CN111337043B

CN111337043B - Path planning method and device, storage medium and electronic equipment

Info

Publication number: CN111337043B
Application number: CN202010186342.2A
Authority: CN
Inventors: 刘梦瑶; 车正平; 史雪凤; 张新圣
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-03-17
Filing date: 2020-03-17
Publication date: 2022-08-02
Anticipated expiration: 2040-03-17
Also published as: CN111337043A

Abstract

The present disclosure provides a method, an apparatus, a storage medium and an electronic device for planning a route, in which a vehicle is used to acquire GPS data and image data of road segments in real time, and a real-time value of at least one road segment feature of each road segment is determined according to the GPS data and the image data; then, determining a comprehensive value of the road section characteristics of all road sections in a preset time range before the current time point based on the real-time value of at least one road section characteristic of each road section; after the starting point position and the end point position of the user are obtained, the current path is determined according to the comprehensive value of at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position, and the current path is recommended to the user. The road section characteristics of the road section can be acquired more comprehensively based on the GPS data and the image data, so that the planned current path is more accurate, and the travel efficiency of the user is improved.

Description

Path planning method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for planning a path, a storage medium, and an electronic device.

Background

The navigation system on the vehicle not only can enable a user to know the exact position of the user at any time and any place in the driving process, but also can plan a reasonable driving path for the user before driving, thereby facilitating the trip of the user.

In the conventional technology, when a navigation system plans a driving path for a user, one method is as follows: the number of vehicles and the driving speed of a certain road section are collected through the laid ground induction coils, and the traffic condition of the road section is judged by utilizing the number of vehicles and the driving speed, however, the ground induction coils are high in cost, and the ground induction coils cannot be laid on all the road sections, so that the condition of missing detection can occur, and the navigation system cannot accurately plan the most reasonable driving path. The other method is as follows: the traffic condition of a road section is judged by using Global Positioning System (GPS) data acquired by floating vehicles, but because the number of the floating vehicles is small, the acquired GPS data is a small sample on the road and cannot represent the real condition of the road, and the most reasonable driving path cannot be accurately planned.

Therefore, a method for planning a path with high accuracy is needed.

Disclosure of Invention

In view of the above, an object of the present disclosure is to provide a method, an apparatus, a storage medium, and an electronic device for planning a path, which can improve the accuracy of a current path.

In a first aspect, the present disclosure provides a method for planning a path, including:

acquiring GPS data and image data of all road sections in real time based on each vehicle;

determining a real-time value of at least one road segment feature for each of the road segments based on the GPS data and the image data;

determining a comprehensive value of the road section characteristics of all the road sections within a preset time range before a current time point based on a real-time value of at least one road section characteristic of each road section;

and determining the current path according to the comprehensive value of at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position.

In one possible embodiment, the determining, from the GPS data and the image data, real-time values of at least one road segment characteristic for each of the road segments, the road segment characteristic including one or more of a speed of travel, a number of lanes, a weather condition, a motor vehicle density, a non-motor/pedestrian density, a violation condition, a road segment occupancy condition, a road surface condition, a night light condition for a given vehicle on the road segment.

In one possible embodiment, in the case where the road section characteristic is a travel speed, the real-time value of each road section characteristic is determined by:

acquiring first GPS data and second GPS data based on the GPS data of the specified vehicle; the first GPS data comprises first time and first position information, and the second GPS data comprises second time and second position information;

determining the travel speed based on the first GPS data and the second GPS data.

In one possible embodiment, in the case where the road section characteristic is the number of lanes, the real-time value of each road section characteristic is determined by:

extracting lane line features from the image data;

determining a lane line in the same direction based on the lane line characteristics;

acquiring the number of the lane lines in the same direction;

and determining the number of lanes in the same direction according to the number of lane lines in the same direction.

In a possible embodiment, in the case where the road section characteristic is a weather condition, the real-time value of each road section characteristic is determined by:

acquiring weather condition standards corresponding to different weather types;

identifying weather features in the image data;

determining a weather condition rating based on the weather condition criteria and the weather feature.

In one possible embodiment, in the case of a road section characteristic of a motor vehicle density, the real-time value of each road section characteristic is determined by:

identifying a motor vehicle in the image data;

calculating the number of motor vehicles in a first preset range;

and determining the motor vehicle density based on the area of the first preset range and the number of the motor vehicles.

In one possible embodiment, in the case of a road segment feature of non-motor vehicle/pedestrian density, the real-time value for each road segment feature is determined by:

identifying non-motor/pedestrian and motor lane features in the image data;

determining the number of non-motor vehicles/pedestrians on the motor vehicle lane within a second preset range based on the non-motor vehicles/pedestrians and the motor vehicle lane characteristics;

determining a non-motor vehicle/pedestrian density based on the area of the second preset range and the number of non-motor vehicles/pedestrians.

In a possible embodiment, in the case where the road section characteristics are violation conditions, the real-time value of each road section characteristic is determined by:

acquiring violation standards corresponding to different violation behaviors;

identifying a violation in the image data;

a violation level is determined based on the violation criteria and the violation.

In a possible embodiment, in the case that the road segment characteristics are road segment occupancy, the real-time value of each road segment characteristic is determined by:

identifying stationary obstacle features in the image data;

determining an occupancy length and an occupancy width based on the stationary obstacle feature;

and calculating to obtain the road section occupation area by using the occupation length and the occupation width.

In one possible embodiment, in the case where the road section characteristic is a road surface condition, the real-time value of each road section characteristic is determined by:

determining the image data corresponding to the travelable region;

extracting road surface features in the image data corresponding to the travelable region;

and determining the grade of the road surface based on the road surface characteristics and the road surface standard acquired in advance.

In a possible embodiment, in the case where the section feature is a night lighting condition, the real-time value of each section feature is determined by:

extracting attribute parameters in the image data in the presence of illumination data in the image data;

determining a night illumination level based on the attribute parameters and pre-acquired illumination criteria.

In a possible embodiment, the determining a current route according to a composite value of at least one feature of the road segment and based on a basic feature value of the road segment based on the starting point position and the ending point position includes:

determining a weight value of each road section characteristic;

determining at least one alternative path based on the starting point position and the end point position, wherein the alternative path is composed of at least one alternative road section;

determining a tendency value of each alternative road section in each alternative path based on the comprehensive value and the weight value of each road section feature;

determining a current path among the alternative paths based on the tendency values and the base feature values.

In a second aspect, the present disclosure further provides a path planning apparatus, including:

the acquisition module is used for acquiring GPS data and image data of all road sections in real time based on each vehicle;

a first determining module, configured to determine a real-time value of at least one road segment feature of each road segment according to the GPS data and the image data;

a second determining module, configured to determine, based on a real-time value of at least one road segment feature of each road segment, a comprehensive value of the road segment features of all the road segments within a preset time range before a current time point;

and the third determining module is used for determining the current path according to the comprehensive value of at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position.

In a third aspect, the present disclosure also provides a computer-readable storage medium, wherein the computer-readable storage medium has stored thereon a computer program, which when executed by a processor performs the steps of the method for planning a path as described.

In a fourth aspect, the present disclosure also provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is operating, the machine-readable instructions when executed by the processor performing the steps of the method of planning a path as described.

The method comprises the steps of acquiring GPS data and image data of road sections in real time by using a vehicle, and determining a real-time value of at least one road section characteristic of each road section according to the GPS data and the image data; then, determining a comprehensive value of the road section characteristics of all road sections in a preset time range before the current time point based on the real-time value of at least one road section characteristic of each road section; after the starting point position and the end point position of the user are obtained, the current path is determined according to the comprehensive value of at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position, and the current path is recommended to the user. The road section characteristics of the road section can be acquired more comprehensively based on the GPS data and the image data, so that the planned current path is more accurate, and the travel efficiency of the user is improved.

In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the present disclosure or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and other drawings can be obtained by those skilled in the art without inventive exercise.

Fig. 1 shows a flow chart of a method for planning a path according to the present disclosure;

fig. 2 shows a flow chart for determining a driving speed in a path planning method provided by the present disclosure;

fig. 3 shows a flow chart of determining the number of lanes in a path planning method provided by the present disclosure;

fig. 4 shows a flow chart of determining a weather condition level in a path planning method provided by the present disclosure;

FIG. 5 is a flow chart illustrating the determination of vehicle density in a method for planning a path according to the present disclosure;

FIG. 6 illustrates a flow chart for determining non-motor vehicle/pedestrian density in a method of planning a path provided by the present disclosure;

fig. 7 illustrates a flow chart for determining violation level in a method for planning a path provided by the present disclosure;

fig. 8 shows a flowchart for determining a road segment occupation area in a path planning method provided by the present disclosure;

fig. 9 shows a flow chart of determining road surface grade in a path planning method provided by the present disclosure;

FIG. 10 illustrates a flow chart for determining night light levels in a method for planning a path provided by the present disclosure;

fig. 11 shows a flow chart of determining a current path in a path planning method provided by the present disclosure;

fig. 12 is a schematic structural diagram illustrating a travel time index determination apparatus provided by the present disclosure;

fig. 13 shows a schematic structural diagram of an electronic device provided by the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more apparent, the technical solutions of the present disclosure will be described clearly and completely below with reference to the accompanying drawings of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.

Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.

To maintain the following description of the present disclosure clear and concise, a detailed description of known functions and known components is omitted from the present disclosure.

For the convenience of understanding the present disclosure, a method for planning a path disclosed in the present disclosure will be described in detail first.

A first aspect of the present disclosure provides a path planning method, and fig. 1 shows a flowchart of the path planning method when a server or a processor is used as an execution subject in the present disclosure, where the specific steps are as follows:

s101, GPS data and image data of all road sections are acquired in real time on the basis of each vehicle.

In a specific implementation, the vehicle includes any vehicle that travels on a road, such as a bus and a taxi, etc. that is equipped with a specific collection device.

Specifically, each vehicle acquires GPS data of all road sections in real time by using an installed positioning system, wherein the GPS data comprises position information of the vehicle, azimuth information from a preset reference object and the like; each vehicle also acquires image data of all road sections by using a driving recorder or pre-installed image acquisition equipment; wherein the image data records the current environment of the vehicle in real time.

Here, the image data includes video and video attributes, picture and picture attributes, time, and the like, and when the image capture device is a laser radar, the image data also includes three-dimensional data of the current environment, and the like.

And S102, determining a real-time value of at least one road section characteristic of each road section according to the GPS data and the image data.

In a specific implementation, in the process of acquiring the GPS data and the image data of the road segments in real time, the real-time value of at least one road segment feature of each road segment may be determined according to the GPS data and the image data. That is, each road segment feature of the current road segment can be calculated in real time.

The road section characteristics of the present disclosure include a running speed of a specified vehicle on the road section, a number of lanes, a weather condition, a motor vehicle density, a non-motor vehicle/pedestrian density, a violation condition, a road section occupancy condition, a road surface condition, and a night illumination condition. Those skilled in the art will appreciate that other road segment characteristics associated with a road segment, or that in some respect can characterize a road segment, are within the scope of the present disclosure.

Wherein the real-time value of at least one road segment characteristic of each road segment comprises the running speed of a specified vehicle, the number of lanes in the same direction, the weather condition level, the motor vehicle density, the non-motor vehicle/pedestrian density, the violation level, the road segment occupation area, the road surface level, the night illumination level and the like on the road segment at any time point.

Next, determination of the traveling speed, the number of lanes, the weather condition, the motor vehicle density, the non-motor vehicle/pedestrian density, the traffic violation condition, the road segment occupancy, the road surface condition, and the night illumination condition will be explained, respectively.

Specifically, in the case where the road section characteristic is the driving speed, the real-time value of each road section characteristic is determined in the manner shown in fig. 2, wherein the specific steps are as follows:

s201, acquiring first GPS data and second GPS data based on GPS data of a specified vehicle; the first GPS data comprises first time and first position information, and the second GPS data comprises second time and second position information;

s202, determining the running speed based on the first GPS data and the second GPS data.

In particular implementations, when the positioning system acquires GPS data for a vehicle, the GPS data is associated with a current acquisition time.

Based on the travel distance being equal to the product of the travel time and the travel speed, first GPS data and second GPS data are acquired based on the GPS data, wherein the first GPS data and the second GPS data belong to the same road segment. Specifically, the first GPS data includes first time and first position information, and the second GPS data includes second time and second position information.

Then, a time interval between the first time and the second time is calculated, and a position distance formed by the first position information and the second position information, namely a distance traveled in the time interval, is calculated. It is to be noted that the position distance is not a straight distance, but is determined according to a travelable route generated by a travelable road.

And finally, taking the time interval as a divisor, taking the position interval as a dividend, and calculating to obtain a quotient, namely the running speed.

Further, in the case that the road section feature is the number of lanes, the real-time value of each road section feature is determined in the manner shown in fig. 3, wherein the specific steps are as follows:

s301, extracting lane line features from the image data;

s302, determining a lane line in the same direction based on the lane line characteristics;

s303, acquiring the number of lane lines in the same direction;

and S304, determining the number of lanes in the same direction according to the number of lane lines in the same direction.

In particular implementations, the number of lanes of a road segment is also an important factor for a user in selecting a driving route. After the image data of the link is acquired, lane line features, such as the color, line type, and the like of the lane line, are extracted in the image data.

Then, the lane line in the same direction is determined based on the lane line feature, where there may be image data in the reverse link in the acquired image data, and at this time, it is not necessary to consider the image data of the reverse link when calculating the travel path, and therefore, after determining the lane line feature, only the lane line in the same direction may be determined.

After the lane lines in the same direction are determined, the number of the lane lines in the same direction is counted, and the number of the lanes in the same direction is determined based on the number of the lane lines in the same direction.

The method can be used for identifying objects in the images, such as lane lines, houses, signs and the like. Specifically, after the picture included in the image data is acquired, the identification model is directly used for identifying the lane lines in the same direction in the picture, the number of the lane lines in the same direction is counted, and the number of the lanes in the same direction is calculated.

Further, in the case that the road section characteristics are weather conditions, the real-time value of each road section characteristic is determined in the manner shown in fig. 4, wherein the specific steps are as follows:

s401, acquiring weather condition standards corresponding to different weather types;

s402, identifying weather features in the image data;

and S403, determining the weather condition grade based on the weather condition standard and the weather characteristics.

In a specific implementation, it is considered that a factor of weather also affects traffic (for example, traffic congestion is easily caused in rainy or snowy weather), and different weather conditions affect the same road section differently, for example, heavy rainy weather affects a road section with a lower terrain differently than a road section with a higher terrain. Thus, weather conditions are also a factor that affects the user's selection of a travel path.

Acquiring preset weather condition standards corresponding to different weather categories, wherein the weather condition standards can be weather condition grades, for example, the weather condition grade corresponding to clear weather is one grade; the grade of the weather condition corresponding to the heavy rainy weather is five grade and the like.

After the image data of the road section is acquired, weather features including wind speed, humidity, visibility and the like are identified in the image data. And then comparing the weather category to which the current weather feature belongs, and determining the weather condition grade based on the weather condition standard and the weather category to which the weather feature belongs.

Of course, the identification model can be used to identify the weather condition grade, and the inquiry can be performed through the weather software of the mobile terminal.

Further, in the case that the road section characteristics are motor vehicle density, the real-time value of each road section characteristic is determined in the manner shown in fig. 5, wherein the specific steps are as follows:

s501, identifying the motor vehicle in the image data;

s502, calculating the number of motor vehicles in a first preset range;

and S503, determining the motor vehicle density based on the area of the first preset range and the number of the motor vehicles.

In the specific implementation, when planning the driving path, the user pays attention to whether the vehicle is blocked, that is, the density of the vehicles on the planned driving path is high, the possibility of traffic blocking is high when the density of the vehicles is high, and the possibility of traffic blocking is low when the density of the vehicles is low.

After acquiring the image data of the road section, identifying the motor vehicle in the image data based on the characteristics of the motor vehicle; then, the number of vehicles within a first preset range is calculated. The first preset range may be a circular area determined by taking the current acquisition position as a center and taking a first preset distance as a radius; and calculating road section areas and the like within a first preset distance before and after the current acquisition position according to the road section direction.

Specifically, the position of each vehicle relative to the current acquisition position is calculated according to the following formulas (1), (2) and (3), wherein the formulas (1), (2) and (3) are as follows:

where h is the installation height of the camera (and may be other image capture devices, as will be described in detail in this disclosure), θ is the downward horizontal downward upward downward upward downward upward downward upward downward upward downward upward downward upward downward upward downward upward downward upward downward upward downward _x Horizontal offset of the optical axis of the camera, c _y Is the vertical offset of the optical axis of the camera, f _x Is the horizontal focal length of the camera, f _y The vertical focal length of the camera is taken as the recognition result of the motor vehicle, the (x, y) is the pixel coordinate of the midpoint of the lower edge of the rectangular frame corresponding to the motor vehicle, the upper left corner of the picture is (0, 0), the lower right corner is (H, W)), and p is _n The position of the vehicle relative to the current acquisition position.

Calculating p _n The number of vehicles falling within the first predetermined range, i.e., the number of vehicles within the first predetermined range.

And then, taking the area of the first preset range as a divisor, taking the number of the motor vehicles as a dividend, and calculating to obtain a quotient, namely the motor vehicle density.

Further, in the case where the road section characteristics are the density of non-motor vehicles/pedestrians, the real-time value of each road section characteristic is determined in the manner shown in fig. 6, wherein the specific steps are as follows:

s601, identifying non-motor vehicle/pedestrian and motor vehicle lane characteristics in the image data;

s602, determining the number of the non-motor vehicles/pedestrians on the motor vehicle lane in a second preset range based on the characteristics of the non-motor vehicles/pedestrians and the motor vehicle lane;

and S603, determining the density of the non-motor vehicles/pedestrians based on the area of the second preset range and the number of the non-motor vehicles/pedestrians.

In the concrete implementation, the number of the existing non-motor vehicles/pedestrians and motor vehicles is different considering the different regional attributes of national road sections, provincial road sections, high road sections and the like. The number of non-motor vehicles/pedestrians and motor vehicles also affects the smoothness of the road section to some extent.

After acquiring the image data of the road section, identifying non-motor vehicles/pedestrians and motor vehicle lane characteristics in the image data; then, the non-motor vehicle/pedestrian and the motor vehicle lane are determined based on the non-motor vehicle/pedestrian and the motor vehicle lane characteristics, and the number of the non-motor vehicles/pedestrians in the second preset range is identified and calculated. The second preset range may be a circular area determined by taking the current acquisition position as a center and taking a preset second distance as a radius; and calculating road section areas and the like within a second preset distance before and after the current acquisition position according to the road section direction. Here, the method for calculating the number of non-motor vehicles/pedestrians in the second preset range is similar to the method for calculating the number of motor vehicles in the first preset range, and detailed description of the specific steps is omitted.

And then, taking the area of the second preset range as a divisor, taking the number of the non-motor vehicles/pedestrians as a dividend, and calculating to obtain a quotient, namely the density of the non-motor vehicles/pedestrians.

Further, in the case that the road section characteristics are violation conditions, the real-time value of each road section characteristic is determined in the manner shown in fig. 7, wherein the specific steps are as follows:

s701, acquiring violation standards corresponding to different violation behaviors;

s702, identifying violation behaviors in the image data;

and S703, determining the violation grade based on the violation standard and the violation behavior.

In the specific implementation, more illegal behaviors exist in part of road sections. Therefore, in planning the driving path, the factor of the violation behavior needs to be taken into consideration. The violation behaviors comprise running a red light, a pedestrian crossing a railing and the like.

Specifically, violation standards corresponding to different violation behaviors are obtained, for example, a red light violation level four corresponds to violation, and a pedestrian crossing a railing corresponds to a violation level one. After the image data of the road section is acquired, the violation behaviors in the image data are identified. In practical application, there may not be any violation behavior in the road section data, and there may be a plurality of violation behaviors.

A violation level is determined based on the violation criteria and the violation. When a plurality of violation behaviors exist in the road section data, the plurality of violation grades can be further calculated to obtain a final violation grade.

Further, in the case that the road segment feature is a road segment occupation situation, the real-time value of each road segment feature is determined in the manner shown in fig. 8, wherein the specific steps are as follows:

s801, identifying static obstacle features in image data;

s802, determining the occupation length and the occupation width based on the static obstacle characteristics;

and S803, calculating to obtain the road section occupation area by using the occupation length and the occupation width.

In the specific implementation, some obstacles, such as vehicles which are not used frequently, roadblocks set by management personnel and the like, can be parked on part of the road section, and the obstacles can greatly influence the smoothness of the current road section.

After the image data of the road segment is acquired, the static obstacle features in the image data are identified, wherein the static obstacle features comprise attribute features, position features and the like of the static obstacle.

And determining the occupied length and the occupied width of the road section occupied by the static obstacle based on the characteristics of the static obstacle, and multiplying the occupied length and the occupied width to obtain a product, namely the occupied area of the road section occupied by the static obstacle.

In practical applications, there may be a plurality of static obstacles occupying the same road section at the same time, and the road section occupied area of the road section is the sum of the road section occupied areas occupied by each static obstacle.

Further, in the case that the road section characteristics are road surface conditions, the real-time value of each road section characteristic is determined in the manner shown in fig. 9, wherein the specific steps are as follows:

s901, determining image data corresponding to a travelable area;

s902, extracting road surface features in image data corresponding to a travelable area;

and S903, determining the road surface grade based on the road surface characteristics and the road surface standards acquired in advance.

In a specific implementation, the acquired image data is all environment data of the road section, including data of a travelable area, data of a sidewalk area, data of a green area, and the like.

After the image data is acquired, the image data corresponding to the travelable region is determined in all the image data, and then the road surface characteristics of the travelable region are extracted from the image data corresponding to the travelable region. The pavement characteristics comprise the material of the pavement, the smoothness of the pavement, the integrity of the pavement and the like.

The road surface grade is determined based on the road surface characteristics and a road surface standard acquired in advance. The road surface standard comprises a mapping relation between road surface characteristics and road surface grades.

Further, in the case where the road section characteristics are the night lighting conditions, the real-time value of each road section characteristic is determined in the manner shown in fig. 10, wherein the specific steps are as follows:

s1001, extracting attribute parameters in image data under the condition that illumination data exists in the image data;

and S1002, determining the night illumination level based on the attribute parameters and the pre-acquired illumination standard.

Considering that the lighting condition on the road section is a factor that the user is more interested in when traveling at night, and the lighting condition is also a factor that affects the driving safety, the lighting condition at night on the road section needs to be considered when planning the route for the user traveling at night.

Specifically, after the image data is acquired, it is determined whether lighting data exists in the image data, that is, the image data indicates that lighting equipment exists in the road segment and the lighting equipment is in an on state. In case of illumination data in the image data, the attribute parameters in the image data are mentioned, wherein the attribute parameters comprise gray scale, contrast, etc.

The night lighting level is determined based on the attribute parameters and pre-acquired lighting criteria. Wherein the lighting criteria comprises a mapping between the attribute parameters and the night lighting levels.

It should be noted that the pictures used in the above description may be pictures directly taken or frame pictures extracted from a taken video.

And S103, determining a comprehensive value of the road section characteristics of all road sections in a preset time range before the current time point based on the real-time value of at least one road section characteristic of each road section.

After determining the real-time value of at least one road section characteristic of each road section, according to the selected current time point, calculating based on the real-time value of at least one road section characteristic of each road section to obtain a comprehensive value of the road section characteristics of all road sections in a preset time range before the current time point.

Here, the integrated value of each link characteristic of the link is integrally determined by all real-time values of the link characteristic in a preset time range before the current time point to integrally reflect the integrated condition of the link characteristic in the specified time range.

In the specific implementation, the real-time requirement on the driving speed, the weather condition, the motor vehicle density, the non-motor vehicle/pedestrian density, the violation condition and the road section occupation condition is high, so that when the comprehensive value of the road section feature of the road section is determined, the GPS data and the image data in a smaller preset time range (for example, one hour) before the current time point can be selected to determine the comprehensive value of the road section feature of the road section.

In addition, the updating frequency of the number of the lanes, the road surface condition and the night illumination condition is low, and in practical application, the real-time requirements on the number of the lanes, the road surface condition and the night illumination condition are low, so that when the comprehensive value of the road section characteristic of the road section is determined, the GPS data and the image data in a larger preset time range (for example, one month) before the current time point can be selected to determine the comprehensive value of the road section characteristic of the road section, and the number of the lanes, the road surface condition and the night illumination condition can be determined in stages, so that the resource waste caused by repeated calculation is avoided.

Determining a composite value of the road section characteristics of the road section based on real-time values of a running speed, a number of lanes, weather conditions, motor vehicle density, non-motor vehicle/pedestrian density, violation conditions, road section occupancy, road surface conditions, and night lighting conditions of a specified vehicle of the road section. Specifically, the mean square error calculation can be performed on the running speed, the number of lanes, the weather condition grade, the motor vehicle density, the non-motor vehicle/pedestrian density, the violation grade, the road section occupation area, the road surface grade and the night illumination grade, and the obtained value is used as the comprehensive value of the road section characteristics of the road section; of course, other calculations may be made, and this disclosure is not limited thereto.

And S104, determining the current path according to the comprehensive value of the at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position.

In a specific implementation, a starting point position and an end point position of a user are determined based on a terminal device, and a current path is determined according to a comprehensive value of at least one road section characteristic and a basic characteristic value based on a road section based on the starting point position and the end point position.

The basic characteristic value based on the road section comprises the grade of the road section, the number of traffic light indicators on the road section, the crowding condition of the road section and the like, and the values are related to the self characteristics and properties of the road section, can be preset values or can be obtained in a real-time mode. In addition, the information such as the grade of the road section, the number of traffic signs on the road section, the congestion state of the road section and the like can be acquired by the vehicle in real time or acquired from other platforms.

Specifically, the current path is determined according to the integrated value of at least one road segment feature and the basic feature value based on the road segment with reference to fig. 11, wherein the specific steps are as follows:

s1101, determining a weight value of each road section characteristic.

Here, the weight values are set in advance for the traveling speed, the number of lanes, the weather condition, the motor vehicle density, the non-motor vehicle/pedestrian density, the traffic violation situation, the road segment occupancy, the road surface condition, and the degree of influence of the night lighting condition on the traveling of the vehicle, based on the traveling speed, the number of lanes, the weather condition level, the motor vehicle density, the non-motor vehicle/pedestrian density, the traffic violation level, the road segment occupancy, the road surface level, and the night lighting level of the specified vehicle, respectively.

S1102, determining at least one alternative path based on the starting point position and the end point position, wherein the alternative path is composed of at least one alternative road section.

In practical applications, at least one alternative path is determined based on the start position and the end position. In more remote areas, there may be only one alternative path; in more busy areas, there may be multiple alternative paths. An alternative path is a path that can be achieved from a starting position to an end position.

Wherein, the smaller the length of the road section is, the more accurate the comprehensive value of the road section is. Thus, the alternative path is composed of at least one alternative road segment when the starting position and the end position are far apart.

S1103, determining a tendency value of each alternative road section in each alternative path based on the comprehensive value and the weight value of each road section feature.

And setting a corresponding weight value for each road section characteristic in advance according to the influence degree of each road section characteristic on the road.

After at least one alternative path is determined, for each alternative path, performing weighted calculation based on the comprehensive value and the weight value of the road segment characteristics of each alternative road segment included in the alternative path to determine the tendency value of each alternative road segment in the alternative path. Wherein the tendency value represents a probability value that the user tends to travel on the alternative road segment.

In practical applications, the user may determine the starting point position and the ending point position, and may also adjust the weight value of each road section feature based on the driving preference of the user, for example, the number of lanes of the road section that the user a wants to drive is large, so the weight value corresponding to the number of lanes may be increased, and the weight values of other road section features may be decreased.

And S1104, determining the current path in the alternative paths based on the tendency values and the basic characteristic values.

After determining the tendency value of each alternative road section in each alternative path, calculating the tendency value of each alternative path based on the tendency value and the basic characteristic value, and determining the current path in the alternative paths based on the tendency value of each alternative path. For example, the candidate path with the largest tendency value is selected as the current path.

The method comprises the steps of acquiring GPS data and image data of road sections in real time by using a vehicle, and determining a real-time value of at least one road section characteristic of each road section according to the GPS data and the image data; then, determining the comprehensive values of the road section characteristics of all road sections in a preset time range before the current time point; after the starting point position and the end point position of the user are obtained, the current path is determined according to the comprehensive value of at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position, and the current path is recommended to the user. The road section characteristics of the road section can be acquired more comprehensively based on the GPS data and the image data, so that the planned current path is more accurate, and the travel efficiency of the user is improved.

Based on the same inventive concept, the second aspect of the present disclosure further provides a path planning apparatus corresponding to the path planning method, and as the principle of the apparatus in the present disclosure for solving the problem is similar to the path planning method in the present disclosure, the implementation of the apparatus may refer to the implementation of the method, and repeated details are not repeated.

Referring to fig. 12, the path planning apparatus includes: the acquisition module 10, the first determination module 20, the second determination module 30 and the third determination module 40; the acquisition module 10 is coupled with the first determination module 20; the first determination module 20 is coupled to the second determination module 30, and the second determination module 30 is coupled to the third determination module 40.

And the acquisition module 10 is used for acquiring the GPS data and the image data of all road sections in real time on the basis of each vehicle. In a specific implementation, the vehicle includes any vehicle that travels on a road, such as a bus and a taxi, etc. that is equipped with a specific collection device.

Specifically, each vehicle acquires the GPS data of all road sections in real time by using an installed positioning system, wherein the GPS data comprises the position information of the vehicle, the azimuth information from a preset reference object and the like; each vehicle also acquires image data of all road sections by using a driving recorder or pre-installed image acquisition equipment; wherein the image data records the current environment of the vehicle in real time.

Here, the image data includes video and video attributes, picture and picture attributes, time, and the like, and when the image capturing device is a laser radar, the image data also includes three-dimensional data of the current environment, and the like.

A first determining module 20, configured to determine real-time values of at least one road segment characteristic of each road segment according to the GPS data and the image data.

In a specific implementation, in the process of acquiring the GPS data and the image data of the road segments in real time, the real-time value of at least one road segment feature of each road segment may be determined according to the GPS data and the image data. That is, each road segment feature of the current road segment may be calculated in real time.

The first determination module 20 includes a first determination unit, a second determination unit, a third determination unit, a fourth determination unit, a fifth determination unit, a sixth determination unit, a seventh determination unit, an eighth determination unit, and a ninth determination unit.

A first determination unit configured to acquire first GPS data and second GPS data based on the GPS data of the specified vehicle; the first GPS data comprises first time and first position information, and the second GPS data comprises second time and second position information; based on the first GPS data and the second GPS data, a travel speed is determined.

A second determination unit configured to extract lane line features in the image data; determining a lane line in the same direction based on the lane line characteristics; acquiring the number of lane lines in the same direction; and determining the number of lanes in the same direction according to the number of lane lines in the same direction.

The third determining unit is used for acquiring weather condition standards corresponding to different weather categories; identifying weather features in the image data; a weather condition rating is determined based on the weather condition criteria and the weather characteristics.

A fourth determination unit for identifying the motor vehicle in the image data; calculating the number of motor vehicles in a first preset range; based on the area of the first preset range and the number of vehicles, vehicle density is determined.

wherein h is the installation height of the camera (other camera devices can be used, and the camera is explained in detail in the disclosure), θ is the horizontal downward depression angle of the camera, and c _x Horizontal offset of the optical axis of the camera, c _y Is the vertical offset of the optical axis of the camera, f _x Is the horizontal focal length of the camera, f _y The vertical focal length of the camera is taken as the recognition result of the motor vehicle, the (x, y) is the pixel coordinate of the midpoint of the lower edge of the rectangular frame corresponding to the motor vehicle, the upper left corner of the picture is (0, 0), the lower right corner is (H, W)), and p is _n The position of the vehicle relative to the current acquisition position.

A fifth determination unit for identifying non-motor/pedestrian and motor lane characteristics in the image data; determining the number of non-motor vehicles/pedestrians on the motor vehicle lane within a second preset range based on the non-motor vehicles/pedestrians and the motor vehicle lane characteristics; determining a non-motor/pedestrian density based on the area of the second preset range and the number of non-motor/pedestrians.

In the concrete implementation, the number of existing non-motor vehicles/pedestrians and motor vehicles is different in consideration of different regional attributes of national road sections, provincial road sections, highway sections and the like. The number of non-motor vehicles/pedestrians and motor vehicles also affects the smoothness of the road section to some extent.

After acquiring the image data of the road section, identifying non-motor vehicles/pedestrians and motor vehicle lane features in the image data; then, the non-motor vehicle/pedestrian and the motor vehicle lane are determined based on the non-motor vehicle/pedestrian and the motor vehicle lane characteristics, and the number of the non-motor vehicles/pedestrians in the second preset range is identified and calculated. The second preset range may be a circular area determined by taking the current acquisition position as a center and taking a preset second distance as a radius; and calculating road section areas and the like within a second preset distance before and after the current acquisition position according to the road section direction. Here, the method for calculating the number of non-motor vehicles/pedestrians in the second preset range is similar to the method for calculating the number of motor vehicles in the first preset range, and detailed description of the specific steps is omitted.

The sixth determining unit is used for acquiring violation standards corresponding to different violation behaviors; identifying a violation in the image data; a violation level is determined based on the violation criteria and the violation.

Specifically, violation standards corresponding to different violation behaviors are obtained, for example, a red light violation level four corresponds to violation, and a pedestrian crossing a railing corresponds to a violation level one. After the image data of the road section is acquired, the violation behaviors in the image data are identified. In practical application, no violation behaviors may exist in the road section data, and multiple violation behaviors may also exist.

A seventh determining unit configured to identify a stationary obstacle feature in the image data; determining an occupancy length and an occupancy width based on the stationary obstacle features; and calculating to obtain the road section occupation area by using the occupation length and the occupation width.

In practical applications, there may be a plurality of static obstacles occupying the same road segment at the same time, and the road segment occupied area of the road segment is the sum of the road segment occupied areas occupied by each static obstacle.

An eighth determining unit configured to determine image data corresponding to a travelable region; extracting road surface features in image data corresponding to the travelable region; the road surface grade is determined based on the road surface characteristics and a road surface standard acquired in advance.

After the image data is acquired, the image data corresponding to the travelable region is determined in all the image data, and then the road surface features of the travelable region are extracted from the image data corresponding to the travelable region. The pavement characteristics comprise the material of the pavement, the smoothness of the pavement, the integrity of the pavement and the like.

A ninth determining unit that extracts the attribute parameter in the image data in a case where the illumination data exists in the image data; the night lighting level is determined based on the attribute parameters and pre-acquired lighting criteria.

A second determining module 30, configured to determine, based on the real-time value of at least one road segment characteristic of each road segment, a comprehensive value of the road segment characteristics of all the road segments within a preset time range before the current time point.

In the specific implementation, the real-time requirement on the running speed, the weather condition, the motor vehicle density, the non-motor vehicle/pedestrian density, the violation condition and the road segment occupation condition of the specified vehicle is high, so that when the comprehensive value of the road segment characteristics of the road segment is determined, the GPS data and the image data in a smaller preset time range (for example, one hour) before the current time point can be selected to determine the comprehensive value of the road segment characteristics of the road segment.

Determining a composite value of the road section characteristics of the road section based on real-time values of a running speed, a number of lanes, weather conditions, motor vehicle density, non-motor vehicle/pedestrian density, violation conditions, road section occupancy, road surface conditions, and night lighting conditions of a specified vehicle of the road section. Specifically, the mean square error calculation can be performed on the real-time values of the running speed, the number of lanes, the weather condition grade, the motor vehicle density, the non-motor vehicle/pedestrian density, the violation grade, the road section occupation area, the road surface grade and the night illumination grade, and the obtained values are used as the comprehensive values of the road section characteristics of the road section; of course, other calculations may be made, and this disclosure is not limited thereto.

And a third determining module 40, configured to determine, based on the start point position and the end point position, a current path according to the integrated value of at least one of the road segment characteristics and a basic characteristic value based on the road segment.

The third determination module 40 includes a tenth determination unit, an eleventh determination unit, a twelfth determination unit, and a thirteenth determination unit. Specifically, the tenth determining unit is configured to determine a weight value of each road segment characteristic.

An eleventh determining unit, configured to determine at least one alternative path based on the start point position and the end point position, where the alternative path is composed of at least one alternative road segment.

And the twelfth determining unit is used for determining the tendency value of each alternative road section in each alternative path based on the comprehensive value and the weight value of each road section characteristic.

And a thirteenth determining unit, configured to determine the current path among the alternative paths based on the tendency value and the basic feature value.

The method comprises the steps of acquiring GPS data and image data of road sections in real time by using a vehicle, and determining a real-time value of at least one road section characteristic of each road section according to the GPS data and the image data; then, determining the comprehensive values of the road section characteristics of all road sections in a preset time range before the current time point; after the starting point position and the ending point position of the user are obtained, the current path is determined according to the comprehensive value of the at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the ending point position, and the current path is recommended to the user. The road section characteristics of the road section can be acquired more comprehensively based on the GPS data and the image data, so that the planned current path is more accurate, and the travel efficiency of the user is improved.

The third aspect of the present disclosure also provides a storage medium, which is a computer-readable medium storing a computer program, and when the computer program is executed by a processor, the computer program implements the method provided in any embodiment of the present disclosure, including the following steps:

s11, acquiring GPS data and image data of all road sections in real time based on each vehicle;

s12, determining a real-time value of at least one road section characteristic of each road section according to the GPS data and the image data;

s13, determining a comprehensive value of the road section characteristics of all the road sections in a preset time range before the current time point based on the real-time value of at least one road section characteristic of each road section;

and S14, determining the current path according to the integrated value of at least one road section characteristic and the basic characteristic value obtained by real-time calculation based on the starting point position and the end point position.

The computer program is executed by the processor to determine a real-time value of at least one road segment feature for each of the road segments based on the GPS data and the image data, the road segment feature including one or more of a speed of travel of a specified vehicle on the road segment, a number of lanes, a weather condition, a motor vehicle density, a non-motor/pedestrian density, a violation condition, a road segment occupancy condition, a road surface condition, a night lighting condition.

When the computer program is executed by the processor to determine a real-time value of at least one road segment feature for each road segment based on the GPS data and the image data when the road segment feature is a driving speed, the processor specifically executes the following steps: acquiring first GPS data and second GPS data based on the GPS data of the specified vehicle; the first GPS data comprises first time and first position information, and the second GPS data comprises second time and second position information; determining the travel speed based on the first GPS data and the second GPS data.

When the road section feature is the number of lanes, the computer program is executed by the processor to determine a real-time value of at least one road section feature of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: extracting lane line features from the image data; determining a lane line in the same direction based on the lane line characteristics; acquiring the number of the lane lines in the same direction; and determining the number of lanes in the same direction according to the number of lane lines in the same direction.

When the road section feature is a weather condition, the computer program is executed by the processor to determine a real-time value of at least one road section feature of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: acquiring weather condition standards corresponding to different weather types; identifying weather features in the image data; determining a weather condition rating based on the weather condition criteria and the weather feature.

When the road section features are motor vehicle density, the computer program is executed by the processor to determine a real-time value of at least one road section feature of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: identifying a motor vehicle in the image data; calculating the number of motor vehicles in a first preset range; and determining the motor vehicle density based on the area of the first preset range and the number of the motor vehicles.

When the road section features are of non-motor vehicle/pedestrian density, the computer program is executed by the processor to determine a real-time value of at least one road section feature of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: identifying non-motor/pedestrian and motor lane features in the image data; determining the number of non-motor vehicles/pedestrians on the motor vehicle lane within a second preset range based on the non-motor vehicles/pedestrians and the motor vehicle lane characteristics; determining a non-motor vehicle/pedestrian density based on the area of the second preset range and the number of non-motor vehicles/pedestrians.

When the road section features are violation conditions, the computer program is executed by the processor to determine the real-time value of at least one road section feature of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: acquiring violation standards corresponding to different violation behaviors; identifying a violation in the image data; a violation level is determined based on the violation criteria and the violation.

When the road segment features are road segment occupation situations, the computer program is executed by the processor to determine a real-time value of at least one road segment feature of each road segment according to the GPS data and the image data, and the processor specifically executes the following steps: identifying stationary obstacle features in the image data; determining an occupancy length and an occupancy width based on the stationary obstacle feature; and calculating to obtain the road section occupation area by using the occupation length and the occupation width.

When the road section characteristics are road surface conditions, the computer program is executed by the processor to determine the real-time value of at least one road section characteristic of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: determining the image data corresponding to the travelable region; extracting road surface features in the image data corresponding to the travelable region; and determining the grade of the road surface based on the road surface characteristics and the road surface standard acquired in advance.

When the road section features are in the night lighting condition, the computer program is executed by the processor to determine the real-time value of at least one road section feature of each road section according to the GPS data and the image data, and the processor specifically executes the following steps: extracting attribute parameters in the image data in the presence of illumination data in the image data; determining a night illumination level based on the attribute parameters and pre-acquired illumination criteria.

When the computer program is executed by the processor to determine the current path based on the starting point position and the end point position, the integrated value of at least one road section characteristic and the basic characteristic value based on the road section, the processor specifically executes the following steps: determining a weight value of each road section characteristic; determining at least one alternative path based on the starting point position and the end point position, wherein the alternative path is composed of at least one alternative road section; determining a tendency value of each alternative road section in each alternative path based on the comprehensive value and the weight value of each road section feature; determining a current path among the alternative paths based on the tendency values and the base feature values.

The fourth aspect of the present disclosure also provides an electronic device, as shown in fig. 13, the electronic device at least includes a memory 1301 and a processor 1302, a computer program is stored on the memory 1301, and the processor 1302 implements the method provided in any embodiment of the present disclosure when executing the computer program on the memory 1301. Illustratively, the method performed by the electronic device computer program is as follows:

s21, acquiring GPS data and image data of all road sections in real time based on each vehicle;

s22, determining a real-time value of at least one road section characteristic of each road section according to the GPS data and the image data;

s23, determining a comprehensive value of the road section characteristics of all the road sections in a preset time range before the current time point based on the real-time value of at least one road section characteristic of each road section;

and S24, determining the current path according to the integrated value of at least one road section characteristic and the basic characteristic value based on the road section based on the starting point position and the end point position.

The processor, when executing the stored real-time values for at least one road segment characteristic for each of the road segments based on the GPS data and the image data, includes one or more of a speed of travel, a number of lanes on a road, a weather condition, a motor vehicle density, a non-motor/pedestrian density, a violation condition, a road segment occupancy condition, a road surface condition, a night light condition for a given vehicle on the road segment.

When the road section feature is a driving speed, the processor executes the following computer program when the processor determines the real-time value of at least one road section feature of each road section according to the GPS data and the image data, which are stored in the memory: acquiring first GPS data and second GPS data based on the GPS data of the specified vehicle; the first GPS data comprises first time and first position information, and the second GPS data comprises second time and second position information; determining the travel speed based on the first GPS data and the second GPS data.

When the road section feature is the number of lanes, the processor executes the real-time value of at least one road section feature of each road section stored in the memory according to the GPS data and the image data, and further executes the following computer program: extracting lane line features from the image data; determining a lane line in the same direction based on the lane line characteristics; acquiring the number of the lane lines in the same direction; and determining the number of lanes in the same direction according to the number of lane lines in the same direction.

When the road section characteristics are weather conditions, the processor executes the computer program which is stored in the memory and determines the real-time value of at least one road section characteristic of each road section according to the GPS data and the image data, wherein the computer program is used for: acquiring weather condition standards corresponding to different weather types; identifying weather features in the image data; determining a weather condition rating based on the weather condition criteria and the weather feature.

When the road section features are motor vehicle density, the processor executes the computer program which is stored in the memory and determines the real-time value of at least one road section feature of each road section according to the GPS data and the image data, wherein the computer program further executes the following steps: identifying a motor vehicle in the image data; calculating the number of motor vehicles in a first preset range; and determining the motor vehicle density based on the area of the first preset range and the number of the motor vehicles.

When the road section features are of non-motor vehicle/pedestrian density, the processor executes the computer program stored in the memory to determine the real-time value of at least one road section feature of each road section according to the GPS data and the image data, wherein the computer program further executes the following steps: identifying non-motor/pedestrian and motor lane features in the image data; determining the number of non-motor vehicles/pedestrians on the motor vehicle lane within a second preset range based on the non-motor vehicles/pedestrians and the motor vehicle lane characteristics; determining a non-motor vehicle/pedestrian density based on the area of the second preset range and the number of non-motor vehicles/pedestrians.

When the road section characteristics are violation conditions, the processor executes the following computer program when determining the real-time value of at least one road section characteristic of each road section according to the GPS data and the image data, which are stored in the memory: acquiring violation standards corresponding to different violation behaviors; identifying a violation in the image data; a violation level is determined based on the violation criteria and the violation.

When the road section features are road section occupation conditions, the processor determines a real-time value of at least one road section feature of each road section according to the GPS data and the image data, which are stored in the execution memory, and executes the following computer program: identifying stationary obstacle features in the image data; determining an occupancy length and an occupancy width based on the stationary obstacle feature; and calculating to obtain the road section occupation area by utilizing the occupation length and the occupation width.

When the road section feature is a road surface condition, the processor determines a real-time value of at least one road section feature of each road section according to the GPS data and the image data, which are stored in the execution memory, and the processor specifically executes the following steps: determining the image data corresponding to the travelable region; extracting road surface features in the image data corresponding to the travelable region; and determining the grade of the road surface based on the road surface characteristics and the road surface standard acquired in advance.

When the road section characteristics are night lighting conditions, the processor executes the computer program which is stored in the memory and determines the real-time value of at least one road section characteristic of each road section according to the GPS data and the image data, wherein the computer program further executes the following steps: extracting attribute parameters in the image data in the presence of illumination data in the image data; determining a night illumination level based on the attribute parameters and pre-acquired illumination criteria.

The processor, when executing the computer program stored in the memory to determine the current route based on the integrated value of at least one of the road segment features and the base feature value based on the road segment, further executes the following computer program: determining a weight value of each road section characteristic; determining at least one alternative path based on the starting point position and the end point position, wherein the alternative path is composed of at least one alternative road section; determining a tendency value of each alternative road section in each alternative path based on the comprehensive value and the weight value of each road section feature; determining a current path among the alternative paths based on the tendency values and the base feature values.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.

The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.

Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It should be noted that the storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in this disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

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

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

While the present disclosure has been described in detail with reference to the embodiments, the present disclosure is not limited to the specific embodiments, and those skilled in the art can make various modifications and alterations based on the concept of the present disclosure, and the modifications and alterations should fall within the scope of the present disclosure as claimed.

Claims

1. A method for planning a path, comprising:

determining a current path according to a comprehensive value of at least one road section characteristic and a basic characteristic value based on the road section based on a starting point position and an end point position, wherein the basic characteristic value of the road section comprises the grade of the road section, the number of traffic light signs on the road section and the congestion condition of street lamps;

the road section characteristics comprise a violation condition, and the real-time value of the violation condition is determined in the following way:

acquiring violation standards corresponding to different violation behaviors;

identifying a violation in the image data;

2. The planning method of claim 1, wherein the determining of the real-time values of at least one road segment characteristic for each of the road segments based on the GPS data and the image data further comprises one or more of a driving speed, a number of lanes, a weather condition, a vehicle density, a non-vehicle/pedestrian density, a road segment occupancy, a road surface condition, and a night lighting condition for a given vehicle on the road segment.

3. A planning method according to claim 2, characterized in that the road segment characteristics further comprise a travel speed, and the real-time value of the travel speed is determined by:

4. The planning method according to claim 2, wherein the road section feature further includes a number of lanes of traffic, and a real-time value of the number of lanes is determined by:

extracting lane line features from the image data;

acquiring the number of the lane lines in the same direction;

5. A planning method according to claim 2, wherein the road segment characteristics further include weather conditions, and wherein the real-time values of the weather conditions are determined by:

acquiring weather condition standards corresponding to different weather types;

identifying weather features in the image data;

6. The planning method according to claim 2, wherein the road segment characteristics further include a vehicle density, and the real-time value of the vehicle density is determined by:

identifying a motor vehicle in the image data;

calculating the number of motor vehicles in a first preset range;

7. The planning method according to claim 2, wherein the road segment characteristics further include a non-motor/pedestrian density, and the real-time value of the non-motor/pedestrian density is determined by:

identifying non-motor/pedestrian and motor lane features in the image data;

8. The planning method according to claim 2, wherein the road segment characteristics further include a road segment occupancy, and the real-time value of the road segment occupancy is determined by:

identifying stationary obstacle features in the image data;

9. The planning method according to claim 2, wherein the road section characteristics further include road surface conditions, and the real-time values of the road surface conditions are determined by:

determining the image data corresponding to the travelable region;

10. The planning method according to claim 2, wherein the road segment characteristics further include a night lighting condition, and the real-time value of the night lighting condition is determined by:

11. The planning method according to claim 1, wherein the determining a current path based on the starting point position and the ending point position according to the integrated value of at least one feature of the road segment and based on the basic feature value of the road segment includes:

determining a weight value of each road section characteristic;

12. A path planning apparatus, comprising:

the second determination module is used for determining a comprehensive value of the road section characteristics of all the road sections in a preset time range before the current time point based on a real-time value of at least one road section characteristic of each road section, wherein the basic characteristic value of each road section comprises the grade of the road section, the number of traffic light indicators on the road section and the congestion condition of street lamps;

the third determining module is used for determining a current path according to a comprehensive value of at least one road section characteristic and a basic characteristic value based on the road section based on a starting point position and an end point position;

acquiring violation standards corresponding to different violation behaviors;

identifying a violation in the image data;

13. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, is adapted to carry out the steps of the method for planning a path according to any one of claims 1 to 11.

14. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions, when executed by the processor, performing the steps of the method of planning a path according to any one of claims 1 to 11.