Road driving risk identification method for static traffic bottlenecks in intelligent connected environment

Abstract

This invention discloses a method for identifying road driving risks in a static traffic bottleneck environment under intelligent connected vehicle conditions. The steps include: 1. Establishing a road plane rectangular coordinate system; 2. Collecting road and vehicle-related information; 3. Determining the road environment field value of the target vehicle under the static traffic bottleneck; 4. Determining the superimposed risk field value of surrounding dangerous vehicles on the target vehicle; 5. Determining the total risk field value of the target vehicle; 6. Judging the risk level of the target vehicle and classifying the risk. This invention can obtain relevant road and vehicle information in an intelligent connected vehicle environment, calculate the dynamic risk value of the target vehicle during its journey, and determine the risk level of the target vehicle, thereby providing a theoretical basis for subsequent risk warning and traffic control.

Description

Technical Field

[0001] This invention relates to the field of road driving risk research in intelligent connected environments, specifically a method for identifying road driving risks in response to static traffic bottlenecks in intelligent connected environments. Background Technology

[0002] With the gradual modernization of cities, the number of cars in cities is increasing year by year. The current level of road development can no longer meet the needs of vehicle traffic. This situation in road development will lead to more frequent traffic bottlenecks, which in turn will increase driving risks.

[0003] The emergence of connected and autonomous vehicles and the continuous development of vehicle-road cooperative technologies have provided new ideas and methods for identifying driving risks on roads. Detection accuracy is higher and information transmission speed is faster in intelligent connected environments. Currently, research on driving risks in intelligent connected environments is limited, and the quantitative models of various driving influencing factors on driving risks in intelligent connected environments are still unclear, making dynamic driving risk identification on roads in intelligent connected environments quite challenging. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention proposes a road driving risk identification method for static traffic bottlenecks in an intelligent connected environment. The aim is to quantify the risk field value of a target vehicle during its journey, based on the fundamental theory of risk field, when traffic bottlenecks occur on the road, thereby determining the risk level of the target vehicle and providing a preliminary theoretical basis for subsequent risk warning and traffic control.

[0005] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0006] The present invention provides a method for identifying road driving risks in a static traffic bottleneck environment under intelligent connected conditions. The vehicles in this intelligent connected environment are all connected autonomous vehicles traveling on a one-way three-lane road. When a static traffic bottleneck occurs in the middle lane, road driving risk identification is performed according to the following steps:

[0007] Step 1: Establish a rectangular coordinate system XOY by taking the starting point of the road boundary line of the innermost lane as the origin O, the road boundary line of the innermost lane as the Y-axis, and the perpendicular line between the road boundary lines of the innermost lane and the road boundary lines of the outermost lane as the X-axis. In the rectangular coordinate system XOY, the positive direction of the Y-axis is the direction of vehicle travel, and the positive direction of the X-axis is the direction from the innermost lane to the outermost lane.

[0008] Step 2: Obtain road-related information, including: the width w of each lane, the length L of the three lanes on the road, the x-coordinate range of the longitudinal boundary line of the static traffic bottleneck area [x1, x2], and the y-coordinate range of the lateral boundary line of the static traffic bottleneck area [y1, y2].

[0009] Step 3: Determine the road environment field value of vehicle j for the static traffic bottleneck in the t-th time interval:

[0010] Step 3.1: Calculate the road boundary line field value generated by the road boundary lines of the innermost and outermost lanes on vehicle j within the t-th time interval using equation (1).

[0011]

[0012] In equation (1), α is the coefficient of the road boundary field; x s,k Represents the x-coordinate of any point on the k-th boundary line; Represents the x-coordinate of vehicle j within the t-th time interval;

[0013] Step 3.2: Determine the risk field value of the boundary line of the static traffic bottleneck for vehicle j within the t-th time interval.

[0014] Step 3.3: Calculate the road environment field value of vehicle j under static traffic bottleneck during the t-th time interval using equation (6).

[0015]

[0016] Step 4: Calculate the vehicle risk field value generated by individual vehicles around vehicle j on vehicle j:

[0017] Step 4.1: Use the intelligent roadside detector to identify the vehicles and the total number of vehicles n in the three surrounding lanes of vehicle j, and number the surrounding vehicles sequentially, where any one of the surrounding vehicles is numbered i, i = 1, ..., n;

[0018] Step 4.2: Let i = 1;

[0019] Step 4.3: Calculate the vehicle risk field value generated by surrounding vehicles i on vehicle j during the t-th time interval using equation (7).

[0020]

[0021] In equation (7), λ represents the coefficient of the vehicle field; β2 represents the undetermined coefficient value related to acceleration; τ represents the critical threshold of the safe distance; μ represents the undetermined coefficient related to speed; m i Indicates the mass of the surrounding vehicle i; represents the speed of the surrounding vehicle i within the t-th time interval; represents the acceleration of the surrounding vehicle i within the t-th time interval; represents the clockwise included angle formed by the connection line between the centroid of vehicle j and the centroid of the surrounding vehicle i within the t-th time interval, and the moving direction of the surrounding vehicle i; represents the coordinates of the surrounding vehicle i within the t-th time interval;

[0022] Step 4.4: After assigning i + 1 to i, if i > n holds, then enter Step 5; otherwise, return to Step 4.3 and execute sequentially;

[0023] Step 5: Determine the dangerous surrounding vehicle m that has an impact on the driving of vehicle j:

[0024] Step 5.1: Initialize i = 1 and m = 1;

[0025] Step 5.2: Judge whether holds. If it holds, then regard the surrounding vehicle i as the dangerous surrounding vehicle m, and assign to the vehicle risk field value generated by the dangerous surrounding vehicle m on vehicle j within the t-th time interval, and enter Step 5.3; otherwise, enter Step 5.4; where, E represents the vehicle risk threshold that can cause an impact on driving; vd represents the vehicle risk threshold that can cause an impact on driving;

[0026] Step 5.3: Judge whether i < n holds. If it holds, assign m + 1 to m and i + 1 to i, and return to Step 5.2 to execute sequentially; otherwise, enter Step 6;

[0027] Step 5.4: Judge whether i < n holds. If it holds, assign i + 1 to i and return to Step 5.2 to execute sequentially; otherwise, enter Step 6;

[0028] Step 6: Calculate the superimposed risk field value generated by all dangerous surrounding vehicles on vehicle j within the t-th time interval by using Equation (8)

[0029]

[0030] In Equation (8), represents the vehicle risk field value generated by the dangerous surrounding vehicle numbered r on vehicle j within the t-th time interval;

[0031] Step 7: Calculate the total risk field value of vehicle j within the t-th time interval by using Equation (9)

[0032]

[0033] In equation (9), γ e Indicates the proportion of the field strength in the road environment; γ v This indicates the proportion of the risk field strength of the vehicle.

[0034] Step 8, let E safe This represents the risk threshold for ensuring safe vehicle operation, and after determining the risk level of vehicle j within the t-th time interval, risk classification is performed:

[0035] like Then, vehicle j within the t-th time interval is classified as a low-risk vehicle;

[0036] like Then, vehicle j within the t-th time interval is classified as a medium-risk vehicle;

[0037] like Then, vehicle j within the t-th time interval is classified as a high-risk vehicle;

[0038] like Then, vehicle j within the t-th time interval is classified as an ultra-high-risk control vehicle;

[0039] Step 9: Assign t+1 to t, then return to step 3 and execute sequentially.

[0040] The method for identifying road driving risks in a static traffic bottleneck environment under intelligent connected conditions, as described in this invention, is also characterized in that step 3.2 includes:

[0041] Step 3.2.1, if Then, using equation (2), the boundary field value of the longitudinal boundary line of the static traffic bottleneck for vehicle j within the t-th time interval is calculated.

[0042]

[0043] In equation (2), β1 represents the coefficient of the boundary field of the static traffic bottleneck; y o The ordinate represents the ordinate of the nearest boundary point on the vertical boundary line from vehicle j to the static traffic bottleneck. Represents the ordinate of vehicle j within the t-th time interval;

[0044] Step 3.2.2, if Equation (3) is used to calculate the boundary field value of the lateral boundary line of the static traffic bottleneck for vehicle j during the t-th time interval.

[0045]

[0046] In equation (3), x oThe x-coordinate represents the nearest boundary point on the lateral boundary line from vehicle j to the static traffic bottleneck.

[0047] Step 3.2.3, if Equation (4) is used to calculate the boundary field values ​​of the horizontal and vertical boundary lines of the static traffic bottleneck for vehicle j during the t-th time interval.

[0048]

[0049] Step 3.2.4, if Equation (5) is used to calculate the boundary field values ​​of the horizontal and vertical boundary lines of the static traffic bottleneck for vehicle j during the t-th time interval.

[0050]

[0051] Step 3.2.5, Judgment Is it true? If it is true, then keep it. Otherwise, let Among them, E ed This indicates the environmental risk threshold that can affect driving.

[0052] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the road driving risk identification method, and the processor is configured to execute the program stored in the memory.

[0053] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the road driving risk identification method.

[0054] Compared with existing technologies, the beneficial technical effects of the present invention are reflected in:

[0055] 1. In the context of intelligent connected vehicles, this invention provides a method for identifying road driving risks in the face of static traffic bottlenecks. By adopting risk field theory, a road environment field model and a vehicle risk field model are established. The two models are superimposed to obtain the total risk field model of the target vehicle. This model considers more risk factors and improves the accuracy of risk quantification.

[0056] 2. When quantifying dynamic road driving risks, the static traffic bottleneck situation of roads is taken into account, and a clearer road risk quantification model under static traffic bottlenecks is established, which has higher applicability.

[0057] 3. This invention utilizes the advantage of real-time sharing of road information and connected autonomous vehicle information in an intelligent connected environment. Based on the acquired real-time road and vehicle-related information, the risk field value of the target vehicle is calculated, and the risk level of the target vehicle is determined based on the risk field value, thereby improving the computational efficiency and accuracy of the identification method. Attached Figure Description

[0058] Figure 1 This is the overall flowchart of the present invention;

[0059] Figure 2 This is a flowchart of the decision-making method of the present invention;

[0060] Figure 3 This is a schematic diagram of a scenario according to the present invention. Detailed Implementation

[0061] In this embodiment, as Figure 3 As shown, in a method for identifying road driving risks in a static traffic bottleneck environment under intelligent connected conditions, all vehicles in the intelligent connected environment are connected autonomous vehicles traveling on a one-way three-lane road; the static traffic bottleneck in this intelligent connected environment occurs in the middle lane, such as... Figure 1 The following steps are shown for identifying road driving risks:

[0062] Step 1: Establish a rectangular coordinate system XOY, with the origin O being the road boundary line of the innermost lane, the Y-axis being the road boundary line of the innermost lane, and the perpendicular line between the road boundary lines of the innermost and outermost lanes being the X-axis. In the XOY coordinate system, the positive Y-axis direction represents the vehicle's travel direction, and the positive X-axis direction represents the direction from the innermost lane to the outermost lane. Figure 3 As shown;

[0063] Step 2: Use intelligent roadside detectors to collect road and vehicle j-related information within the t-th time interval:

[0064] Step 2.1: Obtain road-related information, including: the width w of each lane, the length L of the three lanes on the road, the x-coordinate range [x1, x2] of the longitudinal boundary line of the static traffic bottleneck area, and the y-coordinate range [y1, y2] of the lateral boundary line of the static traffic bottleneck area; such as Figure 3 As shown;

[0065] Step 2.2: In the specific embodiments, the vehicles are all connected autonomous vehicles, capable of information interaction between the vehicle and the intelligent roadside detector, thereby obtaining the coordinates of vehicle j within the t-th time interval. In this embodiment, vehicle and road-related information is obtained through an intelligent connected environment, and real-time interaction between road and vehicle information is achieved through wireless communication, which improves the speed of information transmission and reduces delay errors caused by information transmission.

[0066] Step 3: Determine the road environment field value of vehicle j for the static traffic bottleneck in the t-th time interval:

[0067] Step 3.1: Calculate the road boundary line field value generated by the road boundary lines of the innermost and outermost lanes on vehicle j within the t-th time interval using equation (1).

[0068]

[0069] In equation (1), α is the coefficient of the road boundary field; x s,k Represents the x-coordinate of any point on the k-th boundary line; Represents the x-coordinate of vehicle j within the t-th time interval;

[0070] Step 3.2: Determine the risk field value of the boundary line of the static traffic bottleneck for vehicle j:

[0071] Step 3.2.1, if Then, using equation (2), the boundary field value of the longitudinal boundary line of the static traffic bottleneck for vehicle j within the t-th time interval is calculated.

[0072]

[0073] In equation (2), β1 represents the coefficient of the boundary field of the static traffic bottleneck; y o The ordinate represents the ordinate of the nearest boundary point on the vertical boundary line from vehicle j to the static traffic bottleneck. Represents the ordinate of vehicle j within the t-th time interval;

[0074] Step 3.2.2, if Equation (3) is used to calculate the boundary field value of the lateral boundary line of the static traffic bottleneck for vehicle j during the t-th time interval.

[0075]

[0076] In equation (3), x o The x-coordinate represents the nearest boundary point on the lateral boundary line from vehicle j to the static traffic bottleneck.

[0077] Step 3.2.3, if Equation (4) is used to calculate the boundary field values ​​of the horizontal and vertical boundary lines of the static traffic bottleneck for vehicle j during the t-th time interval.

[0078]

[0079] Step 3.2.4, if Equation (5) is used to calculate the boundary field values ​​of the horizontal and vertical boundary lines of the static traffic bottleneck for vehicle j during the t-th time interval.

[0080]

[0081] Step 3.2.5, Judgment Is it true? If it is true, then keep it. Otherwise, let Among them, E ed This represents the environmental risk threshold that can cause driving impact; the magnitude of the impact of the static traffic bottleneck boundary line on the target vehicle is related to the distance between the vehicle and the static traffic bottleneck boundary line. When the target vehicle is far from the static traffic bottleneck boundary line, the static traffic bottleneck boundary line cannot produce a driving impact on the target vehicle. In this case, the risk field value of the static traffic bottleneck boundary line on the target vehicle is ignored.

[0082] Step 3.3: Calculate the road environment field value of vehicle j under static traffic bottleneck during the t-th time interval using equation (6).

[0083]

[0084] Step 4, as follows Figure 2 As shown, the decision-making method of the present invention for calculating the vehicle risk field value generated by individual vehicles around vehicle j on vehicle j is as follows:

[0085] Step 4.1: Use the intelligent roadside detector to identify the vehicles and the total number of vehicles n in the three surrounding lanes of vehicle j, and number the surrounding vehicles sequentially, where any one of the surrounding vehicles is numbered i, i = 1, ..., n;

[0086] Step 4.2: Let i = 1;

[0087] Step 4.3: Calculate the vehicle risk field value generated by surrounding vehicles i on vehicle j during the t-th time interval using equation (7).

[0088]

[0089] In equation (7), λ represents the coefficient of the vehicle field; β2 represents the undetermined coefficient value related to acceleration; τ represents the critical threshold of the safe distance; μ represents the undetermined coefficient related to speed; m iDenote the mass of the surrounding vehicle i; Denote the speed of the surrounding vehicle i within the t-th time interval; Denote the acceleration of the surrounding vehicle i within the t-th time interval; Denote the clockwise included angle formed by the line connecting the centroid of vehicle j and the centroid of the surrounding vehicle i within the t-th time interval, and the moving direction of the surrounding vehicle i; Denote the coordinates of the surrounding vehicle i within the t-th time interval;

[0090] Step 4.4: After assigning i + 1 to i, if i > n holds, proceed to Step 5; otherwise, return to Step 4.3 and execute sequentially;

[0091] Step 5: As Figure 2 shown, the present invention needs to calculate the vehicle risk field values generated by each surrounding vehicle around the target vehicle on the target vehicle, compare the risk field values generated by each vehicle on the target vehicle with the vehicle risk threshold that affects driving, and then decide the dangerous surrounding vehicle m that affects the driving of vehicle j. The specific decision method is as follows:

[0092] Step 5.1: Initialize i = 1, m = 1;

[0093] Step 5.2: The driving impact of the surrounding vehicle of the target vehicle on the target vehicle is related to the distance between the two vehicles. When the surrounding vehicle is far from the static traffic bottleneck boundary line, this vehicle cannot generate a driving impact on the target vehicle. At this time, the risk field value of this vehicle on the target vehicle is ignored. Judge whether it holds. If it holds, regard the surrounding vehicle i as the dangerous surrounding vehicle m, and assign to the vehicle risk field value generated by the dangerous surrounding vehicle m on vehicle j within the t-th time interval and proceed to Step 5.3; otherwise, proceed to Step 5.4; where E vd Denote the vehicle risk threshold that can cause a driving impact;

[0094] Step 5.3: Judge whether i < n holds. If it holds, assign m + 1 to m, assign i + 1 to i, and return to Step 5.2 to execute sequentially; otherwise, proceed to Step 6;

[0095] Step 5.4: Judge whether i < n holds. If it holds, assign i + 1 to i, and return to Step 5.2 to execute sequentially; otherwise, proceed to Step 6;

[0096] Step 6: Calculate the superimposed risk field value generated by all dangerous surrounding vehicles on vehicle j within the t-th time interval using Equation (8)

[0097]

[0098] In equation (8), This represents the vehicle risk field value generated by the dangerous surrounding vehicle numbered r on vehicle j within the t-th time interval.

[0099] Step 7: Calculate the total risk field value of vehicle j in the t-th time interval using equation (9).

[0100]

[0101] In equation (9), γ e Indicates the proportion of the field strength in the road environment; γ v This indicates the proportion of the risk field strength of the vehicle.

[0102] Step 8, let E safe This indicates the risk threshold for ensuring safe vehicle operation, and as follows: Figure 2 As shown, risk classification is performed after determining the risk level of vehicle j within the t-th time interval:

[0103] like Then, vehicle j within the t-th time interval is classified as a low-risk vehicle;

[0104] like Then, vehicle j within the t-th time interval is classified as a medium-risk vehicle;

[0105] like Then, vehicle j within the t-th time interval is classified as a high-risk vehicle;

[0106] like Then, vehicle j within the t-th time interval is classified as an ultra-high-risk control vehicle;

[0107] Step 9: Assign t+1 to t, then return to step 3 and execute sequentially.

[0108] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0109] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A method for identifying road driving risks in a static traffic bottleneck environment under intelligent connected vehicle conditions, characterized in that, The vehicles in the intelligent connected environment are all connected autonomous vehicles and are driving on a one-way three-lane road; when a static traffic bottleneck occurs in the middle lane, the following steps are taken to identify the road driving risks: Step 1: Take the starting point of the road boundary line of the innermost lane of the road as the origin O, the road boundary line of the innermost lane as the Y-axis, and the perpendicular line between the road boundary line of the innermost lane and the road boundary line of the outermost lane as the X-axis, so as to establish a road rectangular coordinate system XOY; the positive direction of the Y-axis in the rectangular coordinate system XOY is the driving direction of the vehicle, and the positive direction of the X-axis is the direction from the innermost lane to the outermost lane; Step 2: Obtain road-related information, including: the width w of each lane, the length L of the three lanes on the road, the abscissa range [x1, x2] of the longitudinal boundary line of the static traffic bottleneck area, and the ordinate range [y1, y2] of the transverse boundary line of the static traffic bottleneck area; Step 3: Determine the road environment field value of the static traffic bottleneck on vehicle j within the t-th time interval: Step 3.1: Calculate the road boundary line field value generated by the road boundary lines of the innermost and outermost lanes on vehicle j within the t-th time interval using equation (1). In equation (1), α is the coefficient of the road boundary field; x s,k Represents the x-coordinate of any point on the k-th boundary line; Represents the x-coordinate of vehicle j within the t-th time interval; Step 3.2: Determine the risk field value of the boundary line of the static traffic bottleneck for vehicle j within the t-th time interval. Step 3.3: Calculate the road environment field value of vehicle j under static traffic bottleneck during the t-th time interval using equation (6). Step 4: Calculate the vehicle risk field value generated by a single vehicle around vehicle j: Step 4.1: Use an intelligent roadside detector to identify the vehicles on the three lanes around vehicle j and the total number of vehicles n, and sequentially number the surrounding vehicles. The number of any one of the surrounding vehicles is i, where i = 1,..., n; Step 4.2: Let i = 1; Step 4.3: Calculate the vehicle risk field value generated by surrounding vehicles i on vehicle j during the t-th time interval using equation (7). In equation (7), λ represents the coefficient of the vehicle field; β2 represents the undetermined coefficient value related to acceleration; τ represents the critical threshold of the safe distance; μ represents the undetermined coefficient related to speed; m i Indicates the mass of the surrounding vehicle i; This represents the speed of surrounding vehicle i during the t-th time interval; This represents the acceleration of surrounding vehicle i during the t-th time interval; Let represent the clockwise angle formed by the line connecting the centroid of vehicle j and the centroid of the surrounding vehicle i during the t-th time interval, and the direction of motion of the surrounding vehicle i. Represents the coordinates of surrounding vehicle i within the t-th time interval; Step 4.4: After assigning i + 1 to i, if i > n holds, then enter Step 5; otherwise, return to Step 4.3 and execute sequentially; Step 5: Decide the dangerous surrounding vehicle m that has an impact on the driving of vehicle j: Step 5.1: Initialize i = 1 and m = 1; Step 5.2, Judgment If the condition is met, then surrounding vehicle i is considered a dangerous surrounding vehicle m, and... The vehicle risk field value generated by the surrounding vehicles m on vehicle j during the t-th time interval is assigned. Proceed to step 5.3; otherwise, proceed to step 5.4; where E vd This indicates the vehicle risk threshold that can cause driving disruptions; Step 5.3: Judge whether i < n holds. If it holds, assign m + 1 to m and i + 1 to i, and return to Step 5.2 to execute sequentially; otherwise, enter Step 6; Step 5.4: Judge whether i < n holds. If it holds, assign i + 1 to i and return to Step 5.2 to execute sequentially; otherwise, enter Step 6; Step 6: Calculate the superimposed risk field value of all vehicles surrounding the danger to vehicle j within the t-th time interval using equation (8). In equation (8), This represents the vehicle risk field value generated by the dangerous surrounding vehicle numbered r on vehicle j within the t-th time interval. Step 7: Calculate the total risk field value of vehicle j in the t-th time interval using equation (9). In equation (9), γ e Indicates the proportion of the field strength in the road environment; γ v This indicates the proportion of the risk field strength of the vehicle. Step 8, let E safe This represents the risk threshold for ensuring safe vehicle operation, and after determining the risk level of vehicle j within the t-th time interval, risk classification is performed: like Then, vehicle j within the t-th time interval is classified as a low-risk vehicle; like Then, vehicle j within the t-th time interval is classified as a medium-risk vehicle; like Then, vehicle j within the t-th time interval is classified as a high-risk vehicle; like Then, vehicle j within the t-th time interval is classified as an ultra-high-risk control vehicle; Step 9: Assign t + 1 to t and return to Step 3 to execute sequentially.

2. The method for identifying road driving risks in a static traffic bottleneck environment under intelligent connected conditions according to claim 1, characterized in that, Step 3.2 includes: Step 3.2.1, if Then, using equation (2), the boundary field value of the longitudinal boundary line of the static traffic bottleneck for vehicle j within the t-th time interval is calculated. In equation (2), β1 represents the coefficient of the boundary field of the static traffic bottleneck; y o The ordinate represents the ordinate of the nearest boundary point on the vertical boundary line from vehicle j to the static traffic bottleneck. Represents the ordinate of vehicle j within the t-th time interval; Step 3.2.2, if Equation (3) is used to calculate the boundary field value of the lateral boundary line of the static traffic bottleneck for vehicle j during the t-th time interval. In equation (3), x o The x-coordinate represents the nearest boundary point on the lateral boundary line from vehicle j to the static traffic bottleneck. Step 3.2.3, if Equation (4) is used to calculate the boundary field values ​​of the horizontal and vertical boundary lines of the static traffic bottleneck for vehicle j during the t-th time interval. Step 3.2.4, if Equation (5) is used to calculate the boundary field values ​​of the horizontal and vertical boundary lines of the static traffic bottleneck for vehicle j during the t-th time interval. Step 3.2.5, Judgment Is it true? If it is true, then keep it. Otherwise, let Among them, E ed This indicates the environmental risk threshold that can affect driving.

3. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor to execute the road driving risk identification method described in Claim 1 or 2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the road driving risk identification method described in Claim 1 or 2.

Images