flow and collision accident information of the target road section and historical traffic flow and collision accident information. include: The severity of the collision accidents in the target road section is determined based on the number of collisions in the current and historical target road section, the average relative acceleration of the collision vehicles, and the average relative speed of the collision vehicles. The method according to claim 8, characterized in that: The number of collision accidents includes: the number of vehicle-to-vehicle collisions, the number of vehicle-to-person collisions, and the number of vehicle-to-obstacle collisions; The severity S of the collision accident is calculated by the following formula: S＝α ₀ v _r exp(β ₀ a _r )*(α ₁ *N _CC +α ₂ *N _CO +α ₃ *N _CP )*exp(β ₁ N _death ) Among them, v _r represents the average relative speed of the collision vehicle, a _r represents the average relative acceleration of the collision vehicle, N _CC represents the number of collisions between vehicles, N _CO represents the number of collisions between vehicles and obstacles, and N _CP represents The number of collisions between vehicles and people, α ₀ , α ₁ , α ₂ , α ₃ , β ₀ , and β ₁ respectively represent weight parameters, N _death represents the number of deaths in collision accidents in the current and historical target road sections, exp represents the exponential function . The method according to claim 6 or claim 7, characterized in that, based on the current traffic flow and collision accident information of the target road section, as well as the historical traffic flow and collision accident information, determining the collision risk indicator parameters of the target road section includes: Based on the average relative acceleration of the collision vehicle and the average relative speed of the collision vehicle in the current and historical target road section, the controllability of the collision accident in the target road section is determined. The method according to claim 10, characterized in that: The controllability C of the collision accident on the target road section is calculated by the following formula: Among them, a _r represents the average relative acceleration of the collision vehicle, v _r represents the average relative speed of the collision vehicle, β ₂ represents the weight parameter, N _death represents the number of deaths in collision accidents in the current and historical target road section, exp represents an exponential function. The method according to claim 6 or claim 7, characterized in that, based on the current traffic flow and collision accident information of the target road section, as well as the historical traffic flow and collision accident information, determining the collision risk indicator parameters of the target road section includes: Based on the current and historical traffic volume of the target road section and the number of collision accidents, the exposure to collision accidents of the current target road section is determined. The method according to claim 12, characterized in that: The exposure E of the collision accident is calculated by the following formula: Among them, N _accident represents the number of collision accidents, and N _total represents the traffic volume. The method according to claim 3, characterized in that the current traffic condition information of the target road section includes characteristic information of sensitive traffic participants; Determining the current traffic condition information of the target road section based on the current feature information of the target object and/or the historical feature information of the target object includes: Based on the current characteristic information of the target object, sensitive traffic participants are screened out from the target object and the current characteristic information of the sensitive traffic participants is determined, wherein the sensitive traffic participants include at least one of the following Types: Pedestrians, heavy trucks, bicycles, vans. A readable medium, characterized in that instructions are stored on the readable medium, and when executed on an electronic device, the instructions cause the electronic device to execute the road condition detection method described in any one of claims 1 to 14. An electronic device, characterized by including: memory for storing instructions for execution by one or more processors of the electronic device, and The processor is one of the processors of the electronic device and is used to execute the road condition detection method described in any one of claims 1 to 14.