A method, apparatus, electronic device, and storage medium for acquiring lane-level traffic conditions.

Abstract

The embodiments of this specification provide a method for obtaining lane-level road conditions, including: obtaining positioning data collected by a high-precision positioning sensor carried by a vehicle, and sorting the track points according to the sampling time and plane coordinates corresponding to each track point contained in the positioning data to generate a vehicle track sequence; based on the center line of each lane in the preset high-precision map data and the positional relationship between each track point in the track sequence, segmenting the track sequence to determine the track subsequences matching each lane; for any lane, calculating the average speed in the lane based on the track subsequence matching the lane, and determining the road condition of the lane by comparing the average speed with the preset speed of the lane. By combining high-precision positioning data with high-precision map data, accurate track points are obtained, the accuracy of lane and track matching is improved, and the acquisition level of road condition information is upgraded from the road level to the lane level, thereby improving user experience and facilitating user travel.

Description

Technical Field

[0001] The present invention relates to the field of traffic technology, and in particular to a method, device, electronic device and storage medium for acquiring lane-level road conditions. Background Art

[0002] At present, map applications such as in-car maps or mobile phone maps can provide users with traffic information to display road conditions on traffic roads, such as congested, slow-moving and smooth traffic conditions, so that users can change routes in time, bypass congested sections, and reach their destination faster.

[0003] When providing traffic information, the above-mentioned applications are usually based on obtaining satellite positioning signals uploaded by the device where the user terminal is located. For example, when it is determined that a certain road section has gathered multiple vehicles based on the vehicle's GPS data, the road section is determined to be a congested road section.

[0004] However, due to the limited accuracy of the data provided by the satellite positioning signal, the road condition information can only be judged for the entire road and cannot be accurate to the lane level. For example, the accuracy of the GPS satellite positioning signal is 10 to 30 meters, which is much larger than the lateral width of a single lane. Obviously, it is impossible to determine the road conditions in each lane in the horizontal direction of the road.

[0005] Therefore, in order to further facilitate users' travel and improve their experience, it is necessary to improve the accuracy of traffic information from the road level to the lane level. Summary of the invention

[0006] In view of this, one or more embodiments of the present specification provide a lane-level road condition acquisition method, device, electronic device and storage medium to solve the problems existing in the related art.

[0007] To achieve the above objectives, one or more embodiments of this specification provide the following technical solutions:

[0008] According to a first aspect of an embodiment of this specification, a method for acquiring lane-level road conditions is provided, including:

[0009] Acquire positioning data collected by a high-precision positioning sensor carried by the vehicle, and sort the trajectory points according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle;

[0010] Based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, the track sequence is segmented to determine a track subsequence matching each lane;

[0011] For any lane, the average vehicle speed in the lane is calculated based on the trajectory subsequence matching the lane, and the road condition of the lane is determined by comparing the average vehicle speed with the preset vehicle speed of the lane.

[0012] According to a second aspect of an embodiment of this specification, a lane-level road condition acquisition device is provided, including:

[0013] A generation module, which obtains positioning data collected by a high-precision positioning sensor carried by a vehicle, and sorts the trajectory points according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle;

[0014] A matching module, based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, segments the track sequence and determines a track subsequence matching each lane;

[0015] The comparison module calculates, for any lane, an average vehicle speed in the lane based on a trajectory subsequence matching the lane, and determines the road condition of the lane by comparing the average vehicle speed with a preset vehicle speed of the lane.

[0016] According to a third aspect of the embodiments of this specification, there is provided an electronic device, including a communication interface, a processor, a memory and a bus, wherein the communication interface, the processor and the memory are interconnected via the bus;

[0017] The memory stores machine-readable instructions, and the processor executes the above method by calling the machine-readable instructions.

[0018] According to a fourth aspect of the embodiments of this specification, a machine-readable storage medium is provided, wherein the machine-readable storage medium stores machine-readable instructions, and the machine-readable instructions implement the above method when called and executed by a processor.

[0019] The technical solutions provided by the embodiments of this specification may have the following beneficial effects:

[0020] Through the above technical solution, the positioning data collected by the high-precision positioning sensor generates the vehicle trajectory sequence, and the trajectory subsequence matching each lane is determined through the high-precision map data, and then the road condition of the lane is determined by comparing the calculated average speed in the lane with the preset speed of the lane. In the above process, by combining high-precision positioning data with high-precision map data, not only can accurate trajectory points be obtained, but also the accuracy of lane and trajectory matching can be improved, thereby raising the acquisition level of road condition information from the road level to the lane level, greatly improving the user experience and facilitating the user's travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 An application scenario diagram of a lane-level road condition acquisition method provided by an exemplary embodiment of this specification;

[0022] Figure 2 A flowchart of a method for acquiring lane-level road conditions provided by an exemplary embodiment of this specification;

[0023] Figure 3 A schematic diagram of a lane-level road condition acquisition method provided by an exemplary embodiment of this specification;

[0024] Figure 4 A schematic diagram of the structure of an electronic device in which a lane-level road condition acquisition device is provided in an exemplary embodiment of this specification;

[0025] Figure 5 A block diagram of a lane-level road condition acquisition device provided by an exemplary embodiment of this specification. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0027] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0028] At present, due to the low accuracy of positioning data used to obtain traffic information, traffic information can only be determined at the road level, and it is impossible to provide users with lane-level traffic conditions to determine whether the current traffic condition of the lane is congested, unobstructed or slow.

[0029] In view of this, the present specification provides a technical solution for determining accurate road conditions by combining high-precision positioning data with high-precision map data.

[0030] During implementation, positioning data collected by a high-precision positioning sensor carried by a vehicle may be obtained, and the trajectory points may be sorted according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle.

[0031] by Figure 1 For example, Figure 1 An application scenario diagram of a lane-level road condition acquisition method provided in an exemplary embodiment of this specification.

[0032] like Figure 1 As shown, the lane-level road condition acquisition method can be applied to Figure 1 The server 110 in the example can establish a network connection with the vehicle terminal 120 to obtain high-precision positioning data collected by a high-precision positioning sensor carried by the vehicle and obtain various trajectory points of the vehicle.

[0033] Then, the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data are sorted to generate a trajectory sequence of the vehicle.

[0034] The terminal 120 may be a vehicle-mounted terminal or a mobile terminal on the vehicle, and the mobile terminal may be at least one of a mobile phone, a tablet computer, a laptop computer, etc. The vehicle is equipped with a positioning device, a display screen, and a client. The server 110 may be an independent server or a server cluster composed of multiple servers.

[0035] Then, based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, the track sequence may be segmented to determine track subsequences matching each lane.

[0036] Continuing with the example, the server can segment the trajectory sequence based on the center line of each lane in the high-precision map data preset in the server and the positional relationship between each trajectory point in the generated trajectory sequence, and determine the trajectory subsequence that matches each lane.

[0037] Afterwards, for any lane, the average vehicle speed in the lane is calculated based on the trajectory subsequence matching the lane, and the road condition of the lane is determined by comparing the average vehicle speed with the preset vehicle speed of the lane.

[0038] exist Figure 1 In the example, the server can calculate the average speed of any lane based on the trajectory subsequence matching the lane, and determine the road condition of the lane by comparing the average speed with the normal speed of the lane preset in the server. Furthermore, the road condition of the lane is sent to the vehicle terminal through the network, so that the vehicle terminal can feed back to the user through the display screen.

[0039] Through the above technical solution, the positioning data collected by the high-precision positioning sensor generates the vehicle trajectory sequence, and the trajectory subsequence matching each lane is determined through the high-precision map data, and then the road condition of the lane is determined by comparing the calculated average speed in the lane with the preset speed of the lane. In the above process, by combining high-precision positioning data with high-precision map data, not only can accurate trajectory points be obtained, but also the accuracy of lane and trajectory matching can be improved, thereby raising the acquisition level of road condition information from the road level to the lane level, greatly improving the user experience and facilitating the user's travel.

[0040] The lane-level road condition acquisition method of this specification is described in detail below with reference to the accompanying drawings.

[0041] See also Figure 2 , Figure 2 A flowchart of a lane-level road condition acquisition method provided for an exemplary embodiment of this specification includes the following steps:

[0042] Step 201, obtaining positioning data collected by a high-precision positioning sensor carried by a vehicle, and sorting the trajectory points according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle;

[0043] Step 202, based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, the track sequence is segmented to determine a track subsequence matching each lane;

[0044] Step 203 : for any lane, the average vehicle speed in the lane is calculated based on the trajectory subsequence matching the lane, and the road condition of the lane is determined by comparing the average vehicle speed with the preset vehicle speed of the lane.

[0045] It is worth noting that since the above-mentioned lane-level road conditions require a large amount of computing power resources when determining them, it is possible to add a method for judging the existence of lane-level road conditions, so that the above-mentioned lane-level road condition acquisition method is only executed when it is judged that lane-level road conditions exist on the road, so as to greatly reduce the timing of lane-level road condition acquisition and save computing power resources.

[0046] In one embodiment shown, for any road section, satellite positioning data of any vehicle passing through the road section can be obtained, and the average speed of the vehicle when passing through the road section can be calculated based on the satellite positioning data; wherein the accuracy of the satellite positioning data is lower than the accuracy of the positioning data collected by the high-precision positioning sensor;

[0047] Counting the average speeds of the vehicles passing through the road section, and determining whether the statistical results conform to the standard normal distribution;

[0048] If it does, it is determined that the road conditions between the lanes in the road section are the same, and the road section does not have lane-level road conditions; if it does not, it is determined that the road conditions between the lanes in the road section are different, and the road section does have lane-level road conditions.

[0049] For example, for any road section, the satellite positioning data of any vehicle passing through the road section may be obtained, and the average speed of the vehicle when passing through the road section may be calculated based on the satellite positioning data.

[0050] Then, the average speeds of the vehicles passing through the road section are counted to determine whether the statistical results conform to the standard normal distribution.

[0051] If it conforms to the standard normal distribution, it means that the average speeds of most vehicles in the road section are similar. It can be determined that the road conditions between lanes in the road section are the same, and there is no lane-level road condition in the road section.

[0052] If it does not conform to the standard normal distribution, it means that the average speeds of the vehicles in the road section vary greatly. It can be determined that there are large differences in road conditions between the lanes in the road section, and the road section has lane-level road conditions.

[0053] It should be noted that since the accuracy of satellite positioning data is lower than that of positioning data collected by high-precision positioning sensors, and the accuracy of satellite positioning data cannot meet the conditions for obtaining lane-level road conditions, in this manual, satellite positioning data can be used to assist in judgment to determine whether lane-level road conditions exist.

[0054] In this embodiment, positioning data collected by a high-precision positioning sensor carried by the vehicle can be obtained, and the trajectory points are sorted according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle.

[0055] Among them, the high-precision positioning sensor carried by the vehicle can perform sampling periodically, and a series of sampling points can be obtained during the vehicle's driving process, which can also be called trajectory points. These trajectory points are included in the positioning data collected by the positioning sensor.

[0056] For example, by obtaining the positioning data collected by the high-precision positioning sensor carried by the vehicle, these trajectory points can be sorted in chronological order and driving direction according to the sampling time and plane coordinates corresponding to each trajectory point collected during the vehicle's driving process contained in the positioning data to generate a vehicle trajectory sequence.

[0057] by Figure 3 For example, Figure 3 A schematic diagram of a lane-level road condition acquisition method provided by an exemplary embodiment of this specification is shown in FIG. Figure 3 As shown, trajectory points P1 to P12 are trajectory points collected by a high-precision positioning sensor during the vehicle's driving process. By sorting these trajectory points, a trajectory sequence formed by the vehicle driving in the road section can be generated.

[0058] In one embodiment shown, in response to the presence of lane-level road conditions in any road section, positioning data collected by high-precision positioning sensors carried by vehicles passing through the road section within a specified time period can be obtained.

[0059] For example, when it is determined that any road section has a lane-level road condition, the historical positioning data collected by the high-precision positioning sensors carried by vehicles passing through the road section in the past hour can be obtained according to the determined time. The specific time period in the past or future is not limited in this specification, and those skilled in the art can determine it by themselves according to the required amount of data and the upper limit of computing power.

[0060] In this embodiment, the trajectory sequence may be segmented based on the positional relationship between the center line of each lane in the preset high-precision map data and each trajectory point in the trajectory sequence to determine the trajectory subsequences matching the lanes.

[0061] Among them, high-precision maps, also known as high-resolution maps, are different from traditional basic map data. High-precision map data has higher accuracy, which can be accurate to the centimeter level. In addition, high-precision map data has a wider data dimension, not only with road details, such as lane lines, lane center lines, lane attribute changes, etc., but also with traffic-related road surrounding facilities information, such as guardrails, traffic lights, obstacles and other auxiliary information.

[0062] For example, the centerline positions of each lane in the preset high-precision map data, which are accurate to the centimeter level, can be compared with the positions of each trajectory point in the trajectory sequence with the same high precision collected by the high-precision positioning sensor. The trajectory sequence can be segmented according to the positional relationship between the points and the lines, and the trajectory subsequences that match each lane can be determined.

[0063] It is worth mentioning that when facing complex road conditions, such as elevated roads or overpasses with height differences in the same traffic area, matching errors may occur due to the lack of height data in the high-precision map data.

[0064] Therefore, in order to improve the matching accuracy, the altitude contained in the positioning data can be matched with the elevation data (i.e., altitude data) contained in the traditional general map data, so as to determine the target road where the vehicle is located among multiple roads with a hierarchical relationship.

[0065] In one embodiment shown, the positioning data collected by the high-precision positioning sensor also includes the altitude corresponding to each trajectory point;

[0066] Furthermore, based on the trajectory sequence of the vehicle and the altitude, the preset basic map data can be matched to determine the target road where the above-mentioned vehicle is located; wherein the basic map data contains the hierarchical relationship between different roads in height; and according to the mapping relationship between the center line of each lane in the high-precision map data and the center line of the road, the center line of each lane included in the target road that matches the center line of the target road is determined.

[0067] For example, in a certain traffic area, there may be tunnels, ground roads and elevated roads at the same time. In order to determine the correct road, the preset basic map data can be matched based on the vehicle's trajectory sequence and altitude, and the target road that matches the vehicle's trajectory sequence among multiple roads can be determined based on the elevation data in the basic map data.

[0068] After determining the target road where the vehicle is located, it is necessary to match the vehicle's trajectory sequence with the high-precision map data corresponding to the target road. During the matching, the center lines of each lane contained in the target road that matches the center line of the target road can be determined based on the mapping relationship between the center lines of each lane in the high-precision map data and the center lines of the road in the basic map data.

[0069] After determining the trajectory subsequences matching each lane, the trajectory sequence can be segmented according to the positional relationship between the trajectory points and the centerline.

[0070] In one embodiment shown, for any trajectory point in the trajectory sequence, the trajectory point can be vertically projected onto the center line of each lane in the high-precision map data, and the vertical projection distance from the plane coordinate corresponding to the trajectory point to the center line of each lane can be calculated;

[0071] Traversing the trajectory points in the trajectory sequence, and determining the lane corresponding to the center line with the smallest distance from the trajectory point as the lane matching the trajectory point;

[0072] Based on the matching results between the trajectory points and the lanes, the trajectory sequence is segmented to determine trajectory subsequences matching the lanes.

[0073] For example, if Figure 3 As shown, for any trajectory point in the trajectory sequence, taking trajectory point P1 as an example, the trajectory point P1 can be vertically projected onto the center lines L1, L2 and L3 of each lane in the high-precision map data, and the vertical projection distances D1, D2 and D3 from the plane coordinates corresponding to the trajectory point P1 to the center lines of each lane can be calculated.

[0074] Assuming that D1 has the smallest value among D1, D2 and D3, it can be seen that the vertical projection distance from the trajectory point P1 to the center line L1 is the shortest. Therefore, the lane with the center line L1 can be determined as the lane where the trajectory point P1 is located.

[0075] Then, by traversing Figure 3 For all the trajectory points in the trajectory sequence, determine the center line with the smallest vertical projection distance to each trajectory point, and then determine the lane matching each trajectory point.

[0076] Then, the trajectory sequence can be segmented based on the matching results of trajectory points and lanes. During segmentation, the segmentation can be performed based on whether the trajectory points continuously match the same lane, such as Figure 3 In the figure, trajectory points P1~P5 and P7 all match the first lane, but because trajectory point P6 matches the second lane, trajectory point P7 is discontinuous. Therefore, it is possible to divide based on trajectory point P6 to obtain a trajectory subsequence consisting of trajectory points P1~P5 that matches the first lane.

[0077] When segmenting the trajectory sequence as described above, segmentation may also be performed based on the number of specified trajectory points.

[0078] In one embodiment shown, the trajectory sequence may be segmented based on a specified number of trajectory points to obtain trajectory subsequences containing the specified number of trajectory points;

[0079] Based on the matching results of each trajectory point in the trajectory subsequence and the lane, determining whether the number of trajectory points matching the same lane reaches a threshold; if so, determining the trajectory subsequence as a trajectory subsequence matching the lane;

[0080] The trajectory subsequences are traversed to obtain trajectory subsequences matching the lanes.

[0081] For example, if Figure 3 As shown in , assuming that the specified number of trajectory points is 6, that is, a trajectory subsequence is divided every 6 trajectory points, then Figure 3 The trajectory sequence in can be divided into a first trajectory subsequence consisting of trajectory points P1 to P6 and a second trajectory subsequence consisting of trajectory points P7 to P12.

[0082] Then, for the first trajectory subsequence, based on the matching results of each trajectory point and lane in the trajectory subsequence, it can be known that trajectory points P1 to P5 match the first lane, and trajectory point P6 matches the second lane, and then it is determined whether the number of trajectory points matching the same lane reaches a threshold.

[0083] Assuming that the threshold value of the number of matching trajectory points is 4, since the number of trajectory points in the first trajectory subsequence that matches the first lane is 5, which is greater than 4, it can be determined that the first trajectory subsequence is a trajectory subsequence that matches the first lane.

[0084] It can be imagined that when the trajectory sequence contains a large number of trajectory points, the same lane may contain multiple trajectory subsequences. Therefore, the set of trajectory subsequences matching each lane can be determined by traversing the divided trajectory subsequences.

[0085] From the above, it can be seen that since high-precision maps also contain wider data dimensions and have information about traffic-related road facilities, such as guardrails, traffic lights, obstacles and other auxiliary information, trajectory points in parking scenes can be eliminated, such as trajectory points in parking scenes such as railway intersections, toll lanes, stop lines, etc., thereby improving the accuracy of lane-level road conditions.

[0086] In one embodiment shown, in response to the presence of multiple trajectory points located in the same area in the trajectory sequence, it can be determined based on the high-precision map data whether the area is an area corresponding to a parking scene; if so, the trajectory points in the area corresponding to the parking scene are deleted from the trajectory sequence.

[0087] For example, when there are multiple trajectory points located in the same area in the trajectory sequence, for example, the coordinates of 13 out of 15 trajectory points are located in the same area, and the distances between these trajectory points are not much different, it can be considered that the vehicle did not stop due to road conditions, and high-precision map data is needed to determine whether the area corresponds to the parking scene; if so, these trajectory points in the area corresponding to the parking scene need to be deleted from the trajectory sequence to eliminate interference items and improve the accuracy of lane-level road conditions.

[0088] After determining the trajectory subsequences matching each lane, the road conditions of each lane can be judged next.

[0089] In this embodiment, for any lane, the average vehicle speed in the lane can be calculated based on the trajectory subsequence matching the lane, and the road condition of the lane can be determined by comparing the average vehicle speed with the preset vehicle speed of the lane.

[0090] For example, suppose Figure 3 The trajectory subsequence that matches the first lane in contains trajectory points P1 to P5. Based on the coordinates and sampling times corresponding to the starting point P1 and the ending point P5 of this trajectory subsequence, the average speed of the vehicle in the lane corresponding to this trajectory subsequence can be calculated.

[0091] Furthermore, by comparing the calculated average vehicle speed with the preset normal speed of the lane, for example, if the average vehicle speed is within the range of 30% to 80% of the normal speed, the traffic condition of the lane is considered to be slow, and if the average vehicle speed is lower than 30% of the normal speed, the traffic condition of the lane is considered to be congested.

[0092] In one embodiment shown, for any trajectory subsequence matching the lane, the average speed of the vehicle to which the trajectory subsequence belongs traveling in the lane can be calculated according to the sampling time and plane coordinates corresponding to the starting trajectory point and the ending trajectory point of the trajectory subsequence respectively;

[0093] The average vehicle speed in the lane is calculated according to the average speeds respectively corresponding to all trajectory subsequences matching the lane.

[0094] For example, when there are multiple trajectory subsequences matching a lane, the average speed of the vehicle to which each trajectory subsequence belongs in the lane can be calculated respectively. When calculating, the sampling time and plane coordinates corresponding to the starting trajectory point and the ending trajectory point of the trajectory subsequence can be used for calculation.

[0095] After obtaining the average vehicle speeds corresponding to all trajectory subsequences matching the lane, another mean calculation is performed to obtain the average vehicle speed in the lane.

[0096] It is worth noting that in order to improve the accuracy of the lane road condition, the lane can be divided into multiple lane sections, the road condition can be determined for each lane section, and then the road conditions of these lane sections can be summarized as the road condition of the entire lane.

[0097] For example, if the lane length is 1km, and if there is a 500m free-flowing section and a 500m slow-moving section in this 1km length, then when calculating the average speed over the 1km span, the calculated average speed will be lower than the average speed of the 500m slow-moving section, which will lead to inaccurate judgment of the road conditions.

[0098] Therefore, in order to obtain lane-level vehicle conditions at a finer granularity, the lane can be divided into multiple fine-grained lane sections, for example, the span of a lane section can be 50m.

[0099] In one embodiment shown, for any lane, the lane may be divided based on a specified length to obtain a plurality of lane sections;

[0100] For any lane section divided, determine the time difference of the vehicle passing through the lane section according to the trajectory subsequence matching the lane, and calculate the average speed of the vehicle in the lane section according to the time difference and the specified length;

[0101] Calculating the average speed of the lane section according to the average speed of vehicles passing through the lane section within a specified time period;

[0102] Furthermore, the road condition of the lane section may be determined by comparing the average vehicle speed with a preset vehicle speed of the lane section;

[0103] The road condition of the lane is determined by combining the road conditions of the lane sections.

[0104] For example, any lane can be divided based on a specified length to obtain multiple lane sections. For example, the 1km lane mentioned above can be divided based on a unit section length of 50m, so that the 1km lane can be divided into 20 lane sections.

[0105] Then, for any lane section of the 20 lane sections, the time difference of the vehicle passing through the lane section can be determined according to the trajectory subsequence matching the lane. Then, based on the time difference and the section length of 50m, the average speed of the vehicle in the lane section can be obtained.

[0106] Afterwards, the average speed of the vehicles passing through the lane section in a specified time period, such as the average speed of all vehicles passing through the lane section in the past hour, can be further calculated to obtain the average speed of the lane section.

[0107] Then, the road condition of the lane section can be determined by comparing the average vehicle speed of the lane section with the preset vehicle speed of the lane section.

[0108] Finally, the road condition of the lane can be determined by combining the road conditions of the lane sections.

[0109] For example, when combining, the road conditions of each lane section can be output to the display screen in a segmented manner according to the position of the lane section.

[0110] In one embodiment shown, the average vehicle speed may be compared with a plurality of speed intervals divided based on different proportions of the preset vehicle speed of the lane to determine a target speed interval corresponding to the average vehicle speed; wherein different speed intervals correspond to different road conditions;

[0111] The road condition of the lane is determined according to the determined target speed range.

[0112] For example, assuming that the preset normal speed of the lane is 60km / h, the speed range can be divided based on this normal speed according to different proportions. For example, speeds below 20km / h are congested, speeds between 20km / h and 40km / h are slow, and speeds above 40km / h are unobstructed.

[0113] Then when the average vehicle speed is 30km / h, by comparing the average vehicle speed with the above-mentioned multiple speed intervals, it can be determined that the target speed interval corresponding to the lane is 20km / h to 40km / h, and the road condition of the lane is slow.

[0114] Through the above technical solution, the positioning data collected by the high-precision positioning sensor generates the vehicle trajectory sequence, and the trajectory subsequence matching each lane is determined through the high-precision map data, and then the road condition of the lane is determined by comparing the calculated average speed in the lane with the preset speed of the lane. In the above process, by combining high-precision positioning data with high-precision map data, not only can accurate trajectory points be obtained, but also the accuracy of lane and trajectory matching can be improved, thereby raising the acquisition level of road condition information from the road level to the lane level, greatly improving the user experience and facilitating the user's travel.

[0115] In an exemplary embodiment of the present specification, a device capable of implementing the above method is also provided.

[0116] Figure 4 is a schematic structural diagram of a device provided by an exemplary embodiment. Figure 4 At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, a memory 408, and a non-volatile memory 410, and may also include hardware required for other services. One or more embodiments of this specification may be implemented based on software, such as the processor 402 reading the corresponding computer program from the non-volatile memory 410 into the memory 408 and then running it. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0117] Please refer to Figure 5 In a software implementation, a lane-level road condition acquisition device 500 is provided, such as Figure 5 As shown, the device 500 includes:

[0118] The generation module 501 obtains positioning data collected by a high-precision positioning sensor carried by a vehicle, and sorts the trajectory points according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle;

[0119] A matching module 502, based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, segments the track sequence to determine a track subsequence matching each lane;

[0120] The comparison module 503 calculates, for any lane, an average vehicle speed in the lane based on the trajectory subsequence matching the lane, and determines the road condition of the lane by comparing the average vehicle speed with a preset vehicle speed of the lane.

[0121] Optionally, the device 500 further includes:

[0122] The judgment module 504 (not shown in the figure) obtains, for any road section, satellite positioning data of any vehicle passing through the road section, and calculates the average speed of the vehicle when passing through the road section based on the satellite positioning data; wherein the accuracy of the satellite positioning data is lower than the accuracy of the positioning data collected by the high-precision positioning sensor;

[0123] Counting the average speeds of the vehicles passing through the road section, and determining whether the statistical results conform to the standard normal distribution;

[0124] If it does, it is determined that the road conditions between the lanes in the road section are the same, and the road section does not have lane-level road conditions; if it does not, it is determined that the road conditions between the lanes in the road section are different, and the road section does have lane-level road conditions.

[0125] Optionally, the generating module 501 further:

[0126] In response to the presence of lane-level road conditions in any road section, positioning data collected by high-precision positioning sensors carried by vehicles passing through the road section within a specified time period are obtained.

[0127] Optionally, the positioning data collected by the high-precision positioning sensor also includes the altitude corresponding to each trajectory point;

[0128] Furthermore, the device 500 further includes:

[0129] A second matching module 505 (not shown in the figure) matches preset basic map data based on the trajectory sequence of the vehicle and the altitude to determine the target road where the vehicle is located; wherein the basic map data includes the hierarchical relationship between different roads in terms of altitude;

[0130] The mapping module 506 (not shown in the figure) determines the center lines of each lane included in the target road that match the center line of the target road based on the mapping relationship between the center lines of each lane in the high-precision map data and the center lines of the road.

[0131] Optionally, the matching module 502 further:

[0132] For any track point in the track sequence, vertically project the track point onto the center line of each lane in the high-precision map data, and calculate the vertical projection distance from the plane coordinate corresponding to the track point to the center line of each lane;

[0133] Traversing the trajectory points in the trajectory sequence, and determining the lane corresponding to the center line with the smallest distance from the trajectory point as the lane matching the trajectory point;

[0134] Based on the matching results between the trajectory points and the lanes, the trajectory sequence is segmented to determine trajectory subsequences matching the lanes.

[0135] Optionally, the matching module 502 further:

[0136] Based on a specified number of trajectory points, segmenting the trajectory sequence to obtain trajectory subsequences containing the specified number of trajectory points;

[0137] Based on the matching results of each trajectory point in the trajectory subsequence and the lane, determining whether the number of trajectory points matching the same lane reaches a threshold; if so, determining the trajectory subsequence as a trajectory subsequence matching the lane;

[0138] The trajectory subsequences are traversed to obtain trajectory subsequences matching the lanes.

[0139] Optionally, the device 500 further includes:

[0140] The deletion module 507 (not shown in the figure) responds to the presence of multiple trajectory points located in the same area in the trajectory sequence, and determines whether the area is an area corresponding to the parking scene based on the high-precision map data; if so, the trajectory points in the area corresponding to the parking scene are deleted from the trajectory sequence.

[0141] Optionally, the comparison module 503 further:

[0142] For any trajectory subsequence matching the lane, according to the sampling time and plane coordinates corresponding to the starting trajectory point and the ending trajectory point of the trajectory subsequence, respectively, the average speed of the vehicle to which the trajectory subsequence belongs traveling in the lane is calculated;

[0143] The average vehicle speed in the lane is calculated according to the average speeds respectively corresponding to all trajectory subsequences matching the lane.

[0144] Optionally, the comparison module 503 further:

[0145] For any lane, the lane is divided based on a specified length to obtain a plurality of lane sections;

[0146] For any lane section divided, determine the time difference of the vehicle passing through the lane section according to the trajectory subsequence matching the lane, and calculate the average speed of the vehicle in the lane section according to the time difference and the specified length;

[0147] Calculating the average speed of the lane section according to the average speed of vehicles passing through the lane section within a specified time period;

[0148] Determining the road condition of the lane section by comparing the average vehicle speed with a preset vehicle speed of the lane section;

[0149] The road condition of the lane is determined by combining the road conditions of the lane sections.

[0150] Optionally, the comparison module 503 further:

[0151] Comparing the average vehicle speed with a plurality of speed intervals divided based on different proportions of the preset vehicle speed of the lane, and determining a target speed interval corresponding to the average vehicle speed; wherein different speed intervals correspond to different road conditions;

[0152] The road condition of the lane is determined according to the determined target speed range.

[0153] The implementation process of the functions and effects of each module in the above-mentioned device 500 is specifically detailed in the implementation process of the corresponding steps in the above-mentioned lane-level road condition acquisition method. For relevant matters, please refer to the partial description of the method implementation method, which will not be repeated here.

[0154] The device implementation described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the units or modules may be selected according to actual needs to achieve the purpose of the scheme of this specification. Ordinary technicians in this field can understand and implement it without paying creative work.

[0155] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, a game console, a tablet computer, a wearable device or a combination of any of these devices.

[0156] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0157] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0158] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0159] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0160] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0161] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0162] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0163] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. A method for obtaining lane-level road conditions, comprising: For any road section, obtaining satellite positioning data of any vehicle passing through the road section, and calculating the average speed of the vehicle when passing through the road section based on the satellite positioning data; wherein the accuracy of the satellite positioning data is lower than the accuracy of the positioning data collected by the high-precision positioning sensor carried by the vehicle; Counting the average speeds of the vehicles passing through the road section, and determining whether the statistical results conform to the standard normal distribution; If the condition is met, it is determined that the road conditions between the lanes in the road section are the same, and the road section does not have a lane-level road condition; if the condition is not met, it is determined that the road conditions between the lanes in the road section are different, and the road section has a lane-level road condition; Acquire the positioning data collected by the high-precision positioning sensor, and sort the trajectory points according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle; Based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, the track sequence is segmented to determine a track subsequence matching each lane; For any lane, the average vehicle speed in the lane is calculated based on the trajectory subsequence matching the lane, and the road condition of the lane is determined by comparing the average vehicle speed with the preset vehicle speed of the lane.

2. According to the method of claim 1, the step of obtaining the positioning data collected by the high-precision positioning sensor comprises: In response to the presence of lane-level road conditions in any road section, positioning data collected by the high-precision positioning sensor passing through the road section within a specified time period is obtained.

3. According to the method of claim 1, the positioning data collected by the high-precision positioning sensor also includes the altitude corresponding to each trajectory point; The method further comprises: Based on the trajectory sequence of the vehicle and the altitude, matching the preset basic map data, determining the target road where the vehicle is located; wherein the basic map data includes the hierarchical relationship between different roads in terms of altitude; According to the mapping relationship between the center line of each lane and the center line of the road in the high-precision map data, the center line of each lane included in the target road that matches the center line of the target road is determined.

4. The method according to claim 1, wherein the segmenting of the trajectory sequence based on the positional relationship between the center line of each lane in the preset high-precision map data and each trajectory point in the trajectory sequence to determine the trajectory subsequences matching each lane comprises: For any track point in the track sequence, vertically project the track point onto the center line of each lane in the high-precision map data, and calculate the vertical projection distance from the plane coordinate corresponding to the track point to the center line of each lane; Traversing the trajectory points in the trajectory sequence, and determining the lane corresponding to the center line with the smallest distance from the trajectory point as the lane matching the trajectory point; Based on the matching results between the trajectory points and the lanes, the trajectory sequence is segmented to determine trajectory subsequences matching the lanes.

5. The method according to claim 4, wherein the segmenting of the trajectory sequence based on the matching results of the trajectory points and the lanes to determine the trajectory subsequences matching the lanes comprises: Based on a specified number of trajectory points, segmenting the trajectory sequence to obtain trajectory subsequences containing the specified number of trajectory points; Based on the matching results of each trajectory point in the trajectory subsequence and the lane, determining whether the number of trajectory points matching the same lane reaches a threshold; If yes, determining the trajectory subsequence as a trajectory subsequence matching the lane; The trajectory subsequences are traversed to obtain trajectory subsequences matching the lanes.

6. The method according to claim 1, further comprising: In response to the presence of a plurality of trajectory points located in the same area in the trajectory sequence, determining whether the area is an area corresponding to a parking scene based on the high-precision map data; If yes, the track points in the area corresponding to the parking scene are deleted from the track sequence.

7. The method according to claim 1, wherein the calculating the average vehicle speed in the lane based on the trajectory subsequence matching the lane comprises: For any trajectory subsequence matching the lane, according to the sampling time and plane coordinates corresponding to the starting trajectory point and the ending trajectory point of the trajectory subsequence, respectively, the average speed of the vehicle to which the trajectory subsequence belongs traveling in the lane is calculated; The average vehicle speed in the lane is calculated according to the average speeds respectively corresponding to all trajectory subsequences matching the lane.

8. The method according to claim 1, wherein for any lane, calculating the average vehicle speed in the lane based on the trajectory subsequence matching the lane comprises: For any lane, the lane is divided based on a specified length to obtain a plurality of lane sections; For any lane section divided, determine the time difference of the vehicle passing through the lane section according to the trajectory subsequence matching the lane, and calculate the average speed of the vehicle in the lane section according to the time difference and the specified length; Calculating the average speed of the lane section according to the average speed of vehicles passing through the lane section within a specified time period; The determining the road condition of the lane by comparing the average vehicle speed with the preset vehicle speed of the lane includes: Determining the road condition of the lane section by comparing the average vehicle speed with a preset vehicle speed of the lane section; The road condition of the lane is determined by combining the road conditions of the lane sections.

9. The method according to claim 1, wherein determining the road condition of the lane by comparing the average vehicle speed with the preset vehicle speed of the lane comprises: Comparing the average vehicle speed with a plurality of speed intervals divided based on different proportions of the preset vehicle speed of the lane, and determining a target speed interval corresponding to the average vehicle speed; wherein different speed intervals correspond to different road conditions; The road condition of the lane is determined according to the determined target speed range.

10. A lane-level road condition acquisition device, comprising: a determination module, for any road section, obtaining satellite positioning data of any vehicle passing through the road section, and calculating the average speed of the vehicle when passing through the road section based on the satellite positioning data; wherein the accuracy of the satellite positioning data is lower than the accuracy of the positioning data collected by the high-precision positioning sensor carried by the vehicle; counting the average speeds corresponding to each vehicle passing through the road section, and determining whether the statistical result conforms to the standard normal distribution; if so, determining that the road conditions between the lanes of the road section are the same, and the road section does not have a lane-level road condition; if not, determining that the road conditions between the lanes of the road section are different, and the road section has a lane-level road condition; A generation module, which obtains the positioning data collected by the high-precision positioning sensor, and sorts the trajectory points according to the sampling time and plane coordinates corresponding to each trajectory point contained in the positioning data to generate a trajectory sequence of the vehicle; A matching module, based on the positional relationship between the center line of each lane in the preset high-precision map data and each track point in the track sequence, segments the track sequence and determines a track subsequence matching each lane; The comparison module calculates, for any lane, an average vehicle speed in the lane based on a trajectory subsequence matching the lane, and determines the road condition of the lane by comparing the average vehicle speed with a preset vehicle speed of the lane.

11. An electronic device, comprising: processor; a memory for storing processor-executable instructions; The processor implements the method according to any one of claims 1 to 9 by running the executable instructions.

12. A machine-readable storage medium having machine-readable instructions stored thereon, which implement the steps of the method according to any one of claims 1 to 9 when executed by a processor.

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