Road condition recognition method and device, electronic equipment and readable storage medium

Abstract

The application provides a road condition recognition method and device, electronic equipment and readable storage medium. The method comprises the following steps: receiving at least P data information sent by at least one vehicle, P being a positive integer; determining M vehicles traveling in a target road section in a target time period according to the P data information, M being a positive integer; obtaining speed values of the M vehicles in the target road section in the target time period from the P data information, obtaining N speed values, N being an integer greater than M; determining a travel speed value of the target road section in the target time period according to the N speed values; and determining a road condition of the target road section in the target time period according to the travel speed value. The application can enrich the collected data information, thereby improving the reliability of road condition recognition.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of traffic, in particular to a road condition recognition method and device, electronic equipment and readable storage medium. BACKGROUND

[0002] With the progress of the times and the improvement of the quality of life, cars gradually enter ordinary families. With more and more vehicles and more and more complex road traffic, it is particularly important for traffic authorities and drivers to understand and update road conditions in a timely manner. At present, road conditions are generally recognized by setting cameras on roads to collect vehicle data, but the collection range of the cameras is limited, which can easily cause low reliability of road condition recognition. SUMMARY

[0003] Embodiments of the present application provide a road condition recognition method, device, electronic equipment and readable storage medium to solve the problem of low reliability of road condition recognition caused by collecting vehicle data by setting cameras on roads.

[0004] To solve the above problems, the present application is implemented as follows:

[0005] In a first aspect, embodiments of the present application provide a road condition recognition method, comprising:

[0006] receiving at least P data information sent by at least one vehicle, P being a positive integer;

[0007] determining M vehicles traveling in a target road section in a target time period according to the P data information, M being a positive integer;

[0008] obtaining speed values of the M vehicles in the target road section in the target time period from the P data information, to obtain N speed values, N being an integer greater than M;

[0009] determining a travel speed value of the target road section in the target time period according to the N speed values;

[0010] determining a road condition of the target road section in the target time period according to the travel speed value.

[0011] In a second aspect, embodiments of the present application also provide a road condition recognition device, comprising:

[0012] a transceiver configured to receive at least P data information sent by at least one vehicle, P being a positive integer;

[0013] a processor configured to:

[0014] determine M vehicles traveling in a target road section in a target time period according to the P data information, M being a positive integer;

[0015] obtaining speed values of the M vehicles in the target road section in the target time period from the P data information, to obtain N speed values, N being an integer greater than M;

[0016] determining a travel speed value of the target road section in the target time period according to the N speed values;

[0017] determining a road condition of the target road section in the target time period according to the travel speed value.

[0018] In a third aspect, an electronic device is provided, which comprises a transceiver, a memory, a processor, and a program stored in the memory and capable of running on the processor; the processor is configured to read the program in the memory to implement the steps in the method according to the first aspect.

[0019] In a fourth aspect, a readable storage medium is provided, which is used to store a program; when the program is executed by a processor, the steps in the method according to the first aspect are implemented.

[0020] In the embodiments of the present application, the data information is collected by vehicles, and the electronic device determines the vehicles traveling in each road section in a target time period based on the data information sent by the vehicles, and further determines the road conditions of each road section in the target time period. Since the vehicles are mobile, the collected data information can be enriched, and the reliability of the road condition recognition can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is one of the flowcharts of the road condition recognition method provided by the embodiments of the present application;

[0023] Figure 2 is another flowchart of the road condition recognition method provided by the embodiments of the present application;

[0024] Figure 3 is a flowchart of the obstacle recognition provided by the embodiments of the present application;

[0025] Figure 4 is a data acquisition schematic diagram provided by the embodiments of the present application;

[0026] Figure 5This is a schematic diagram of the road condition recognition device provided in this application;

[0027] Figure 6 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The terms "first," "second," etc., used in the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices. Additionally, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: including A alone, B alone, C alone, and the presence of both A and B, both B and C, both A and C, and the presence of A, B, and C.

[0030] The following describes the road condition recognition method provided in the embodiments of this application.

[0031] The road condition recognition method of this application can be applied to electronic devices. In practical applications, the electronic device can be a server or a traffic management platform, etc.

[0032] like Figure 1 As shown, the road condition recognition method may include the following steps:

[0033] Step 101: Receive at least P data messages sent by at least one vehicle, where P is a positive integer.

[0034] Each of the at least one vehicle can transmit one or more data messages. The data messages transmitted by the vehicles may include: a first array and a second array.

[0035] The first array may include at least:

[0036] The time field indicates the time when the data was collected.

[0037] The vehicle information field is used to indicate relevant information about the vehicle.

[0038] The vehicle information field may include:

[0039] The first identifier (id) field is used to indicate the vehicle's identifier;

[0040] The first location field is used to indicate the vehicle's location information;

[0041] The first speed field indicates the vehicle's speed value.

[0042] Furthermore, the vehicle information field may also include at least one of the following:

[0043] The heading field indicates the direction of travel of the vehicle.

[0044] The license plate field is used to indicate the vehicle's license plate number;

[0045] The autodrive domain is used to indicate whether the vehicle is an autonomous vehicle;

[0046] The motortype field indicates the vehicle's driving mode.

[0047] The second array may include subarrays that correspond one-to-one with objects scanned by the vehicle. The objects scanned by the vehicle may include at least one of vehicles and stationary objects (such as obstacles). Each object's subarray may include at least: an object information field, used to indicate relevant information about the object.

[0048] The object information field may include:

[0049] The second identifier (id) field is used to indicate the object's identifier;

[0050] The second location field is used to indicate the location information of the object;

[0051] The second speed field indicates the speed value of the object.

[0052] Furthermore, the object information field may also include at least one of the following:

[0053] The type field indicates the type of the object;

[0054] The relative orientation field is used to indicate the direction of travel of the object relative to the vehicle.

[0055] The size field indicates the dimensions of the object.

[0056] The lane field indicates the lane number where the object is located.

[0057] Furthermore, the location field may include: a longitude field, used to indicate longitude location; and a latitude field, used to indicate latitude location. The size field may include: a width field, used to indicate width; and a length field, used to indicate length.

[0058] It should be noted that in practical applications, if the vehicle collects information corresponding to a certain field, the corresponding information can be filled into that field; if no information corresponding to a certain field is collected, the field can be left empty.

[0059] The at least one vehicle may include at least one of the following vehicle types: manually driven vehicle; autonomous vehicle. The vehicle can scan the object's information using holographic scanning or front-end scanning.

[0060] Step 102: Based on the P data information, determine the M vehicles traveling in the target road segment within the target time period, where M is a positive integer.

[0061] The duration of the target time period can be preset, such as 5 minutes or 15 minutes. The start time of the target time period can be set according to requirements, and this application embodiment does not limit this.

[0062] In specific implementation, the electronic device can match the location information in the P data pieces with the location information of the target road segment, and match the collection time in the P data pieces with the target time period to determine the vehicles traveling on each road segment within the target time period. The target road segment can be any road segment within the road network. It should be understood that the M vehicles can include at least one of the following: the vehicle sending the data information; and the vehicles scanned by the vehicle sending the data information.

[0063] Step 103: Obtain the speed values ​​of the M vehicles within the target time period in the target road segment from the P data information to obtain N speed values, where N is an integer greater than M.

[0064] As can be seen from the foregoing, the data information includes a speed domain. Therefore, after determining the M vehicles, the electronic device can extract the speed values ​​of the M vehicles within the target time period and on the target road segment from the P data information to obtain the N speed values.

[0065] Step 104: Determine the travel speed value of the target road segment within the target time period based on the N speed values.

[0066] In a specific implementation, in one approach, the electronic device can determine the average value of the N speed values ​​as the travel speed value of the target road segment within the target time period; in another approach, the electronic device can select a portion of the speed values ​​from the N speed values ​​according to certain conditions, and then determine the travel speed value of the target road segment within the target time period based on the selected portion of speed values. The specific implementation can be determined according to the actual situation, and this application embodiment does not limit this.

[0067] Step 105: Determine the road conditions of the target road segment within the target time period based on the travel speed value.

[0068] Optionally, traffic conditions can be characterized by the degree of congestion. In this case, the following congestion levels can be preset: smooth, mostly smooth, lightly congested, moderately congested, and severely congested. Of course, in other embodiments, traffic conditions can also be characterized by other parameters, which can be set according to actual needs, and this application embodiment does not limit this.

[0069] In one implementation, the electronic device can pre-set a correspondence between travel speed ranges and congestion levels. In this implementation, after determining the travel speed value, the electronic device can first determine the travel speed range to which the travel speed value belongs, and then determine the congestion level corresponding to the travel speed range as the congestion level of the target road segment within the target time period.

[0070] In another implementation, the electronic device can determine the congestion level of the target road segment within the target time period based on the travel speed value V1 and the free-flow speed V2 of the target road segment. Specifically, the electronic device can pre-set the correspondence between the comparison results of V1 and V2 and the congestion level, as shown in Table 1.

[0071] Table 1. Correspondence between "Comparison Results of V1 and V2" and "Congestion Level"

[0072]

[0073] In this implementation, after determining the travel speed value, the electronic device can first compare V1 and V2, and then, based on the comparison result of V1 and V2, determine the congestion level corresponding to the comparison result as the congestion level of the target road segment in the target time period.

[0074] The road condition recognition method of this application involves collecting data information from moving vehicles. An electronic device determines the vehicles traveling on each road segment within a target time period based on the received data, thereby determining the road conditions on each road segment within the target time period. Because vehicles are mobile, the collected data information is enriched, thus improving the reliability of road condition recognition.

[0075] The implementation of step 102 is explained in detail below.

[0076] Optionally, determining the M vehicles traveling on the target road segment within the target time period based on the P data pieces includes:

[0077] Data cleaning is performed on the P data information to obtain Q valid data information, wherein the valid data information includes relevant information of the first vehicle that sent the valid data information, and relevant information of the object scanned by the first vehicle. The object includes a vehicle or a stationary object. The relevant information includes identification information, location information and speed value, and Q is a positive integer less than or equal to P.

[0078] Determine the first object and the second object among K objects, where K is the number of identification information included in the Q valid data information, the speed value of the first object is less than a preset speed value, and the speed value of the second object is greater than or equal to the preset speed value;

[0079] Based on the location information of the second object, the objects located in the target road segment are identified as M vehicles traveling in the target road segment within the target time period.

[0080] In this optional embodiment, the electronic device can first perform data cleaning on the P data information to remove invalid data information from the P data information, and only use the valid data information for traffic condition analysis, thereby reducing the operating burden of the electronic device.

[0081] Optionally, the data information includes a first array and a second array. The first array includes: a time field for indicating the acquisition time of the data information; a vehicle information field for indicating relevant information of the second vehicle that sent the data information; and the second array includes an object information field for indicating relevant information of the objects scanned by the second vehicle.

[0082] The step of cleaning the P data information to obtain Q valid data information includes:

[0083] If the target data information does not meet the first condition, the target data information will be determined as invalid data information;

[0084] The first condition includes: the time of collection of the target data information is not within the target time period.

[0085] If the collection time of a certain data information does not fall within the target time period, the data information can be directly determined as invalid data information.

[0086] Furthermore, the first condition may also include any of the following: the target field in the second array is missing, the target field being used to indicate at least one of position information and velocity value; or the second array is empty.

[0087] The missing target field in the second array can be manifested as: the target field in the second array is empty, or the second array does not include the target field. An empty second array can be manifested as: the field in the second array is empty, or the second array does not include the field. In this case, the data information can only be determined as valid data information if it satisfies all the conditions in the first condition, thereby reducing the operational burden on the terminal.

[0088] It is understood that if the Q valid data information includes K identification information, the electronic device can locate K objects based on the K identification information. Then, the terminal can compare the speed values ​​of the K objects with a preset speed value to distinguish whether the object is a vehicle or a stationary object. The preset speed value can be pre-set, for example, to 0.2 meters per second.

[0089] For the first object whose speed value is less than the preset speed value, it is very likely to be a stationary object and can be excluded from the road condition statistics; for the second object whose speed value is greater than or equal to the preset speed value, it is very likely to be a moving vehicle and can be included in the road condition statistics.

[0090] After identifying the second object, i.e., the vehicle, the vehicle's location information can be matched with the location information of the target road segment to determine the M vehicles traveling within the target road segment during the target time period. Specifically, if the location information of a vehicle falls within the target road segment, it can be determined that the vehicle is traveling within the target road segment during the target time period; otherwise, it can be determined that it is not a vehicle traveling within the target road segment during the target time period.

[0091] By using the above method, the P data points are first cleaned to retain valid data. Then, vehicles are selected from the K objects. The location information of the vehicles can be matched with the location information of the target road segment to determine the M vehicles traveling in the target road segment within the target time period, thereby further improving the reliability of road condition recognition.

[0092] Optionally, after determining the first object and the second object among the K objects, the method further includes:

[0093] Determine whether the location information of the first object is included in the set of information about stationary objects;

[0094] If the location information of the first object is not included in the set of static object information, the location information of the first object is marked in the set of suspected static object information.

[0095] If the number of times the location information of the first object is marked in the suspected stationary object information set reaches a preset threshold, the first object is identified as a stationary object in the road segment, and the location information of the first object is stored in the stationary object information set.

[0096] In this optional embodiment, the electronic device may pre-store a set of stationary object information and a set of suspected stationary object information. The set of stationary object information stores the location information of identified stationary objects; the set of suspected stationary object information stores the location information of objects identified as suspected stationary objects. For each suspected stationary object, a number of markings is also set to indicate the number of times the object has been marked as a suspected stationary object.

[0097] For each of the first objects identified by the electronic device, the electronic device may first determine whether the location information of the first object is included in the set of stationary object information.

[0098] If so, the first object can be determined to be a stationary object, and it will not be included in the traffic statistics. If not, the electronic device can mark the location information of the first object in the suspected stationary object information set. It should be noted that if the suspected stationary object information set stores the location information of the first object, the number of times the first object is marked can be increased by a preset step size. If the suspected stationary object information set does not store the location information of the first object, the location information of the first object can be stored in the suspected stationary object information set, the number of times the first object is marked can be set to 0, and the number of times the first object is marked can be increased by a preset step size.

[0099] Subsequently, the electronic device can determine whether the number of times the location information of the first object is marked in the suspected stationary object information set has reached a preset threshold. If so, the first object can be identified as a stationary object in the road segment, and the location information of the first object can be stored in the stationary object information set for subsequent stationary object identification.

[0100] The above methods can improve the reliability of static object recognition.

[0101] Optionally, determining the travel speed value of the target road segment within the target time period based on the N speed values ​​includes:

[0102] Calculate the average of the N velocity values;

[0103] The target speed value among the N speed values ​​is added to the dataset, and the standard deviation of the target speed value and the average value is within a preset range;

[0104] The average speed value in the dataset is determined as the travel speed value of the target road segment within the target time period.

[0105] In this optional embodiment, considering the complexity of road conditions, the N speed values ​​included in the road condition calculation for the road segment may not form a standard normal distribution. Therefore, speed values ​​among the N speed values ​​whose standard deviation from the average of the N speed values ​​is within a preset range can be selected. For example, speed values ​​whose average value is within one standard deviation (after conversion to a standard normal distribution) can be selected to determine the travel speed value of the target road segment in the target time period, thereby improving the accuracy of road condition identification.

[0106] It should be noted that the various optional implementation methods described in the embodiments of this application can be combined with each other or implemented individually without conflict, and the embodiments of this application do not limit this.

[0107] For ease of understanding, the following example is provided:

[0108] This application can determine the road conditions of the road the vehicle is traveling on by identifying and judging obstacles based on vehicle-mounted holographic scanning perception data. This application can monitor and calculate the road conditions of the road segment the vehicle is traveling on in real time as the vehicle-mounted device moves.

[0109] like Figure 2 As shown, the road condition recognition method may include the following steps:

[0110] Step 201: Receive vehicle holographic scan data.

[0111] Step 202: Filter vehicle holographic scan data.

[0112] Vehicle holographic scan data can be exemplified as follows:

[0113] First array:

[0114] "time": 1590982713716;

[0115] "id": 1234567890ABCDEF;

[0116] "longitude": 120.1234567;

[0117] "latitude": 31.7654321;

[0118] "speed": 30;

[0119] "heading": 30.197;

[0120] "plate": ”Su E888888“;

[0121] "autodrive": 1;

[0122] "motortype": 10;

[0123] Second array:

[0124] Information related to Object 1:

[0125] "id": 11,

[0126] "location": {"longitude": 120.1234567, "latitude": 31.7654321};

[0127] "type": 1;

[0128] "speed": 30.15;

[0129] "orientation": 30.197;

[0130] "size": {"width": 80, "length": 50};

[0131] "lane": 1;

[0132] Information related to Object 2:

[0133] [[ID=...]] "id": 15,

[0134] "location": {"longitude": 122.9234567, "latitude": 31.0654321};

[0135] "type": 3;

[0136] "speed": 36.65;

[0137] "orientation": 79.126;

[0138] It should be noted that in the original text, the tag [[ID=...]] seems to have some irregularities in numbering (e.g., [[ID=...]] in the middle). Please check and correct it if necessary for a more accurate translation. Also, some text like "苏E888888" is directly transliterated here. If there is a specific English equivalent for license plate in a particular context, it may need to be adjusted accordingly."size": {"width": 80, "length": 50};

[0139] "lane": 2.

[0140] In practice, invalid data such as (1) empty arrays, (2) abnormal timestamps, and (3) missing important fields in the array (latitude and longitude coordinates, speed values, etc.) can be cleaned.

[0141] Step 203: Identify whether it is a vehicle.

[0142] Judging stationary objects: Based on experience, when the speed of the target object in the road is less than 0.2m / s, it is considered to be stationary, and then an obstacle is determined.

[0143] Obstacle identification: The coordinates of the object are compared with the set of obstacle coordinates. If the object is in the set, it is considered an obstacle. If the object's coordinates are not in the set of obstacles, it is marked as a suspected obstacle and compared with the set of suspected obstacles.

[0144] If the object does not appear in the suspected obstacle set, it is marked as a suspected obstacle. If the object already appears in the suspected obstacle set, its marking count is incremented by one. When the suspected obstacle's coordinates are marked three times, the object is considered an obstacle within that time period. The obstacle's coordinates are then updated in the obstacle data set for future reference.

[0145] Since the data transmitted each time is uploaded by different moving vehicle-mounted devices, and the vehicle-mounted devices monitor vehicles and objects within the lane range, if three different vehicle-mounted devices all detect that the object at the coordinates is not moving, it can be considered as a stationary obstacle on that road segment within a unit of time period and is not included in the road condition calculation.

[0146] Obstacle identification diagram as follows Figure 3 As shown.

[0147] If the vehicle is identified as not a vehicle, step 204 can be executed; if the vehicle is identified as a vehicle, step 205 can be executed.

[0148] Step 204: Use it as an obstacle marker or for display.

[0149] Step 205: Incorporate it into road condition calculations.

[0150] Step 206: Perform road condition calculations based on the vehicles included in the road condition calculations.

[0151] The following explains road condition recognition:

[0152] 1) Matching of road segment speed sets per unit time:

[0153] 1.1 Match the latitude and longitude coordinates of the target vehicle data included in the road condition calculation with the static latitude and longitude information of the road to identify the road segment in which it is located;

[0154] 1.2 Statistically analyze the speed data for the same road segment within this unit of time (usually 5 minutes). Based on experience, when the amount of data is sufficient, the statistical data should basically follow a normal distribution.

[0155] 2) Speed ​​values ​​for road sections:

[0156] 2.1 Considering the complexity of road conditions, the amount of data in the dataset {X1} included in the road condition calculation for this period may not conform to a standard normal distribution. To avoid numerous extreme values ​​due to insufficient data collection and statistics, such as... Figure 4 As shown, values ​​within one standard deviation (converted to a standard normal distribution), i.e., about 68% of the dataset {X2}, that are deviating from the mean by one standard deviation, can be used as input for the average velocity calculation.

[0157] 2.2 Calculation of average travel speed

[0158] Average the values ​​within the velocity set {X2}:

[0159] x∈{X2}

[0160] Given this unit of time, the representative speed v of this road segment is obtained.

[0161] 3) Road condition output

[0162] You can use v to refer to Table 1 above to get the corresponding road conditions.

[0163] In this embodiment, obstacles in the road can be dynamically sensed by the vehicle-mounted sensing device, and the obstacles can be judged and distinguished to construct a holographic view of traffic. The travel speed that reflects the driving conditions of most vehicles on the road segment can be calculated by combining the vehicle-mounted sensing device with statistical laws and engineering experience. The real road conditions of the time period and the road segment can be obtained with low computational resources and low model training overhead.

[0164] Compared to existing technologies, this application does not require extensive model training and parameter tuning. The model training cost of this proposal is lower, the model parameters are more transparent, and the engineering operability is stronger. Based on obstacle recognition, it combines richer road traffic-related information to construct a holographic scanning overview of traffic and identify the overall traffic conditions. The data source is different. The vehicle-side data source is usually driving vehicles, which is different from the traditional traffic vehicle collection method. This data source has the effect of "traffic probe" and does not require all vehicles on the road to build a road condition recognition model for that section.

[0165] See Figure 5 , Figure 5 This is a structural diagram of the road condition recognition device provided in an embodiment of this application. Figure 5 As shown, the road condition recognition device 500 includes:

[0166] Transceiver 501 is used to: receive at least P data messages transmitted by at least one vehicle, where P is a positive integer;

[0167] Processor 502 is used for:

[0168] Based on the P data points, determine the M vehicles traveling on the target road segment within the target time period, where M is a positive integer;

[0169] From the P data information, obtain the speed values ​​of the M vehicles in the target road segment within the target time period, and obtain N speed values, where N is an integer greater than M;

[0170] Based on the N speed values, determine the travel speed value of the target road segment within the target time period;

[0171] Based on the travel speed value, the road conditions of the target road segment within the target time period are determined.

[0172] Optionally, the processor 502 is configured to:

[0173] Data cleaning is performed on the P data information to obtain Q valid data information, wherein the valid data information includes relevant information of the first vehicle that sent the valid data information, and relevant information of the object scanned by the first vehicle. The object includes a vehicle or a stationary object. The relevant information includes identification information, location information and speed value, and Q is a positive integer less than or equal to P.

[0174] Determine the first object and the second object among K objects, where K is the number of identification information included in the Q valid data information, the speed value of the first object is less than a preset speed value, and the speed value of the second object is greater than or equal to the preset speed value;

[0175] Based on the location information of the second object, the objects located in the target road segment are identified as M vehicles traveling in the target road segment within the target time period.

[0176] Optionally, the processor 502 is further configured to:

[0177] Determine whether the location information of the first object is included in the set of information about stationary objects;

[0178] If the location information of the first object is not included in the set of static object information, the location information of the first object is marked in the set of suspected static object information.

[0179] If the number of times the location information of the first object is marked in the suspected stationary object information set reaches a preset threshold, the first object is identified as a stationary object in the road segment, and the location information of the first object is stored in the stationary object information set.

[0180] Optionally, the data information includes a first array and a second array. The first array includes: a time field for indicating the acquisition time of the data information; a vehicle information field for indicating relevant information of the second vehicle that sent the data information; and the second array includes an object information field for indicating relevant information of the objects scanned by the second vehicle.

[0181] The processor is used for:

[0182] If the target data information meets the first condition, the target data information is determined to be invalid data information;

[0183] The first condition includes the time information not being within the target time period.

[0184] Optionally, the processor 502 is configured to:

[0185] Calculate the average of the N velocity values;

[0186] The target speed value among the N speed values ​​is added to the dataset, and the standard deviation of the target speed value and the average value is within a preset range;

[0187] The average speed value in the dataset is determined as the travel speed value of the target road segment within the target time period.

[0188] The road condition recognition device 500 can achieve the functions described in the embodiments of this application. Figure 1 The various processes in the method embodiments, and the ways to achieve the same beneficial effects, will not be repeated here to avoid repetition.

[0189] This application also provides an electronic device. Please refer to [link to relevant documentation]. Figure 6 The electronic device may include a processor 601, a memory 602, and a program 6021 stored in the memory 602 and executable on the processor 601. When the program 6021 is executed by the processor 601, it can achieve... Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0190] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium. This application also provides a readable storage medium storing a computer program, which, when executed by a processor, can implement the above-described methods. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0191] The storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0192] The above description represents the preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles described in this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A road condition recognition method, characterized in that, include: Receive at least P data messages sent by at least one vehicle, where P is a positive integer; Based on the P data points, determine the M vehicles traveling on the target road segment within the target time period, where M is a positive integer; From the P data information, obtain the speed values ​​of the M vehicles in the target road segment within the target time period, and obtain N speed values, where N is an integer greater than M; Based on the N speed values, determine the travel speed value of the target road segment within the target time period; Based on the travel speed value and the free-flow speed of the target road segment, the road conditions of the target road segment within the target time period are determined; The step of determining M vehicles traveling on a target road segment within a target time period based on the P data points includes: Data cleaning is performed on the P data information to obtain Q valid data information, wherein the valid data information includes relevant information of the first vehicle that sent the valid data information, and relevant information of the object scanned by the first vehicle. The object includes a vehicle or a stationary object. The relevant information includes identification information, location information and speed value, and Q is a positive integer less than or equal to P. Determine the first object and the second object among K objects, where K is the number of identification information included in the Q valid data information, the speed value of the first object is less than a preset speed value, and the speed value of the second object is greater than or equal to the preset speed value; Based on the location information of the second object, the objects located in the target road segment of the second object are identified as M vehicles traveling in the target road segment within the target time period; After determining the first object and the second object among the K objects, the method further includes: Determine whether the location information of the first object is included in the set of information about stationary objects; If the location information of the first object is not included in the set of static object information, the location information of the first object is marked in the set of suspected static object information. If the number of times the location information of the first object is marked in the suspected stationary object information set reaches a preset threshold, the first object is identified as a stationary object in the road segment, and the location information of the first object is stored in the stationary object information set.

2. The method according to claim 1, characterized in that, The data information includes a first array and a second array. The first array includes: a time field, used to indicate the time of data collection; and a vehicle information field, used to indicate relevant information about the second vehicle that sent the data information. The second array includes an object information field, used to indicate relevant information about the objects scanned by the second vehicle. The step of cleaning the P data information to obtain Q valid data information includes: If the target data information meets the first condition, the target data information is determined to be invalid data information; The first condition includes the time information not being within the target time period.

3. The method according to claim 1, characterized in that, Determining the travel speed value of the target road segment within the target time period based on the N speed values ​​includes: Calculate the average of the N velocity values; The target speed value among the N speed values ​​is added to the dataset, and the standard deviation of the target speed value and the average value is within a preset range; The average speed value in the dataset is determined as the travel speed value of the target road segment within the target time period.

4. A road condition recognition device, characterized in that, include: A transceiver is used to receive at least P data messages transmitted by at least one vehicle, where P is a positive integer. Processor, used for: Based on the P data points, determine the M vehicles traveling on the target road segment within the target time period, where M is a positive integer; From the P data information, obtain the speed values ​​of the M vehicles in the target road segment within the target time period, and obtain N speed values, where N is an integer greater than M; Based on the N speed values, determine the travel speed value of the target road segment within the target time period; Based on the travel speed value and the free-flow speed of the target road segment, the road conditions of the target road segment within the target time period are determined; The processor is used for: Data cleaning is performed on the P data information to obtain Q valid data information, wherein the valid data information includes relevant information of the first vehicle that sent the valid data information, and relevant information of the object scanned by the first vehicle. The object includes a vehicle or a stationary object. The relevant information includes identification information, location information and speed value, and Q is a positive integer less than or equal to P. Determine the first object and the second object among K objects, where K is the number of identification information included in the Q valid data information, the speed value of the first object is less than a preset speed value, and the speed value of the second object is greater than or equal to the preset speed value; Based on the location information of the second object, the objects located in the target road segment of the second object are identified as M vehicles traveling in the target road segment within the target time period; The processor is also used for: Determine whether the location information of the first object is included in the set of information about stationary objects; If the location information of the first object is not included in the set of static object information, the location information of the first object is marked in the set of suspected static object information. If the number of times the location information of the first object is marked in the suspected stationary object information set reaches a preset threshold, the first object is identified as a stationary object in the road segment, and the location information of the first object is stored in the stationary object information set.

5. The apparatus according to claim 4, characterized in that, The data information includes a first array and a second array. The first array includes: a time field, used to indicate the time of data collection; and a vehicle information field, used to indicate relevant information about the second vehicle that sent the data information. The second array includes an object information field, used to indicate relevant information about the objects scanned by the second vehicle. The processor is used for: If the target data information meets the first condition, the target data information is determined to be invalid data information; The first condition includes the time information not being within the target time period.

6. The apparatus according to claim 4, characterized in that, The processor is used for: Calculate the average of the N velocity values; The target speed value among the N speed values ​​is added to the dataset, and the standard deviation of the target speed value and the average value is within a preset range; The average speed value in the dataset is determined as the travel speed value of the target road segment within the target time period.

7. An electronic device, comprising: A transceiver, a memory, a processor, and a program stored in the memory and executable on the processor; characterized in that the processor is configured to read the program in the memory to implement the steps of the road condition recognition method as described in any one of claims 1 to 3.

8. A readable storage medium for storing a program, characterized in that, When the program is executed by the processor, it implements the steps in the road condition recognition method as described in any one of claims 1 to 3.

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