A method and device for detecting uneven objects on a road

Abstract

The present application relates to a method and device for detecting bumps and bumps on a road, and to the field of intelligent transportation technology. First, the acceleration signals of each wheel of a vehicle are obtained, and the acceleration signals are processed by framing. The acceleration difference and acceleration ratio of each frame of the acceleration signal of each wheel are calculated respectively. If the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, it is determined that the corresponding wheel has passed through a bump and bump in the corresponding frame, and the data information of each bump and bump passed by the vehicle is obtained respectively. The data information of all bumps and bumps is transmitted to the traffic department server, and the traffic department server evaluates each bump and bump using the data information of the bump and bump and the vehicle information of the corresponding vehicle. The road bump and bump detection method provided by the present application solves the problems in the related art of poor detection effectiveness of bump and bump on the road, low accuracy of the detection structure, and the need to improve the overall efficiency.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method and device for detecting road bumps and concavities. Background Art

[0002] At present, due to various reasons, there are often some bumps on the road. When vehicles pass these bumps, especially when they pass these bumps at high speed, it will cause discomfort to the passengers, and may also cause damage to the vehicle and the items inside, causing traffic accidents, and even causing the loss of life and property. Therefore, it is necessary and important to detect bumps on the road, especially those on the highway.

[0003] In the related art, there are generally two ways to detect bumps and bumps on the road. The first way is to use manual visual inspection, which is a purposeless detection method that mainly relies on manual inspection, is extremely inefficient, and requires huge human, material, financial and time costs. The feedback method is relatively long, and the bumps and bumps cannot be repaired in a timely manner, which prolongs the time when safety hazards exist. The second way is to report the incident by phone, which is a passive detection method. It mainly relies on vehicle drivers to call the traffic department to report the incident. In this way, relatively few bumps and bumps on the road are discovered, and the loopholes are relatively large. It is impossible to conduct a comprehensive detection of bumps and bumps on the road, and it is also impossible to quantify the degree of danger of the bumps and bumps and the urgency of repair, which is not conducive to the development of road bump and bump repair work.

[0004] In addition to the two commonly used methods mentioned above, there is currently another method that obtains an image pair for the road to be detected. The image pair often includes a first image and a second image obtained at different shooting positions. After determining the optical flow data of the image pair, the pixel matching relationship between the first image and the second image is determined based on the optical flow data of the image pair, and then the damage of the road to be detected is determined based on the pixel matching relationship between the first image and the second image. Although this method has improved detection efficiency compared to the previous two methods and is less likely to produce more loopholes, its overall detection algorithm is quite complex and the data processing time is relatively long. In addition, the detection result accuracy is not high enough and it cannot distinguish whether the bumps are real obstacles. Summary of the invention

[0005] The embodiments of the present application provide a method and device for detecting bumps and uneven objects on a road, so as to solve the problems in the related art of poor detection effectiveness of bumps and uneven objects on a road, low accuracy of the detection structure, and the need to improve the overall efficiency.

[0006] In a first aspect, a method for detecting uneven objects on a road is provided, the steps of which include:

[0007] Obtain acceleration signals of each wheel of the vehicle;

[0008] The acceleration signal is subjected to frame processing, and the acceleration difference and acceleration ratio of each frame acceleration signal of each wheel are calculated by using the acceleration signal after frame processing, and if the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, it is determined that the corresponding wheel has passed through a concave-convex object in the corresponding frame;

[0009] The data information of each of the uneven objects passed by the vehicle is obtained respectively, and the data information of all the uneven objects is transmitted to the traffic department server, and the traffic department server evaluates each of the uneven objects using the data information of the uneven objects and the vehicle information of the corresponding vehicle.

[0010] In some embodiments, if the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, it is determined that the corresponding wheel has passed through a concave-convex object in the corresponding frame, including:

[0011] Calculating the maximum number of frames between two wheels symmetrically arranged on the vehicle when they respectively pass through the same speed bump or speed limit barrier;

[0012] If the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, then confirm whether the other wheel corresponding to the wheel has also passed through the bump within the maximum frame time range after the wheel passed through the bump. If so, it is determined that the two wheels passed through a speed bump or a speed limit barrier in the corresponding frame. If not, it is determined that the corresponding wheel passed through the bump in the corresponding frame.

[0013] In some embodiments, the calculating of the maximum number of frames between two symmetrically arranged wheels of the vehicle when they respectively pass through the same speed bump or speed limit barrier comprises:

[0014] Obtaining the maximum wheelbase between two symmetrically arranged wheels of the vehicle, obtaining the minimum vehicle speed when the wheels pass through a speed bump or a speed limit barrier, and calculating the maximum time interval between the two symmetrically arranged wheels when they respectively pass through the same speed bump or speed limit barrier based on the maximum wheelbase and the minimum vehicle speed;

[0015] The maximum number of frames between the two wheels when they respectively pass through the same speed bump or speed limit barrier is calculated based on the maximum duration, the frame length of each frame and the sampling frequency of the acceleration signal.

[0016] In some embodiments, the step of respectively acquiring data information of each of the concave-convex objects passed by the vehicle and transmitting the data information of all the concave-convex objects to a traffic department server includes:

[0017] Acquire the actual vehicle speed when the vehicle passes the corresponding uneven object, and calculate the longitudinal length information of the corresponding uneven object according to the actual vehicle speed and the acceleration ratio when the vehicle passes the corresponding uneven object;

[0018] Obtaining geographic location coordinate information of the vehicle when passing the corresponding concave-convex object;

[0019] The longitudinal length information and the corresponding geographic location coordinate information are packaged into a data packet, and the data packet is transmitted to the traffic department server.

[0020] In some embodiments, the traffic department server evaluates each of the concave-convex objects using the data information of the concave-convex objects and the vehicle information of the corresponding vehicle, including:

[0021] The traffic department server receives the data packets transmitted by different vehicles, and compares and analyzes the currently received data packets with the previously received data packets respectively;

[0022] If the current data packet and one of the previous data packets correspond to the same concave-convex object, the two data packets are divided into the same area; if the current data packet and the previous data packet correspond to different concave-convex objects, the current data packet is divided into a new area.

[0023] In some embodiments, comparing and analyzing the currently received data packet with the previously received data packet respectively includes:

[0024] Acquire geographic location coordinate information from the current data packet, and calculate the distance between the concave-convex object corresponding to the current data packet and the concave-convex object corresponding to the previously acquired data packet according to the currently acquired geographic location coordinate information and the previously acquired geographic location coordinate information;

[0025] Determine whether the calculated distance is not greater than the preset distance. If so, determine that the current data packet and the corresponding data packet previously obtained correspond to the same concave-convex object. If not, determine that the current data packet and the corresponding data packet previously obtained correspond to different concave-convex objects.

[0026] In some embodiments, the traffic department server evaluates each of the concave-convex objects using the data information of the concave-convex objects and the vehicle information of the corresponding vehicle, further comprising:

[0027] Determine the lateral length information of the concave-convex object corresponding to the area according to the vehicle information of the vehicle;

[0028] Calculate the risk factor of the concave-convex object using the transverse length information and the longitudinal length information of the concave-convex object;

[0029] The risk coefficient is graded according to its size. If the risk coefficient is less than 0.2, the risk level of the bumps and concave objects corresponding to the area is determined to be a slight level. If the risk coefficient is not less than 0.2 and less than 0.5, the risk level of the bumps and concave objects corresponding to the area is determined to be a medium level. If the risk coefficient is not less than 0.5, the risk level of the bumps and concave objects corresponding to the area is determined to be a severe level.

[0030] In some embodiments, determining the lateral length information of the concave-convex object corresponding to the area according to the vehicle information of the vehicle further includes:

[0031] Obtaining vehicle information of the data packet corresponding to the area within a preset time period;

[0032] If the vehicle information corresponding to the data packet is the same as that of other data packets, the data packets with the same vehicle information are assigned the same sequence number; if the vehicle information corresponding to the data packet is different from that of all other data packets, a new sequence number is assigned to the corresponding data packet;

[0033] The number of the vehicles corresponding to the area is determined according to the serial number with the largest value, and the lateral length information of the concave-convex object corresponding to the area is calculated according to the number.

[0034] In some embodiments, the sampling frequency of the acceleration signal ranges from 4500 to 5500 Hz, and the frame length of each frame of the acceleration signal ranges from 950 to 1050.

[0035] In a second aspect, a road bump detection device is provided, and the road bump detection device is used to implement the above-mentioned road bump detection method, and comprises:

[0036] A monitoring module, comprising a plurality of acceleration monitors respectively arranged on each tire of the vehicle, wherein the acceleration monitors are used to obtain acceleration signals of each wheel of the vehicle;

[0037] A data acquisition and analysis module is used to receive the acceleration signal and perform frame processing on the acceleration signal. The data acquisition and analysis module is also used to use the acceleration signal after frame processing to respectively calculate the acceleration difference and acceleration ratio of each frame acceleration signal of each wheel, and to determine whether the corresponding acceleration difference satisfies and is greater than the corresponding preset acceleration difference threshold, and whether the corresponding acceleration ratio satisfies and is greater than the corresponding preset acceleration ratio threshold. If both are satisfied, it is determined that the corresponding wheel has passed through a concave-convex object in the corresponding frame.

[0038] The beneficial effects of the technical solution provided by this application include:

[0039] The embodiment of the present application provides a method for detecting bumps and bumps on a road, which uses the acceleration signals of each wheel of a vehicle, and uses the acceleration signals after frame processing to respectively calculate the acceleration difference and acceleration ratio of each frame acceleration signal of each wheel, and respectively compares the corresponding acceleration difference with a preset acceleration difference threshold, and the corresponding acceleration ratio with a preset acceleration ratio threshold, so as to accurately determine whether the wheel has passed a bump and bump in the corresponding frame. The method for detecting bumps and bumps on a road uses the calculation and comparison results of the acceleration difference and the acceleration ratio to determine whether there are bumps and bumps on the road, thereby improving the effectiveness of the detection. The method for detecting bumps and bumps on a road has a simple detection principle, a concise detection algorithm, and a high effectiveness and accuracy of the detection method, which greatly reduces the manpower, material, financial and time costs, and greatly facilitates the detection of bumps and bumps on a road. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A schematic diagram of the process of detecting road bumps and concavities provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of a process of determining whether a wheel has passed through an uneven object in a corresponding frame in a method for detecting uneven objects on a road provided in an embodiment of the present application;

[0043] Figure 3 A schematic flow chart of the maximum number of frames between two wheels of the road bump detection method provided in an embodiment of the present application when they pass through the same speed bump or speed limit barrier respectively;

[0044] Figure 4A schematic diagram of a scenario in which a vehicle is at a speed bump or speed limit barrier according to the road bump detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0046] The embodiment of the present application provides a method for detecting bumps and bumps on a road, which can solve the problems in the related art of poor effectiveness in detecting bumps and bumps on a road, low accuracy of the detection structure, and the need to improve the overall efficiency.

[0047] See also Figure 1 As shown, the steps of the road bump detection method include: first, acquiring the acceleration signal of each wheel of the vehicle, then framing the acceleration signal, and using the acceleration signal after framing to calculate the acceleration difference and acceleration ratio of each frame acceleration signal of each wheel, if the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, it is determined that the corresponding wheel has passed the bump in the corresponding frame, and finally, data information of each bump passed by the vehicle is obtained, and the data information of all the bumps is transmitted to the traffic department server, and the traffic department server evaluates each bump using the data information of the bump and the vehicle information of the corresponding vehicle.

[0048] Specifically, the acceleration information needs to be preprocessed before the acceleration signal is framed. After the vehicle is started, the acceleration signals of each wheel are collected. Taking a four-wheeled vehicle as an example, the acceleration signals of the four wheels are FL(i), FR(i), RL(i), and RR(i), which represent the acceleration signal sequences of the front left, front right, rear left, and rear right wheels, respectively, where i is 1, 2, 3, etc. in sequence. Generally speaking, the lower limit of the size of the bumps in the direction of vehicle travel that need to be repaired on the road is 80mm. The fastest speed of a general vehicle is 120km / h. Therefore, if the vehicle passes this bump at a speed of 120km / h, the time it takes is as follows:

[0049] (80 / 1000) / (120 / 3.6)=2.4*10 -3 s=2.4ms Formula 1

[0050] The number of data points collected during this period cannot be too low, but it cannot increase the calculation burden too much. Therefore, the sampling frequency of the acceleration signal is determined to be in the range of 4500 to 5500 Hz based on the elapsed time calculated by Formula 1 to ensure that there are enough sampling data points when driving over bumps and concave objects.

[0051] Specifically, the purpose of framing the acceleration signal is to ensure that the acceleration signal analysis has a sufficiently high time resolution, and N continuous sampling points are grouped into a data analysis unit, called a frame, where N is the frame length. When framing, the acceleration signal {FL(i)} is intercepted with N as the data length. Similarly, the i1-th frame data of the four acceleration signals corresponding to the four wheels are FL_frame(i1)={FL((i1-1)*N+1)~FL(i1*N)}, FR_frame(i1)={FR((i1-1)*N+1)~FR(i1*N)}, RL_frame(i1)={RL((i1-1)*N+1)~RL(i1*N)}, RR_frame(i1)={RR((i1-1)*N+1)~RR(i1*N)}, and i1 is 1, 2, 3, etc. in order.

[0052] In order to ensure that the acceleration signal analysis has a sufficiently high time resolution, N cannot be too large. Specifically, to ensure the effectiveness and accuracy of the road bump detection method, a frame of data must contain the entire time history of a certain wheel of the vehicle passing through the bump. Generally speaking, the upper limit of the size of the bump in the direction of the vehicle that needs to be repaired on the road is 250mm. Assuming that the vehicle passes through the bump at the lowest possible speed of 10km / h, the time it takes is as follows:

[0053] (250 / 1000) / (10 / 3.6)=90*10 -3 s=90ms Formula 2

[0054] According to the sampling frequency, the number of data points collected during this period is about 405 to 495, among which the sampling frequency is preferably 5000 Hz, and 450 data points are the upper limit of the number of data points contained in the time history of a complete wheel of a vehicle passing over a bumpy object. Therefore, the value of N needs to be greater than 450. Here, the frame length N of each frame of the acceleration signal ranges from 950 to 1050 to ensure the effectiveness and accuracy of the detection.

[0055] Specifically, when one of the wheels of the vehicle passes over a bump, the corresponding acceleration signal will change suddenly. Take the i1 frame data FL_frame(i1) of the left front wheel as an example. Assume that its data sequence is {a(i2)}, i2 takes values ​​of 1, 2, 3...N, and N is the frame length. The difference calculation is as follows:

[0056] b(i3)=(a(i3+1)-a(i3)) Formula 3

[0057] The value of i3 in formula 3 is 1, 2, 3...N-1, and the data sequence b(i3) is the difference of FL_frame(i1), recorded as FL_diff(i1). However, it is not sufficient to use only the differential algorithm to detect bumps and concave objects, and the effectiveness and accuracy cannot be guaranteed, because the differential algorithm calculates the difference between the acceleration moments before and after, without considering the influence of the road. The differential value when driving over bumps and concave objects on a smooth road may be similar to the differential value when driving normally on a bad road, that is, without driving over bumps and concave objects. In this case, the detection result of using the differential algorithm to detect bumps and concave objects is very inaccurate. Therefore, on this basis, the acceleration ratio is also considered.

[0058] Specifically, the left front wheel i1 frame data FL_frame(i1) is still used as an example for explanation, assuming that its data sequence is {a(i2)}, i2 takes values ​​of 1, 2, 3...N, and N is the frame length. The multiple ratio calculation is as follows:

[0059] c(i3)=a(i3+1) / a(i3) Formula 4

[0060] The value of i3 in Formula 4 is also 1, 2, 3, ... N-1, and the data sequence c(i3) is a multiple of FL_frame(i1).

[0061] Therefore, when b(i3) is greater than the preset acceleration difference threshold, and c(i3) is greater than the preset acceleration ratio threshold, it means that the vehicle is passing over an uneven object. According to the acceleration test results of each road, the statistical values ​​of the preset acceleration difference and the preset acceleration ratio, diff_limit and ratio_limit, can be obtained respectively. Here, diff_limit is defined as the preset acceleration difference threshold, and ratio_limit is defined as the preset acceleration ratio threshold. Therefore, when the following formula is satisfied:

[0062]

[0063] This means that the corresponding wheel has passed through a bump, which also means that there are bumps on the road.

[0064] For further information, see Figure 2As shown, since speed bumps or speed limit barriers are set up at special sections such as intersections or schools on the current roads, if the vehicle passes over the speed bump or speed limit barrier, the above formula 5 cannot exclude the speed bump or speed limit barrier passed by. Therefore, if the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, it is determined that the corresponding wheel has passed through the concave-convex object in the corresponding frame. The steps specifically include: calculating the maximum number of frames between two symmetrically arranged wheels of the vehicle when they pass through the same speed bump or speed limit barrier respectively. If the corresponding acceleration difference is greater than the corresponding preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the corresponding preset acceleration ratio threshold, confirming whether the other wheel corresponding to the wheel has also passed through the concave-convex object within the maximum frame time range after the wheel passes through the concave-convex object. If so, it is determined that the two wheels passed through the speed bump or speed limit barrier in the corresponding frame. If not, it is determined that the corresponding previous wheel passed through the concave-convex object in the corresponding frame.

[0065] For further information, see Figure 3 As shown, the specific steps of calculating the maximum number of frames between two symmetrically arranged wheels of a vehicle when they pass the same speed bump or speed limit barrier respectively include: first obtaining the maximum wheelbase between the two symmetrically arranged wheels of the vehicle, then obtaining the minimum vehicle speed when the wheels pass the speed bump or speed limit barrier, and calculating the maximum time interval between the two symmetrically arranged wheels when they pass the same speed bump or speed limit barrier respectively according to the maximum wheelbase and the minimum vehicle speed; finally, calculating the maximum number of frames between the two wheels when they pass the same speed bump or speed limit barrier respectively according to the maximum time interval, the frame length of each frame and the sampling frequency of the acceleration signal.

[0066] For details, see Figure 4 As shown in the figure, the vehicle may pass through the speed bump or speed limit barrier directly vertically, or pass through the speed bump or speed limit barrier at a certain yaw angle, including Figure 4 In the three cases 1, 2 and 3 shown in the figure, when the vehicle passes by vertically, the bumps detected by the two wheels on the left and right sides will appear in the same frame sequence. When the vehicle passes by speed bumps or speed limit barriers at a certain yaw angle, the bumps detected by the two wheels on the left and right sides will appear in different frame sequences. For example, the bumps on the left front wheel appear in the i5th frame, and the bumps on the right front wheel appear in the i6th frame. Generally speaking, when the vehicle is driving, the upper limit of the yaw angle is 45°, so the maximum time interval between the bumps detected by the left and right wheels is calculated as follows:

[0067] duration=distance_tire / (v / 3.6)=3.6*distance_tire / v Formula 6

[0068] In formula 6, distance_tire is the wheelbase of the vehicle, which is 2m, and v is the vehicle speed. In order to calculate the upper limit of duration, the vehicle speed v is taken as the lower limit of 10km / h. Finally, the longest time interval between the bumps detected by the left and right wheels is 0.72s. Therefore, the maximum number of frames between the bumps detected by the left and right wheels is calculated as follows:

[0069] frame_interval=duration / (N / fs)=3.6*distance_tire / (v*N / fs) Formula 7

[0070] Wherein, in Formula 7, N is the frame length, and fs is the sampling frequency. Substituting N as 1000 and fs as 5000 Hz into Formula 7, the maximum number of frames frame_interval between the concave and convex objects detected by the left and right wheels is calculated to be 4 frames. According to different vehicle models, the sizes of different parameters can be changed, such as the vehicle wheelbase distance_tire.

[0071] After calculating that the maximum number of frames between the bumps detected by the left and right wheels is 4 frames, for example, the bump detection result variable of the left front wheel is set to L_detect, and the bump detection result variable of the right front wheel is set to R_detect. When the left front wheel detects a bump, L_detect takes a value of 1, otherwise it takes a value of 0. Similarly, when the right front wheel detects a bump, R_detect takes a value of 1, otherwise it takes a value of 0. According to the maximum number of frames between the bumps detected by the left and right wheels, if L_detect takes a value of 1, then it is detected whether the right front wheel has passed a bump within 4 frames after detecting that the left front wheel has passed the bump, that is, whether the value of R_detect within 4 frames is 1. If yes, it is determined that the bump is a speed bump or a speed limit barrier. If not, it is determined that the bump is a real bump. After the 4-frame monitoring is completed, a new round of detection is performed.

[0072] Furthermore, the steps of respectively acquiring data information of each uneven object passed by the vehicle and transmitting the data information of all the uneven objects to the traffic department server specifically include: first acquiring the actual speed of the vehicle when passing the corresponding uneven object, calculating the longitudinal length information of the corresponding uneven object based on the actual speed and the acceleration ratio when passing the corresponding uneven object, then acquiring the geographical location coordinate information of the vehicle when passing the corresponding uneven object, and finally packaging the longitudinal length information and the corresponding geographical location coordinate information into a data packet, and transmitting the data packet to the traffic department server via wireless.

[0073] Specifically, when an uneven object is detected, it is very meaningful to estimate its longitudinal length, which can be used to determine the danger level of the uneven object and the urgency of road maintenance. Generally speaking, the larger the longitudinal length of the uneven object, the greater the acceleration change when the vehicle passes it at the same speed. On the other hand, for the same uneven object, the acceleration change is also different when the same vehicle passes it at different speeds. Generally, the greater the speed, the greater the acceleration change. In order to eliminate the influence of vehicle speed on acceleration change, the following formula is used to characterize the longitudinal length:

[0074] uneven_length=c(i3) / (v / 3.6)=3.6*c(i3) / v Formula 8

[0075] Wherein, c(i3) is the calculation result of Formula 4, v is the vehicle speed, and v is the vehicle speed at the moment corresponding to c(i3), and uneven_length is the longitudinal length representation of the uneven object.

[0076] Specifically, when the detected bumps are real bumps, data information of the bumps is sent to the traffic department server, including the longitudinal length representation of the bumps and the geographic location coordinates of the vehicle when the vehicle passes the bumps, such as longitude and latitude.

[0077] Furthermore, the step of the traffic department server evaluating each uneven object using the data information of the uneven object and the vehicle information of the corresponding vehicle specifically includes: the traffic department server receives data packets transmitted by different vehicles, and compares and analyzes the currently received data packets with the previously received data packets respectively; if the current data packet and one of the previous data packets correspond to the same uneven object, the two data packets are divided into the same area; if the current data packet and the previous data packet correspond to different uneven objects, the current data packet is divided into a new area.

[0078] Furthermore, the specific steps of comparing and analyzing the currently received data packet with the previously received data packets include: obtaining geographic location coordinate information from the current data packet, and calculating the distance between the concave-convex object corresponding to the current data packet and the concave-convex object corresponding to the previously obtained data packet based on the currently obtained geographic location coordinate information and the previously obtained geographic location coordinate information, and judging whether the calculated distance is not greater than a preset distance; if so, judging that the current data packet and the previously obtained corresponding data packet correspond to the same concave-convex object; if not, judging that the current data packet and the previously obtained corresponding data packet correspond to different concave-convex objects.

[0079] Specifically, when the traffic department server receives the first data packet, it extracts the vehicle's geographic location coordinates, including longitude and latitude, from it, and records them as longitude_1 and latitude_1 respectively, and sets the longitude variable long_center_1 to be equal to longitude_1, and the latitude variable lat_center_1 to be equal to latitude_1. When the traffic department server receives the second data packet, it similarly extracts the vehicle's geographic location coordinates from it, and records them as longitude_2 and latitude_2 respectively, and determines whether longitude_2 and latitude_2 satisfy the following formula:

[0080] (longitude_2-long_center_1) 2 +(latitude_2-lat_center_1) 2 ≤R 2 Formula 9

[0081] Among them, the meaning of formula 9 is to draw a circle with coordinates (long_center_1, lat_center_1) as the center and R as the radius. If the variable (longitude_2, latitude_2) falls inside or on this circle, it is considered that the position of the concave and convex object corresponding to the first data packet and the position of the concave and convex object corresponding to the second data packet belong to the same region. If the variable (longitude_2, latitude_2) falls outside the circle, it is considered that the positions of the concave and convex object corresponding to the two data packets belong to different regions. If the variable (longitude_2, latitude_2) satisfies formula 9, the positions of the concave and convex object corresponding to the first and second data packets belong to the same region, recorded as region 1, and its center coordinate value is (long_center_1, lat_center_1). The values ​​of long_center_1 and lat_center_1 are calculated as follows:

[0082]

[0083]

[0084] The meaning of formula 10 and formula 11 is to update the coordinate value of the center of the region to the average value of the positions of the concave and convex objects corresponding to each data packet in the region.

[0085] If the variables (longitude_2, latitude_2) do not satisfy formula 9, a new area 2 is created, and its center coordinate value is (long_center_2, lat_center_2), where long_center_2 takes the value of longitude_2 and lat_center_2 takes the value of latitude_2. When the traffic department server receives the third data packet, it extracts the vehicle's geographic location coordinates from it and determines whether it belongs to area 1 or area 2. If it does, the area center coordinate value is updated. If it does not, a new area 3 is created and the area center coordinate value is assigned an initial value. Similarly, when the traffic department server receives the fourth, fifth, and so on data packets, similar processing is performed according to the above method.

[0086] Furthermore, the traffic department server evaluates each uneven object using the data information of the uneven objects and the vehicle information of the corresponding vehicle, and the steps also include: determining the lateral length information of the uneven objects corresponding to the corresponding area according to the vehicle information of the vehicle, calculating the danger coefficient of the uneven objects using the lateral length information and longitudinal length information of the uneven objects, and finally grading them according to the size of the danger coefficient. If the danger coefficient is less than 0.2, the danger level of the uneven objects corresponding to the area is judged to be a minor level; if the danger coefficient is not less than 0.2 and less than 0.5, the danger level of the uneven objects corresponding to the area is judged to be a medium level; if the danger coefficient is not less than 0.5, the danger level of the uneven objects corresponding to the area is judged to be a severe level.

[0087] Furthermore, the step of determining the lateral length information of the uneven objects corresponding to the corresponding area based on the vehicle information of the vehicle includes: obtaining the vehicle information of the data packet of the corresponding area within a preset time period; if the vehicle information corresponding to the data packet is the same as other data packets, the data packets with the same vehicle information are assigned the same serial number; if the vehicle information corresponding to the data packet is different from all other data packets, the corresponding data packet is assigned a new serial number; the number of vehicles corresponding to the corresponding area is determined according to the serial number with the largest value, and the lateral length information of the uneven objects corresponding to the area is calculated according to the number.

[0088] Specifically, set a time period, such as from 7 am to 12 noon, and count the bump data information received by the traffic department server during this time period. Count the total number of data packets in a certain area, and assign corresponding serial numbers in turn according to the vehicle information corresponding to each data packet. Let the largest serial number in a certain area be num. This num value is the number of vehicles that sent data packets to the traffic department server in this geographical area, that is, there are num vehicles passing the bumps in this area. The variable num represents the horizontal length of the bumps to a certain extent, because the larger the horizontal length of the bumps, the more vehicles pass it, and the larger num is. Let the average of the uneven_length data representing the longitudinal length of the bumps in each data packet in this area be ave_uneven_length, and use the following formula to calculate the danger coefficient of the bumps in this area, danger_ratio:

[0089]

[0090] Among them, num_worst in formula 12 is the maximum number of vehicles that can be allowed to pass through the bumps and concave objects in the corresponding time period of each area, and length_worst is the maximum allowable value of the longitudinal length of the bumps and concave objects on the road. If danger_ratio is less than 0.2, the danger level of the bumps and concave objects corresponding to the judgment area is a minor level, and it can continue to be observed and tracked. The progress of its danger level can be flexibly carried out; if danger_ratio is between 0.2 and 0.5, the danger level of the bumps and concave objects corresponding to the judgment area is a medium level, and the repair work can wait for a certain period of time, and concentrated repairs can be carried out when the bumps and concave objects to be repaired on the road accumulate to a certain number; if danger_ratio is not less than 0.5, the danger level of the bumps and concave objects corresponding to the judgment area is a serious level, and the repair work must be carried out immediately.

[0091] Furthermore, the present application also provides a road bump detection device, which is used to implement the above-mentioned road bump detection method, and includes a monitoring module and a data acquisition and analysis module. The monitoring module includes multiple acceleration monitors respectively arranged on each tire of the vehicle, and the acceleration monitor is used to obtain the acceleration signal of each wheel of the vehicle. The data acquisition and analysis module is used to receive the acceleration signal and perform frame processing on the acceleration signal. The data acquisition and analysis module is also used to use the acceleration signal after frame processing to respectively calculate the acceleration difference and acceleration ratio of each frame acceleration signal of each wheel, and to judge whether the corresponding acceleration difference satisfies and is greater than the corresponding preset acceleration difference threshold, and whether the corresponding acceleration ratio satisfies and is greater than the corresponding preset acceleration ratio threshold. If both are satisfied, it is determined that the corresponding wheel has passed through the bump in the corresponding frame.

[0092] Among them, the functional implementation of each module in the above-mentioned road bump detection device corresponds to each step in the above-mentioned road bump detection method, and its functions and implementation processes are no longer repeated here.

[0093] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0094] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0095] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for detecting uneven objects on a road, characterized in that: The steps include: Obtain acceleration signals of each wheel of the vehicle; The acceleration signal is subjected to frame processing, and the acceleration difference and acceleration ratio of each frame of the acceleration signal of each wheel are calculated by using the acceleration signal after the frame processing, and if the corresponding acceleration difference is greater than a preset acceleration difference threshold, and the corresponding acceleration ratio is greater than a preset acceleration ratio threshold, it is determined that the corresponding wheel has passed through a concave-convex object in the corresponding frame; Respectively obtain data information of each concave-convex object passed by the vehicle, and transmit the data information of all the concave-convex objects to the traffic department server, and the traffic department server evaluates each concave-convex object by using the data information of the concave-convex object and the vehicle information of the corresponding vehicle; If the corresponding acceleration difference is greater than a preset acceleration difference threshold, and the corresponding acceleration ratio is greater than a preset acceleration ratio threshold, it is determined that the corresponding wheel has passed through a concave-convex object in the corresponding frame, including: Calculate the maximum number of frames between two symmetrically-set wheels of a vehicle when they pass the same speed bump or speed limit barrier respectively; If the acceleration difference corresponding to one of the two wheels is greater than the preset acceleration difference threshold, and the corresponding acceleration ratio is greater than the preset acceleration ratio threshold, confirm whether the other wheel of the two wheels also passes through the concave-convex object within the time range corresponding to the maximum number of frames after the one wheel passes through the concave-convex object, and if so, determine that the two wheels pass through a speed bump or a speed limit barrier in the corresponding frame, and if not, determine that one of the wheels passes through the concave-convex object in the corresponding frame; The calculation of the maximum number of frames between two symmetrically arranged wheels of the vehicle when they respectively pass through the same speed bump or speed limit barrier includes: Obtaining the maximum wheelbase between two symmetrically arranged wheels of the vehicle, obtaining the minimum vehicle speed when the wheels pass through a speed bump or speed limit barrier, and calculating the maximum time interval between the two wheels when they respectively pass through the same speed bump or speed limit barrier based on the maximum wheelbase and the minimum vehicle speed; The maximum number of frames between the two wheels when they respectively pass through the same speed bump or speed limit barrier is calculated according to the maximum duration, the frame length of each frame and the sampling frequency of the acceleration signal.

2. A method for detecting uneven objects on a road as claimed in claim 1, characterized in that: The method of respectively acquiring data information of each concave-convex object passed by the vehicle and transmitting the data information of all the concave-convex objects to a traffic department server includes: Acquire the actual vehicle speed when the vehicle passes the corresponding concave-convex object, and calculate the longitudinal length information of the corresponding concave-convex object according to the actual vehicle speed and the acceleration ratio when the vehicle passes the corresponding concave-convex object; Obtaining geographic location coordinate information of the vehicle when passing through the corresponding concave-convex object; The longitudinal length information and the corresponding geographic location coordinate information are packaged into a data packet, and the data packet is transmitted to the traffic department server.

3. A method for detecting uneven objects on a road as claimed in claim 2, characterized in that: The traffic department server evaluates each of the concave-convex objects using the data information of the concave-convex objects and the vehicle information of the corresponding vehicle, including: The traffic department server receives data packets transmitted by different vehicles, and compares and analyzes the currently received data packets with the previously received data packets respectively; If the current data packet and one of the previous data packets correspond to the same concave-convex object, the two data packets corresponding to the same concave-convex object are divided into the same area. If the current data packet and the previous data packet correspond to different concave-convex objects, the current data packet is divided into a new area.

4. A method for detecting uneven objects on a road as claimed in claim 3, characterized in that: The comparing and analyzing the currently received data packet with the previously received data packets respectively includes: Acquire geographic location coordinate information from the current data packet, and calculate the distance between the concave-convex object corresponding to the current data packet and the concave-convex object corresponding to the previously acquired data packet according to the currently acquired geographic location coordinate information and the previously acquired geographic location coordinate information; It is determined whether the distance is not greater than a preset distance. If so, it is determined that the current data packet and the previously acquired data packet correspond to the same concave-convex object. If not, it is determined that the current data packet and the previously acquired data packet correspond to different concave-convex objects.

5. A method for detecting uneven objects on a road as claimed in claim 4, characterized in that: The traffic department server evaluates each of the concave-convex objects using the data information of the concave-convex objects and the vehicle information of the corresponding vehicle, and further includes: Determine the lateral length information of the concave-convex object in the target area according to the vehicle information of the vehicle; Calculate the risk factor of the concave-convex object using the transverse length information and the longitudinal length information of the concave-convex object; The corresponding uneven objects are graded according to the size of the danger coefficient. If the danger coefficient is less than 0.2, the danger level of the uneven objects in the target area is determined to be a slight level. If the danger coefficient is not less than 0.2 and less than 0.5, the danger level of the uneven objects in the target area is determined to be a medium level. If the danger coefficient is not less than 0.5, the danger level of the uneven objects in the target area is determined to be a severe level.

6. A method for detecting uneven objects on a road as claimed in claim 5, characterized in that: The determining the lateral length information of the concave-convex object in the corresponding area according to the vehicle information of the vehicle also includes: Obtain vehicle information corresponding to all data packets corresponding to the target area within a preset time period; If the vehicle information corresponding to one of the data packets is the same as the vehicle information corresponding to any other data packets, the data packets with the same vehicle information are assigned the same sequence number; if the vehicle information corresponding to one of the data packets is different from the vehicle information corresponding to all other data packets, the one of the data packets is assigned a new sequence number in sequence; The number of vehicles in the target area is determined according to the sequence number with the largest value, and the lateral length information of the concave-convex object corresponding to the target area is calculated according to the number.

7. A method for detecting uneven objects on a road as claimed in claim 1, characterized in that: The sampling frequency of the acceleration signal ranges from 4500 to 5500 Hz, and the frame length of each frame of the acceleration signal ranges from 950 to 1050.

8. A road bump detection device, comprising: The monitoring module and the data collection and analysis module are characterized in that the device is used to implement the road bump detection method as described in claim 1.

Images