Urban underground tunnel diversion area driving risk assessment method

Abstract

The invention provides an urban underground tunnel diversion area driving risk assessment method. The method comprises the following steps: constructing an urban underground tunnel diversion area driving risk assessment model; obtaining driving data, and inputting the driving data into the urban underground tunnel diversion area driving risk assessment model to obtain a driving risk assessment result; and obtaining a driving division result according to the driving risk assessment result. According to the invention, based on upgrading from a single physical rule to multi-factor coupling risk assessment, traffic environment and driver behavior changes can be responded in real time. And the deployment cost is reduced through simulation and data driving. The method is suitable for high-risk areas of scenes such as urban underground tunnels, highway confluence areas and smart city complex road networks.

Description

Technical Field

[0001] The present invention relates to the technical field of driving planning, and particularly to a method for evaluating driving risks in the diversion area of an urban underground tunnel. Background Art

[0002] With the acceleration of the urbanization process, due to problems such as enclosed space, poor lighting conditions, and sidewall obstruction in urban underground tunnels, the driver's field of vision is limited, especially the driving risk in the diversion section is significantly increased. Most of the existing driving risk assessment methods are aimed at ground roads and lack adaptability to the special environment of tunnels. Traditional models are difficult to quantify the dynamic interaction risks among people, vehicles, and roads in tunnels, and there are no effective line-of-sight induction facilities in tunnels, which are prone to traffic accidents.

[0003] The present invention proposes a driving safety assessment and optimization strategy for the diversion area of an urban underground tunnel based on a risk field. By constructing a comprehensive risk field model of a potential energy field, a kinetic energy field, and a behavior field, the risk value of the tunnel diversion area is quantified, and a differentiated path induction scheme is designed to reduce the accident risk. Summary of the Invention

[0004] Based on this, it is necessary to provide a method for evaluating driving risks in the diversion area of an urban underground tunnel in view of the above technical problems.

[0005] A method for evaluating driving risks in the diversion area of an urban underground tunnel includes the following steps:

[0006] Construct a driving risk assessment model for the diversion area of an urban underground tunnel:

[0007] E = E R + E V + E D

[0008] Wherein, E represents the driving risk assessment result, E R represents the potential energy field value, E V represents the vehicle kinetic energy field value, E D represents the behavior field value;

[0009] Obtain driving data, input the driving data into the driving risk assessment model for the diversion area of an urban underground tunnel, and obtain the driving risk assessment result;

[0010] Obtain the driving division result according to the driving risk assessment result.

[0011] In one embodiment, it further includes:

[0012] Calculate the potential energy field value of each position in the tunnel diversion area according to the following formula:

[0013]

[0014] Among them, E R represents the potential energy field value, q represents the parameters related to the tunnel sidewall, r represents the road influence factor, represents the illuminance influence factor, y(t) represents the real-time position of the vehicle, y b represents the abscissa of the left and right sidewalls of the tunnel. In one of the embodiments, it further includes:

[0015] Calculate the vehicle kinetic energy field value according to the following formula:

[0016]

[0017] Among them, E V represents the vehicle kinetic energy field value, M i represents the equivalent mass of vehicle i, λ represents a constant to be determined, β represents the parameters related to the acceleration of vehicle i, a represents the acceleration of vehicle i, θ represents the angle between the speed direction of vehicle i and d, d ij represents the distance between vehicle i and vehicle j;

[0018] Calculate the equivalent mass of the vehicle according to the following formula:

[0019]

[0020] Among them, M i represents the equivalent mass of vehicle i, m i represents the mass of vehicle i, T i represents the type of vehicle i, v i represents the speed of vehicle i;

[0021] Calculate the distance between two vehicles according to the following formula:

[0022]

[0023] Among them, d ij represents the distance between vehicle i and vehicle j, l represents the vehicle length, w represents the vehicle width, (x i ,y i ) represents the coordinates of vehicle i, (x j ,y j ) represents the coordinates of vehicle j.

[0024] In one of the embodiments, it further includes:

[0025] Calculate the behavior field value according to the following formula:

[0026] E D =E V `S n

[0027] Among them, E D represents the behavior field value, EV Represents the vehicle kinetic energy field value, S n Represents the driving style influence factor of the nth type of driver;

[0028] The driving style influence factor is calculated according to the following formula:

[0029] S n = exp(γ1σ1 + γ2σ2 + γ3σ3)

[0030] where S n Represents the driving style influence factor of the nth type of driver, σ1 represents the standard deviation of acceleration, σ2 represents the standard deviation of speed, σ3 represents the standard deviation of jerk, and γ1, γ2, and γ3 are the weights corresponding to the standard deviation of acceleration, the standard deviation of speed, and the standard deviation of jerk, respectively.

[0031] In one embodiment, after obtaining the driving risk assessment result, it further includes:

[0032] Using visualization software, generate a comprehensive risk field view according to the driving risk assessment result.

[0033] In one embodiment, obtaining the driving division result according to the driving risk assessment result includes:

[0034] Obtain the judgment distance, adjustment distance, lane change distance, and safety confirmation distance according to the driving risk assessment result;

[0035] Obtain the driving division result according to the judgment distance, the adjustment distance, the lane change distance, and the safety confirmation distance; wherein, the driving division result includes: advance point signs, action point signs, confirmation point signs, and road surface indication markings.

[0036] In one embodiment, obtaining the driving division result according to the judgment distance, the adjustment distance, the lane change distance, and the safety confirmation distance includes:

[0037] Calculate the advance point position coordinates as follows:

[0038] D1 = L1 + L 21 + L 22 + L3

[0039] where D1 represents the advance point position coordinates, L1 represents the judgment distance, L 21 represents the adjustment distance, L 22 represents the lane change distance, and L3 represents the safety confirmation distance;

[0040] Calculate the action point position coordinates as follows:

[0041] L = L 21 + L 22+L3

[0042] Among them, L represents the calculation of the position coordinates of the action point, and L 21 represents the adjustment distance, and L 22 represents the lane change distance, and L3 represents the safety confirmation distance.

[0043] An urban underground tunnel diversion area driving risk assessment system for implementing the above-mentioned urban underground tunnel diversion area driving risk assessment method, including:

[0044] A model construction module for constructing an urban underground tunnel diversion area driving risk assessment model:

[0045] E = E R +E V +E D

[0046] Among them, E represents the driving risk assessment result, and E R represents the potential energy field value, and E V represents the vehicle kinetic energy field value, and E D represents the behavior field value;

[0047] A result acquisition module for acquiring driving data, inputting the driving data into the urban underground tunnel diversion area driving risk assessment model, and obtaining a driving risk assessment result;

[0048] A driving planning module for obtaining a driving division result according to the driving risk assessment result.

[0049] A device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the above-mentioned urban underground tunnel diversion area driving risk assessment method in each embodiment.

[0050] A storage medium stores a computer program, and when the program is executed by a processor, it implements the steps of the above-mentioned urban underground tunnel diversion area driving risk assessment method in each embodiment.

[0051] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: By constructing a comprehensive risk field model of potential energy field, kinetic energy field and behavior field, the present invention quantifies the risk value of the tunnel diversion area and designs a differential path induction scheme to reduce the accident risk. It is upgraded from a single physical rule to a multi-factor coupling risk assessment, and can respond to the changes of traffic environment and driver behavior in real time. The deployment cost is reduced through simulation and data driving. It is applicable to high-risk areas in various scenarios such as urban underground tunnels, highway merging areas, and complex road networks in smart cities. Description of the Drawings

[0052] Figure 1 It is a schematic flow chart of a driving risk assessment method for the diversion area of an urban underground tunnel in an embodiment;

[0053] Figure 2 It is a schematic diagram of the comprehensive risk field in an embodiment;

[0054] Figure 3 It is a schematic diagram of the diversion area in an embodiment;

[0055] Figure 4 It is a schematic diagram of the pre-warning point sign in an embodiment;

[0056] Figure 5 It is a schematic diagram of the action point sign in an embodiment;

[0057] Figure 6 It is a schematic diagram of the confirmation point sign in an embodiment;

[0058] Figure 7 It is a schematic diagram of the road surface indication marking in an embodiment;

[0059] Figure 8 It is a schematic structural diagram of a driving risk assessment system for the diversion area of an urban underground tunnel in an embodiment;

[0060] Figure 9 It is a schematic diagram of the internal structure of the equipment in an embodiment. Detailed implementation manners

[0061] Before describing the detailed implementation manners of the present invention, the overall concept of the present invention is described as follows:

[0062] The present invention is mainly developed for the driving process in tunnels. Most of the existing driving risk field modeling methods are for surface roads. Compared with surface roads, the structure of underground roads, especially underground tunnels, is closed, the internal environment is dim, and the driving environment changes greatly. The original driving risk field modeling methods are not suitable for urban underground tunnels.

[0063] Therefore, in view of the special driving environment of urban underground tunnels, the present invention establishes a comprehensive driving risk field model that considers the potential energy field of the tunnel environment, the kinetic energy field of dynamic vehicles, and the behavior field of driver factors to evaluate the driving risk of urban underground tunnels and visualize the driving risk, providing a reference for the optimal design of urban underground tunnels.

[0064] After introducing the overall concept of the present invention, in order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below through specific implementation manners in conjunction with the accompanying drawings.

[0065] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of this specification should have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains. The terms "first", "second" and similar words used in one or more embodiments of this specification do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0066] In one embodiment, as Figure 1 shown, a driving risk assessment method for the diversion area of urban underground tunnels is provided, including the following steps:

[0067] Step S101, constructing a driving risk assessment model for the diversion area of urban underground tunnels:

[0068] E = E R + E V + E D

[0069] wherein, E represents the driving risk assessment result, E R represents the potential energy field value, E V represents the vehicle kinetic energy field value, E D represents the behavior field value.

[0070] Specifically, when constructing a driving risk assessment model for the diversion area of urban underground tunnels, a potential energy field is established for the special driving environment of urban underground tunnels, a kinetic energy field is established considering the influence of dynamic vehicles around the road on driving safety, and a behavior field is established considering the influence of driving styles on driving safety.

[0071] On this basis,

[0072] According to the following formula, calculate the potential energy field value at each position in the tunnel diversion area:

[0073]

[0074] wherein, E R represents the potential energy field value, q represents the relevant parameters of the tunnel sidewall, r represents the road influence factor, represents the illumination influence factor, y(t) represents the real-time position of the vehicle, y b represents the abscissa of the left and right sidewalls of the tunnel.

[0075] Specifically, a potential field is established based on the static road facilities, alignment conditions, and illumination conditions in the diversion area of the urban underground tunnel to obtain the impact of static road objects in the tunnel diversion area on driving safety.

[0076] According to the alignment conditions in the tunnel diversion area, the road impact factor r is determined i ; then, using the illumination data, the illumination change rate in the diversion area is calculated Then, based on the tunnel sidewall, road alignment conditions, and tunnel illumination change rate, a potential field E is established R , to calculate the potential field values at various positions in the tunnel diversion area.

[0077]

[0078] In the formula, E R represents the potential field, q represents the parameters related to the tunnel sidewall, r represents the road impact factor, represents the illumination impact factor, y(t) represents the real-time position of the vehicle, and y b represents the abscissas of the left and right sidewalls of the tunnel.

[0079] On this basis, the vehicle kinetic energy field value is calculated according to the following formula:

[0080]

[0081] Among them, E V represents the vehicle kinetic energy field value, M i represents the equivalent mass of vehicle i, λ represents a constant to be determined, β represents the parameters related to the acceleration of vehicle i, a represents the acceleration of vehicle i, θ represents the angle between the velocity direction of vehicle i and d, and d ij represents the distance between vehicle i and vehicle j;

[0082] The equivalent mass of the vehicle is calculated according to the following formula:

[0083]

[0084] Among them, M i represents the equivalent mass of vehicle i, m i represents the mass of vehicle i, T i represents the type of vehicle i, and v i represents the speed of vehicle i;

[0085] The distance between two vehicles is calculated according to the following formula:

[0086]

[0087] Among them, d ijDenote the distance between vehicle i and vehicle j, l denote the vehicle length, w denote the vehicle width, (x i , y i ) denote the coordinates of vehicle i, and (x j , y j ) denote the coordinates of vehicle j.

[0088] Specifically, use a non-contact speedometer and MobilEye to collect the speed information of vehicles, and establish a vehicle kinetic energy field in combination with the attributes and motion states of target vehicles, so as to obtain the

[0089] Under good road conditions, the field strength formed by a vehicle depends on its mass, speed, and the relative distance between vehicles. The impact of vehicle attributes on driving risk is represented by virtual mass, and the calculation formula is:

[0090]

[0091] In the formula, M i is the equivalent mass of vehicle i, m i is the mass of vehicle i, T i is the type of vehicle, and v i is the speed of the vehicle. For vehicles of the same type, the greater the speed of the vehicle, the greater the potential collision risk and the more serious the collision degree.

[0092] Take the forward direction of the road as the horizontal axis and the upward direction perpendicular to the road as the vertical axis to establish a plane rectangular coordinate system. Let the coordinates of the surrounding vehicles be (x i , y i ), and the coordinates of the target vehicle be (x j , y j ) to calculate the actual distance between the two. Since vehicles in reality are not a point mass, therefore, when calculating the distance between two vehicles in this method, the length and width of the vehicle are considered. The specific formula is as follows:

[0093]

[0094] In the formula, d ij represents the distance between vehicle i and vehicle j; l represents the vehicle length; w represents the vehicle width.

[0095] Establish a kinetic energy field and calculate the risk generated by the vehicles driving in the tunnel to the surrounding area. The calculation formula is as follows:

[0096]

[0097] In the formula, E V represents the vehicle kinetic energy field, λ is a constant to be determined, β represents the parameter related to the acceleration of vehicle i, a represents the acceleration of vehicle i, and θ represents the angle between the speed direction of vehicle i and d, with the clockwise direction being positive.

[0098] On this basis, the behavior field value is calculated according to the following formula:

[0099] E D = E V `S n

[0100] where E D represents the behavior field value, E V represents the vehicle kinetic energy field value, and S n represents the driving style influence factor of the nth type of driver;

[0101] The driving style influence factor is calculated according to the following formula:

[0102] S n = exp(γ1σ1 + γ2σ2 + γ3σ3)

[0103] where S n represents the driving style influence factor of the nth type of driver; σ1 represents the standard deviation of acceleration; σ2 represents the standard deviation of speed; σ3 represents the standard deviation of jerk; γ1, γ2, and γ3 are the weights corresponding to the standard deviation of acceleration, the standard deviation of speed, and the standard deviation of jerk, respectively.

[0104] Specifically, according to the vehicle speed and acceleration information, the driving behavior of the driver is extracted to obtain the driving style influence factor of the driver. The vehicle kinetic energy field and the driving style influence factor are combined to establish a behavior field, and the driving behavior of the driver himself and the danger caused by the vehicle he drives to the surrounding environment are obtained. Introducing the driving style factor makes the safety model adapt to different driver characteristics.

[0105] According to the speed and acceleration information collected by the non-contact speedometer and MobilEye, the driving styles of drivers are classified, and the standard deviation of acceleration and the standard deviation of speed of each type of driver are extracted to calculate the driving style influence factor. The calculation formula is as follows:

[0106] S n = exp(γ1σ1 + γ2σ2 + γ3σ3)

[0107] In the formula, S n represents the driving style influence factor of the nth type of driver, σ1 represents the standard deviation of acceleration, σ2 represents the standard deviation of speed, σ3 represents the standard deviation of jerk, and γ1, γ2, and γ3 are the weights corresponding to the standard deviation of acceleration, the standard deviation of speed, and the standard deviation of jerk, respectively.

[0108] A behavior field is established, and the calculation formula is:

[0109] E D = E V ·S n

[0110] In the formula, E D represents the behavior field value, and E V represents the vehicle kinetic energy field value, and S n represents the driving style influence factor of the nth type of driver.

[0111] Step S102: Obtain driving data, input the driving data into the driving risk assessment model for the urban underground tunnel diversion area, and obtain the driving risk assessment result.

[0112] Specifically, obtaining driving data includes: static road facilities, alignment conditions, illumination conditions in the underground tunnel diversion area, speed information of the vehicle, attributes of the target vehicle, motion state, speed, acceleration information, driving style categories of the driver, standard deviation of acceleration and standard deviation of speed for each type of driver, and calculating the risk values at each position in the tunnel diversion area using the comprehensive risk field model.

[0113] On this basis, after obtaining the driving risk assessment result, it further includes:

[0114] Generating a comprehensive risk field view according to the driving risk assessment result through visualization software.

[0115] Specifically, as Figure 2 shown, visualize the result to intuitively display the gradient change characteristics of the risk field.

[0116] Step S103: Obtain the driving division result according to the driving risk assessment result.

[0117] Specifically, design the sight guidance system for the urban underground tunnel diversion area according to the driving risk assessment result.

[0118] On this basis, obtaining the driving division result according to the driving risk assessment result includes:

[0119] Obtaining the judgment distance, adjustment distance, lane change distance, and safety confirmation distance according to the driving risk assessment result;

[0120] Obtaining the driving division result according to the judgment distance, the adjustment distance, the lane change distance, and the safety confirmation distance; wherein, the driving division result includes: advance point signs, action point signs, confirmation point signs, and pavement indication markings.

[0121] Specifically, as Figure 3 shown, divide the tunnel diversion area into a pre-warning area, an action area, and a confirmation area according to the position area of the underground tunnel and the order of driving tasks.

[0122] The judgment distance, adjustment distance, lane change distance and safety confirmation distance are calculated based on the driving risk assessment results. High-risk areas require smaller acceptable gaps. High-risk areas require longer lane change distances to reduce lateral conflicts. High-risk areas require advance speed or heading adjustments, including but not limited to calculations using this formula:

[0123]

[0124] On this basis, the driving division result obtained according to the judgment distance, the adjustment distance, the lane change distance and the safety confirmation distance includes:

[0125] The predicted point position coordinates are calculated as follows:

[0126] D1=L1+L 21 +L 22 +L3

[0127] Among them, D1 represents the position coordinates of the prediction point, L1 represents the judgment distance, and L 21 Indicates the adjustment distance, L 22 Indicates lane change distance, L3 indicates safety confirmation distance;

[0128] The action point position coordinates are calculated as follows:

[0129] L=L 21 +L 22 +L3

[0130] Among them, L represents the calculated action point position coordinates, L 21 Indicates the adjustment distance, L 22 It indicates the lane change distance, and L3 indicates the safety confirmation distance.

[0131] Specifically, warning signs are set up at exit warning points to inform drivers of the location and direction of the exit in advance. Figure 4 As shown, the warning point sign adopts a hanging flat design, which includes the place name, distance and direction arrow. The calculation formula for the setting position of the warning point is as follows:

[0132] D1=L1+L 21 +L 22 +L3

[0133] In the formula, D1 represents the position coordinate of the prediction point, L1 represents the judgment distance, and L 21 Indicates the adjustment distance, L 22 It indicates the lane change distance, and L3 indicates the safety confirmation distance.

[0134] Action point signs are designed to alert drivers to the lane change point. The action point sign should include the exit name and arrow, and distinguish the main line from the ramp text color.Figure 5 As shown, the ramp name and direction arrow should be set to warning yellow, while the main line should be white. The calculation formula for the position of the action point sign is as follows:

[0135] L = L 21 + L 22 + L3

[0136] In the formula, L represents the calculated position coordinate of the action point, L 21 represents the adjustment distance, and L 22 represents the lane change distance, and L3 represents the safety confirmation distance.

[0137] As Figure 6 shown, a confirmation point sign is set at the nose end of the underground tunnel shunt, and its layout information should preferably be arranged vertically.

[0138] The designed road surface indication markings should include the exit name and indication arrow, mainly including three levels, and are set in an information progressive manner. As Figure 7 shown, in the first level, road surface indication texts for the ramp exits are set on all three lanes at the end of the main line segment to enhance the visibility of the ramp exits and guide the driver to change lanes; in the second level, two exit ramp guiding markings are set on the middle lane and the outermost lane in the weaving shunt area, and one main line road surface indication text is set on the innermost lane to prompt the driver to change lanes to the outermost lane in a timely manner; in the third level, one ramp exit guiding mark is set on the outermost lane before the nose end of the shunt, and two main line road surface indication texts are set on the middle lane and the innermost lane.

[0139] The present invention constructs a comprehensive risk field model of potential energy field, kinetic energy field and behavior field, integrates static and dynamic risks, quantifies the inherent risks of tunnel structures (curves, narrow roads, low illuminance) in the potential energy field, the kinetic energy field reflects the vehicle motion state (speed, acceleration) in real time, the behavior field captures the driver style (aggressive / conservative), and the superposition of the three can accurately evaluate the human-vehicle-road collaborative risk. It can quantify the risk value of the tunnel shunt area and design a differentiated path guidance scheme to reduce the accident risk. It upgrades from a single physical rule to a multi-factor coupled risk assessment, and can respond to changes in traffic environment and driver behavior in real time. It reduces the deployment cost through simulation and data driving. It is applicable to high-risk areas in various scenarios such as urban underground tunnels, highway confluence areas, and complex road networks in smart cities.

[0140] It should be noted that the method of the embodiment of the present invention can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by the cooperation of multiple devices. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiment of the present invention, and these multiple devices will interact with each other to complete the described method.

[0141] It should be noted that some embodiments of the present invention have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0142] Based on the same inventive concept, corresponding to any of the above-described method embodiments, the present invention further provides a driving risk assessment system for an urban underground tunnel diversion area.

[0143] Referring to Figure 8 , the driving risk assessment system for an urban underground tunnel diversion area includes:

[0144] A model construction module 801 for constructing a driving risk assessment model for an urban underground tunnel diversion area:

[0145] E = E R + E V + E D

[0146] where E represents the driving risk assessment result, E R represents the potential energy field value, E V represents the vehicle kinetic energy field value, E D represents the behavior field value;

[0147] A result acquisition module 802 for acquiring driving data, inputting the driving data into the driving risk assessment model for the urban underground tunnel diversion area, and obtaining a driving risk assessment result;

[0148] A driving planning module 803 for obtaining a driving division result according to the driving risk assessment result.

[0149] For the sake of convenience of description, when describing the above system, it is divided into various modules according to functions and described separately. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0150] The system of the above embodiment is used to implement the corresponding driving risk assessment method for an urban underground tunnel diversion area in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0151] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for evaluating driving risks in the diversion area of an urban underground tunnel described in any one of the above embodiments.

[0152] Figure 9 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0153] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0154] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0155] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0156] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0157] The bus 1050 includes a path for transmitting information between various components of the device, such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040.

[0158] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0159] The electronic device of the above embodiment is used to implement the corresponding method for evaluating the driving risk in the diversion area of the urban underground tunnel in any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0160] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute a method for evaluating the driving risk in the diversion area of the urban underground tunnel as described in any one of the above embodiments.

[0161] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette tapes, magnetic disk storage, or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0162] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute a method for evaluating the driving risk in the diversion area of the urban underground tunnel as described in any one of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0163] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the present invention (including the claims) is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present invention as described above, and they are not provided in detail for the sake of brevity.

[0164] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a way that is not in the order shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0165] In the case where specific details are set forth to describe exemplary embodiments of the present invention, it will be apparent to those skilled in the art that the embodiments of the present invention can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive. Although the present invention has been described in conjunction with specific embodiments of the present invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description.

[0166] The embodiments of the present invention are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present invention shall be included within the protection scope of the present invention.

Claims

1. A driving risk assessment method for the diversion area of urban underground tunnels, characterized in that, Including: Construct a driving risk assessment model for the diversion area of urban underground tunnels: E = E R + E V + E D Among them, E represents the driving risk assessment result, E R represents the potential energy field value, E V represents the vehicle kinetic energy field value, E D represents the behavior field value; Obtain driving data, input the driving data into the driving risk assessment model for the diversion area of urban underground tunnels, and obtain a driving risk assessment result; Obtain a driving division result according to the driving risk assessment result.

2. The method for evaluating driving risks in the diversion area of an urban underground tunnel according to claim 1, wherein Also including: Calculate the potential energy field value at each position in the tunnel diversion area according to the following formula: Among them, E R represents the potential field value, q represents the parameters related to the tunnel sidewall, r represents the road influence factor, represents the illuminance influence factor, y(t) represents the real-time position of the vehicle, and y b represents the abscissas of the left and right sidewalls of the tunnel.

3. The driving risk assessment method for the diversion area of urban underground tunnels according to claim 1, characterized in that Also including: Calculate the vehicle kinetic energy field value according to the following formula: Among them, E V represents the vehicle kinetic energy field value, M i represents the equivalent mass of vehicle i, λ represents a constant to be determined, β represents the acceleration-related parameter of vehicle i, a represents the acceleration of vehicle i, θ represents the angle between the velocity direction of vehicle i and d, d ij represents the distance between vehicle i and vehicle j; Calculate the equivalent mass of the vehicle according to the following formula: Among them, M i represents the equivalent mass of vehicle i, m i represents the mass of vehicle i, T i represents the type of vehicle i, v i represents the speed of vehicle i; Calculate the distance between two vehicles according to the following formula: where d ij represents the distance between vehicle i and vehicle j, l represents the vehicle length, w represents the vehicle width, (x i , y i ) represents the coordinates of vehicle i, and (x j , y j ) represents the coordinates of vehicle j.

4. The method for evaluating driving risks in the diversion area of an urban underground tunnel according to claim 3, wherein, Also including: Calculate the behavior field value according to the following formula: E D = E V S n Among them, E D represents the behavior field value, and E V represents the vehicle kinetic energy field value. S n represents the driving style influence factor of the nth type of driver; Calculate the driving style influence factor according to the following formula: S n = exp(γ1σ1 + γ2σ2 + γ3σ3) Among them, S n represents the driving style influence factor of the nth type of driver, σ1 represents the standard deviation of acceleration, σ2 represents the standard deviation of speed, σ3 represents the standard deviation of jerk, and γ1, γ2, and γ3 are the weights corresponding to the standard deviation of acceleration, the standard deviation of speed, and the standard deviation of jerk, respectively.

5. The driving risk assessment method for the diversion area of urban underground tunnels according to claim 1, characterized in that After obtaining the driving risk assessment result, it also includes: Generate a comprehensive risk field view according to the driving risk assessment result through visualization software.

6. The method for evaluating driving risks in the diversion area of an urban underground tunnel according to claim 1, wherein, The obtaining the driving division result according to the driving risk assessment result includes: Obtain a judgment distance, an adjustment distance, a lane change distance, and a safety confirmation distance according to the driving risk assessment result; Obtain a driving division result according to the judgment distance, the adjustment distance, the lane change distance, and the safety confirmation distance; wherein, the driving division result includes: a warning point sign, an action point sign, a confirmation point sign, and a road surface indication marking.

7. The driving risk assessment method for the diversion area of urban underground tunnels according to claim 6, characterized in that The obtaining the driving division result according to the judgment distance, the adjustment distance, the lane change distance, and the safety confirmation distance includes: Calculate the warning point position coordinates through the following: D1 = L1 + L 21 + L 22 + L3 Among them, D1 represents the position coordinates of the warning point, L1 represents the judgment distance, and L 21 represents the adjustment distance, and L 22 represents the lane change distance, and L3 represents the safety confirmation distance; Calculate the action point position coordinates through the following: L = L 21 + L 22 + L3 Among them, L represents the calculation of the position coordinates of the action point, L 21 represents the adjustment distance, L 22 represents the lane change distance, and L3 represents the safety confirmation distance.

8. An urban underground tunnel diversion area driving risk assessment system, characterized in that, Used to implement a driving risk assessment method for the diversion area of urban underground tunnels as described in any one of claims 1-7, including: A model construction module for constructing a driving risk assessment model for the diversion area of urban underground tunnels: E = E R + E V + E D Among them, E represents the driving risk assessment result, E R represents the potential energy field value, E V represents the vehicle kinetic energy field value, E D represents the behavior field value; A result acquisition module for obtaining driving data, inputting the driving data into the driving risk assessment model for the diversion area of urban underground tunnels, and obtaining a driving risk assessment result; A driving planning module for obtaining a driving division result according to the driving risk assessment result.

9. An apparatus, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 7.

Images