Road condition recognition method, device, equipment, medium and product

Abstract

The invention discloses a road condition recognition method, device and equipment, a medium and a product. The method comprises the steps of determining vehicle position data and vehicle driving data of at least one target vehicle in a target road in response to a road condition recognition request for the target road; determining road network parameters of the target road according to the vehicle position data of each target vehicle; determining lane state parameters of at least one lane in the target road according to the road network parameters and the vehicle driving data; determining road state information of the target road according to the lane state parameters of the lanes; and if the road state information is that the road state is abnormal, determining road abnormal parameters in the target road, and generating a road condition recognition result of the target road according to the road abnormal parameters. According to the scheme of the embodiment, the road condition recognition result of the target road can be generated according to the road abnormal parameters when it is determined that the road state of the target road is abnormal, and the road condition recognition efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a road condition recognition method, device, equipment, medium, and product. Background Technology

[0002] Road congestion is a significant issue in urban management. With the rapid increase in the number of cars in cities, urban road congestion has become increasingly serious. Currently, cities are investing considerable human and material resources in addressing urban road congestion. Effective traffic management requires the rapid and accurate assessment of road conditions and the acquisition of abnormal road parameters that affect these conditions.

[0003] Current methods for assessing road conditions and acquiring road anomaly parameters are mostly based on single-dimensional data or simplified road network models. Road condition assessment often uses the global average speed of a road segment as the core indicator and classifies road conditions into smooth, slow, or congested categories using fixed thresholds. Braking frequency is also used as a supplementary indicator. However, this method relies on a single dimension, making it prone to misjudgments. Acquiring road anomaly parameters depends on camera vision detection or lateral position data from RSUs (Roadside Units). By comparing vehicle lateral offset with lane width, road anomaly parameters are obtained. However, this method lacks integration with features such as road network exits and lane boundaries, resulting in poor adaptability to complex road conditions. Consequently, the acquisition of road anomaly parameters for the target road is inefficient and incomplete, leading to low efficiency and poor accuracy in identifying road conditions.

[0004] Therefore, it is necessary to improve the efficiency and comprehensiveness of acquiring abnormal road parameters in the target road, and to enhance the efficiency and accuracy of road condition identification of the target road. Summary of the Invention

[0005] This invention provides a road condition recognition method, device, equipment, medium, and product to identify road conditions of a target road from multiple dimensions using vehicle data, thereby improving the efficiency and comprehensiveness of acquiring abnormal road parameters in the target road and enhancing the efficiency and accuracy of road condition recognition of the target road.

[0006] According to one aspect of the present invention, a road condition recognition method is provided, comprising:

[0007] In response to a road condition recognition request for a target road, determine the vehicle location data and vehicle driving data of at least one target vehicle on the target road;

[0008] Based on the vehicle location data of each target vehicle, the road network parameters of the target road are determined;

[0009] Based on the road network parameters and the vehicle driving data, determine the lane status parameters of at least one lane in the target road;

[0010] Based on the lane status parameters of each lane, the road status information of the target road is determined;

[0011] If the road status information indicates an abnormal road condition, then the abnormal road parameters in the target road are determined, and a road condition identification result for the target road is generated based on the abnormal road parameters.

[0012] According to another aspect of the present invention, a road condition recognition device is provided, comprising:

[0013] The identification request processing module is used to determine the vehicle location data and vehicle driving data of at least one target vehicle on the target road in response to a road condition identification request for the target road.

[0014] The road network parameter determination module is used to determine the road network parameters of the target road based on the vehicle location data of each target vehicle.

[0015] The state parameter determination module is used to determine the lane state parameters of at least one lane in the target road based on the road network parameters and the vehicle driving data.

[0016] The status information determination module is used to determine the road status information of the target road based on the lane status parameters of each lane.

[0017] The identification result generation module is used to determine the road anomaly parameters in the target road if the road condition information indicates an abnormal road condition, and generate a road condition identification result for the target road based on the road anomaly parameters.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the road condition recognition method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the road condition recognition method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the traffic condition recognition method according to any embodiment of the present invention.

[0023] The technical solution of this invention, in response to a road condition recognition request for a target road, determines the vehicle location data and driving data of at least one target vehicle on the target road; determines the road network parameters of the target road based on the vehicle location data of each target vehicle; determines the lane state parameters of at least one lane on the target road based on the road network parameters and the driving data; determines the road state information of the target road based on the lane state parameters of each lane; if the road state information indicates an abnormal road state, determines the abnormal road parameters in the target road, and generates a road condition recognition result for the target road based on the abnormal road parameters. This embodiment can determine the road state information of the target road based on the vehicle information of the target vehicles, and when the road state information is determined to be abnormal, it generates a road condition recognition result for the target road based on the abnormal road parameters. It can perform multi-dimensional recognition of the road conditions of the target road using vehicle data, improving the efficiency and comprehensiveness of obtaining abnormal road parameters present in the target road, and enhancing the efficiency and accuracy of road condition recognition for the target road.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a road condition recognition method provided in Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of a road condition recognition method provided in Embodiment 2 of the present invention;

[0028] Figure 3 This is a schematic diagram of the structure of a road condition recognition device according to Embodiment 3 of the present invention;

[0029] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the road condition recognition method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises 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 such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a road condition recognition method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where road condition recognition of a target road is inefficient and inaccurate, and where the efficiency and comprehensiveness of obtaining abnormal road parameters are insufficient when road conditions are abnormal. This method can be executed by a road condition recognition device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0034] S110. In response to a road condition recognition request for a target road, determine the vehicle position data and vehicle driving data of at least one target vehicle on the target road.

[0035] The vehicle location data can be the real-time location information of the target vehicle on the target road, specifically including the target vehicle's coordinates, direction of travel, and acceleration. The vehicle driving data can be a set of data information describing the vehicle's body state and vehicle operation during travel, specifically including vehicle speed, throttle opening, braking status, and lane departure frequency.

[0036] This can be achieved by collecting vehicle location and driving data of all vehicles traveling on the target road based on the obtained road condition recognition request for the target road, through RSU roadside units deployed on the target road. Specifically, vehicle location and driving data can be collected through data interaction between the RSU roadside units on the target road and the target vehicles. It should be noted that the target road in this embodiment can be a road area with abnormal traffic conditions.

[0037] S120. Determine the road network parameters of the target road based on the vehicle location data of each target vehicle.

[0038] The road network parameters can be structured and quantified indicators used to describe the road attributes of the target road, specifically including the number of lanes, lane centerlines and boundary lines, speed limits, and merging points. Specifically, the acquired vehicle position data of each target vehicle can be smoothed, for example, by using Kalman filtering to filter and de-jitter the acquired vehicle position information, resulting in processed vehicle position data. Then, the lateral coordinates of each target vehicle in the processed vehicle position data can be clustered to obtain the vehicle clustering results for each target vehicle. Specifically, based on the lateral coordinates of each target vehicle in its position data, the lane in which each target vehicle is located can be identified, and each target vehicle can be assigned to a corresponding vehicle cluster, resulting in vehicle clusters corresponding to each target vehicle. The number of vehicle clusters corresponding to each target vehicle is determined, and based on the number of vehicle clusters, the number of lanes in the target road is determined.

[0039] Furthermore, for any given lane, the lane parameters of the lane containing the vehicle cluster can be determined based on the lateral coordinates of each target vehicle within that lane's vehicle cluster. These lane parameters can include lane coordinates, lane centerline coordinates, and lane boundary coordinates. Based on these lane parameters, the road network parameters of the target road can then be determined.

[0040] S130. Based on road network parameters and vehicle driving data, determine the lane status parameters of at least one lane in the target road.

[0041] Lane status parameters can be indices describing the real-time operating status and lane capacity of a lane, specifically including average lane speed, cross-lane interference coefficient, and lane node speed matching degree. Lane nodes can be the coordinate points at the points where the lane length is divided into equal intervals. For example, if the lane is divided into intervals of 500 meters, then the coordinate points at intervals of 500 meters, 1000 meters, and 1500 meters can be identified as lane nodes. Specifically, based on road network parameters, the lane centerline and lane boundary lines of at least one lane in the target road can be determined, and the vehicle speeds of each target vehicle can be determined based on vehicle driving data. Furthermore, the lane status parameters of each lane in the target road can be determined based on the lane centerline, lane boundary lines, and vehicle speeds.

[0042] S140. Determine the road status information of the target road based on the lane status parameters of each lane.

[0043] The road condition information can describe the traffic status of vehicles on the target road, specifically including smooth traffic, slow traffic, and congestion. Specifically, the lane condition parameters of each lane can be compared with pre-defined threshold ranges for condition parameters to obtain comparison results. Based on these comparison results, the road condition information of the target road is determined. Alternatively, at least one condition parameter value from each lane's lane condition parameters can be compared with its corresponding threshold range to obtain comparison results for each value, thereby determining the road condition information of the target road. By combining the comparison results of multiple condition parameter indicators, a multi-dimensional determination of the road condition information can be achieved, avoiding misjudgments based on a single condition parameter indicator.

[0044] S150. If the road condition information indicates an abnormal road condition, determine the abnormal road parameters in the target road and generate a road condition identification result for the target road based on the abnormal road parameters.

[0045] The road anomaly parameters can be parameters reflecting abnormal states that cause abnormal traffic flow on the target road. Specifically, these can include slow-moving interference coefficients, obstacle avoidance coefficients, merging interference coefficients, and speed limit adaptation coefficients. The slow-moving interference coefficient reflects the degree to which slow-moving vehicles on the target road obstruct traffic flow; the obstacle avoidance coefficient reflects the degree to which vehicles avoid obstacles and thus affect traffic flow; the merging interference coefficient reflects the degree to which merging vehicles entering the target road through merging points affect traffic flow; and the speed limit adaptation coefficient reflects the degree to which speed-limited zones on the target road affect traffic flow.

[0046] Specifically, when the road condition information of the target road is determined to be abnormal, abnormal state parameters affecting the road traffic condition are acquired, and a road condition identification result for the target road is generated based on the acquired abnormal state parameters. For example, the abnormal state parameters can be at least one of a slow-moving vehicle interference coefficient, an obstacle avoidance coefficient, a merging interference coefficient, and a speed limit adaptation coefficient. Based on these abnormal state parameters, a road condition identification result for the target road can be generated. For example, if the abnormal state parameter is a slow-moving vehicle interference coefficient, the road condition identification result for the target road can be road congestion caused by slow-moving vehicles. Alternatively, if the abnormal state parameters are a merging interference coefficient and a speed limit adaptation coefficient, the road condition identification result for the target road can be road congestion caused by traffic merging at intersections and speed limit zones.

[0047] Optionally, if the road anomaly parameter is a slow-moving vehicle interference coefficient, then the road anomaly parameter in the target road is determined, and a road condition recognition result for the target road is generated based on the road anomaly parameter, including: determining at least one slow-moving vehicle in the target road based on the vehicle speed and travel time of each target vehicle in the target road; determining the proportion parameter of slow-moving vehicles in the target road based on the number of each slow-moving vehicle and the total number of each target vehicle; determining the overlap rate between each slow-moving vehicle and the target road based on the vehicle trajectory of each slow-moving vehicle in the target road; and obtaining the throttle parameters of other vehicles in the target road; other vehicles are vehicles in the target road other than each slow-moving vehicle; determining the slow-moving vehicle interference coefficient in the target road based on the slow-moving vehicle proportion parameter, overlap rate, and throttle coefficient of each other vehicle; and generating a road condition recognition result for the target road based on the slow-moving vehicle interference coefficient.

[0048] Specifically, the vehicle speed and travel time of each target vehicle on the target road can be determined, where the travel time can be the duration of the target vehicle's journey on the target road. Then, the vehicle speed and travel time of each target vehicle are compared with pre-set slow-moving vehicle identification conditions for consistency. Based on the consistency comparison result, at least one slow-moving vehicle on the target road is identified. For example, the pre-set slow-moving vehicle identification conditions could be a vehicle speed ≤ 30 km / h and the target vehicle continuously traveling at that speed for ≥ 10 seconds. Therefore, if a target vehicle on the target road is determined to be traveling at 25 km / h for 15 seconds, that target vehicle is identified as a slow-moving vehicle. This process is repeated to identify all slow-moving vehicles on the target road and obtain the number of slow-moving vehicles on the target road. This embodiment does not impose specific limitations on this.

[0049] Furthermore, the vehicle speed and travel time of each slow-moving vehicle can be obtained, and based on these data, the vehicle trajectory of each slow-moving vehicle on the target road can be determined. Then, based on the vehicle trajectory and the length of the target road, the overlap rate between each slow-moving vehicle and the target road can be determined.

[0050] Among them, the vehicle throttle parameter can refer to the throttle opening of the vehicle, specifically the average throttle opening of all non-slow-moving vehicles on the target road.

[0051] The slow-moving interference coefficient in the target road can be determined using the following formula, based on the proportion of slow-moving vehicles, overlap rate, and throttle coefficients of other vehicles:

[0052] ;

[0053] in, This can represent the slow-moving interference coefficient; This can be a parameter representing the proportion of slow-moving vehicles to the total number of vehicles on the target road. This can represent the number of slow-moving vehicles on the target road that meet the criteria for slow-moving vehicle recognition. This can represent the total number of target vehicles on the target road; It can be the overlap rate, which represents the proportion of the vehicle trajectory of each slow-moving vehicle to the length of the target road; To represent the following throttle coefficient, where This can be the throttle coefficient for each other vehicle, representing the average throttle opening of non-slow-moving vehicles on the target road. Among them, To represent the proportion of slow-moving vehicles. Following vehicle throttle coefficient and overlap rate The importance proportion of the slow-moving interference coefficient, and For example, the proportion of slow-moving vehicles. When it is ≥30%, then it can be determined. The throttle coefficient for each of the other vehicles is 0.4. When the following throttle coefficient is ≤20%, If the value is ≥0.33, then it can be determined. The value is 0.3, and the overlap rate between the vehicle trajectory of each slow-moving vehicle and the length of the target road is also considered. When it is ≥80%, then it can be determined. The value is 0.3, but the specific value can be automatically generated based on the actual situation.

[0054] Optionally, the slow-moving interference coefficient in the target road is compared with a pre-set slow-moving interference coefficient threshold to obtain a coefficient comparison result. Based on the coefficient comparison result, a road condition identification result for the target road is generated. For example, if the slow-moving interference coefficient in the target road is 0.7, which is greater than the pre-set slow-moving interference coefficient threshold of 0.65, and the lane in the target road where each slow-moving vehicle is traveling is the same as the lane with the most slow-moving vehicles in the target road, and this lane has no exits, speed limits, or other road features, then the road condition identification result for the target road can be generated as slow-moving vehicles causing road congestion. This embodiment does not impose specific limitations on this.

[0055] Optionally, if the road anomaly parameter is an obstacle avoidance coefficient, then the road anomaly parameter in the target road is determined, and a road condition recognition result for the target road is generated based on the road anomaly parameter, including: determining at least one avoidance vehicle in the target road based on the vehicle steering angle and vehicle braking state of each target vehicle in the target road; determining the avoidance vehicle ratio parameter in the target road based on the number of each avoidance vehicle and the total number of each target vehicle; determining the lateral offset parameter of each avoidance vehicle in the target road, and determining the spatial concentration coefficient of avoidance vehicles performing continuous avoidance operations in the target road; determining the obstacle avoidance coefficient in the target road based on the avoidance vehicle ratio parameter, lateral offset parameter, and spatial concentration coefficient; and generating a road condition recognition result for the target road based on the obstacle avoidance coefficient.

[0056] Specifically, this can be achieved by determining the steering angle and braking status of each target vehicle on the target road. The steering angle refers to the relationship between the steering wheel rotation angle and the actual steering angle of the front wheels, specifically the deflection angle of the front wheels relative to the vehicle's straight-line direction of travel when turning. This can include the maximum steering angle, steering angle response speed, and steering angle linearity. The braking status can be light braking, moderate braking, or heavy braking, and can be determined based on the brake pedal position of each target vehicle. Furthermore, based on the steering angle and braking status of each target vehicle, it is determined whether the target vehicle meets the pre-set avoidance vehicle identification conditions. If so, the target vehicle is identified as an avoidance vehicle, and all avoidance vehicles among the target vehicles are identified. For example, the preset vehicle avoidance identification conditions can be that the target vehicle's steering angle is ≥10° and the brake pedal travel is ≥15mm, that is, the vehicle's braking state is moderate braking. Then, when it is determined that the target vehicle in the target road has a steering angle of 15° and a brake pedal travel of 18mm, that is, the braking state is moderate braking, the target vehicle is identified as an avoidance vehicle. In this way, all avoidance vehicles in the target road are identified, and the number of avoidance vehicles in the target road is obtained. This embodiment does not impose specific limitations on this.

[0057] The lateral offset parameter refers to a quantitative indicator of the target vehicle's deviation from the lane centerline in the lateral direction within the target road lane. It can include the lateral offset direction and intensity, where the lateral offset intensity can be determined based on the lateral offset distance of the avoiding vehicle. The spatial concentration coefficient refers to the degree of clustering of highly similar lateral offset behaviors among target vehicles within the same road region of the target road.

[0058] The obstacle avoidance coefficient in the target road can be determined using the following formula, based on the avoidance vehicle ratio parameter, lateral offset parameter, and spatial concentration coefficient. The specific formula is as follows:

[0059] ;

[0060] in, It can represent the obstacle avoidance coefficient; This can be a parameter representing the proportion of vehicles that need to give way to other vehicles on the target road, out of the total number of vehicles on the target road. It can represent the number of slow-moving vehicles that meet the vehicle avoidance recognition conditions on the target road; This can represent the total number of target vehicles on the target road; It can be a lateral offset parameter, representing the consistency of the lateral offset direction of each target vehicle in the lane of the target road; It can be a lateral offset parameter, representing the intensity of the lateral offset of each target vehicle in the lane of the target road; Let represent the spatial concentration factor, where Xm can be the length of the same road region where highly similar lateral deviation behaviors occur for each target vehicle on the target road. Xm can be a pre-set threshold for judging the length of the same road region where highly similar lateral deviation behaviors occur; this threshold can be pre-set by technicians based on actual needs and experience. To represent the proportion of vehicles that give way. Lateral offset parameters Lateral offset parameters Spatial Concentration Factor The proportion of importance in the obstacle avoidance coefficient, and For example, the proportion of vehicles that need to avoid a collision. When it is ≥50%, then it can be determined. The consistency of the lateral offset direction of each target vehicle in the lane of the target road is 0.3. When it is ≥80%, then it can be determined. The lateral offset intensity of each target vehicle in the lane of the target road is 0.25. When it is ≥60%, then it can be determined. The value is 0.25, and the spatial concentration factor is... When the length of the same road region where each target vehicle exhibits highly similar lateral offset behavior on the target road is greater than a pre-set length threshold, then it can be determined that... The value is 0.2, but the specific value can be automatically generated based on the actual situation.

[0061] Optionally, the obstacle avoidance coefficient in the target road is compared with a pre-set obstacle avoidance coefficient threshold to obtain a coefficient comparison result. Based on the coefficient comparison result, a road condition identification result for the target road is generated. For example, if the obstacle avoidance coefficient in the target road is 0.79, which is greater than the pre-set obstacle avoidance coefficient threshold of 0.7, and the road area where each avoiding vehicle travels is not an exit or a speed limit area, and the number of lanes on the target road within a certain distance upstream and downstream of this road area has not changed, and no vehicle malfunction has been identified, then the road condition identification result for the target road can be generated as road congestion caused by road surface water and / or debris. This embodiment does not impose specific limitations on this.

[0062] Optionally, if the road anomaly parameter is an inflow interference coefficient, then the road anomaly parameter in the target road is determined, and a road condition recognition result for the target road is generated based on the road anomaly parameter, including: determining the inflow vehicle ratio parameter in the target road based on the number of inflow vehicles and the total number of all target vehicles; determining the vehicle speed parameter of each inflow vehicle and the braking increment coefficient of the main road vehicles in the target road; and determining the position matching coefficient between the vehicle inflow position of each inflow vehicle and the state anomaly position in the target road; the main road vehicles are the vehicles that were traveling in the target road before each inflow vehicle merged into the target road; determining the inflow interference coefficient in the target road based on the inflow vehicle ratio parameter, vehicle speed parameter, position matching coefficient, and braking increment coefficient of the main road vehicles; and generating a road condition recognition result for the target road based on the inflow interference coefficient.

[0063] The vehicle speed parameter can be the vehicle speed state value of the merging vehicles when entering the target road, specifically the vehicle speed deceleration coefficient when entering the target road, which can be determined based on the average throttle opening of each merging vehicle. The braking increment coefficient can be the difference between the number of braking operations of each target vehicle in the target road and the number of braking operations in the road area other than the target road. The target road can refer to a road area with abnormal traffic conditions, while the road area other than the target road can be a road area with normal traffic conditions adjacent to the target road, or a road area with normal traffic conditions not adjacent to the target road.

[0064] Among them, the abnormal location in the target road can be the abnormal center location when the traffic condition of the target road is abnormal, and the location matching coefficient can be used to quantify the distance relationship between the abnormal center location when the traffic condition of the target road is abnormal and the merging point location where merging vehicles enter the target road.

[0065] Specifically, the merging interference coefficient in the target road can be determined using the following formula, based on the merging vehicle ratio parameters, vehicle speed parameters, position matching coefficients of each merging vehicle, and the braking increment coefficient of the main road vehicles:

[0066] ;

[0067] in, This can represent the number of vehicles merging into the target road; This can be a parameter representing the proportion of vehicles merging into the target road, indicating the percentage of such vehicles relative to the total number of vehicles on the target road. This can represent the total number of target vehicles on the target road; This can be represented by the braking increment coefficient, where It can represent the difference between the number of times each target vehicle brakes on the target road and the number of times it brakes in road areas other than the target road; This can be a vehicle speed parameter, specifically the vehicle speed deceleration coefficient when merging into the target road. This can represent the average throttle opening of each merging vehicle; This can be a location matching coefficient, where represents the distance between the anomaly center location when the target road's traffic status is abnormal and the merging point location where merging vehicles enter the target road. To represent the proportion of each merging vehicle. Braking increment coefficient for vehicles on main roads Vehicle speed parameters and position matching coefficient The importance proportion of the input interference coefficient, and For example, the merged vehicle ratio parameter. When it is ≥20%, then it can be determined. The braking increment coefficient for vehicles on the main road is 0.3. When it is ≥30%, then it can be determined. The vehicle speed parameter is 0.25. When it is ≥20%, then it can be determined. The value is 0.25, and the position matching coefficient is... When the percentage is 61% at 50 meters from the merging point and 14% at 200 meters from the merging point, then it can be determined that... The value is 0.2, but the specific value can be automatically generated based on the actual situation.

[0068] Optionally, the merging interference coefficient in the target road is compared with a pre-set merging interference coefficient threshold to obtain a coefficient comparison result. Based on the coefficient comparison result, a road condition identification result for the target road is generated. For example, if the merging interference coefficient in the target road is 0.75, which is greater than the pre-set merging interference coefficient threshold of 0.65, then the road condition identification result for the target road can be generated as traffic congestion caused by traffic merging at the intersection. This embodiment does not impose specific limitations on this.

[0069] Optionally, if the road anomaly parameter is a speed limit adaptation coefficient, then the road anomaly parameter in the target road is determined, and a road condition recognition result for the target road is generated based on the road anomaly parameter, including: determining at least one speed-limited vehicle in the target road based on the vehicle braking state and vehicle speed state of each target vehicle in the target road; determining the speed-limited vehicle ratio parameter in the target road based on the number of each speed-limited vehicle and the total number of each target vehicle; determining the speed adaptation parameter of each speed-limited vehicle based on the vehicle speed value after braking and the preset standard vehicle speed value; determining the throttle adaptation parameter of each speed-limited vehicle based on the vehicle throttle opening value after braking; determining the position matching degree value between each speed-limited vehicle and the target road based on the vehicle speed limit position of each speed-limited vehicle in the target road; determining the speed limit adaptation coefficient in the target road based on the speed limit vehicle ratio coefficient, speed adaptation parameter, throttle adaptation parameter, and position matching degree value of each speed-limited vehicle; and generating a road condition recognition result for the target road based on the speed limit adaptation coefficient.

[0070] The vehicle speed state refers to the speed change of a vehicle during travel, specifically the speed reduction of a target vehicle after braking within a speed-limited area. Specifically, the braking and speed states of each target vehicle on the target road can be determined, and braking and speed parameters for each target vehicle can be calculated. These parameters are then compared with pre-set threshold values ​​for speed-limited vehicle identification to obtain the comparison results. Based on these results, at least one speed-limited vehicle on the target road can be identified. For example, the preset threshold for identifying speed-limited vehicles can be that the brake pedal travel of the target vehicle is ≥20mm, i.e., heavy braking, and the speed reduction is ≥30km / h. Then, when the speed reduction of the target vehicle in the target road is determined to be 35km / h and the brake pedal travel is 23mm, i.e., heavy braking, the target vehicle is identified as a speed-limited vehicle. In this way, all speed-limited vehicles in the target road are identified, and the number of speed-limited vehicles in the target road is obtained. This embodiment does not impose specific limitations on this.

[0071] Among them, the throttle adaptation parameter can be a quantitative indicator used to quantify the relationship between the average throttle opening value of a vehicle after braking by a speed-limited vehicle on the target road and a preset vehicle throttle opening value, such as a preset vehicle throttle opening value of 25%.

[0072] The standard vehicle speed value can refer to the permitted vehicle speed within the speed-limited area of ​​the target road, such as a standard vehicle speed of 60 km / h at the tunnel entrance. The speed adaptation parameter can be a parameter used to quantify the relationship between the average vehicle speed of each target vehicle after braking and the standard vehicle speed value. For example, if a target vehicle is braking while passing through a tunnel with a speed limit of 60 km / h, and the average vehicle speed of all target vehicles after braking is 65 km / h, then the speed adaptation parameter for that target road can be determined to be 92%. This embodiment does not impose specific limitations on this. The position matching degree value can be a parameter used to quantify the distance relationship between the abnormal center position when the traffic condition of the target road is abnormal and the position where each speed-limited vehicle performs a speed-limiting operation. The speed-limiting operation position can be the location where vehicle speed limit signs are set on the target road.

[0073] Specifically, the speed limit adaptation coefficient in the target road can be determined using the following formula, based on the speed limit vehicle ratio coefficient, speed adaptation parameters, throttle adaptation parameters, and position matching degree value for each speed-limited vehicle. The formula is as follows:

[0074] ;

[0075] in, It can represent the speed limit adaptation coefficient in the target road; This can be the speed-limited vehicle proportion coefficient, representing the proportion of speed-limited vehicles in the target road to the total number of target vehicles. This can represent the number of vehicles on the target road that meet the speed limit recognition criteria. This can represent the total number of target vehicles on the target road; A speed adaptation parameter can be provided, representing the relationship between the vehicle speed of each target vehicle after braking and the speed of a standard vehicle. This can represent the average vehicle speed of all vehicles on the target road after braking. It can represent the permitted vehicle speed value in the speed-limited area of ​​the target road; A throttle adaptation parameter can be provided, representing the relationship between the average throttle opening value of the target vehicle after braking and a preset throttle opening value. It can represent the average throttle opening value of each speed-limited vehicle after braking. This can be a location matching degree value, representing the distance relationship between the anomaly center location when the traffic condition of the target road is abnormal and the location where each speed-limited vehicle performs speed-limiting operations. This can be represented as the distance between the anomaly center location and the location where each speed-limited vehicle executes its speed limit operation when the traffic condition of the target road is abnormal. To represent the proportion of speed-limited vehicles for each speed-limited vehicle Speed ​​adaptation parameters Throttle adaptation parameters Location matching value The importance proportion of the speed limit adaptation factor, and For example, the proportion of speed-limited vehicles for each speed-limited vehicle. When it is ≥50%, then it can be determined. 0.3, speed adaptation parameter When ≥90%, it can be determined 0.3, throttle adaptation parameters When it is ≥80%, then it can be determined. The value is 0.2, and the position matching degree value is... If the percentage is 67% at 50 meters from the speed limit and 30% at 150 meters from the speed limit, then it can be determined that... The value is 0.2, but the specific value can be automatically generated based on the actual situation.

[0076] Optionally, the speed limit adaptation coefficient in the target road is compared with a pre-set speed limit adaptation coefficient threshold to obtain a coefficient comparison result. Based on the coefficient comparison result, a road condition identification result for the target road is generated. For example, if the speed limit adaptation coefficient in the target road is 0.8, which is greater than the pre-set speed limit adaptation coefficient threshold of 0.75, then the road condition identification result for the target road can be generated as "speed limit area causing road congestion". This embodiment does not impose specific limitations on this.

[0077] It should be noted that when the road condition information of the target road is abnormal, the abnormal road parameters can be determined based on the vehicle position data and driving data of each target vehicle on the target road, as well as road network parameters. These abnormal road parameters can be one or more of the following: slow-moving interference coefficient, obstacle avoidance coefficient, merging interference coefficient, and speed limit adaptation coefficient. In other words, when the road condition information of the target road is determined to be abnormal, all possible abnormal road parameters can be calculated to obtain at least one abnormal road parameter. Then, based on these abnormal road parameters, a road condition identification result for the target road is generated.

[0078] The technical solution of this invention, in response to a road condition recognition request for a target road, determines the vehicle location data and driving data of at least one target vehicle on the target road; determines the road network parameters of the target road based on the vehicle location data of each target vehicle; determines the lane state parameters of at least one lane on the target road based on the road network parameters and the driving data; determines the road state information of the target road based on the lane state parameters of each lane; if the road state information indicates an abnormal road state, determines the abnormal road parameters in the target road, and generates a road condition recognition result for the target road based on the abnormal road parameters. This embodiment can determine the road state information of the target road based on the vehicle information of the target vehicles, and when the road state information is determined to be abnormal, it generates a road condition recognition result for the target road based on the abnormal road parameters. It can perform multi-dimensional recognition of the road conditions of the target road using vehicle data, improving the efficiency and comprehensiveness of obtaining abnormal road parameters present in the target road, and enhancing the efficiency and accuracy of road condition recognition for the target road.

[0079] Example 2

[0080] Figure 2 This is a flowchart of a road condition recognition method provided in Embodiment 2 of the present invention. This embodiment further optimizes the above-mentioned road condition recognition method based on the embodiments described above.

[0081] Furthermore, the steps "obtaining page access requests, parsing page access requests to obtain user access tokens and page request access addresses" are refined into "determining the lane centerline and lane boundary lines of at least one lane in the target road based on road network parameters; and determining the speed change values ​​of each target vehicle in the target road based on vehicle driving data; determining the number of target vehicles in each lane within a preset time period based on the lane centerline, as well as the corresponding travel time and travel distance for each target vehicle, and further refining the process based on the number of target vehicles and their corresponding travel time and travel distance..." The process involves determining the average lane speed for each lane; based on lane boundary lines, determining the number of lane changes by target vehicles in each lane and the lateral offset of lane-changing vehicles within each target vehicle; and determining the cross-lane interference coefficient for each lane based on the number of lane changes and the lateral offset of lane-changing vehicles within each target vehicle. Based on speed change values, the speed matching degree value between each target vehicle and associated road nodes in the target road is determined. The average lane speed, cross-lane interference coefficient, and speed matching degree value are then used as lane state parameters for each lane in the target road. This improves the road condition recognition method for the target road. Figure 2 As shown, the method includes:

[0082] S210. In response to a road condition recognition request for a target road, determine the vehicle position data and vehicle driving data of at least one target vehicle on the target road.

[0083] S220. Based on the vehicle location data of each target vehicle, determine the road network parameters of the target road.

[0084] S230. Based on road network parameters, determine the lane centerline and lane boundary line of at least one lane in the target road; and based on vehicle driving data, determine the speed change value of each target vehicle in the target road.

[0085] Among them, the speed change value can refer to the quantitative indicator of how the vehicle speed changes with the vehicle's travel time and the changes in the target road during the target vehicle's travel on the target road. Specifically, it can refer to the difference in the speed values ​​of the target vehicle at different road nodes on the target road.

[0086] S240. Based on the lane centerline, determine the number of target vehicles in each lane within a preset time period, as well as the corresponding travel time and distance for each target vehicle. Based on the number of target vehicles and the corresponding travel time and distance for each target vehicle, determine the average speed of each lane.

[0087] This involves matching target vehicles to their corresponding lanes based on lane centerlines, determining the number of target vehicles passing through each lane within a preset time period, as well as the travel time and distance of each target vehicle, and calculating the average lane speed for each lane on the target road. Specifically, the average lane speed for each lane can be determined using the following formula, based on the number of target vehicles and their respective travel time and distance:

[0088] ;

[0089] in, It can represent the average speed of each lane in the target road; This can represent the number of vehicles passing through each lane in the i-th lane within a preset time period; This can be represented as the distance traveled by the j-th vehicle in the i-th lane of the target road; Let represent the travel time of the j-th vehicle in the i-th lane of the target road.

[0090] S250. Based on the lane boundary lines, determine the number of lane changes of the target vehicles in each lane and the lateral offset of the lane-changing vehicles in each target vehicle. Based on the number of lane changes of the target vehicles and the lateral offset of the lane-changing vehicles in each target vehicle, determine the cross-lane interference coefficient corresponding to each lane.

[0091] The number of lane changes refers to the number of times the target vehicle crosses the lane boundary line, and the lateral offset refers to the distance the target vehicle deviates from the lane boundary line in the lateral direction. Specifically, the cross-lane interference coefficient for each lane can be determined using the following formula, based on the number of lane changes of the target vehicle and the lateral offset of each lane-changing vehicle:

[0092] ;

[0093] in, It can be the cross-lane interference coefficient, which represents the frequency of the target vehicle crossing lanes and the degree of interference with the traffic on the target road; This can be represented as the number of target vehicles traveling across lanes; It can be used to represent the maximum lateral offset of the j-th vehicle in the target road, which can be calculated based on the vehicle position data of the j-th vehicle and the lane boundary line of the initial lane in which the vehicle is located. It can be a pointer function, if If I = 1, then I = 1; otherwise I = 0. The width of vehicles on the target road.

[0094] S260. Based on the speed change value, determine the speed matching degree value between the relevant road nodes of each target vehicle in the target road.

[0095] The associated road nodes can be adjacent road areas within the target road. Specifically, the speed matching degree between each target vehicle and the associated road nodes within the target road can be determined using the following formula, based on the speed change value:

[0096] ;

[0097] in, This can represent the speed matching degree value; This can be the number of vehicles representing a change in vehicle speed; It can be the vehicle speed value of the j-th vehicle when passing through the k-th and k+1-th road nodes in the target road; This can represent the ideal speed change of the j-th vehicle as it passes through the k-th and k+1-th road nodes in the target road. Specifically, it can be set based on the road network parameters of the target road. For example, when the target vehicle passes through a lane merge exit... =-10km / h; For example, when passing through ordinary road sections =0, which can be pre-set by technical personnel based on actual needs and experience.

[0098] S270. The average lane speed, cross-lane interference coefficient, and speed matching degree value are determined as the lane state parameters for each lane in the target road.

[0099] Specifically, the average lane speed, cross-lane interference coefficient, and speed matching degree value can be combined to generate a multi-dimensional judgment matrix for judging the road condition of the target road. By identifying the lane status of each lane in the target road, the road condition of the target road can be identified, avoiding misjudgments caused by a single speed indicator.

[0100] S280. Determine the road status information of the target road based on the lane status parameters of each lane.

[0101] Specifically, at least one state parameter value included in the lane state parameters of each lane can be compared with the corresponding state parameter threshold range to obtain the parameter comparison result corresponding to each state parameter value. For example, for lane-level average speed, it can be compared with the lane speed limit of each lane in the target road. If a comparison is made, ≥0.9 If the average speed is 0.8, it indicates that the average speed of each lane on the target road is normal, and the road condition information of the target road can be determined to be unobstructed; > ≥0.75 If the average lane speed is 0.65, it indicates an anomaly in the average lane speed of each lane on the target road, and the road condition information of the target road can be determined to be slow-moving; ≥ ≥0.4 This indicates that the average lane speed of each lane in the target road is abnormal, and it can be determined that the road condition information of the target road is congested. Regarding the cross-lane interference coefficient, if... If ≤0.1, it indicates that the target vehicle's cross-lane interference is small, and the road condition information of the target road can be determined to be unobstructed; if 0.1 < If the value is ≤0.3, it indicates that the interference to the target vehicle is moderate, and the road condition information of the target road can be determined to be slow-moving; if A value >0.3 indicates severe cross-lane interference from the target vehicle, and confirms that the road condition of the target road is congested. Regarding the speed matching score, if... If ≥0.8, it indicates that the vehicle speed change value of the target vehicle on the target road conforms to the ideal speed change value of the target road, and it can be determined that the road condition information of the target road is unobstructed; if 0.6≤ If the value is less than 0.8, it indicates an anomaly in the vehicle speed change of the target vehicle on the target road, and it can be determined that the road condition information of the target road is slow-moving; if A value less than 0.6 indicates an abnormal change in vehicle speed on the target road, confirming congestion as the road's condition. Furthermore, the average lane speed, cross-lane interference coefficient, and speed matching degree can be combined to determine the target road's condition. By comparing the results of multiple condition parameters, a multi-dimensional assessment of the target road's condition can be achieved, avoiding misjudgments based on a single parameter.

[0102] S290. If the road condition information indicates an abnormal road condition, determine the abnormal road parameters in the target road and generate a road condition identification result for the target road based on the abnormal road parameters.

[0103] The technical solution of this invention includes: determining the lane centerline and lane boundary line of at least one lane in a target road based on road network parameters; determining the speed change value of each target vehicle in the target road based on vehicle driving data; determining the number of target vehicles in each lane and the corresponding travel time and distance of each target vehicle within a preset time period based on the lane centerline, and determining the average lane speed of each lane based on the number of target vehicles and the corresponding travel time and distance; determining the number of lane changes of target vehicles in each lane and the lateral offset of lane-changing vehicles in each target vehicle based on the lane boundary line, and determining the cross-lane interference coefficient of each lane based on the number of lane changes of target vehicles and the lateral offset of lane-changing vehicles in each target vehicle; determining the lane state parameters of each lane in the target road based on the average lane speed, the cross-lane interference coefficient, and the speed matching degree value; and determining the road state information of the target road based on the lane state parameters of each lane. This embodiment can determine the road condition of the target road based on three dimensions: lane-level average speed, cross-lane interference coefficient, and road network node speed matching degree, according to the vehicle information of the target vehicle in the target road and the road network parameters. This enables multi-dimensional identification of the road condition of the target road, improves the efficiency and comprehensiveness of obtaining abnormal road parameters in the target road, and enhances the efficiency and accuracy of road condition identification of the target road.

[0104] Example 3

[0105] Figure 3 This is a schematic diagram of a road condition recognition device provided in Embodiment 3 of the present invention. The road condition recognition device provided in this embodiment of the present invention is applicable to situations where the efficiency and accuracy of road condition recognition of a target road are poor, and where the efficiency and comprehensiveness of acquiring abnormal road parameters are insufficient when road conditions are abnormal. This road condition recognition device can be implemented in hardware and / or software, such as... Figure 3 As shown, it specifically includes: an identification request processing module 310, a road network parameter determination module 320, a status parameter determination module 330, a status information determination module 340, and an identification result generation module 350. Among them,

[0106] The identification request processing module 310 is used to determine the vehicle position data and vehicle driving data of at least one target vehicle in the target road in response to a road condition identification request for the target road.

[0107] The road network parameter determination module 320 is used to determine the road network parameters of the target road based on the vehicle location data of each target vehicle.

[0108] The state parameter determination module 330 is used to determine the lane state parameters of at least one lane in the target road based on the road network parameters and the vehicle driving data.

[0109] The status information determination module 340 is used to determine the road status information of the target road based on the lane status parameters of each lane.

[0110] The identification result generation module 350 is used to determine the road anomaly parameters in the target road if the road state information indicates that the road state is abnormal, and to generate a road condition identification result for the target road based on the road anomaly parameters.

[0111] This solution verifies the target user's role and operation permissions after the target user's identity information is verified. Upon successful verification, the target user can perform corresponding operations within the power IoT system. This achieves effective management of operation permission information within the power IoT, ensuring the overall security and stability of the system. It also solves the problem of existing authorization management methods being unable to flexibly adapt to complex user structures.

[0112] Optionally, if the road anomaly parameter is a slow-moving interference coefficient, the identification result generation module 350 is specifically used to determine at least one slow-moving vehicle in the target road based on the vehicle speed and vehicle travel time of each target vehicle in the target road.

[0113] The proportion of slow-moving vehicles in the target road is determined based on the number of each type of slow-moving vehicle and the total number of each type of target vehicle.

[0114] Based on the vehicle travel trajectories of each of the slow-moving vehicles on the target road, the overlap rate between each of the slow-moving vehicles and the target road is determined; and the throttle parameters of other vehicles on the target road are obtained; the other vehicles are vehicles on the target road other than each of the slow-moving vehicles.

[0115] Based on the slow-moving vehicle ratio parameter, overlap rate, and throttle coefficient of each of the other vehicles, the slow-moving interference coefficient in the target road is determined; and based on the slow-moving interference coefficient, the road condition identification result of the target road is generated.

[0116] Optionally, if the road anomaly parameter is an obstacle avoidance coefficient, the identification result generation module 350 is specifically used to determine at least one avoidance vehicle in the target road based on the vehicle steering angle and vehicle braking state of each target vehicle in the target road.

[0117] The proportion of vehicles that need to give way in the target road is determined based on the number of each of the vehicles that need to give way and the total number of each of the target vehicles.

[0118] Determine the lateral offset parameters of each of the avoidance vehicles in the target road, and determine the spatial concentration coefficient of the avoidance vehicles performing continuous avoidance operations in the target road;

[0119] The obstacle avoidance coefficient in the target road is determined based on the avoidance vehicle ratio parameter, lateral offset parameter and spatial concentration coefficient of the avoidance vehicle.

[0120] Based on the obstacle avoidance coefficient, a road condition identification result for the target road is generated.

[0121] Optionally, if the road anomaly parameter is the merging interference coefficient, the identification result generation module 350 is specifically used to determine the merging vehicle ratio parameter in the target road based on the number of merging vehicles in the target road and the total number of each target vehicle.

[0122] The vehicle speed parameters of each merging vehicle and the braking increment coefficient of the main road vehicles in the target road are determined; and the position matching coefficient between the merging position of each merging vehicle and the abnormal position in the target road is determined; the main road vehicles are the vehicles that were traveling in the target road before each merging vehicle merged into the target road.

[0123] The merging interference coefficient in the target road is determined based on the merging vehicle ratio parameters, vehicle speed parameters, and position matching coefficients of each merging vehicle, as well as the braking increment coefficient of the main road vehicles.

[0124] Based on the input interference coefficient, a road condition identification result for the target road is generated.

[0125] Optionally, if the road anomaly parameter is a speed limit adaptation coefficient, the identification result generation module 350 is specifically used to determine at least one speed-limited vehicle in the target road based on the vehicle braking state and vehicle speed state of each target vehicle in the target road.

[0126] The proportion of speed-limited vehicles in the target road is determined based on the number of each speed-limited vehicle and the total number of each target vehicle.

[0127] Based on the vehicle speed value of each speed-limited vehicle after braking and the preset standard vehicle speed value, determine the speed adaptation parameters of each speed-limited vehicle; and based on the throttle opening value of each speed-limited vehicle after braking, determine the throttle adaptation parameters of each speed-limited vehicle.

[0128] Based on the speed limit position of each speed-limited vehicle on the target road, determine the position matching degree value between each speed-limited vehicle and the target road;

[0129] Based on the speed limit vehicle ratio coefficient, speed adaptation parameters, throttle adaptation parameters, and position matching degree value of each speed limit vehicle, the speed limit adaptation coefficient in the target road is determined.

[0130] Based on the speed limit adaptation coefficient, a road condition identification result for the target road is generated.

[0131] Optionally, the state parameter determination module 330 is used to determine the lane center line and lane boundary line of at least one lane in the target road based on the road network parameters; and to determine the speed change value of each target vehicle in the target road based on the vehicle driving data.

[0132] Based on the lane centerline, determine the number of target vehicles in each lane within a preset time period, as well as the travel time and distance of each target vehicle. Based on the number of target vehicles and the travel time and distance of each target vehicle, determine the average speed of each lane.

[0133] Based on the lane boundary lines, determine the number of lane changes of the target vehicles in each lane and the lateral offset of the lane-changing vehicles in each target vehicle, and determine the cross-lane interference coefficient corresponding to each lane based on the number of lane changes of the target vehicles and the lateral offset of the lane-changing vehicles in each target vehicle.

[0134] Based on the speed change value, determine the speed matching degree value between the target vehicles and the associated road nodes in the target road;

[0135] The average lane speed, the cross-lane interference coefficient, and the speed matching degree value are determined as the lane state parameters for each lane in the target road.

[0136] The road condition recognition device provided in the embodiments of the present invention can execute the road condition recognition method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0137] Example 4

[0138] Figure 4A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0140] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0141] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as road condition recognition methods.

[0142] In some embodiments, the traffic condition recognition method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the traffic condition recognition method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the traffic condition recognition method by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0147] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0148] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability.

[0149] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A road condition recognition method, characterized in that, include: In response to a road condition recognition request for a target road, determine the vehicle location data and vehicle driving data of at least one target vehicle on the target road; Based on the vehicle location data of each target vehicle, the road network parameters of the target road are determined; Based on the road network parameters and the vehicle driving data, determine the lane status parameters of at least one lane in the target road; Based on the lane status parameters of each lane, the road status information of the target road is determined; If the road status information indicates an abnormal road condition, then the abnormal road parameters in the target road are determined, and a road condition identification result for the target road is generated based on the abnormal road parameters.

2. The method according to claim 1, characterized in that, If the road anomaly parameter is a slow-moving traffic interference coefficient, then determining the road anomaly parameter in the target road and generating a road condition identification result for the target road based on the road anomaly parameter includes: Based on the vehicle speed and travel time of each target vehicle on the target road, at least one slow-moving vehicle on the target road is determined; The proportion of slow-moving vehicles in the target road is determined based on the number of each type of slow-moving vehicle and the total number of each type of target vehicle. Based on the vehicle travel trajectories of each of the slow-moving vehicles on the target road, the overlap rate between each of the slow-moving vehicles and the target road is determined; and the throttle parameters of other vehicles on the target road are obtained; the other vehicles are vehicles on the target road other than each of the slow-moving vehicles. Based on the slow-moving vehicle ratio parameter, overlap rate, and throttle coefficient of each of the other vehicles, the slow-moving interference coefficient in the target road is determined; and based on the slow-moving interference coefficient, the road condition identification result of the target road is generated.

3. The method according to claim 1, characterized in that, If the road anomaly parameter is an obstacle avoidance coefficient, then determining the road anomaly parameter in the target road and generating a road condition identification result for the target road based on the road anomaly parameter includes: Based on the vehicle steering angle and braking status of each target vehicle on the target road, determine at least one vehicle to avoid; The proportion of vehicles that need to give way in the target road is determined based on the number of each of the vehicles that need to give way and the total number of each of the target vehicles. Determine the lateral offset parameters of each of the avoidance vehicles in the target road, and determine the spatial concentration coefficient of the avoidance vehicles performing continuous avoidance operations in the target road; The obstacle avoidance coefficient in the target road is determined based on the avoidance vehicle ratio parameter, lateral offset parameter and spatial concentration coefficient of the avoidance vehicle. Based on the obstacle avoidance coefficient, a road condition identification result for the target road is generated.

4. The method according to claim 1, characterized in that, If the road anomaly parameter is an influencing factor, then determining the road anomaly parameter in the target road and generating a road condition identification result for the target road based on the road anomaly parameter includes: The proportion of vehicles merging into the target road is determined based on the number of vehicles merging into the target road and the total number of vehicles in each target road. The vehicle speed parameters of each merging vehicle and the braking increment coefficient of the main road vehicles in the target road are determined; and the position matching coefficient between the merging position of each merging vehicle and the abnormal position in the target road is determined; the main road vehicles are the vehicles that were traveling in the target road before each merging vehicle merged into the target road. The merging interference coefficient in the target road is determined based on the merging vehicle ratio parameters, vehicle speed parameters, and position matching coefficients of each merging vehicle, as well as the braking increment coefficient of the main road vehicles. Based on the input interference coefficient, a road condition identification result for the target road is generated.

5. The method according to claim 1, characterized in that, If the road anomaly parameter is a speed limit adaptation coefficient, then determining the road anomaly parameter in the target road and generating a road condition identification result for the target road based on the road anomaly parameter includes: Based on the vehicle braking status and vehicle speed status of each target vehicle on the target road, at least one speed-limited vehicle on the target road is determined. The proportion of speed-limited vehicles in the target road is determined based on the number of each speed-limited vehicle and the total number of each target vehicle. Based on the vehicle speed value of each speed-limited vehicle after braking and the preset standard vehicle speed value, determine the speed adaptation parameters of each speed-limited vehicle; and based on the throttle opening value of each speed-limited vehicle after braking, determine the throttle adaptation parameters of each speed-limited vehicle. Based on the speed limit position of each speed-limited vehicle on the target road, determine the position matching degree value between each speed-limited vehicle and the target road; Based on the speed limit vehicle ratio coefficient, speed adaptation parameters, throttle adaptation parameters, and position matching degree value of each speed limit vehicle, the speed limit adaptation coefficient in the target road is determined. Based on the speed limit adaptation coefficient, a road condition identification result for the target road is generated.

6. The method according to claim 1, characterized in that, The step of determining the lane state parameters of at least one lane in the target road based on the road network parameters and the vehicle driving data includes: Based on the road network parameters, determine the lane centerline and lane boundary line of at least one lane in the target road; and based on the vehicle driving data, determine the speed change value of each of the target vehicles in the target road. Based on the lane centerline, determine the number of target vehicles in each lane within a preset time period, as well as the travel time and distance of each target vehicle. Based on the number of target vehicles and the travel time and distance of each target vehicle, determine the average speed of each lane. Based on the lane boundary lines, determine the number of lane changes of the target vehicles in each lane and the lateral offset of the lane-changing vehicles in each target vehicle, and determine the cross-lane interference coefficient corresponding to each lane based on the number of lane changes of the target vehicles and the lateral offset of the lane-changing vehicles in each target vehicle. Based on the speed change value, determine the speed matching degree value between the target vehicles and the associated road nodes in the target road; The average lane speed, the cross-lane interference coefficient, and the speed matching degree value are determined as the lane state parameters for each lane in the target road.

7. A road condition recognition device, characterized in that, include: The identification request processing module is used to determine the vehicle location data and vehicle driving data of at least one target vehicle on the target road in response to a road condition identification request for the target road. The road network parameter determination module is used to determine the road network parameters of the target road based on the vehicle location data of each target vehicle. The state parameter determination module is used to determine the lane state parameters of at least one lane in the target road based on the road network parameters and the vehicle driving data. The status information determination module is used to determine the road status information of the target road based on the lane status parameters of each lane. The identification result generation module is used to determine the road anomaly parameters in the target road if the road condition information indicates an abnormal road condition, and generate a road condition identification result for the target road based on the road anomaly parameters.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the road condition recognition method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the road condition recognition method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the road condition recognition method according to any one of claims 1-6.

Images