Improved traffic conflict risk evaluation method and system based on TTC index and relative speed difference, storage medium and equipment

Abstract

The invention discloses an improved traffic conflict risk evaluation method and system based on a TTC index and a relative speed difference, a storage medium and equipment, and belongs to the technical field of safety risk prediction of extra-long tunnels of highways in mountainous areas. In order to solve the problems that an existing traffic conflict risk evaluation index cannot better quantify the degree of traffic conflict risks when the speed of a rear vehicle is smaller than the speed of a front vehicle and cannot more meticulously distinguish traffic conflicts under different distance speed combinations, the method comprises the following steps of: reading multi-frame data within one second according to a time sequence; according to the vehicle numbers appearing in the multiple frames, if the number of the vehicle numbers is larger than a vehicle number threshold value, it is judged that conflicts occur, traffic conflict indexes are calculated for each frame in which two vehicles exist at the same time, and evaluation is conducted through the traffic conflict indexes including the TTC-IM indexes.

Description

Technical Field

[0001] The present invention belongs to the technical field of safety risk prediction for extra-long tunnels on mountain highways, and specifically relates to a traffic conflict risk assessment method, system, storage medium and equipment. Background Art

[0002] With the acceleration of urbanization, road traffic volume is increasing, and the incidence of traffic accidents is also increasing. Among many traffic accidents, conflict risk caused by driver error or complex traffic environment is one of the main reasons. In order to improve traffic safety and reduce the occurrence of traffic accidents, traffic conflict risk assessment has become an important direction of traffic safety research.

[0003] Traditional traffic conflict risk assessment methods mainly rely on a single indicator, such as the Time to Collision (TTC) indicator, which evaluates the conflict risk by calculating the time required for a vehicle to reach a potential collision state from its current state. However, the evaluation method based on a single indicator has the following limitations: relying solely on the TTC indicator cannot fully reflect the complex situation of traffic conflicts, such as the influence of factors such as driver reaction time, vehicle performance, and road conditions; the TTC indicator has poor adaptability to different traffic scenarios, and it is difficult to accurately assess the conflict risk in various complex situations. Therefore, it is very meaningful to combine multiple indicators to evaluate traffic conflicts and propose an improved traffic conflict risk assessment method based on the TTC indicator and relative speed difference. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing traffic conflict risk evaluation index cannot better quantify the degree of traffic conflict risk when the speed of the rear vehicle is lower than the speed of the front vehicle and cannot more finely distinguish traffic conflicts under different distance-speed combinations.

[0005] An improved traffic conflict risk assessment method based on TTC index and relative speed difference includes the following steps:

[0006] Firstly, the road video surveillance data is obtained, and the traffic flow data is obtained based on the road video surveillance data;

[0007] Then, multiple frames of data within one second are read in chronological order, and the vehicle numbers appearing in the multiple frames within this second are stored. If the number of vehicle numbers is greater than the vehicle number threshold, it is determined that a conflict has occurred;

[0008] According to the extracted number of seconds of conflict occurrence, a traffic conflict index is calculated for each frame in which two vehicles exist at the same time, wherein the traffic conflict index includes a TTC-IM index;

[0009] The TTC-IM indicators are as follows:

[0010]

[0011] In the formula, x i-1 (t) is the position of the preceding vehicle i-1 at time t; x i (t) is the position of vehicle i at time t; l i-1 is the vehicle length of the preceding vehicle i-1; is the speed of vehicle i at time t; is the speed of the preceding vehicle i-1 at time t; λ1 is the relative speed correction coefficient; λ2 is the vehicle length-to-distance ratio correction coefficient; λ3 is the safety distance coefficient;

[0012] Finally, the traffic conflict risk is evaluated based on traffic conflict indicators.

[0013] Furthermore, the road video surveillance data is video surveillance data of a highway.

[0014] Furthermore, the vehicle quantity threshold is 1.

[0015] Furthermore, the process of acquiring traffic flow data based on road video surveillance data is implemented using a neural network model.

[0016] An improved traffic conflict risk assessment system based on TTC index and relative speed difference, comprising:

[0017] Traffic flow data analysis module: obtains road video surveillance data, and obtains traffic flow data based on the road video surveillance data;

[0018] Traffic conflict preliminary identification module: reads multiple frames of data within one second in chronological order, stores the vehicle numbers that appear in the multiple frames within this second, and if the number of vehicle numbers is greater than the vehicle number threshold, it is determined that a conflict has occurred;

[0019] Traffic conflict index calculation module: according to the extracted number of seconds of conflict occurrence, the traffic conflict index is calculated for each frame in which two vehicles exist at the same time, and the traffic conflict index includes the TTC-IM index;

[0020] The TTC-IM indicators are as follows:

[0021]

[0022] In the formula, x i-1 (t) is the position of the preceding vehicle i-1 at time t; x i (t) is the position of vehicle i at time t; l i-1 is the vehicle length of the preceding vehicle i-1; is the speed of vehicle i at time t; is the speed of the preceding vehicle i-1 at time t; λ1 is the relative speed correction coefficient; λ2 is the vehicle length-to-distance ratio correction coefficient; λ3 is the safety distance coefficient.

[0023] Conflict risk evaluation module: Evaluate traffic conflict risks based on traffic conflict indicators.

[0024] Furthermore, the road video surveillance data is video surveillance data of a highway.

[0025] Furthermore, the vehicle quantity threshold is 1.

[0026] Furthermore, the process of acquiring traffic flow data based on road video surveillance data is implemented using a neural network model.

[0027] A computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to execute an improved traffic conflict risk assessment system based on a TTC index and a relative speed difference.

[0028] A device for evaluating the risk of an improved traffic conflict based on a TTC index and a relative speed difference. The device comprises a processor and a memory. The memory stores at least one instruction. The at least one instruction is loaded and executed by the processor to evaluate the risk of an improved traffic conflict based on a TTC index and a relative speed difference.

[0029] The beneficial effects of the present invention are:

[0030] The present invention can solve the situation where the TTC index appears negative when the speed of the rear vehicle is less than the speed of the front vehicle by analyzing the risk of traffic conflict under different speed and distance combinations when the speed of the rear vehicle is less than the speed of the front vehicle, and better quantify the degree of traffic conflict risk in this specific situation. At the same time, it can more finely distinguish the traffic conflict risk under different distance and speed combinations, thereby improving the accuracy of risk assessment. The above two analysis methods are effectively integrated, based on the TTC conflict evaluation index, combined with the situation where the speed of the rear vehicle is less than the speed of the front vehicle and different speed and distance combinations, an improved traffic conflict risk assessment method is proposed, which can well quantify the degree of traffic conflict risk when the speed of the rear vehicle is less than the speed of the front vehicle, and more finely distinguish traffic conflicts under different distance and speed combinations, thereby enhancing the comprehensiveness and systematicness of the evaluation method. The present invention is particularly suitable for the field of safety risk prediction of extra-long tunnels of mountain highways, which helps to identify and warn potential safety hazards in advance, reduce the occurrence of traffic accidents, ensure the safety of drivers, and improve the level of traffic safety management of extra-long tunnels of mountain highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1This is a flow chart of improved traffic conflict risk assessment based on TTC index and relative speed difference.

[0032] Figure 2 These are four examples of vehicle types of driving data used in the present invention.

[0033] Figure 3 This is the TTC-IM accuracy test chart.

[0034] Figure 4 This is the TTC accuracy test chart.

[0035] Figure 5 This is a DRAC accuracy test chart.

[0036] Figure 6 This is the TTC-IM readability test chart.

[0037] Figure 7 This is the TTC readability test chart.

[0038] Figure 8 This is a DRAC readability test chart. DETAILED DESCRIPTION

[0039] Specific implementation method 1: Combination Figure 1 To explain this embodiment,

[0040] This embodiment is an improved traffic conflict risk assessment method based on TTC index and relative speed difference, comprising the following steps:

[0041] Step 1: Using the surveillance cameras currently installed at intervals (150m-500m) and at the entrances and exits in the Guming Tunnel and Jiangmaoshan Tunnel of the Sannan Expressway (Xinliu South Section) in Guangxi Zhuang Autonomous Region, and considering the direction, speed and field of view requirements of vehicles, as well as factors such as avoiding obstructions and shadows, the cameras are installed on the tunnel wall on the right side of the up and down lanes inside the expressway tunnel and on the light poles of the central dividing strip of the tunnel entrance and exit to obtain the best field of view and coverage. The video surveillance data currently obtained is shown in Table 1.

[0042] Table 1 The collected video data

[0043]

[0044]

[0045] Step 2: Based on this, traffic flow data is obtained through video detection and image recognition technology based on Yolov7, including the model, instantaneous speed, acceleration, displacement trajectory of each vehicle, as well as the traffic volume, traffic composition, average speed, etc. in each fixed time period (30s), providing a theoretical basis for the subsequent analysis of traffic conflicts.

[0046] Examples of specific data types involved are as follows:

[0047] (1) Instantaneous vehicle speed (m / s)

[0048] The instantaneous speed of the vehicle is obtained for each frame. The data is subsequently processed preliminarily to calculate the average and standard deviation of the speed per second. Taking vehicle No. 2 as an example, its instantaneous speed within 1 second is shown in Table 2.

[0049] Table 2 Vehicle instantaneous speed data

[0050]

[0051] (2) Vehicle instantaneous acceleration (m / s) 2 )

[0052] The vehicle instantaneous acceleration is calculated from the instantaneous speed of the vehicle in each frame. The instantaneous speed of the vehicle can be obtained from every 13 consecutive frames. Taking vehicle No. 2 as an example, its acceleration is shown in Table 3.

[0053] Table 3 Vehicle instantaneous acceleration data

[0054]

[0055] (3) Vehicle displacement (m)

[0056] The vehicle displacement is obtained for each frame, and the distance between the vehicle and the detection starting point at each frame is measured. Taking vehicle No. 2 as an example, its displacement is shown in Table 4.

[0057] Table 4 Vehicle displacement data

[0058]

[0059] (4) Vehicle type

[0060] There are four types of vehicles: car, bus, van, and truck. See the attached image for examples of specific vehicle types. Figure 2 , where (a) is a car, (b) is a bus, (c) is a van, and (d) is a truck.

[0061] (5) 30s traffic volume (pcu)

[0062] The traffic volume is calculated every 30 seconds. The total traffic volume and the traffic volume of small vehicles and large vehicles in the 30 seconds before this moment are calculated. The data from 31s to 42s are used as an example, as shown in Table 5.

[0063] Table 5 30s traffic volume data

[0064]

[0065]

[0066] (6) 30s average speed (m / s)

[0067] The average speed is calculated every 30 seconds. The total average of the 30 seconds before that moment and the average speed of small cars and large cars are calculated. The data from 31s to 42s are used as an example, as shown in Table 6.

[0068] Table 6 30s average vehicle speed data

[0069]

[0070] Step 3: Perform preliminary identification of conflicts on traffic flow data. Because the acquired data is instantaneous data of 30 frames within one second, and the required data is conflict data in seconds, the first step is to perform preliminary identification of the seconds when the conflict occurred. The basic logic of the screening is to read 30 frames of data within one second in chronological order and store the vehicle numbers that appear in the 30 frames of this second. If the number of vehicle numbers is greater than 1, it is determined that a conflict has occurred. Taking the conflict data of the 4th second as an example, the data of some frames are shown in Table 7.

[0071] Table 7 Conflicting data examples

[0072]

[0073]

[0074] Step 4: Based on the number of seconds when the collision occurs, traffic conflict indicators are calculated for each frame in which two vehicles exist at the same time, including TTC (Time to Collision) and DRAC (Deceleration Rate to Avoid the Crush).

[0075] TTC is the distance between two objects divided by their relative speed. The calculation formula is shown in formula (1):

[0076]

[0077] In the formula, x i-1 (t)——the position of the preceding vehicle i-1 at time t, m; x i (t)——the position of vehicle i at time t, m; l i-1 ——the vehicle length of the preceding vehicle i-1, in m; ——The speed of the preceding vehicle i-1 at time t, m / s; ——The speed of vehicle i at time t, m / s.

[0078] DRAC is the square of the velocity difference between two objects divided by twice their distance. The calculation formula is shown in formula (2):

[0079]

[0080] In the formula, x i-1 (t)——the position of the preceding vehicle i-1 at time t, m; x i (t)——the position of vehicle i at time t, m; l i-1 ——the vehicle length of the preceding vehicle i-1, in m; ——The speed of vehicle i at time t, m / s; ——The speed of the preceding vehicle i-1 at time t, m / s.

[0081] The minimum value of TTC and the maximum value of DRAC within one second are selected as the representative values ​​of each traffic conflict index in that second. The conflict evaluation index values ​​at some conflict moments are taken as examples. The data examples are shown in Table 8.

[0082] Table 8 Values ​​of conflict evaluation indicators at some conflict moments

[0083]

[0084]

[0085] Step 5: Improve the TTC indicator expression:

[0086] (1) Find the reciprocal of the original TTC index;

[0087] (2) Based on the improved TTC in (1), the relative speed part is introduced, with the exponential function as the base function and the difference between the speed of the rear vehicle and the speed of the front vehicle as the independent variable. The specific expression is shown in formula (3):

[0088]

[0089] In the formula, ——The speed of the preceding vehicle i-1 at time t, m / s; ——The speed of the rear vehicle i at time t, m / s; λ1——Relative speed correction coefficient.

[0090] When the speed of the rear vehicle is greater than that of the front vehicle, the value of the relative speed part is greater than 0, and the value of formula (3) is greater than 1, increasing with the increase of the relative speed and exponentially growing; when the speed of the rear vehicle is less than that of the front vehicle, the value of the relative speed part is less than 1, decreasing with the increase of the relative speed. The relative speed correction coefficient λ1 (taken as 0.2 in this embodiment) is used to correct the growth degree of the exponential function in order to better measure the risk of traffic conflict when the speed of the rear vehicle is less than the drag speed.

[0091] (3) Based on the improved TTC in (2), the vehicle length to distance ratio part is introduced. The vehicle length to distance ratio part uses an exponential function as the base function and the ratio of vehicle length to headway time as the independent variable. The specific expression is shown in formula (4).

[0092]

[0093] Where x i-1 (t)——the position of the preceding vehicle i-1 at time t, m; x i (t)——the position of vehicle i at time t, m; l i-1 ——Vehicle length of the preceding vehicle i-1, m; λ2——Vehicle length to distance ratio correction factor; λ3——Safety distance factor.

[0094] The ratio of vehicle length to headway is a number less than or equal to 1. The smaller the headway, the closer the ratio is to 1, and the larger the corresponding exponential function value will be; the larger the headway, the smaller the ratio is, and the smaller the corresponding exponential function value will be. The safety distance coefficient λ3 is the ratio of the average vehicle length of 5m to the recommended safety distance of 100m (recommended safety distance at 100km / h), λ3=0.05, and the vehicle length-to-distance ratio correction coefficient λ2 (0.2 in this embodiment) is used to correct the growth degree of the exponential function, in order to distinguish the risk of traffic conflicts under different distance-speed combinations.

[0095] (4) Substitute the relative speed part and the vehicle length-to-distance ratio part in (2) and (3) into the numerator of the improved TTC index in (1), and multiply the numerator by the speed of the following vehicle to obtain the comprehensive improved TTC-IM index. The specific expression of TTC-IM is shown in formula (5):

[0096]

[0097] Where x i-1 (t)——the position of the preceding vehicle i-1 at time t, m; x i (t)——the position of vehicle i at time t, m; l i-1 ——the vehicle length of the preceding vehicle i-1, in m; ——The speed of vehicle i at time t, m / s; ——The speed of the preceding vehicle i-1 at time t, m / s; λ1——Relative speed correction coefficient; λ2——Vehicle length-to-distance ratio correction coefficient; λ3——Safety distance coefficient.

[0098] Step 6: Compare the accuracy of TTC-IM, TTC and DRAC indicators to verify the advantages of TTC-IM as a traffic conflict evaluation indicator in terms of accuracy, precision and completeness. The test data comes from the 1h (15:42:32-16:42:32) traffic conflict data of the high-risk section (K352+368) of the Maoshan Tunnel obtained through video surveillance in step 2 and the second series data of TTC and DRAC indicators calculated in step 4. The accuracy test of TTC, DRAC and TTC-IM is based on existing relevant traffic conflict research, with manual inspection as the benchmark criterion, to evaluate the accuracy of the three conflict indicators in detecting traffic conflicts.

[0099] First, based on the video detection data, the number of seconds of traffic conflicts was selected through manual detection, and a total of 209 conflict data were obtained from 3600 data. The data were divided into two parts with equal total data volume in chronological order, one part was the training set (127 / 1800), which was used to select the thresholds of TTC, DRAC, and TTC-IM, and the other part was the test set (82 / 1800), which was used to verify the accuracy of TTC, DRAC, and TTC-IM.

[0100] The corresponding TTC, DRAC, and TTC-IM values ​​are extracted from the training set and the average values ​​are taken, as shown in formula (6), formula (7), and formula (8).

[0101] TTC 平均 =2.778s (6)

[0102] DRAC 平均 =2.854m / s 2 (7)

[0103] TTC-IM 平均 =0.51s -1 (8)

[0104] The above values ​​are used as the thresholds of TTC, DRAC, and TTC-IM traffic conflict analysis indicators and tested in the test set.

[0105] (1) TTC test results

[0106] According to the TTC threshold, we finally selected data values ​​below 70 threshold values, that is, 70 traffic conflict seconds were detected, and the detected data were manually verified by video. After inspection, 65 of the 70 data were identified as traffic conflict events, 5 were false detections, and 16 traffic conflict data were not detected. The final TTC traffic conflict risk index accuracy was 98.8% [(65+1713) / 1800*100%], the precision was 92.9% [65 / 70*100%], and the recall was 79.3% [65 / 82*100%]. The specific confusion matrix is ​​attached. Figure 3 .

[0107] (2) DRAC test results

[0108] According to the threshold of TTC-IM, 66 data values ​​above the threshold were finally selected, 66 traffic conflict seconds were detected, and the detected data were manually verified by video. After inspection, 60 of the 66 data were identified as traffic conflict events, 6 were false detections, and 22 conflict data were not detected. The final DRAC traffic conflict risk index accuracy was 98.4% [(60+1712) / 1800*100%], the precision was 90.9% [60 / 66*100%], and the recall was 73.1% [60 / 82*100%]. The specific confusion matrix is ​​attached. Figure 4 .

[0109] (3) TTC-IM test results

[0110] According to the threshold of TTC-IM, 79 data values ​​above the threshold were finally selected, that is, 79 traffic conflict seconds were detected, and the detected data were manually verified by video. After inspection, 78 of the 79 data were identified as traffic conflict events, 1 was a false detection, and 4 traffic conflict data were not detected. Finally, the accuracy of the TTC-IM traffic conflict risk index was 99.7% [(78+1717) / 1800*100%], the precision was 98.7% [78 / 79*100%], and the recall was 95.1% [78 / 82*100%]. The specific confusion matrix is ​​attached. Figure 5 .

[0111] (4) Comprehensive comparative analysis

[0112] The comparison of accuracy, precision and recall of the three conflict indicators is shown in Table 9:

[0113] Table 9 Comparison of conflict indicator recognition accuracy

[0114]

[0115] As can be seen from the table, the accuracy, precision and recall of TTC-IM are the highest, and all reach more than 95%. Therefore, the accuracy of this traffic conflict analysis indicator is good.

[0116] Step 7: Perform a readability comparison test on the TTC-IM index, TTC index and DRAC index to verify the advantages of the TTC-IM index as a traffic conflict evaluation index in terms of accuracy, precision and completeness. The test data comes from the 209 traffic conflict data within 1 hour (15:42:32-16:42:32) of the detection point in the high-risk section (K352+368) of the Maoshan Tunnel obtained through video surveillance in Step 2. The readability test of TTC, DRAC and TTC-IM is based on the principles of mathematical statistics. The frequency histogram and probability distribution of the indicators are used as the benchmark judgment criteria to evaluate the readability of the three conflict indicators in detecting traffic conflicts, and to test the continuity, uniformity and readability of the three evaluation indicators. Statistical histogram analysis of TTC, DRAC and TTC-IM was performed on some of the 209 conflict data detected above. The frequency histogram of TTC-IM is attached. Figure 6 、TTC frequency histogram attached Figure 7 , DRAC frequency histogram attached Figure 8 .

[0117] As can be seen from the figure, the TTC value is generally dispersed and has a relatively obvious risk threshold. However, since it cannot estimate the situation where the speed of the rear vehicle is lower than that of the front vehicle, it will miss some traffic conflict risk situations. And because its threshold is high, it is difficult to estimate the degree of change in conflict risk more sensitively.

[0118] DRAC presents an approximate Weibull distribution, indicating that its risk sensitivity is low and it is difficult to better estimate the degree of conflict risk between similar values. At the same time, compared with TTC, the thresholds for dividing its various risk levels are also difficult to determine through more precise values.

[0119] TTC-IM presents an exponential distribution as a whole and makes up for the shortcomings of the above two indicators. For example, when the speed of the rear vehicle is lower than that of the front vehicle, TTC-IM can accurately assess the degree of conflict risk, and the TTC-IM value distribution is relatively uniform, ensuring that moderate sensitivity can be obtained in more detailed micro-traffic conflict analysis, thereby better dividing the risk level.

[0120] The above implementation is only an implementation case and does not limit the scope of implementation of the present invention. The data of the implementation case is only used as a reference for the specific application calculation of the method of the present invention, and it is not guaranteed that the exemplified data is completely true and reasonable. It should be understood that the data calculation in the implementation case may cause some results to be slightly different due to the difference in the number of significant digits retained; since it is only for reference in the calculation process, the significant digits of the original data and the calculation results (including intermediate results and final results) in the embodiment are retained without considering the actual rationality. In the actual application process, the significant digits of reasonable digits should be retained according to the actual significance and calculation accuracy requirements. The present invention can also have other various implementation cases. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and deformations according to the present invention, but these corresponding changes and deformations should belong to the protection scope of the claims attached to the present invention. Specific implementation method 2:

[0122] This embodiment is an improved traffic conflict risk assessment system based on TTC index and relative speed difference, which is actually a program product or computer software corresponding to the improved traffic conflict risk assessment method based on TTC index and relative speed difference. The improved traffic conflict risk assessment system based on TTC index and relative speed difference described in this embodiment specifically includes:

[0123] Traffic flow data analysis module: obtains road video surveillance data, and obtains traffic flow data based on the road video surveillance data; further, the road video surveillance data is video surveillance data of a highway, and the process of obtaining traffic flow data based on the road video surveillance data is implemented using a neural network model.

[0124] Traffic conflict preliminary identification module: reads multiple frames of data within one second in chronological order, stores the vehicle numbers that appear in the multiple frames within this second, and if the number of vehicle numbers is greater than the vehicle number threshold, it is determined that a conflict has occurred; further, the vehicle number threshold is 1.

[0125] Traffic conflict index calculation module: according to the extracted number of seconds of conflict occurrence, the traffic conflict index is calculated for each frame in which two vehicles exist at the same time, and the traffic conflict index includes the TTC-IM index;

[0126] The TTC-IM indicators are as follows:

[0127]

[0128] In the formula, x i-1 (t) is the position of the preceding vehicle i-1 at time t; x i (t) is the position of vehicle i at time t; l i-1 is the vehicle length of the preceding vehicle i-1; is the speed of vehicle i at time t; is the speed of the preceding vehicle i-1 at time t; λ1 is the relative speed correction coefficient; λ2 is the vehicle length-to-distance ratio correction coefficient; λ3 is the safety distance coefficient.

[0129] Conflict risk evaluation module: Evaluate traffic conflict risks based on traffic conflict indicators. Specific implementation method three:

[0131] This embodiment is a computer storage medium, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to execute the improved traffic conflict risk assessment system based on TTC index and relative speed difference.

[0132] It should be understood that the instructions include computer program products, software or computerized methods corresponding to any method described in the present invention; the instructions can be used to program a computer system, or other electronic device. Computer storage media may include readable media on which instructions are stored, which may include but are not limited to magnetic storage media, optical storage media; magneto-optical storage media include read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM) and flash memory layers, or other types of media suitable for storing electronic instructions. Specific implementation method four:

[0134] This embodiment is an improved traffic conflict risk assessment device based on TTC index and relative speed difference, the device includes a processor and a memory, it should be understood that including any device including a processor and a memory described in the present invention, the device may also include other units and modules that perform display, interaction, processing, control, etc. and other functions through signals or instructions;

[0135] The memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to execute the improved traffic conflict risk assessment system based on TTC index and relative speed difference.

[0136] Those skilled in the art will appreciate that at least one instruction stored is a computer program product corresponding to the method or system. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application, and may also be used for corresponding devices. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0142] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. An improved traffic conflict risk assessment method based on TTC index and relative speed difference, characterized in that: The following steps are involved: Firstly, the road video surveillance data is obtained, and the traffic flow data is obtained based on the road video surveillance data; Then, multiple frames of data within one second are read in chronological order, and the vehicle numbers appearing in the multiple frames within this second are stored. If the number of vehicle numbers is greater than the vehicle number threshold, it is determined that a conflict has occurred; According to the extracted number of seconds of conflict occurrence, a traffic conflict index is calculated for each frame in which two vehicles exist at the same time, wherein the traffic conflict index includes a TTC-IM index; The TTC-IM indicators are as follows: In the formula, x i-1 (t) is the position of the preceding vehicle i-1 at time t; x i (t) is the position of vehicle i at time t; l i-1 is the vehicle length of the preceding vehicle i-1; is the speed of vehicle i at time t; is the speed of the preceding vehicle i-1 at time t; λ1 is the relative speed correction coefficient; λ2 is the vehicle length-to-distance ratio correction coefficient; λ3 is the safety distance coefficient; Finally, the traffic conflict risk is evaluated based on traffic conflict indicators.

2. The improved traffic conflict risk assessment method based on TTC index and relative speed difference according to claim 1 is characterized in that: The road video surveillance data is video surveillance data of a highway.

3. The improved traffic conflict risk assessment method based on TTC index and relative speed difference according to claim 2 is characterized in that: The vehicle quantity threshold is 1.

4. The improved traffic conflict risk assessment method based on TTC index and relative speed difference according to any one of claims 1 to 3, characterized in that: The process of acquiring traffic flow data based on road video surveillance data is implemented using a neural network model.

5. An improved traffic conflict risk assessment system based on TTC index and relative speed difference, characterized in that: include: Traffic flow data analysis module: obtains road video surveillance data, and obtains traffic flow data based on the road video surveillance data; Traffic conflict preliminary identification module: reads multiple frames of data within one second in chronological order, stores the vehicle numbers that appear in the multiple frames within this second, and if the number of vehicle numbers is greater than the vehicle number threshold, it is determined that a conflict has occurred; Traffic conflict index calculation module: according to the extracted number of seconds of conflict occurrence, the traffic conflict index is calculated for each frame in which two vehicles exist at the same time, and the traffic conflict index includes the TTC-IM index; The TTC-IM indicators are as follows: In the formula, x i-1 (t) is the position of the preceding vehicle i-1 at time t; x i (t) is the position of vehicle i at time t; l i-1 is the vehicle length of the preceding vehicle i-1; is the speed of vehicle i at time t; is the speed of the preceding vehicle i-1 at time t; λ1 is the relative speed correction coefficient; λ2 is the vehicle length-to-distance ratio correction coefficient; λ3 is the safety distance coefficient. Conflict risk evaluation module: Evaluate traffic conflict risks based on traffic conflict indicators.

6. The improved traffic conflict risk assessment system based on TTC index and relative speed difference according to claim 5 is characterized in that: The road video surveillance data is video surveillance data of a highway.

7. The improved traffic conflict risk assessment system based on TTC index and relative speed difference according to claim 6 is characterized in that: The vehicle quantity threshold is 1.

8. The improved traffic conflict risk assessment system based on TTC index and relative speed difference according to any one of claims 5 to 7, characterized in that: The process of acquiring traffic flow data based on road video surveillance data is implemented using a neural network model.

9. A computer storage medium, characterized in that The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to execute the improved traffic conflict risk assessment system based on TTC index and relative speed difference as described in any one of claims 5 to 8.

10. An improved traffic conflict risk assessment device based on TTC index and relative speed difference, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to execute an improved traffic conflict risk assessment system based on TTC index and relative speed difference as described in any one of claims 5 to 8.

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