Non-motor vehicle key risk point segment identification method, system and device

Abstract

The invention discloses a non-motor vehicle key risk point segment identification method, system and device, and the method comprises the steps: obtaining urban road network data, and determining a non-motor vehicle road segment set from the urban road network data; determining a position relationship between each non-motor vehicle road section in the non-motor vehicle road section set and an intersected road section according to the urban road network data; determining candidate hidden danger road sections from the non-motor vehicle road section set according to the position relation; determining a risk assessment value corresponding to each candidate hidden danger road section according to a preset calculation model; and determining a target risk road section according to the risk assessment value corresponding to each candidate hidden danger road section. Therefore, a traffic manager can timely and quickly find hidden dangers of the non-motorized vehicle lane in the urban road in planning and design, so that the risk road section can be timely re-planned, and unnecessary traffic safety accidents can be reduced.

Description

Technical Field

[0001] This application relates to the field of traffic engineering, and particularly to a method, system and device for identifying key risk point segments of non-motor vehicles on urban roads. Background Art

[0002] Identifying traffic risk hazards on non-motor vehicle lanes of urban roads is the primary problem faced by current urban traffic governance. Currently, urban traffic managers often pay more attention to the risk hazards of motor vehicle lanes when identifying risk hazards on urban roads, and the risk hazards of non-motor vehicle lanes often become an aspect that is easily overlooked. Practitioners usually focus on indicators such as the width of non-motor vehicle lanes, whether there are non-motor vehicle and motor vehicle isolation facilities, roadside parking, and road continuity when identifying non-motor vehicle lanes, lacking attention to the rationality of the structural design of non-motor vehicle lanes and intersecting roads. The intersections of intersecting roads are often prominent points with frequent traffic accidents and traffic hazards.

[0003] Therefore, the existing methods for identifying traffic risk hazards on non-motor vehicle lanes cannot timely and quickly discover prominent problems such as potential hazards in the planning and design of non-motor vehicle lanes on urban roads. Summary of the Invention

[0004] In view of this, embodiments of this application are expected to provide a system, method and device for identifying key risk point segments of non-motor vehicles to at least solve the above technical problems.

[0005] To achieve the above object, the technical solution of this application is realized as follows:

[0006] According to one aspect of the embodiments of this application, a method for identifying key risk point segments of non-motor vehicles is provided, including:

[0007] Obtain urban road network data, and determine a set of non-motor vehicle road segments from the urban road network data;

[0008] According to the urban road network data, determine the positional relationship between each non-motor vehicle road segment in the set of non-motor vehicle road segments and the intersecting road segments;

[0009] Determine at least one candidate hazard road segment from the set of non-motor vehicle road segments according to the positional relationship;

[0010] Determine the risk assessment value corresponding to each candidate hazard road segment according to a pre-designed calculation model;

[0011] Determine the target risk road segment from the at least one candidate hazard road segment according to the risk assessment value.

[0012] Optionally, the determining the positional relationship between each non-motor vehicle road segment and the intersecting road segments according to the set of non-motor vehicle road segments includes:

[0013] Based on the urban road network data, determine at least one intersection section with a non-motor vehicle lane in the set of non-motor vehicle road sections;

[0014] Calculate the included angle relationship between the center line of the non-motor vehicle road section in the intersection section and the center line of the right intersection section; wherein, the included angle relationship is used to indicate the positional relationship between the non-motor vehicle road section and the intersection section.

[0015] Optionally, the urban road network data includes data of the signal control system included in the intersection section;

[0016] The determining, based on the urban road network data, at least one intersection section with a non-motor vehicle lane in the set of non-motor vehicle road sections includes:

[0017] Based on the data of the signal control system included in the intersection section, determine at least one intersection section with a non-motor vehicle lane and without a signal control system from the set of non-motor vehicle road sections.

[0018] Optionally, the determining at least one candidate hidden danger road section from the set of non-motor vehicle road sections according to the positional relationship includes:

[0019] Based on the included angle relationship, determine the intersection sections with included angles within a preset range from the at least one intersection section as candidate hidden danger road sections.

[0020] Optionally, the determining the risk assessment value corresponding to each candidate hidden danger road section according to a preset calculation model includes:

[0021] Determine the included angle amplitude of the intersecting roads corresponding to each candidate hidden danger road section according to the preset calculation model;

[0022] Obtain the traffic accident data related to each candidate hidden danger road section, and determine the traffic accident intensity corresponding to each candidate hidden danger road section according to the traffic accident data;

[0023] Determine the stopping sight distance of the intersecting roads corresponding to each candidate hidden danger road section;

[0024] Determine the risk assessment value corresponding to the candidate hidden danger road section according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads.

[0025] Optionally, the determining the risk assessment value corresponding to the candidate hidden danger road section according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads includes:

[0026] Determine a first evaluation value according to the included angle amplitude of the intersecting roads and a first preset scoring model;

[0027] Determine a second evaluation value according to the traffic accident intensity and the second preset scoring model;

[0028] Determine a third evaluation value according to the stopping sight distance of the intersecting road and the third preset scoring model;

[0029] Perform weighted calculation on the first evaluation value, the second evaluation value and the third evaluation value to obtain the risk evaluation value corresponding to the candidate potential hazard section.

[0030] Optionally, the determining the target risk section from the at least one candidate potential hazard section according to the risk evaluation value includes:

[0031] From the at least one candidate potential hazard section, determine the candidate potential hazard sections whose risk evaluation values exceed a preset threshold as the target risk sections.

[0032] According to a second aspect of the present application, there is provided a non-motor vehicle key risk point section identification system, the system includes:

[0033] A non-motor vehicle lane determination module, configured to obtain urban road network data and determine a non-motor vehicle road section set from the urban road network data;

[0034] A position relationship determination module, configured to determine the position relationship between each non-motor vehicle road section in the non-motor vehicle road section set and the intersecting road section according to the urban road network data;

[0035] A candidate potential hazard section determination module, configured to determine at least one candidate potential hazard section from the non-motor vehicle road section set according to the position relationship;

[0036] A risk evaluation value determination module, configured to determine the risk evaluation value corresponding to each candidate potential hazard section according to a preset calculation model;

[0037] A target risk section determination module, configured to determine a target risk section from the at least one candidate potential hazard section according to the risk evaluation value.

[0038] Optionally, the position relationship determination module includes:

[0039] An intersecting road section determination sub-module, configured to determine at least one intersecting road section provided with a non-motor vehicle lane in the non-motor vehicle road section set according to the urban road network data;

[0040] A position relationship determination sub-module, configured to determine the included angle relationship between the center line of the non-motor vehicle road section in the intersecting road section and the center line of the intersecting road section on its right, and determine the included angle relationship as the position relationship between the non-motor vehicle road section and the intersecting road section.

[0041] According to a third aspect of the present application, there is provided a non-motor vehicle key risk point segment identification device, the device comprising:

[0042] at least one processor; and

[0043] a memory communicatively connected to the at least one processor; wherein:

[0044] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the non-motor vehicle key risk point segment identification method described in any one of the above.

[0045] The non-motor vehicle key risk point segment identification method, system and device provided by the present application are a solution for identifying non-motor vehicle key risk point segments. Specifically, by obtaining urban road network data, a non-motor vehicle road segment set is identified from the urban road network data, and the positional relationship between each non-motor vehicle road segment and the intersecting road segments is determined; based on this positional relationship, further analysis is carried out to determine the target risk road segments, so that traffic managers can timely and quickly discover potential problems in the planning and design of non-motor vehicle lanes in urban roads, and thus re-plan the risk road segments in a timely manner to reduce the occurrence of unnecessary traffic safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic flow chart of the steps of the non-motor vehicle key risk point segment identification method in the present application;

[0047] Figure 2 is a schematic diagram of the included angle relationship between the center line of a non-motor vehicle road segment and the center line of the intersecting road segment on the right side in the present application;

[0048] Figure 3 is a schematic diagram of another included angle relationship between the center line of a non-motor vehicle road segment and the center line of the intersecting road segment on the right side in the present application;

[0049] Figure 4 is a schematic diagram of the structural composition of the non-motor vehicle key risk point segment identification system in the present application;

[0050] Figure 5 is a schematic diagram of the structural composition of the non-motor vehicle key risk point segment identification device in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The technical solutions of the present application will be further described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0052] For each specific technical feature in the various embodiments described in the specific implementation manners, various combinations can be made without conflict. For example, different embodiments can be formed by combining different specific technical features. To avoid unnecessary repetition, various possible combination manners of the specific technical features in this application will not be described separately.

[0053] It should be noted that the terms "first / second / third" involved in the embodiments of this application are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged in a specific order or sequence when permitted. It should be understood that the objects distinguished by "first / second / third" can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here.

[0054] First, to facilitate the understanding of the technical solutions provided in the embodiments of this application by those skilled in the art, the related technologies will be described below:

[0055] In the prior art, urban traffic managers often pay more attention to the potential risks in the motor vehicle lanes during the identification of potential risks on urban roads, while the potential risks in the non-motor vehicle lanes often become an aspect that is easily overlooked. Practitioners usually focus on indicators such as the width of the non-motor vehicle lane, whether there are non-motor vehicle and motor vehicle isolation facilities, roadside parking, and the continuity of the road when identifying non-motor vehicle lanes. However, there is a lack of attention to the rationality of the structural design of the non-motor vehicle lane and the intersecting roads, and the intersection of the intersecting roads is often a prominent point where traffic accidents occur frequently and there are traffic hazards.

[0056] Therefore, the embodiments of this application provide a technical solution for identifying key risk point segments of non-motor vehicles. In this technical solution, by obtaining urban road network data, a set of non-motor vehicle road segments is determined from the urban road network data; according to the urban road network data, the positional relationship between each non-motor vehicle road segment in the set of non-motor vehicle road segments and the intersecting road segments is determined; at least one candidate hazard road segment is determined from the set of non-motor vehicle road segments according to the positional relationship; a risk assessment value corresponding to each candidate hazard road segment is determined according to a pre-designed calculation model; and a target risk road segment is determined from the at least one candidate hazard road segment according to the risk assessment value. By obtaining urban road network data and using the urban road network data to construct a positional relationship model between non-motor vehicle road segments and intersecting road segments, the target risk road segment can be analyzed based on this positional relationship model, so that traffic managers can timely and quickly discover the potential hazards in the planning and design of non-motor vehicle lanes on urban roads, and then re-plan the risk road segments in a timely manner to reduce the occurrence of unnecessary traffic safety accidents.

[0057] The technical solutions of the present application will be introduced through multiple embodiments. It should be noted that these embodiments can be implemented in various different forms and should not be construed as being limited only to the embodiments described herein.

[0058] Embodiment 1

[0059] Figure 1 It is a schematic diagram of the process implementation of the non-motor vehicle key risk point segment identification method in the present application. As Figure 1 shown, the method includes steps S101 - S105, where

[0060] Step S101: Obtain urban road network data and determine a non-motor vehicle road segment set from the urban road network data;

[0061] Specifically, the urban road network data is collected through monitoring devices and / or checkpoint monitoring devices set by traffic managers on various traffic roads. The urban road network data mainly refers to the vector data of urban road segments, including basic data such as road segment names, vehicle driving directions, signal control systems included in intersecting road segments, and non-motor vehicle lanes included in intersecting road segments.

[0062] In this embodiment, by identifying and analyzing the obtained urban road network data, a non-motor vehicle road segment set can be determined from the urban road network data. Among them, the non-motor vehicle road segment set may include non-motor vehicle road segments and road segment sets associated with each non-motor vehicle road segment respectively.

[0063] Step S102: According to the urban road network data, determine the positional relationship between each non-motor vehicle road segment in the non-motor vehicle road segment set and the intersecting road segments;

[0064] Specifically, the positional relationship between the non-motor vehicle road segment and the intersecting road segment can be represented by the included angle relationship between the two roads. By calculating the included angle between the center line of the non-motor vehicle road segment and the center line of the intersecting road segment. In specific implementation, since the traffic rule is that non-motor vehicles drive on the right side, at the intersecting road segment, non-motor vehicles are prone to collide with motor vehicles coming from the right-side motor vehicle road segment. Therefore, the included angle relationship between the center line of the non-motor vehicle road segment and the center line of the right-side intersecting road segment can be calculated to indicate the positional relationship between the non-motor vehicle road segment and the intersecting road segment.

[0065] After calculating the positional relationships between all non-motor vehicle road segments in the non-motor vehicle road segment set and the intersecting road segments, a positional relationship model between the non-motor vehicle road segments and the intersecting road segments is constructed for subsequent analysis to determine the segments with relatively high risks.

[0066] Step S103: Determine at least one candidate hidden danger road section from the set of non-motor vehicle road sections according to the position relationship.

[0067] Specifically, by determining the position relationship between the non-motor vehicle road section and the intersecting road section, the non-motor vehicle road section with the included angle between the two road sections less than the preset range is determined as the candidate hidden danger road section. Among them, the preset range is the maximum critical value and the minimum critical value of the included angle set in advance, which is used to initially screen out the non-motor vehicle road sections that may have risks. For example, the preset range can be from 0° to 70°, or other included angle intervals, and the embodiments of the present application do not make specific limitations on this.

[0068] Step S104: Determine the risk assessment value corresponding to each candidate hidden danger road section according to the pre-designed calculation model.

[0069] Among them, the pre-designed calculation model can be a logical framework preset for describing the calculation process of the risk assessment value. By using the traffic data of the urban road that can be obtained and calculating according to the pre-designed calculation model, the risk assessment value corresponding to each candidate hidden danger road section can be determined.

[0070] Specifically, to analyze the risks existing in the road, three evaluation indicators, namely the included angle amplitude of the intersecting roads, the intensity of traffic accidents, and the stopping sight distance of the intersecting roads, can be used. By calculating the scores of the three evaluation indicators respectively and calculating the weighted value, the final required risk assessment value can be obtained.

[0071] Step S105: Determine the target risk road section from the at least one candidate hidden danger road section according to the risk assessment value.

[0072] In this embodiment, the target risk road section can be determined from the candidate hidden danger road sections according to the risk assessment value calculated by the above steps. As an example, a threshold can be preset in advance to judge whether each risk assessment value is greater than the preset threshold. If the risk assessment value is greater than or equal to the preset threshold, this candidate hidden danger road section can be determined as the target risk road section. If the risk assessment value is less than the preset threshold, this candidate hidden danger road section can be determined not to be the target risk road section. As another example, it is also possible to first sort according to the risk assessment value, and then determine the N candidate hidden danger road sections with the largest risk assessment values as the target risk road sections, where N can be set according to the road planning indicators required by traffic managers. For example, if N is 10, then the 10 candidate hidden danger road sections with the largest risk assessment values are selected as the target risk road sections.

[0073] In a preferred embodiment of the present application, the step S102: Determine the position relationship between each non-motor vehicle road section in the set of non-motor vehicle road sections and the intersecting road section according to the urban road network data, including:

[0074] Based on the urban road network data, determine at least one intersecting road section with a non-motor vehicle lane in the set of non-motor vehicle road sections; calculate the angular relationship between the center line of the non-motor vehicle road section in the intersecting road section and the center line of the right intersecting road section; wherein, the angular relationship is used to indicate the positional relationship between the non-motor vehicle road section and the intersecting road section.

[0075] In this embodiment, the urban road network data may include basic data such as road section names, vehicle driving directions, signal control systems included in intersecting road sections, non-motor vehicle lanes included in intersecting road sections, etc. By analyzing according to the urban road network data, determine at least one intersecting road section with a non-motor vehicle lane in the set of non-motor vehicle road sections, and further calculate the angular relationship between the center line of the non-motor vehicle road section in each intersecting road section and the center line of the right intersecting road section; wherein, the angular relationship is used to indicate the positional relationship between the non-motor vehicle road section and the intersecting road section.

[0076] As an example, such as Figure 2 and Figure 3 show a schematic diagram of the angular relationship between the center line of the non-motor vehicle road section and the center line of the right intersecting road section, wherein, Figure 2 in, the angle between the center line of the non-motor vehicle road section and the center line of the right intersecting road section is 21°, Figure 3 in, the angle between the center line of the non-motor vehicle road section and the center line of the right intersecting road section is 62°

[0077] In a preferred embodiment of the present application, the urban road network data includes data of the signal control system included in the intersecting road section;

[0078] The step of determining at least one intersecting road section with a non-motor vehicle lane in the set of non-motor vehicle road sections according to the urban road network data includes:

[0079] According to the data of the signal control system included in the intersecting road section, determine at least one intersecting road section with a non-motor vehicle lane and without a signal control system from the set of non-motor vehicle road sections.

[0080] In this embodiment, since intersections with signal control systems can control vehicles to pass safely through traffic lights, while intersections without signal control systems are more prone to traffic accidents. Therefore, according to the data of the signal control system included in the intersecting road section, at least one intersecting road section with a non-motor vehicle lane and without a signal control system can be initially screened out from the set of non-motor vehicle road sections.

[0081] In a preferred embodiment of the present application, the step of determining at least one candidate hidden danger road section from the set of non-motor vehicle road sections according to the positional relationship includes:

[0082] According to the included angle relationship, determine the intersecting road segments with the included angle within a preset range from the at least one intersecting road segment as candidate potential hazard road segments.

[0083] In this embodiment, by determining the position relationship between the non-motor vehicle road segment and the intersecting road segment, determine the non-motor vehicle road segment with the included angle between the two road segments less than the preset range as the candidate potential hazard road segment. Wherein, the preset range is the maximum critical value and the minimum critical value of the included angle set in advance, which is used to initially screen out the non-motor vehicle road segments that may have risks. For example, the preset range can be 0° to 70°, or other included angle intervals, and the embodiments of the present application do not make specific limitations on this.

[0084] As an example, assume that the preset range is 0° to 70°, Figure 2 and Figure 3 the intersecting road segments in can be determined as candidate potential hazard road segments.

[0085] In a preferred embodiment of the present application, the determining the risk assessment value corresponding to each candidate potential hazard road segment according to the preset calculation model includes:

[0086] Determine the included angle amplitude of the intersecting roads corresponding to each candidate potential hazard road segment according to the preset calculation model;

[0087] Obtain the traffic accident data related to each candidate potential hazard road segment, and determine the traffic accident intensity corresponding to each candidate potential hazard road segment according to the traffic accident data;

[0088] Determine the stopping sight distance of the intersecting roads corresponding to each candidate potential hazard road segment;

[0089] Determine the risk assessment value corresponding to the candidate potential hazard road segment according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads.

[0090] In this embodiment, when calculating the risk assessment value corresponding to the candidate potential hazard road segment, three evaluation indicators, namely, the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads, can be used. Determine the included angle amplitude of the intersecting roads corresponding to each candidate potential hazard road segment according to the preset calculation model; obtain the traffic accident data related to each candidate potential hazard road segment, and determine the traffic accident intensity corresponding to each candidate potential hazard road segment according to the traffic accident data; determine the stopping sight distance of the intersecting roads corresponding to each candidate potential hazard road segment; finally, determine the risk assessment value corresponding to the candidate potential hazard road segment according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads.

[0091] In a preferred embodiment of the present application, determining the risk assessment value corresponding to the candidate hidden danger section according to the intersection road angle amplitude, traffic accident intensity, and stopping sight distance of the intersection road includes:

[0092] Determining a first evaluation value according to the intersection road angle amplitude and a first preset scoring model;

[0093] Determining a second evaluation value according to the traffic accident intensity and a second preset scoring model;

[0094] Determining a third evaluation value according to the stopping sight distance of the intersection road and a third preset scoring model;

[0095] Performing weighted calculation on the first evaluation value, the second evaluation value, and the third evaluation value to obtain the risk assessment value corresponding to the candidate hidden danger section.

[0096] Among them, the first preset scoring model is used to evaluate and score according to the angle amplitude. The second preset scoring model is used to evaluate and score according to the traffic accident intensity. The third preset scoring model is used to evaluate and score according to the stopping sight distance. In this embodiment, by determining the first evaluation value according to the intersection road angle amplitude and the first preset scoring model; determining the second evaluation value according to the traffic accident intensity and the second preset scoring model; determining the third evaluation value according to the stopping sight distance of the intersection road and the third preset scoring model, finally, performing weighted calculation on the first evaluation value, the second evaluation value, and the third evaluation value to obtain the risk assessment value corresponding to the candidate hidden danger section.

[0097] Specifically, the calculation processes of the three evaluation indicators are as follows:

[0098] 1. Road angle amplitude: Divide the angle of the intersection road at intervals of 20° to determine the road angle risk level I. As an example, the evaluation scores corresponding to the angle risk levels are shown in Table 1 below:

[0099] Risk level Included angle Evaluation score Level 1 0°-20° 4 Level 2 20°-40° 3 Level 3 40°-60° 2 Level 4 60°-70° 1

[0100] Table 1

[0101] 2. Traffic accident intensity; Determine the traffic accident intensity D of each candidate hidden danger section. The accident intensity is the number of non-motor vehicle and motor vehicle accidents that occurred on this intersection section in the past year. Its risk level is divided as follows:

[0102] D = αω

[0103] Where: α represents the absolute number of each type of accident, and ω represents the weight of each type of accident.

[0104] The weights corresponding to each type of accident are shown in Table 2 below:

[0105] Accident casualties Weight No casualties 1 1 minor injury 2 2 or more minor injuries 3 1 serious injury 4 1 or more deaths / 2 or more serious injuries 5

[0106] Table 2

[0107] As an example, the evaluation scores corresponding to each accident intensity are as shown in Table 3 below:

[0108] Risk level Accident intensity (D) Evaluation score Remarks Level 1 D＞10 4 Extremely high risk hazards Level 2 7＜D≤10 3 Relatively high risk hazards Level 3 4＜D≤7 2 There are certain risk hazards Level 4 0＜D≤4 1 There may be risk hazards

[0109] Table 3

[0110] 3. Stopping sight distance of intersecting roads: The intersection of the center line of the rightmost straight lane of the straight road and the driving track line of the left-turn lane closest to the center line of the intersecting road is the most dangerous conflict point. Measure the stopping sight distance S backward along the center line and the driving track line from the most dangerous conflict point T , and connect the ends of the stopping sight distance to form a sight triangle. There should be no obstruction within the sight triangle. As an example, the standard that the stopping sight distance S T should meet is as shown in Table 4 below:

[0111] Design speed (km / h) 20 30 40 50 Stopping sight distance / m 15 25 30 45

[0112] Table 4

[0113] The risk levels of the stopping sight distances of different roads are classified as S, and the evaluation scores corresponding to each stopping sight distance are as shown in Table 5 below:

[0114]

[0115] Table 5

[0116] Finally, according to the scores calculated above, calculate the comprehensive evaluation index Z. The specific calculation formula is as follows:

[0117] Z = βI + γD + εS

[0118] Among them, β, γ, and ε are the weights of the road intersection angle amplitude, traffic accident intensity, and stopping sight distance of the intersecting road. As an example, according to the Delphi method, the weights of each index can be taken as β = 0.5, γ = 0.3, and ε = 0.2. In addition, the weights of each index can also be set according to needs, and the embodiments of the present application do not make specific restrictions on this

[0119] In a preferred embodiment of the present application, the determining the target risk section from the at least one candidate hidden danger section according to the risk assessment value includes:

[0120] Determine, from the at least one candidate hidden danger section, the candidate hidden danger section whose risk assessment value exceeds the preset threshold as the target risk section

[0121] Among them, the preset threshold is a pre-set scoring critical value. In this embodiment, by presetting a threshold, it is determined whether each risk assessment value is greater than the preset threshold. If the risk assessment value is greater than or equal to the preset threshold, it can be determined that this candidate hidden danger road section is a target risk road section. If the risk assessment value is less than the preset threshold, it can be determined that this candidate hidden danger road section is not a target risk road section.

[0122] In this embodiment, by obtaining urban road network data and using the urban road network data to construct a position relationship model between non-motor vehicle road sections and intersecting road sections, the target risk road sections can be analyzed based on this position relationship model, so that traffic managers can timely and quickly discover the hidden dangers existing in the planning and design of non-motor vehicle lanes in urban roads, and then re-plan the risk road sections in time to reduce the occurrence of unnecessary traffic safety accidents.

[0123] Embodiment 2

[0124] Figure 4 It is a schematic structural composition diagram of the non-motor vehicle key risk point section identification system in this application. This system can be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the embodiments of this application. The program modules referred to in the embodiments of this application refer to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment. As Figure 4 shown, the non-motor vehicle key risk point section identification system may include the following modules: a non-motor vehicle lane determination module 401, a position relationship determination module 402, a candidate hidden danger road section determination module 403, a risk assessment value determination module 404, and a target risk road section determination module 405, where:

[0125] The non-motor vehicle lane determination module 401 is used to obtain urban road network data and determine a non-motor vehicle road section set from the urban road network data;

[0126] The position relationship determination module 402 is used to determine the position relationship between each non-motor vehicle road section in the non-motor vehicle road section set and an intersecting road section according to the urban road network data;

[0127] The candidate hidden danger road section determination module 403 is used to determine at least one candidate hidden danger road section from the non-motor vehicle road section set according to the position relationship;

[0128] The risk assessment value determination module 404 is used to determine the risk assessment value corresponding to each candidate hidden danger road section according to a pre-designed calculation model;

[0129] A target risk road section determination module 405, configured to determine a target risk road section from the at least one candidate potential hazard road section according to the risk assessment value.

[0130] In a preferred embodiment of the present application, the position relationship determination module 402 includes:

[0131] An intersecting road section determination sub-module, configured to determine at least one intersecting road section with a non-motor vehicle lane set in the non-motor vehicle road section set according to the urban road network data;

[0132] A position relationship determination sub-module, configured to determine an included angle relationship between the center line of the non-motor vehicle road section in the intersecting road section and the center line of the intersecting road section on its right, and determine the included angle relationship as the position relationship between the non-motor vehicle road section and the intersecting road section.

[0133] In a preferred embodiment of the present application, the urban road network data includes data of a signal control system included in the intersecting road section;

[0134] The intersecting road section determination sub-module is specifically configured to:

[0135] Determine at least one intersecting road section with a non-motor vehicle lane set and not including a signal control system from the non-motor vehicle road section set according to the data of the signal control system included in the intersecting road section.

[0136] In a preferred embodiment of the present application, the candidate potential hazard road section determination module 403 is specifically configured to:

[0137] Determine, according to the included angle relationship, an intersecting road section with an included angle within a preset range from the at least one intersecting road section as a candidate potential hazard road section.

[0138] In a preferred embodiment of the present application, the risk assessment value determination module 404 includes:

[0139] An included angle amplitude determination sub-module, configured to determine the included angle amplitude of the intersecting roads corresponding to each candidate potential hazard road section according to a pre-designed calculation model;

[0140] A traffic accident intensity determination sub-module, configured to obtain traffic accident data related to each candidate potential hazard road section, and determine the traffic accident intensity corresponding to each candidate potential hazard road section according to the traffic accident data;

[0141] A stopping sight distance determination sub-module, configured to determine the stopping sight distance of the intersecting roads corresponding to each candidate potential hazard road section;

[0142] A risk assessment value determination sub-module, configured to determine the risk assessment value corresponding to the candidate potential hazard road section according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads.

[0143] In a preferred embodiment of the present application, the risk assessment value determination sub-module is specifically configured to:

[0144] Determine a first evaluation value according to the intersection road angle amplitude and a first preset scoring model;

[0145] Determine a second evaluation value according to the traffic accident intensity and a second preset scoring model;

[0146] Determine a third evaluation value according to the stopping sight distance of the intersecting road and a third preset scoring model;

[0147] Perform a weighted calculation on the first evaluation value, the second evaluation value, and the third evaluation value to obtain the risk assessment value corresponding to the candidate hidden danger section.

[0148] In a preferred embodiment of the present application, the target risk section determination module 405 is specifically configured to:

[0149] Determine, from the at least one candidate hidden danger section, a candidate hidden danger section whose risk assessment value exceeds a preset threshold as the target risk section.

[0150] It should be noted that the non-motor vehicle key risk point section identification system provided in the above embodiment and the above Figure 1 The provided non-motor vehicle key risk point section identification method belong to the same concept. The specific implementation process can refer to the above system embodiment and will not be elaborated here.

[0151] Embodiment III

[0152] The present application provides a non-motor vehicle key risk point section identification device, and the device includes:

[0153] At least one processor; and

[0154] A memory communicatively connected to the at least one processor; wherein:

[0155] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the non-motor vehicle key risk point section identification method described in any one of the above.

[0156] Embodiment IV

[0157] Figure 5 For the non-motor vehicle key risk point section identification device in the present application, as Figure 5As shown, the non-motor vehicle key risk point segment identification device 500 includes at least one processor 501 and a memory 502 for storing a computer program that can run on the processor 501. When the processor 501 is used to run the computer program, it executes the non-motor vehicle key risk point segment identification method prompted by the above embodiments of the present application. The non-motor vehicle key risk point segment identification device 500 further includes at least one network interface 504 and a user interface 503. Each component in the non-motor vehicle key risk point segment identification device 500 is coupled together through a bus system 505. It can be understood that the bus system 505 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 5 all kinds of buses are labeled as the bus system 505.

[0158] Among them, the user interface 503 may include a display, a keyboard, a mouse, a trackball, a click wheel, a button, a button, a touchpad, or a touch screen, etc.

[0159] It can be understood that the memory 502 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), an erasable programmable read-only memory (EPROM, Erasable Programmable Read-Only Memory), an electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read-Only Memory), a ferromagnetic random access memory (FRAM, ferromagnetic random access memory), a flash memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM, Compact Disc Read-Only Memory); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM, Random Access Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as a static random access memory (SRAM, Static Random Access Memory), a synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory), a dynamic random access memory (DRAM, Dynamic Random Access Memory), a synchronous dynamic random access memory (SDRAM, SynchronousDynamic Random Access Memory), a double data rate synchronous dynamic random access memory (DDRSDRAM, Double Data Rate Synchronous Dynamic Random Access Memory), an enhanced synchronous dynamic random access memory (ESDRAM, Enhanced Synchronous Dynamic Random Access Memory), a sync link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), a direct rambus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 502 described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memory.

[0160] The memory 502 in the embodiments of the present application is used to store various types of data to support the operation of the non-motor vehicle key risk point segment identification device 500. Examples of such data include: any computer programs for operating on the non-motor vehicle key risk point segment identification device 500, such as the operating system 5021, application programs 5022, and the non-motor vehicle key risk point segment identification system 5023; among them, the operating system 5021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application programs 5022 can include various application programs, such as a Media Player, a Browser, etc., for implementing various application services. The non-motor vehicle key risk point segment identification system 5023 is the system Figure 4 shown in this application, used to implement the cep simulation. The program for implementing the method of the embodiments of the present application can be included in the application program 5022 or in the non-motor vehicle key risk point segment identification system 5023.

[0161] The processor 501 may be an integrated circuit chip with the ability to process signals. In the implementation process, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 501 or by instructions in the form of software. The above-mentioned processor 501 can be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 501 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. Combining the steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or by a combination of the hardware and software modules in the decoding processor. The software module can be located in the storage medium, and this storage medium is located in the memory 502. The processor 501 reads the information in the memory 502 and combines its hardware to complete the steps of the foregoing method.

[0162] In an exemplary embodiment, the non-motor vehicle critical risk point segment identification device 500 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, microcontroller units (MCUs), microprocessors, or other electronic components, and is used to execute the foregoing method.

[0163] In an exemplary embodiment, the embodiment of the present application further provides a computer-readable storage medium, such as a memory 502 including a computer program. The foregoing computer program can be executed by a processor 501 of the non-motor vehicle critical risk point segment identification device 500 to complete the steps of the foregoing method. The computer-readable storage medium may be a FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.; it may also be various devices including one or any combination of the foregoing memories, such as a computer, a tablet device, a personal digital assistant, etc.

[0164] The embodiment of the present application also records a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the surge warning method of the compressor prompted by the foregoing embodiment of the present application.

[0165] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. In addition, the features disclosed in several method or device embodiments provided by the present application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0166] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application.

Claims

1. A method for identifying key risk point segments of non-motor vehicles, characterized in that, The method includes: Obtaining urban road network data and determining a set of non-motor vehicle road segments from the urban road network data; Determining the positional relationship between each non-motor vehicle road segment in the set of non-motor vehicle road segments and the intersecting road segments according to the urban road network data; Determining at least one candidate hidden danger road segment from the set of non-motor vehicle road segments according to the positional relationship; Determining the risk assessment value corresponding to each candidate hidden danger road segment according to a pre-designed calculation model; Determining the target risk road segment from the at least one candidate hidden danger road segment according to the risk assessment value.

2. The non-motor vehicle key risk point section identification method according to claim 1, wherein, The determining the positional relationship between each non-motor vehicle road segment in the set of non-motor vehicle road segments and the intersecting road segments according to the urban road network data includes: Determining at least one intersecting road segment with a non-motor vehicle lane in the set of non-motor vehicle road segments according to the urban road network data; Calculating the included angle relationship between the center line of the non-motor vehicle road segment in the intersecting road segment and the center line of the right intersecting road segment; wherein, the included angle relationship is used to indicate the positional relationship between the non-motor vehicle road segment and the intersecting road segment.

3. The method for identifying key risk point segments of non-motor vehicles according to claim 2, characterized in that The urban road network data includes data of the signal control system contained in the intersecting road segments; The determining at least one intersecting road segment with a non-motor vehicle lane in the set of non-motor vehicle road segments according to the urban road network data includes: Determining at least one intersecting road segment with a non-motor vehicle lane and not containing a signal control system from the set of non-motor vehicle road segments according to the data of the signal control system contained in the intersecting road segments.

4. The method for identifying key risk point segments of non-motor vehicles according to claim 1, characterized in that The determining at least one candidate hidden danger road segment from the set of non-motor vehicle road segments according to the positional relationship includes: Determining, according to the included angle relationship, the intersecting road segments with an included angle within a preset range from the at least one intersecting road segment as candidate hidden danger road segments.

5. The method for identifying key risk point segments of non-motor vehicles according to claim 1, wherein The determining the risk assessment value corresponding to each candidate hidden danger road segment according to a pre-designed calculation model includes: Determining the included angle amplitude of the intersecting roads corresponding to each candidate hidden danger road segment according to a pre-designed calculation model; Obtaining traffic accident data related to each candidate hidden danger road segment and determining the traffic accident intensity corresponding to each candidate hidden danger road segment according to the traffic accident data; Determining the stopping sight distance of the intersecting roads corresponding to each candidate hidden danger road segment; Determining the risk assessment value corresponding to the candidate hidden danger road segment according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads.

6. The method for identifying key risk point segments of non-motor vehicles according to claim 1, characterized in that The determining the risk assessment value corresponding to the candidate hidden danger road segment according to the included angle amplitude of the intersecting roads, the traffic accident intensity, and the stopping sight distance of the intersecting roads includes: Determining a first evaluation value according to the included angle amplitude of the intersecting roads and a first preset scoring model; Determining a second evaluation value according to the traffic accident intensity and a second preset scoring model; Determining a third evaluation value according to the stopping sight distance of the intersecting roads and a third preset scoring model; Performing weighted calculation on the first evaluation value, the second evaluation value, and the third evaluation value to obtain the risk assessment value corresponding to the candidate hidden danger road segment.

7. The method for identifying key risk point segments of non-motor vehicles according to claim 1, characterized in that, The determining the target risk road segment from the at least one candidate hidden danger road segment according to the risk assessment value includes: From the at least one candidate hidden danger road section, determine the candidate hidden danger road section whose risk assessment value exceeds the preset threshold as the target risk road section.

8. A non-motor vehicle critical risk point segment identification system, characterized in that, The system includes: A non-motor vehicle lane determination module, configured to obtain urban road network data and determine a non-motor vehicle road section set from the urban road network data; A position relationship determination module, configured to determine the position relationship between each non-motor vehicle road section in the non-motor vehicle road section set and the intersecting road section according to the urban road network data; A candidate hidden danger road section determination module, configured to determine at least one candidate hidden danger road section from the non-motor vehicle road section set according to the position relationship; A risk assessment value determination module, configured to determine the risk assessment value corresponding to each candidate hidden danger road section according to a preset calculation model; A target risk road section determination module, configured to determine the target risk road section from the at least one candidate hidden danger road section according to the risk assessment value.

9. The non-motor vehicle key risk point segment identification system according to claim 8, wherein, The position relationship determination module includes: An intersecting road section determination sub-module, configured to determine at least one intersecting road section with a non-motor vehicle lane in the non-motor vehicle road section set according to the urban road network data; A position relationship determination sub-module, configured to determine the included angle relationship between the center line of the non-motor vehicle road section in the intersecting road section and the center line of the intersecting road section on its right, and determine the included angle relationship as the position relationship between the non-motor vehicle road section and the intersecting road section.

10. A non-motor vehicle key risk point section identification device, characterized in that The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein: The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the non-motor vehicle key risk point section identification method according to any one of claims 1 to 7.

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