Method and apparatus for identifying road collapses based on mobile phone navigation data

Abstract

This invention relates to a method and apparatus for identifying road collapses based on mobile phone navigation data, belonging to the field of road safety monitoring technology. The method includes monitoring the vehicle speed on a highway based on the positioning data of a mobile phone inside the target vehicle; if the vehicle speed changes from a first speed to a second speed within a predetermined time, it is determined that the vehicle has experienced a driving anomaly; in the case of the vehicle experiencing a driving anomaly, the collision acceleration vector direction and driving acceleration of the target vehicle are obtained based on the IMU data of the mobile phone, and based on the collision acceleration vector direction and driving acceleration, it is determined whether the driving anomaly is a collapse or fall; if the driving anomaly is a collapse or fall, it is determined that a road collapse has occurred on the highway, thereby facilitating efficient, accurate, and low-cost identification of road collapse conditions.

Description

Technical Field

[0001] This invention relates to the field of road safety monitoring technology, and in particular to a method and apparatus for identifying road collapses based on mobile phone navigation data. Background Technology

[0002] In road traffic, traditional infrastructure monitoring methods are inefficient and unable to detect sudden collapses in real time. In fall accidents, vehicles may fall into low-lying areas with poor signal coverage, where signals may be lost or delayed, failing to provide timely warnings to vehicles behind. Therefore, there is an urgent need for a method that can efficiently, accurately, and cost-effectively identify genuine road collapses from massive amounts of crowdsourced navigation data to overcome the shortcomings of existing technologies. Summary of the Invention

[0003] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method and apparatus for identifying road collapses based on mobile phone navigation data, which solves the technical problem of road collapse identification.

[0004] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0005] The first aspect of this invention provides a method for identifying road collapses based on mobile phone navigation data.

[0006] The method for identifying road collapses based on mobile phone navigation data proposed in this invention includes:

[0007] The vehicle speed on the highway is monitored based on the location data of the mobile phone inside the target vehicle.

[0008] If the vehicle speed of the target vehicle is detected to change from the first speed to the second speed within a predetermined time, it is determined that the target vehicle has a driving abnormality.

[0009] In the event of a driving abnormality in the target vehicle, the collision acceleration vector direction and driving acceleration of the target vehicle are obtained based on the IMU data of the mobile phone when the vehicle speed changes, and the driving abnormality of the target vehicle is determined based on the collision acceleration vector direction and driving acceleration.

[0010] If the abnormal driving behavior of the target vehicle is a collapse or fall, then it is determined that a road surface collapse has occurred on the highway.

[0011] In some instances, determining whether the driving anomaly of the target vehicle is a collapse or fall based on the direction of the collision acceleration vector and the vehicle acceleration includes:

[0012] Based on the collision acceleration vector direction and the driving acceleration, the vehicle posture of the target vehicle when driving abnormality occurs is obtained;

[0013] Based on the vehicle posture of the target vehicle exhibiting abnormal driving behavior, determine whether the abnormal driving behavior of the target vehicle is a collapse or fall.

[0014] In some instances, determining whether the abnormal driving behavior of the target vehicle, based on its vehicle posture, constitutes a collapse or fall includes:

[0015] If the trend of the vehicle's attitude change is weightlessness-front drop-collision, then the driving abnormality of the target vehicle is determined to be a collapse and fall.

[0016] If the trend of the vehicle's posture change is collision-airborne-secondary collision, then the driving abnormality of the target vehicle is determined to be impact-airborne.

[0017] In some instances, obtaining the vehicle attitude indicating an abnormal driving behavior of the target vehicle based on the collision acceleration vector direction and the driving acceleration includes:

[0018] Based on the three-axis gyroscope data in the IMU data, the angle Δθ of the vehicle's front end sinking is obtained;

[0019] If the angle between the collision acceleration vector direction of the target vehicle and the forward acceleration vector direction of the target vehicle is... The error between the angle Δθ of the vehicle's front end and the angle between the collision acceleration vector direction of the target vehicle and the vertical acceleration vector direction of the target vehicle is within a predetermined range. If the angle is less than 90 degrees, the vehicle posture of the target vehicle that is exhibiting abnormal driving behavior is determined to be a downward tilt of the vehicle's front end;

[0020] If the vertical acceleration of the target vehicle is N times the gravitational acceleration and the duration is within the first duration, then the vehicle posture of the target vehicle exhibiting abnormal driving behavior is determined to be weightlessness, where 0.7≤N≤1;

[0021] If the vertical acceleration of the target vehicle is gravitational acceleration and the duration exceeds the second duration, then the vehicle posture of the target vehicle exhibiting abnormal driving behavior is determined to be airborne; wherein, the second duration is greater than the first duration.

[0022] If the absolute value of either forward acceleration or lateral acceleration of the target vehicle exceeds a predetermined threshold within a predetermined time period, then the abnormal vehicle posture of the target vehicle is determined to be a collision.

[0023] In some instances, the driving acceleration includes: forward acceleration in the X-axis direction, lateral acceleration in the Y-axis direction, and vertical acceleration in the Z-axis direction;

[0024] The method further includes:

[0025] The vector sum of the forward acceleration in the X-axis direction, the lateral acceleration in the Y-axis direction, and the vertical acceleration in the Z-axis direction is taken as the collision acceleration of the target vehicle.

[0026] In some instances, the angle between the direction of the collision acceleration vector of the target vehicle and the direction of the forward acceleration vector of the target vehicle is... for:

[0027] ;in, The direction of the collision acceleration vector of the target vehicle. The direction of the forward acceleration vector of the target vehicle;

[0028] The angle between the collision acceleration vector direction of the target vehicle and the vertical acceleration vector direction of the target vehicle. for:

[0029] ;in, The direction of the collision acceleration vector of the target vehicle. The direction of the vertical acceleration vector of the target vehicle.

[0030] In some instances, the IMU data used to obtain the collision acceleration vector direction and driving acceleration of the target vehicle is data collected by a mobile phone fixed inside the target vehicle.

[0031] A second aspect of this invention provides a device for identifying road collapses based on mobile phone navigation data, comprising:

[0032] The data monitoring module is used to monitor the vehicle speed on the highway based on the location data of the mobile phone inside the target vehicle.

[0033] The driving anomaly analysis module is used to determine that the target vehicle has a driving anomaly if the vehicle speed is detected to change from a first speed to a second speed within a predetermined time.

[0034] The vehicle attitude analysis module is used to obtain the collision acceleration vector direction and driving acceleration of the target vehicle when the vehicle speed changes based on the IMU data of the mobile phone when the target vehicle experiences a driving abnormality, and to determine whether the driving abnormality of the target vehicle is a collapse or fall based on the collision acceleration vector direction and driving acceleration.

[0035] The road collapse identification module is used to determine that a road collapse has occurred on the highway if the abnormal driving behavior of the target vehicle is a collapse or fall.

[0036] A third aspect of the present invention provides a computer-readable storage medium storing a program for identifying road collapses based on mobile phone navigation data. When the program for identifying road collapses based on mobile phone navigation data is executed by a processor, it implements the method for identifying road collapses based on mobile phone navigation data described in the first aspect.

[0037] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program for identifying road collapses based on mobile phone navigation data, stored in the memory and executable on the processor. When the processor executes the program for identifying road collapses based on mobile phone navigation data, it implements the method for identifying road collapses based on mobile phone navigation data described in the first aspect above.

[0038] The present invention discloses a method for identifying road surface collapse based on mobile phone navigation data, comprising: monitoring the vehicle speed of the target vehicle on a highway based on the positioning data of the mobile phone in the target vehicle; if the vehicle speed of the target vehicle changes from a first speed to a second speed within a predetermined time, it is determined that the target vehicle has a driving abnormality; in the case of the target vehicle having a driving abnormality, acquiring the collision acceleration vector direction and driving acceleration of the target vehicle when the vehicle speed changes based on the IMU data of the mobile phone, and determining whether the driving abnormality of the target vehicle is a collapse or fall based on the collision acceleration vector direction and driving acceleration; if the driving abnormality of the target vehicle is a collapse or fall, it is determined that a road surface collapse has occurred on the highway. In this application, the vehicle's speed, collision acceleration vector direction, and acceleration can be effectively analyzed using the positioning data and IMU data from the mobile phone inside the vehicle. Based on the collision acceleration vector direction and acceleration of the target vehicle, it can be analyzed whether the driving anomaly of the target vehicle is a collapse or fall. If the driving anomaly of the target vehicle is a collapse or fall, it is determined that a road surface collapse has occurred on the highway, which is conducive to efficiently, accurately, and cost-effectively identifying the road surface collapse state. Attached Figure Description

[0039] Figure 1 A flowchart illustrating a method for identifying road collapses based on mobile phone navigation data, provided in an embodiment of the present invention;

[0040] Figure 2 A flowchart for identifying road collapses based on mobile phone navigation data is provided as an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the device structure for identifying road collapses based on mobile phone navigation data, provided in an embodiment of the present invention. Detailed Implementation

[0042] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] The method for identifying road collapses based on mobile phone navigation data proposed in this invention addresses the problem of road collapse identification. By using the positioning data and IMU data from the mobile phone inside the vehicle, the method can effectively analyze the vehicle's speed, collision acceleration vector direction, and acceleration. Based on the collision acceleration vector direction and acceleration of the target vehicle, it analyzes whether the abnormal driving behavior of the target vehicle indicates a collapse or fall. If the abnormal driving behavior of the target vehicle indicates a collapse or fall, it is determined that a road collapse has occurred on the highway, thus facilitating efficient, accurate, and low-cost identification of road collapse conditions.

[0044] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0045] Figure 1 This is a flowchart illustrating a method for identifying road collapses based on mobile phone navigation data, provided as an embodiment of the present invention. Figure 1 As shown in the embodiment of the present invention, the method for identifying road collapses based on mobile phone navigation data includes:

[0046] Step 100: Monitor the vehicle speed on the highway based on the location data of the mobile phone inside the target vehicle;

[0047] Step 110: If it is detected that the speed of the target vehicle changes from the first speed to the second speed within a predetermined time, then it is determined that the target vehicle has a driving abnormality.

[0048] Step 120: In the event of a driving abnormality in the target vehicle, the collision acceleration vector direction and driving acceleration of the target vehicle are obtained based on the IMU data of the mobile phone when the vehicle speed changes, and the driving abnormality of the target vehicle is determined based on the collision acceleration vector direction and driving acceleration to determine whether the driving abnormality of the target vehicle is a collapse or fall.

[0049] Step 130: If the abnormal driving behavior of the target vehicle is a collapse or fall, then it is determined that a road surface collapse has occurred on the highway.

[0050] In this exemplary embodiment, the positioning data includes GPS (Global Positioning System) data. GPS data includes latitude and longitude, and heading angle.

[0051] Based on GPS data and high-precision map information, it is determined whether the vehicle is on a regular section of the highway and special sections such as bridges, tunnels, and ramps are excluded to ensure that the vehicle is on a typical section of road where a collapse may occur.

[0052] In this exemplary embodiment, the predetermined time can be a relatively short duration. When the target vehicle's speed changes from a first speed to a second speed within the predetermined time, it can be considered that the target vehicle has experienced a sudden deceleration anomaly. Here, the first speed is greater than the second speed.

[0053] For example, if the vehicle speed drops sharply from the normal highway speed (e.g., above 80 km / h) to an extremely low speed (e.g., below 3 km / h) or zero within a very short period of time (e.g., 2-3 seconds) after the occurrence of weightlessness, it is determined that the vehicle has encountered an abnormal situation.

[0054] In this exemplary embodiment, collapse and fall refers to a vehicle falling due to road surface subsidence.

[0055] In this application, the vehicle's speed, collision acceleration vector direction, and acceleration can be effectively analyzed using the positioning data and IMU data from the mobile phone inside the vehicle. Based on the collision acceleration vector direction and acceleration of the target vehicle, it can be analyzed whether the driving anomaly of the target vehicle is a collapse or fall. If the driving anomaly of the target vehicle is a collapse or fall, it is determined that a road surface collapse has occurred on the highway, which is conducive to efficiently, accurately, and cost-effectively identifying the road surface collapse state.

[0056] In some instances, determining whether the driving anomaly of the target vehicle is a collapse or fall based on the direction of the collision acceleration vector and the vehicle acceleration includes:

[0057] Based on the collision acceleration vector direction and the driving acceleration, the vehicle posture of the target vehicle when driving abnormality occurs is obtained;

[0058] Based on the vehicle posture of the target vehicle exhibiting abnormal driving behavior, determine whether the abnormal driving behavior of the target vehicle is a collapse or fall.

[0059] In this exemplary embodiment, the vehicle's posture may include weightlessness, nose-down, collision, airborne, etc.

[0060] The step of determining whether the abnormal driving behavior of the target vehicle is a collapse or fall based on the vehicle's posture indicates an abnormal driving behavior includes:

[0061] If the trend of the vehicle's attitude change is weightlessness-front drop-collision, then the driving abnormality of the target vehicle is determined to be a collapse and fall.

[0062] If the trend of the vehicle's posture change is collision-airborne-secondary collision, then the driving abnormality of the target vehicle is determined to be impact-airborne.

[0063] In this exemplary embodiment, impact-induced airborne refers to the vehicle being airborne after a collision.

[0064] In this exemplary embodiment, weightlessness-front drop-collision refers to the change in vehicle posture from weightlessness to front drop, and then to collision.

[0065] In this exemplary embodiment, the collision-airborne-secondary collision refers to the change in vehicle posture from a collision to airborne, and then to a secondary collision.

[0066] In some instances, obtaining the vehicle attitude indicating an abnormal driving behavior of the target vehicle based on the collision acceleration vector direction and the driving acceleration includes:

[0067] Based on the three-axis gyroscope data in the IMU data, the angle Δθ of the vehicle's front end sinking is obtained;

[0068] If the angle between the collision acceleration vector direction of the target vehicle and the forward acceleration vector direction of the target vehicle is... The error between the angle Δθ of the vehicle's front end and the angle between the collision acceleration vector direction of the target vehicle and the vertical acceleration vector direction of the target vehicle is within a predetermined range. If the angle is less than 90 degrees, the vehicle posture of the target vehicle that is exhibiting abnormal driving behavior is determined to be a downward tilt of the vehicle's front end;

[0069] If the vertical acceleration of the target vehicle is N times the gravitational acceleration and the duration is within the first duration, then the vehicle posture of the target vehicle exhibiting abnormal driving behavior is determined to be weightlessness, where 0.7≤N≤1;

[0070] If the vertical acceleration of the target vehicle is gravitational acceleration and the duration exceeds the second duration, then the vehicle posture of the target vehicle exhibiting abnormal driving behavior is determined to be airborne; wherein, the second duration is greater than the first duration.

[0071] If the absolute value of either forward acceleration or lateral acceleration of the target vehicle exceeds a predetermined threshold within a predetermined time period, then the abnormal vehicle posture of the target vehicle is determined to be a collision.

[0072] In this exemplary embodiment, the driving acceleration includes: forward acceleration in the X-axis direction, lateral acceleration in the Y-axis direction, and vertical acceleration in the Z-axis direction;

[0073] The method further includes:

[0074] The vector sum of the forward acceleration in the X-axis direction, the lateral acceleration in the Y-axis direction, and the vertical acceleration in the Z-axis direction is taken as the collision acceleration of the target vehicle.

[0075] In this exemplary embodiment, a collision acceleration vector is defined. At the moment of collision, the triaxial accelerometer readings are extracted, and a = [ax, ay, az]. T Defined as the collision acceleration vector. Here, ax, ay, and az represent the acceleration values ​​along the three axes in the phone's coordinate system, respectively. The direction of the collision acceleration vector is determined. The IMU data includes three-axis acceleration data and three-axis gyroscope data.

[0076] In some instances, the angle between the direction of the collision acceleration vector of the target vehicle and the direction of the forward acceleration vector of the target vehicle is... for:

[0077] ;in, The direction of the collision acceleration vector of the target vehicle. The direction of the forward acceleration vector of the target vehicle;

[0078] The angle between the collision acceleration vector direction of the target vehicle and the vertical acceleration vector direction of the target vehicle. for:

[0079] ;in, The direction of the collision acceleration vector of the target vehicle. The direction of the vertical acceleration vector of the target vehicle.

[0080] In this exemplary embodiment, the angular velocity component characterizing the pitch angle change is extracted from the gyroscope data. (Usually the Y-axis). Integrate this angular velocity component to calculate the angle Δθ of the vehicle's nose sinking during the time interval Δt. That is, .

[0081] Collision Direction Analysis: Extract the axial component of the acceleration vector with the maximum value at the time of the collision. Calculate the angle between this component and the vehicle's forward direction and the direction of gravity.

[0082] In this exemplary embodiment, the angle between the collision acceleration vector direction of the target vehicle and the forward acceleration vector direction of the target vehicle is... for:

[0083] ;in, The direction of the collision acceleration vector of the target vehicle. The direction of the forward acceleration vector of the target vehicle;

[0084] The angle between the collision acceleration vector direction of the target vehicle and the vertical acceleration vector direction of the target vehicle. for:

[0085] ;in, The direction of the collision acceleration vector of the target vehicle. The direction of the vertical acceleration vector of the target vehicle.

[0086] In this exemplary embodiment, the forward acceleration vector direction f can be defined as the front axis direction (+X axis) of the vehicle coordinate system. It can be determined by the GPS heading angle or the heading angle estimated by the IMU at the moment before weightlessness.

[0087] Vertical acceleration vector direction During the normal driving phase before weightlessness occurs, an accurate estimate can be made by averaging accelerometer data. In the vehicle coordinate system, it typically points in the vertical downward direction (-Z axis) of the vehicle.

[0088] In this exemplary embodiment, when an impact-induced airborne event occurs, if a high-intensity, short-duration impact signal appears in the forward acceleration or lateral acceleration along the three axes, and the absolute value of the acceleration exceeds a predetermined threshold (e.g., 5g), then it can be determined that the target vehicle exhibits an abnormal driving posture as a collision.

[0089] In some instances, the IMU data used to obtain the collision acceleration vector direction and driving acceleration of the target vehicle is data collected by a mobile phone fixed inside the target vehicle.

[0090] In this exemplary embodiment, after the navigation system starts and IMU data is acquired, the three-axis angular velocities can be acquired after two minutes. Acquisition is performed once every 0.5 seconds, with three angular velocities acquired each time. Each angular velocity is acquired 100 times consecutively. If the standard deviation of these 100 data points is less than or equal to 3% of the average of these 100 data points, the phone is considered fixed along that axis. If all three axes satisfy the above rule, the phone is considered fixed to the bracket. If the judgment result does not meet the above rule, the algorithm terminates. After 5 minutes, the algorithm is restarted and the judgment is performed again.

[0091] Figure 2 This is a flowchart illustrating a method for identifying road collapses based on mobile phone navigation data, provided as an embodiment of the present invention. Figure 2 As shown, the process of identifying road collapses based on mobile phone navigation data includes:

[0092] Step 20: Begin;

[0093] Step 21, Data Acquisition;

[0094] Step 22: Determine if the vehicle is on a highway;

[0095] Step 23: If the vehicle is on a highway, analyze the vehicle's three-axis accelerometer data;

[0096] Step 24: Determine if there are any abnormalities in the car;

[0097] Step 25: If the car exhibits any abnormal behavior, analyze the vehicle's abnormal events.

[0098] Step 26: The vehicle abnormality event is determined to be a vehicle fall event;

[0099] Step 27: If the abnormal vehicle event is determined to be a vehicle falling event, then the road surface collapse is confirmed.

[0100] Step 28: If the road surface collapses, warn oncoming vehicles.

[0101] Step 29: The abnormal vehicle event is determined to be a vehicle collision event;

[0102] Step 30: Once the vehicle abnormality event is determined to be a vehicle collision event, the process ends.

[0103] This invention provides a device for identifying road collapses based on mobile phone navigation data. Figure 3 This is a schematic diagram of the device structure for identifying road collapses based on mobile phone navigation data, provided in an embodiment of the present invention. Figure 3 As shown, it includes:

[0104] The data monitoring module 30 is used to monitor the vehicle speed of the target vehicle on the highway based on the positioning data of the mobile phone inside the target vehicle.

[0105] The driving anomaly analysis module 31 is used to determine that the target vehicle has a driving anomaly if the vehicle speed of the target vehicle changes from a first speed to a second speed within a predetermined time.

[0106] The vehicle attitude analysis module 32 is used to obtain the collision acceleration vector direction and driving acceleration of the target vehicle when the vehicle speed changes based on the IMU data of the mobile phone when the target vehicle experiences a driving abnormality, and to determine whether the driving abnormality of the target vehicle is a collapse or fall based on the collision acceleration vector direction and driving acceleration.

[0107] The road collapse identification module 33 is used to determine that a road collapse has occurred on the highway if the driving abnormality of the target vehicle is a collapse or fall.

[0108] In this exemplary embodiment, the positioning data includes GPS (Global Positioning System) data. GPS data includes latitude and longitude, and heading angle.

[0109] Based on GPS data and high-precision map information, it is determined whether the vehicle is on a regular section of the highway and special sections such as bridges, tunnels, and ramps are excluded to ensure that the vehicle is on a typical section of road where a collapse may occur.

[0110] In this exemplary embodiment, the predetermined time can be a relatively short duration. When the target vehicle's speed changes from a first speed to a second speed within the predetermined time, it can be considered that the target vehicle has experienced a sudden deceleration anomaly. Here, the first speed is greater than the second speed.

[0111] For example, if the vehicle speed drops sharply from the normal highway speed (e.g., above 80 km / h) to an extremely low speed (e.g., below 3 km / h) or zero within a very short period of time (e.g., 2-3 seconds) after the occurrence of weightlessness, it is determined that the vehicle has encountered an abnormal situation.

[0112] In this exemplary embodiment, "impact-induced airborne" refers to the vehicle being airborne after a collision, while "collapse-induced fall" refers to the vehicle falling due to road surface subsidence.

[0113] In this application, the vehicle's speed, collision acceleration vector direction, and acceleration can be effectively analyzed using the positioning data and IMU data from the mobile phone inside the vehicle. Based on the collision acceleration vector direction and acceleration of the target vehicle, it can be analyzed whether the driving anomaly of the target vehicle is a collapse or fall. If the driving anomaly of the target vehicle is a collapse or fall, it is determined that a road surface collapse has occurred on the highway, which is conducive to efficiently, accurately, and cost-effectively identifying the road surface collapse state.

[0114] Since the systems / devices described in the above embodiments of the present invention are systems / devices used to implement the methods of the above embodiments of the present invention, those skilled in the art can understand the specific structure and modifications of the systems / devices based on the methods described in the above embodiments of the present invention, and therefore will not be repeated here. All systems / devices used in the methods of the above embodiments of the present invention fall within the scope of protection of the present invention.

[0115] This invention provides a computer-readable storage medium storing a program for identifying road collapses based on mobile phone navigation data. When the program is executed by a processor, it implements the method for identifying road collapses based on mobile phone navigation data described in the above embodiments.

[0116] This invention provides an electronic device, including a memory, a processor, and a program for identifying road collapses based on mobile phone navigation data, stored in the memory and executable on the processor. When the processor executes the program for identifying road collapses based on mobile phone navigation data, it implements the method for identifying road collapses based on mobile phone navigation data described in the above embodiments.

[0117] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0118] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0119] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "over," or "on top" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," or "beneath" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0120] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0121] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for identifying a road collapse based on mobile phone navigation data, characterized in that, The method comprises: monitoring driving speed of a target vehicle on a highway based on positioning data of a mobile phone in the target vehicle; if the driving speed of the target vehicle is monitored to change from a first speed to a second speed within a predetermined time, determining that the target vehicle has driving abnormality; the predetermined time is 2-3 seconds; in the case that the target vehicle has driving abnormality, obtaining a collision acceleration vector direction and driving acceleration of the target vehicle when the speed changes based on IMU data of the mobile phone, and determining whether the driving abnormality of the target vehicle is collapse and falling based on the collision acceleration vector direction and the driving acceleration; if the driving abnormality of the target vehicle is collapse and falling, determining that the highway has road collapse; the determination whether the driving abnormality of the target vehicle is collapse and falling based on the collision acceleration vector direction and the driving acceleration comprises: obtaining a vehicle posture of the target vehicle when the driving abnormality occurs based on the collision acceleration vector direction and the driving acceleration; the obtaining the vehicle posture of the target vehicle when the driving abnormality occurs based on the collision acceleration vector direction and the driving acceleration comprises: obtaining an angle Δθ of the head sinking based on three-axis gyroscope data in the IMU data; if the angle between the collision acceleration vector direction of the target vehicle and the forward acceleration vector direction of the target vehicle is less than 90 degrees, it is determined that the target vehicle has a vehicle posture of a vehicle abnormality of a vehicle head down. if the angle between the collision acceleration vector direction of the target vehicle and the forward acceleration vector direction of the target vehicle is less than 90 degrees, it is determined that the target vehicle has a vehicle posture of a vehicle abnormality of a vehicle head down. if the angle between the collision acceleration vector direction of the target vehicle and the forward acceleration vector direction of the target vehicle is less than 90 degrees, it is determined that the target vehicle has a vehicle posture of a vehicle abnormality of a vehicle head down. if the vertical acceleration of the target vehicle is N times of gravity acceleration and the duration is within a first duration, determining that the vehicle posture of the target vehicle when the driving abnormality occurs is weightlessness, wherein 0.7≤N≤1; if the vertical acceleration of the target vehicle is gravity acceleration and the duration exceeds a second duration, determining that the vehicle posture of the target vehicle when the driving abnormality occurs is emptying; wherein the second duration is greater than the first duration; if any acceleration absolute value of the forward acceleration or the lateral acceleration in the driving acceleration of the target vehicle exceeds a predetermined threshold within a predetermined duration, determining that the vehicle posture of the target vehicle when the driving abnormality occurs is collision; determining whether the driving abnormality of the target vehicle is collapse and falling based on the vehicle posture of the target vehicle when the driving abnormality occurs.

2. The method for identifying road collapse based on mobile navigation data according to claim 1, wherein, the determination whether the driving abnormality of the target vehicle is collapse and falling based on the vehicle posture of the target vehicle when the driving abnormality occurs comprises: if the change trend of the vehicle posture is weightlessness-head sinking-collision, determining that the driving abnormality of the target vehicle is collapse and falling; if the change trend of the vehicle posture is collision-emptying-second collision, determining that the driving abnormality of the target vehicle is impact emptying.

3. The method for identifying road collapse based on mobile navigation data of claim 1, wherein, the driving acceleration comprises: forward acceleration in X-axis direction, lateral acceleration in Y-axis direction, and vertical acceleration in Z-axis direction; the method further comprises: taking the vector sum of the forward acceleration in X-axis direction, the lateral acceleration in Y-axis direction, and the vertical acceleration in Z-axis direction as the collision acceleration of the target vehicle.

4. The method for identifying road collapse based on mobile navigation data of claim 1, wherein, an angle between a collision acceleration vector direction of the target vehicle and a forward acceleration vector direction of the target vehicle is: ; wherein is a collision acceleration vector direction of the target vehicle, is a forward acceleration vector direction of the target vehicle; an angle between a collision acceleration vector direction of the target vehicle and a vertical acceleration vector direction of the target vehicle is: ; wherein is a collision acceleration vector direction of the target vehicle, is a vertical acceleration vector direction of the target vehicle.

5. The method of identifying road collapse based on mobile phone navigation data according to any one of claims 1-4, wherein, the IMU data used to obtain the collision acceleration vector direction and the driving acceleration of the target vehicle is data collected by a mobile phone fixed in the target vehicle.

6. A device for identifying road collapses based on mobile phone navigation data, characterized in that, The method comprises: The data monitoring module is configured to monitor a driving speed of the target vehicle on the expressway based on positioning data of a mobile phone in the target vehicle. The driving abnormality analysis module is configured to determine that the target vehicle has a driving abnormality if the driving speed of the target vehicle changes from a first speed to a second speed within a predetermined time. The predetermined time is 2-3 seconds. The driving posture analysis module is configured to, in a case where the target vehicle has a driving abnormality, acquire a collision acceleration vector direction and a driving acceleration of the target vehicle when the driving speed changes based on IMU data of the mobile phone, and determine whether the driving abnormality of the target vehicle is collapse and falling based on the collision acceleration vector direction and the driving acceleration. The road collapse identification module is configured to determine that the expressway has a road collapse if the driving abnormality of the target vehicle is collapse and falling. The determination whether the driving abnormality of the target vehicle is collapse and falling based on the collision acceleration vector direction and the driving acceleration includes: acquiring a vehicle posture of the target vehicle when the driving abnormality occurs based on the collision acceleration vector direction and the driving acceleration. The acquiring the vehicle posture of the target vehicle when the driving abnormality occurs based on the collision acceleration vector direction and the driving acceleration includes: acquiring an angle Δθ of a sinking of a vehicle head based on three-axis gyroscope data in the IMU data; if an angle between a collision acceleration vector direction of the target vehicle and a forward acceleration vector direction of the target vehicle is greater than 90 degrees and an angle between the nose-down angle Δθ and the error is within a predetermined range, and an angle between a collision acceleration vector direction of the target vehicle and a vertical acceleration vector direction of the target vehicle is less than 90 degrees, then it is determined that the target vehicle has a vehicle attitude of a driving abnormality of a nose-down ​ determining that the vehicle posture of the target vehicle when the driving abnormality occurs is weightlessness if a vertical acceleration of the target vehicle is N times of gravity acceleration and a duration is within a first duration, where 0.7≤N≤1; determining that the vehicle posture of the target vehicle when the driving abnormality occurs is emptying if the vertical acceleration of the target vehicle is gravity acceleration and the duration exceeds a second duration, where the second duration is greater than the first duration; determining that the vehicle posture of the target vehicle when the driving abnormality occurs is collision if an absolute value of any one of a forward acceleration or a lateral acceleration in the driving acceleration of the target vehicle exceeds a predetermined threshold within a predetermined duration; and determining whether the driving abnormality of the target vehicle is collapse and falling based on the vehicle posture of the target vehicle when the driving abnormality occurs.

7. A computer readable storage medium characterized in that, A program for identifying a road collapse based on mobile phone navigation data is stored on the computer readable storage medium, and the program is executed by the processor to implement the method for identifying a road collapse based on mobile phone navigation data according to any one of claims 1-5.

8. An electronic device, comprising: The computer device includes a memory, a processor, and a program for identifying a road collapse based on mobile phone navigation data stored on the memory and executable on the processor, and the processor executes the program for identifying a road collapse based on mobile phone navigation data to implement the method for identifying a road collapse based on mobile phone navigation data according to any one of claims 1-5.

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