CN114737455B

CN114737455B - Pavement detection method, device and equipment

Info

Publication number: CN114737455B
Application number: CN202210421702.1A
Authority: CN
Inventors: 陈野; 张骞; 杨明; 周欣如; 章婉霞; 赵红宇; 承楠
Original assignee: Neusoft Corp
Current assignee: Neusoft Corp
Priority date: 2022-04-21
Filing date: 2022-04-21
Publication date: 2023-09-05
Anticipated expiration: 2042-04-21
Also published as: CN114737455A

Abstract

The application discloses a pavement detection method, a pavement detection device and pavement detection equipment, which are used for collecting the vertical acceleration of a vehicle and the position of the vehicle in real time. When the vertical direction acceleration of the vehicle satisfies a first preset condition, a front wheel vibration moment, a first vertical direction acceleration, a target vehicle position, and a first acceleration sequence including the first vertical direction acceleration at this time are acquired. And determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel. And in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle meets the first preset condition, acquiring a second vertical acceleration and a second acceleration sequence comprising the second vertical acceleration. And calculating the correlation degree of the first acceleration sequence and the second acceleration sequence. When the degree of correlation satisfies a preset range, it is determined that the road surface at the target vehicle position is an uneven road surface. The method is suitable for different vehicle parameters, so that the detection accuracy of the uneven road surface is high.

Description

Pavement detection method, device and equipment

Technical Field

The present application relates to the field of detection technologies, and in particular, to a method, an apparatus, and a device for detecting a road surface.

Background

Typically, uneven roads, such as potholes and raised roads, are present on the road. The vehicle is driven on uneven road surfaces, which accelerates wear of the vehicle and even causes accidents. It is therefore important to determine the location of the rough road and to maintain and repair the rough road.

In order to solve the problem, some vehicles have an uneven road surface detection function, and can acquire the position information of an uneven road surface during running and send the position information to related departments so as to improve the repairing efficiency of the road surface.

Currently, some vehicles detect rough surfaces by acquiring data through an acceleration sensor and comparing the acquired data with a fixed threshold. However, the manner of fixing the threshold value makes the accuracy of the detection result lower due to the different vehicle parameters of different vehicles.

Disclosure of Invention

In order to solve the technical problems, the application provides a pavement detection method, a pavement detection device and pavement detection equipment, which can improve the accuracy of uneven pavement detection.

In order to achieve the above object, the technical solution provided by the embodiments of the present application is as follows:

the embodiment of the application provides a pavement detection method, which comprises the following steps:

Collecting the vertical acceleration of the vehicle and the vehicle position in real time;

acquiring front wheel vibration moment, first vertical acceleration and target vehicle position when the vertical acceleration of the vehicle meets a first preset condition;

acquiring a first acceleration sequence comprising the first vertical acceleration;

determining an estimated rear wheel vibration time interval according to the front wheel vibration time;

acquiring a second vertical acceleration meeting the first preset condition when the vertical acceleration of the vehicle meets the first preset condition in the estimated rear wheel vibration time interval;

acquiring a second acceleration sequence comprising the second vertical acceleration;

and calculating the correlation degree of the first acceleration sequence and the second acceleration sequence, and if the correlation degree meets a preset range, determining that the road surface at the position of the target vehicle is in an uneven state.

In one possible implementation, the uneven state includes a depressed state or a raised state, and the method further includes:

acquiring a plurality of preselected acceleration sequences; the preselected acceleration sequence corresponds to a label; the label comprises a hollow state or a convex state;

If the vertical acceleration of the vehicle meets the first preset condition in the estimated rear wheel vibration time interval, the first acceleration sequence and the second acceleration sequence form a target acceleration sequence;

based on a dynamic time warping algorithm, respectively calculating a warping path distance between the target acceleration sequence and each pre-selected acceleration sequence;

and determining the label corresponding to the preselected acceleration sequence of which the regular path distance meets a third preset condition as the label of the road surface at the position of the target vehicle.

In one possible implementation, the method further includes:

collecting steering wheel rotation angle data of a vehicle in real time;

and in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle meets the second preset condition, determining that the road surface at the position of the target vehicle is in an uneven state.

If the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle meet the second preset condition in the estimated rear wheel vibration time interval, taking the first acceleration sequence as a target acceleration sequence;

In one possible implementation, the method further includes:

and in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle does not meet the second preset condition, determining that the road surface at the position of the target vehicle is in a flat state.

In a possible implementation manner, the acquiring a first acceleration sequence including the first vertical acceleration includes:

acquiring adjacent first acceleration sub-sequences before the first vertical acceleration; the length of the adjacent first acceleration sub-sequence is the first acceleration sampling length;

Acquiring an adjacent second acceleration sub-sequence after the first vertical acceleration; the adjacent second acceleration subsequence is a second acceleration sampling length;

forming the adjacent first acceleration sub-sequence, the first vertical acceleration and the adjacent second acceleration sub-sequence into a first acceleration sequence;

the acquiring a second acceleration sequence including the second vertical acceleration includes:

acquiring an adjacent third acceleration sub-sequence before the second vertical acceleration; the length of the adjacent third acceleration sub-sequence is the third acceleration sampling length;

acquiring an adjacent fourth acceleration sub-sequence after the second vertical acceleration; the adjacent fourth acceleration sub-sequence is a fourth acceleration sampling length;

and the adjacent third acceleration sub-sequence, the second vertical acceleration and the adjacent fourth acceleration sub-sequence are combined into a second acceleration sequence.

In one possible implementation, the first acceleration sampling length, the second acceleration sampling length, the third acceleration sampling length, and the fourth acceleration sampling length are all determined according to a preset rough road section length, a vehicle speed, a vehicle acceleration sampling rate, and a sampling point selection coefficient.

In one possible implementation manner, the determining the estimated rear wheel vibration time interval according to the front wheel vibration time includes:

calculating an interval time period according to the vehicle wheelbase and the vehicle speed;

determining the time when the front wheel vibration time passes through the interval time period as a target time;

acquiring an adjacent first estimated rear wheel vibration time subinterval before the target time and an adjacent second estimated rear wheel vibration time subinterval after the target time;

and forming the adjacent first estimated rear wheel vibration time subinterval, the target time and the adjacent second estimated rear wheel vibration time subinterval into an estimated rear wheel vibration time interval.

The embodiment of the application also provides a road surface detection device, which comprises:

the first acquisition unit is used for acquiring the vertical acceleration of the vehicle and the position of the vehicle in real time;

a first acquiring unit configured to acquire a front wheel vibration time when a vertical direction acceleration of the vehicle satisfies a first preset condition, a first vertical direction acceleration, and a target vehicle position;

a second acquisition unit configured to acquire a first acceleration sequence including the first vertical-direction acceleration;

The first determining unit is used for determining an estimated rear wheel vibration time interval according to the front wheel vibration time;

a third obtaining unit, configured to obtain, in the estimated rear wheel vibration time interval, a second vertical acceleration when the first preset condition is satisfied when the vertical acceleration of the vehicle satisfies the first preset condition;

a fourth acquisition unit configured to acquire a second acceleration sequence including the second vertical-direction acceleration;

and the first calculation unit is used for calculating the correlation degree of the first acceleration sequence and the second acceleration sequence, and determining that the road surface at the position of the target vehicle is in an uneven state if the correlation degree meets a preset range.

The embodiment of the application also provides road surface detection equipment, which comprises: the road surface detection system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the road surface detection method according to any one of the above when executing the computer program.

The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device is caused to execute the pavement detection method according to any one of the above.

According to the technical scheme, the application has the following beneficial effects:

the embodiment of the application provides a pavement detection method, a pavement detection device and pavement detection equipment, which are used for collecting the vertical acceleration of a vehicle and the position of the vehicle in real time. When the vertical acceleration of the vehicle satisfies a first preset condition, it indicates that the front wheels of the vehicle are vibrated. The front wheel shake timing, the first vertical direction acceleration, the target vehicle position, and the first acceleration sequence including the first vertical direction acceleration at this time are acquired. And determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel. And in the estimated vibration time interval of the rear wheels, when the vertical acceleration of the vehicle meets a first preset condition, indicating that the rear wheels of the vehicle vibrate. The second vertical-direction acceleration at this time and a second acceleration sequence including the second vertical-direction acceleration are acquired. When the front and rear wheels of the vehicle pass through the uneven road surface at the same position to cause the vibration of the wheels, the correlation degree of the first acceleration sequence and the second acceleration sequence can meet the preset range. Thus, the degree of correlation of the first acceleration sequence and the second acceleration sequence is calculated. When the degree of correlation satisfies a preset range, it is determined that the vibration of the front and rear wheels of the vehicle is vibration caused by passing over an uneven road surface at the same position, that is, a road surface at the position of the target vehicle. It is known that the method for determining whether the road surface is uneven by calculating the correlation degree of the first acceleration sequence when the front wheel possibly vibrates and the second acceleration sequence when the rear wheel possibly vibrates and analyzing the correlation degree is suitable for different vehicle parameters, so that the detection accuracy of the uneven road surface is high.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of a frame of an exemplary application scenario provided in an embodiment of the present application;

fig. 2 is a flowchart of a road surface detection method according to an embodiment of the present application;

FIG. 3 is a flowchart of another pavement detection method according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a pavement detection apparatus according to an embodiment of the present application.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of embodiments of the application will be rendered by reference to the appended drawings and appended drawings.

In order to facilitate understanding and explanation of the technical solutions provided by the embodiments of the present application, the following description will first explain the background technology of the embodiments of the present application.

Specifically, some vehicles acquire a road surface image using an in-vehicle video apparatus, and process and analyze the road surface image to obtain a road surface detection result. However, the road surface image is affected by the brightness of the light, so that the accuracy of the road surface detection result is not high. In addition, some vehicles collect acceleration data via acceleration sensors and compare the collected acceleration data to a fixed threshold, and when the fixed threshold is exceeded, the road surface is determined to be rough. However, since the vehicle parameters of different vehicles are different, the same fixed threshold value is used for judging, and the accuracy is low.

Based on the above, the embodiment of the application provides a pavement detection method, a pavement detection device and pavement detection equipment, which are used for collecting the vertical acceleration of a vehicle and the position of the vehicle in real time. When the vertical acceleration of the vehicle satisfies a first preset condition, it indicates that the front wheels of the vehicle are vibrated. The front wheel shake timing, the first vertical direction acceleration, the target vehicle position, and the first acceleration sequence including the first vertical direction acceleration at this time are acquired. And determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel. And in the estimated vibration time interval of the rear wheels, when the vertical acceleration of the vehicle meets a first preset condition, indicating that the rear wheels of the vehicle vibrate. The second vertical-direction acceleration at this time and a second acceleration sequence including the second vertical-direction acceleration are acquired. When the front and rear wheels of the vehicle pass through the uneven road surface at the same position to cause the vibration of the wheels, the correlation degree of the first acceleration sequence and the second acceleration sequence can meet the preset range. Thus, the degree of correlation of the first acceleration sequence and the second acceleration sequence is calculated. When the degree of correlation satisfies a preset range, it is determined that the vibration of the front and rear wheels of the vehicle is vibration caused by passing over an uneven road surface at the same position, that is, a road surface at the position of the target vehicle. It is known that the method for determining whether the road surface is uneven by calculating the correlation degree of the first acceleration sequence when the front wheel possibly vibrates and the second acceleration sequence when the rear wheel possibly vibrates and analyzing the correlation degree is suitable for different vehicle parameters, so that the detection accuracy of the uneven road surface is high.

In order to facilitate understanding of the road surface detection method provided by the embodiment of the present application, the following description is made with reference to the example of the scene shown in fig. 1. Referring to fig. 1, the diagram is a schematic frame diagram of an exemplary application scenario provided in an embodiment of the present application.

In practical applications, the vertical acceleration of the vehicle and the vehicle position are collected in real time.

When the vertical acceleration of the vehicle meets a first preset condition, the front wheels of the vehicle are determined to vibrate. But at this time the cause of the front wheel shake cannot be determined. And acquiring the front wheel vibration moment, the first vertical acceleration and the target vehicle position when the vertical acceleration of the vehicle meets the first preset condition. Further, a first acceleration sequence including a first vertical acceleration is acquired.

And determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel. In the case where the front wheel vibration is caused by an uneven road surface, the rear wheel may pass through the same uneven road surface to generate vibration in a predicted rear wheel vibration time zone.

And in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle meets a first preset condition, the rear wheel of the vehicle vibrates. At this time, the second vertical acceleration when the first preset condition is satisfied is acquired. Further, a second acceleration sequence including a second vertical acceleration is acquired.

And calculating the correlation degree of the first acceleration sequence and the second acceleration sequence, and if the correlation degree meets the preset range, indicating that the front wheel vibration and the rear wheel vibration are caused by the fact that the same uneven road surface is passed. At this time, the road surface at the target vehicle position is determined to be in an uneven state.

Those skilled in the art will appreciate that the frame diagram shown in fig. 1 is but one example in which embodiments of the present application may be implemented. The scope of applicability of the embodiments of the application is not limited in any way by the framework.

In order to facilitate understanding of the present application, a road surface detection method provided by an embodiment of the present application is described below with reference to the accompanying drawings.

Referring to fig. 2, fig. 2 is a schematic diagram of a road surface detection method according to an embodiment of the present application. As shown in fig. 2, the method includes S201 to S207:

s201: the vertical acceleration of the vehicle and the vehicle position are acquired in real time.

In practice, the acceleration sensor and the global positioning system GPS are mounted in the middle of the roof of the vehicle body by means of magnets or other means. The vehicle acceleration sensor and GPS are used to collect the vehicle vertical acceleration and vehicle position in real time while the vehicle is running.

As an alternative example, the acceleration sensor is a three-axis acceleration sensor.

S202: and acquiring the front wheel vibration moment, the first vertical acceleration and the target vehicle position when the vertical acceleration of the vehicle meets the first preset condition.

The acceleration of the vehicle in the vertical direction changes significantly when the vehicle vibrates. Therefore, it is possible to know whether the vehicle is shocked or not by detecting a change in the vertical acceleration of the vehicle.

As an alternative example, it is detected in real time whether the vertical acceleration of the vehicle satisfies a first preset condition. When the vertical acceleration of the vehicle satisfies the first preset condition, it is considered that the front wheels of the vehicle vibrate to cause the vehicle to vibrate.

It should be noted that, in the embodiment of the present application, when the vertical acceleration of the vehicle detected in real time satisfies the first preset condition, a scene in which the vehicle vibrates due to vibration of the rear wheels of the vehicle is not considered. The vehicle vertical acceleration satisfying the first preset condition in this step is the acquired vertical acceleration when the first preset condition is satisfied.

In one or more embodiments, the first preset condition is that the vertical acceleration of the vehicle is greater than a set first threshold Y1.

Recording the front wheel vibration moment when the vertical acceleration of the vehicle meets the first preset condition, the first vertical acceleration and the target vehicle position. As an alternative example, the front wheel vibration moment may be expressed in terms of a specific time, such as xx hours xx minutes xx seconds, where the front wheel vibration moment is xx years xx month xx days.

As another alternative example, the front wheel shake time may also be represented by a discrete sampling time of the acceleration sensor, such as a sampling time of the front wheel shake. The sampling time is the position of the sampling point acquired by the acceleration sensor, and when the sampling time of the front wheel vibration is a, the sampling point representing the vertical acceleration during the front wheel vibration is the a sampling point.

At the same time, the first vertical acceleration at this time is recorded as s _a The target vehicle position is P _a 。

It is understood that when determining the vibration of the front wheels of the vehicle, it is impossible to determine whether the front wheels of the vehicle are subjected to vibration caused by uneven road surfaces or vibration caused by noise of the vehicle, such as vibration of the front wheels of the vehicle caused by sudden large-amplitude movements of the rear-row personnel of the vehicle. Thus, further determination is required.

S203: a first acceleration sequence including a first vertical acceleration is acquired.

Since it is impossible to determine whether the front wheels of the vehicle are passing by the uneven road surface or the vehicle is noisy. At this time, based on the first vertical direction acceleration, a first acceleration sequence including the first vertical direction acceleration is acquired, the first acceleration sequence being used for subsequently determining whether the vehicle shake is a shake caused by passing through an uneven road surface.

It will be appreciated that the first vertical acceleration is only a determined acceleration value acquired when the front wheels of the vehicle are in vibration, and if the vehicle is in vibration caused by an uneven road surface, the uneven road surface may have a certain length, and the vertical acceleration data of the front wheels of the vehicle in vibration is not only one value. Thus, the acquired first acceleration sequence may be understood as vehicle vibration data generated when the front wheels of the vehicle may pass over an uneven road surface in the case where the uneven road surface causes the vehicle to vibrate.

In one possible implementation, an embodiment of the present application provides a specific implementation for acquiring a first acceleration sequence including a first vertical acceleration, see in particular the following A1-A3.

S204: and determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel.

In general, if the vehicle vibration is caused by uneven road surface, after the front wheels vibrate, if the vehicle does not turn, the rear wheels also pass through the uneven road surface at the same position, so that the rear wheels vibrate. Based on the estimated vibration time interval of the rear wheels is determined according to the vibration time of the front wheels. The rear wheel vibration time interval is estimated and the rear wheel of the vehicle may vibrate.

In one possible implementation manner, the embodiment of the present application provides a specific implementation manner of determining the estimated rear wheel vibration time interval according to the front wheel vibration time, and please refer to the following B1-B4.

S205: and in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle meets a first preset condition, acquiring a second vertical acceleration meeting the first preset condition.

And in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle meets a first preset condition, determining that the rear wheel of the vehicle vibrates again. The vibration of the vehicle is caused by the fact that the rear wheels of the vehicle possibly vibrate on the same uneven road surface.

It should be noted that, in this step, the vertical acceleration of the vehicle that satisfies the first preset condition is the vertical acceleration when the first preset condition is satisfied in the estimated rear wheel vibration time interval.

Recording the second vertical acceleration s when the first preset condition is met in the estimated rear wheel vibration time interval _c And records the rear wheel vibration time and the vehicle position P at the time _c . The rear wheel vibration time can be represented by a sampling time and is marked as c. It will be appreciated that the rear wheel vibration moment is the moment when the vehicle vibration actually occurs, and is not estimated.

S206: a second acceleration sequence is acquired that includes a second vertical acceleration.

To verify that the vehicle vibration occurring at the front wheel vibration moment and the rear wheel vibration moment is vibration caused by the vehicle passing through the same uneven road surface. A second acceleration sequence comprising a second vertical acceleration is also acquired based on the second vertical acceleration. The second acceleration sequence is used to subsequently determine whether the vehicle shock is a shock caused by passing over an uneven road surface.

It will be appreciated that the second vertical acceleration is only a determined acceleration value acquired when the rear wheels of the vehicle are vibrated, and if the vehicle is vibrated due to an uneven road surface, the uneven road surface may have a certain length, and the vertical acceleration data is not only a value when the front wheels of the vehicle are passed over the uneven road surface. Thus, the acquired second acceleration sequence may be understood as vehicle vibration data generated when the rear wheels of the vehicle may pass over an uneven road surface in the case where the uneven road surface causes the vehicle to vibrate.

In one possible implementation, the embodiment of the present application provides a specific implementation for acquiring the second acceleration sequence including the second vertical acceleration, specifically please refer to C1-C3 below.

S207: and calculating the correlation degree of the first acceleration sequence and the second acceleration sequence, and determining that the road surface at the position of the target vehicle is in an uneven state if the correlation degree meets the preset range.

The first acceleration sequence may be understood as vehicle shock data generated when the front wheels of the vehicle may pass over an uneven road surface in case the uneven road surface causes the vehicle to vibrate. The second acceleration sequence may be understood as vehicle vibration data generated when the rear wheels of the vehicle may pass over an uneven road surface in the case where the uneven road surface causes the vehicle to vibrate.

Moreover, if the front and rear wheels of the vehicle pass over the uneven road surface at the same position to cause vibration of the vehicle, the correlation degree between the first acceleration sequence and the second acceleration sequence should meet the preset range. Specifically, when the front and rear wheels of the vehicle pass through the uneven road surface at the same position to cause vibration of the vehicle, the correlation degree of the first acceleration sequence and the second acceleration sequence should meet the requirement of negative correlation, namely, the theoretical value of the correlation degree is-1, which indicates that the first acceleration sequence and the second acceleration sequence are in negative correlation.

Based on this, a degree of correlation of the first acceleration sequence and the second acceleration sequence is calculated. Whether the front wheel shake and the rear wheel shake of the vehicle are caused by passing over an uneven road surface is determined by whether the degree of correlation satisfies a preset range. As an alternative example, the preset range is smaller than the set second threshold Y2, such as-0.2.

In an alternative example, the correlation coefficients of the first acceleration sequence and the second acceleration sequence are calculated, and the correlation degree is represented by the correlation coefficients. Specifically, the calculation formula of the correlation coefficient of the first acceleration sequence and the second acceleration sequence is as follows:

wherein S is ₁ For the first acceleration sequence, S ₂ For the second acceleration sequence of the device,is S ₁ And S is ₂ Is used for the correlation coefficient of the (c). Wherein cov (S) ₁ ,S ₂ )＝E(S ₁ S ₂ )-E(S ₁ )E(S ₂ )，cov(S ₁ ,S ₂ ) Represent S ₁ And S is ₂ E (·) represents mathematical expectations and σ (·) represents standard deviation.

If it isAnd when the preset range is met, the fact that the vehicle passes through the uneven road surface is confirmed, and the front wheel vibration and the rear wheel vibration of the vehicle pass through the same uneven road surface, namely the road surface at the position of the target vehicle. At this time, the vehicle will position information P _a And uploading the detection result to a cloud platform, and providing the detection result to highway maintenance related departments, so that the pavement can be repaired in time. To make the detection result more detailed, P can also be used _c And also uploaded to the cloud platform. In addition, if->If the preset range is not satisfied, the vehicle is considered to run on a flat road.

It can be understood that the method for determining whether the vehicle passes through the uneven road surface by calculating the correlation degree of the first acceleration sequence and the second acceleration sequence can reduce the interference to the detection of the uneven road surface caused by vibration caused by vehicle noise, and can be suitable for different vehicle parameters, so that the detection accuracy of the uneven road surface is high.

Based on the content of S201-S207, the embodiment of the application provides a road surface detection method, which collects the acceleration in the vertical direction of the vehicle and the vehicle position in real time. When the vertical acceleration of the vehicle satisfies a first preset condition, it indicates that the front wheels of the vehicle are vibrated. The front wheel shake timing, the first vertical direction acceleration, the target vehicle position, and the first acceleration sequence including the first vertical direction acceleration at this time are acquired. And determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel. And in the estimated vibration time interval of the rear wheels, when the vertical acceleration of the vehicle meets a first preset condition, indicating that the rear wheels of the vehicle vibrate. The second vertical-direction acceleration at this time and a second acceleration sequence including the second vertical-direction acceleration are acquired. When the front and rear wheels of the vehicle pass through the uneven road surface at the same position to cause the vibration of the wheels, the correlation degree of the first acceleration sequence and the second acceleration sequence can meet the preset range. Thus, the degree of correlation of the first acceleration sequence and the second acceleration sequence is calculated. When the degree of correlation satisfies a preset range, it is determined that the vibration of the front and rear wheels of the vehicle is vibration caused by passing over an uneven road surface at the same position, that is, a road surface at the position of the target vehicle. It is known that the method for determining whether the road surface is uneven by calculating the correlation degree of the first acceleration sequence when the front wheel possibly vibrates and the second acceleration sequence when the rear wheel possibly vibrates and analyzing the correlation degree is suitable for different vehicle parameters, so that the detection accuracy of the uneven road surface is high.

In a possible implementation manner, the embodiment of the present application provides a specific implementation manner of acquiring a first acceleration sequence including a first vertical acceleration in S203, including:

a1: acquiring adjacent first acceleration sub-sequences before the first vertical acceleration; the length of the adjacent first acceleration sub-sequence is the first acceleration sampling length.

A2: acquiring adjacent second acceleration sub-sequences after the first vertical acceleration; the adjacent second acceleration sub-sequence is a second acceleration sample length.

After the first vertical acceleration is acquired, an adjacent first acceleration sub-sequence before the first vertical acceleration and an adjacent second acceleration sub-sequence after the first vertical acceleration are acquired based on the first vertical acceleration.

The first vertical acceleration is preceded by a first vertical acceleration, and the first vertical acceleration is followed by a sampling time of the first vertical acceleration. The adjacent first acceleration sub-sequence and the adjacent second acceleration sub-sequence are adjacent to the first vertical acceleration.

In one or more embodiments, the first acceleration sampling length, the second acceleration sampling length are determined based on a preset rough road segment length, a vehicle speed, a vehicle acceleration sampling rate, and a sampling point selection coefficient.

Specifically, the vehicle acceleration sensor sampling rate is h. For example, at a sampling rate of 500 hertz, 500 sampling points (i.e., vertical acceleration) may be acquired for 1 second. The vehicle speed is v meters per second. The acceleration sampling length is d, which represents a total of d sampling points, and the calculation formula is as follows:

wherein r is the length of the preset uneven road section. The uneven state includes a depressed state or a raised state. When the uneven road surface is in a convex state, the length of the preset uneven road section is the length of the convex road surface. When the uneven road surface is in a hollow state, the length of the preset uneven road section is the length of the hollow road surface. The preset uneven road section length is determined according to an empirical value, for example, 0.3 meter.

Beta is a sampling point selection coefficient that is typically greater than 1/2, for example, preferably 2/3, in order to more fully cover the vehicle vibration data. The symbol [. Cndot ] represents an upward rounding.

For example, if h=500, v=10, r=0.3, β=2/3, then d=10.

That is, the first acceleration sampling length and the second acceleration sampling length may both be determined according to the above-described calculation formula of the acceleration sampling length d. It can be understood that when the first acceleration sampling length and the second acceleration sampling length are calculated respectively, if the parameters in the above formula are different, the calculated first acceleration sampling length and the calculated second acceleration sampling length are different, and if the parameters in the formula are identical, the calculated first acceleration sampling length and the calculated second acceleration sampling length are identical.

It should be noted that, the embodiment of the present application is not limited to the first acceleration sampling length and the second acceleration sampling length, and may be determined according to actual situations. As an alternative example, the first acceleration sample length and the second acceleration sample length are the same.

For example, when the first acceleration sub-sequence and the second acceleration sub-sequence are identical in length and denoted by d, the first vertical acceleration s is recorded and saved _a D sampling points before and after each. Obtaining a first acceleration sub-sequence of [ s ] _a-d ,…,s _a-1 ]The second acceleration sub-sequence is [ s ] _a+1 ,…,s _a+d ]。

A3: the adjacent first acceleration sub-sequence, the first vertical acceleration and the adjacent second acceleration sub-sequence are combined into a first acceleration sequence.

After the adjacent first acceleration sub-sequence and the adjacent second acceleration sub-sequence are acquired, the adjacent first acceleration sub-sequence, the first vertical acceleration and the adjacent second acceleration sub-sequence are formed into a first acceleration sequence, denoted as S ₁ S, i.e ₁ ＝[s _a-d ,…,s _a-1 ,s _a ,s _a+1 ,…,s _a+d ]。

It will be appreciated that the first acceleration sequence may be vehicle body vibration data generated as the front wheels of the vehicle pass over uneven road surfaces.

Based on the content of A1-A3, based on the first vertical acceleration and the first and second acceleration sampling lengths, adjacent first and second acceleration sub-sequences of the first vertical acceleration may be determined. Further, a first acceleration sequence consisting of adjacent first acceleration sub-sequences, first vertical acceleration and adjacent second acceleration sub-sequences may be obtained.

In a possible implementation manner, the embodiment of the present application provides a specific implementation manner of determining an estimated rear wheel vibration time interval according to a front wheel vibration time in S204, including:

b1: the interval period is calculated from the vehicle wheelbase and the vehicle speed.

The spacing between the front wheels and the rear wheels of the vehicle can be expressed in terms of the wheelbase of the vehicle. When the front wheels and the rear wheels of the vehicle pass through the same uneven road surface, the moving distance of the vehicle is the distance represented by the vehicle wheelbase.

Then in this step the interval time is the quotient of the vehicle wheelbase and the vehicle speed and is used to represent the estimated interval time between when the rear wheels of the vehicle are likely to vibrate and when the front wheels of the vehicle are likely to vibrate.

As an alternative example, the interval period may be represented in terms of a specific time. For example, the vehicle wheelbase is L, for example, the vehicle wheelbase is 3 meters. The interval period is b, b=l/v. For example v=10, l=3, b=0.3 s.

As an alternative example, the interval period may be represented in terms of a sampling time. For example, the vehicle wheelbase is L, for example, the vehicle wheelbase is 3 meters, and the interval period is b, b= [ h (L/v) ]. For example h=500, v=10, l=3, then b=150. b represents the number of sampling points between the time when the estimated rear wheel of the vehicle is likely to vibrate and the time when the front wheel is vibrated.

B2: the time when the interval period elapses at the front wheel shake time is determined as the target time.

The target time is the estimated time when the rear wheels of the vehicle may vibrate.

For example, if the front wheel shake time is 3s and the interval period is 0.3s, the time when the interval period elapses at the front wheel shake time is 3.3s, and this time is determined as the target time.

In another example, when the current wheel shake time is represented by a discrete sampling time a, the target time is represented by a+b.

B3: and acquiring an adjacent first estimated rear wheel vibration time subinterval before the target time and an adjacent second estimated rear wheel vibration time subinterval after the target time.

Since the vehicle rear wheel shock does not occur only at one target time, it may last for a period of time. Therefore, the adjacent first estimated rear wheel vibration time subinterval before the target time and the adjacent second estimated rear wheel vibration time subinterval after the target time are acquired.

The target time and the adjacent second estimated rear wheel vibration time subinterval are estimated time periods and times when the rear wheel of the vehicle may vibrate.

B4: and forming an estimated rear wheel vibration time interval by the adjacent first estimated rear wheel vibration time subinterval, the target time and the adjacent second estimated rear wheel vibration time subinterval.

It can be understood that the pre-estimated rear wheel vibration time interval after composition is the time period when the pre-estimated rear wheel of the vehicle possibly vibrates after the front wheel of the vehicle vibrates.

As an alternative example, the estimated rear wheel shake time interval is represented by a specific time period. For example, when the target time is 3.3s and the adjacent first estimated rear wheel vibration time subinterval and the adjacent second estimated rear wheel vibration time subinterval are both 1s, the estimated rear wheel vibration time interval is 2.3s to 4.3s.

As an alternative example, the estimated rear wheel shake time interval may be represented by a sampling time. For example, when the adjacent first estimated rear wheel vibration time subinterval and the adjacent second estimated rear wheel vibration time subinterval are each represented by a sampling time d sampling points, the estimated rear wheel vibration time interval is a+b-d sampling time to a+b+d sampling time.

Based on the knowledge of B1-B4, the estimated rear wheel vibration time may be obtained based on the interval time period and the front wheel vibration time, so as to obtain the estimated rear wheel vibration time interval.

In a possible implementation manner, the embodiment of the present application provides a specific implementation manner of acquiring the second acceleration sequence including the second vertical acceleration in S206, including:

C1: acquiring an adjacent third acceleration sub-sequence before the second vertical acceleration; the length of the adjacent third acceleration sub-sequence is the third acceleration sampling length.

C2: acquiring an adjacent fourth acceleration sub-sequence after the second vertical acceleration; the adjacent fourth acceleration sub-sequence is a fourth acceleration sample length.

After the second vertical acceleration is acquired, an adjacent third acceleration sub-sequence before the second vertical acceleration and an adjacent fourth acceleration sub-sequence after the second vertical acceleration are acquired based on the second vertical acceleration.

The first vertical acceleration is a first vertical acceleration, and the second vertical acceleration is a second vertical acceleration. The adjacent third acceleration sub-sequence and the adjacent fourth acceleration sub-sequence are adjacent to the second vertical acceleration.

In one or more embodiments, the third acceleration sample length and the fourth acceleration sample length are each determined based on a preset rough road segment length, a vehicle speed, a vehicle acceleration sample rate, and a sample point selection coefficient. Details of the specific implementation can be found in A2.

It will be appreciated that the embodiment of the present application is not limited to the third acceleration sampling length and the fourth acceleration sampling length, and may be determined according to practical situations. As an alternative example, the first acceleration sample length, the second acceleration sample length, the third acceleration sample length, and the fourth acceleration sample length are the same.

For example, when the third acceleration is in the sub-orderWhen the length of the column and the length of the fourth acceleration sub-sequence are the same and the length is denoted by d, the second vertical acceleration s is recorded and saved _c D sampling points before and after each. Obtaining a third acceleration sub-sequence of [ s ] _c-d ,…,s _c-1 ]The fourth acceleration sub-sequence is [ s ] _c+1 ,…,s _c+d ]。

And C3: and forming a second acceleration sequence from the adjacent third acceleration sub-sequence, the second vertical acceleration sub-sequence and the adjacent fourth acceleration sub-sequence.

After the adjacent third acceleration sub-sequence and the adjacent fourth acceleration sub-sequence are acquired, the adjacent third acceleration sub-sequence, the second vertical acceleration and the adjacent fourth acceleration sub-sequence are formed into a second acceleration sequence, denoted as S ₂ S, i.e ₂ ＝[s _c-d ,…,s _c-1 ,s _c ,s _c+1 ,…,s _c+d ]The value of d can be referred to as A2.

It will be appreciated that the second acceleration sequence may be vehicle body vibration data generated as the rear wheels of the vehicle pass over uneven road surfaces.

Based on the content of C1-C3, based on the second vertical acceleration, the third acceleration sampling length and the fourth acceleration sampling length, an adjacent third acceleration sub-sequence and an adjacent fourth acceleration sub-sequence of the second vertical acceleration can be determined. Further, a second acceleration sequence consisting of an adjacent third acceleration sub-sequence, a second vertical direction acceleration and an adjacent fourth acceleration sub-sequence may be obtained.

It will be appreciated that S204 describes a scenario in which the front wheels of the vehicle vibrate, the vehicle is not turned, and the rear wheels of the vehicle are passing over the same rough road. However, there are cases where the driver of the vehicle turns the steering wheel to steer the vehicle after learning that the front wheels of the vehicle are vibrating, so as to avoid the rear wheels of the vehicle passing over the same uneven road surface. Based on this, the embodiment of the application also discloses technical details for judging whether the front wheel vibration of the vehicle is caused by the uneven road surface or not in the situation. The method specifically comprises the following steps:

d1: steering wheel angle data of the vehicle is collected in real time.

The steering wheel angle data of the vehicle can be acquired in real time through the angle sensor.

D2: and in the estimated rear wheel vibration time interval, when the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle meets the second preset condition, determining that the road surface at the position of the target vehicle is in an uneven state.

And in the estimated rear wheel vibration time interval, if the vertical acceleration of the vehicle does not meet the first preset condition all the time, checking steering wheel rotation angle data of the vehicle. If steering wheel rotation angle data of the vehicle determine that the front wheels of the vehicle pass through an uneven road surface when vibrating, and a driver vibrates again to avoid the rear wheels, the steering wheel is rotated to realize avoiding adjustment.

As an alternative example, the second preset condition is that the steering wheel angle exceeds a set third threshold. The third threshold value may be set according to practical situations, which is not limited in the embodiment of the present application.

In addition, if the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle does not meet the second preset condition in the estimated rear wheel vibration time interval, the road surface at the position of the target vehicle is determined to be in a flat state. That is, when the steering wheel angle is always smaller than the set third threshold value, it is determined that the vehicle vibration caused by the front wheel vibration timing is noise. Based on the above, by detecting steering wheel angle data of the vehicle, detection misjudgment caused by vibration noise of the vehicle body can be overcome.

Since the uneven state of the road surface includes a pothole state or a raised state, it is determined that the vehicle is in vibration caused by the vehicle passing over the uneven road surface, and then the vehicle can be continuously identified as the pothole state or the raised state. Wherein it can be determined that the vehicle vibration is caused by road surface unevenness in the case where there are two kinds of vehicle vibration. One is determined in the case where vibration occurs in both front and rear wheels of the vehicle (see S201 to S207). The other is determined in the case that only the front wheels of the vehicle vibrate and the rear wheels of the vehicle are subjected to evasion adjustment (see D1-D2). In a first vehicle shock event, a first acceleration sequence and a second acceleration sequence may be acquired. In the case of a second vehicle vibration, only the first acceleration sequence can be acquired.

Based on the foregoing, in a first vehicle vibration situation, in one possible implementation manner, an embodiment of the present application provides a specific implementation manner for identifying a vehicle uneven state, including:

e1: acquiring a plurality of preselected acceleration sequences; the preselected acceleration sequence corresponds to a label; the label includes a depressed state or a raised state.

It is understood that the preselected acceleration sequence may be used as a reference sequence for a shock acceleration sequence generated by a vehicle shock during actual travel of the vehicle. In the first vehicle vibration condition, the vibration acceleration sequence is a first acceleration sequence and a second acceleration sequence. In the second vehicle shock situation, the shock acceleration sequence is only the first acceleration sequence.

There are a plurality of pre-selected acceleration sequences, and each pre-selected acceleration sequence corresponds to a respective tag. Wherein the label includes a depressed state or a raised state. For example, the tag corresponding to the preselected acceleration sequence 1 is in a depressed state. The label corresponding to the preselected acceleration sequence 1 is in a convex state.

The pre-selected acceleration sequence needs to be collected and stored in advance, for example, when the uneven road surface is a hollow road surface, the acceleration sequence generated by the vibration of the vehicle when the vehicle speed is 8m/s (or 10m/s, 12m/s, 14m/s, etc.) and the length of the hollow of the road surface is 0.2m (or 0.3m, 0.4m, etc.) is collected, and the acceleration sequence is taken as the pre-selected acceleration sequence, and the corresponding label is marked as the hollow state. Wherein, vehicle speed is vehicle parameter, and road surface pothole or protruding length is road parameter. The process of collecting and storing the preselected acceleration sequence when the uneven road surface is in a convex state is similar to that described above, and will not be repeated here.

In addition, under each set of vehicle parameters and road parameters, corresponding to two vehicle vibration cases in which both the front and rear wheels of the vehicle vibrate and only the front wheels of the vehicle vibrate, it is necessary to store the acceleration sequence of the combination of the front and rear wheels of the vehicle and the acceleration sequence of only the front wheels of the vehicle, respectively. For either a pothole or a bump condition, a plurality of preselected acceleration sequences of different lengths are associated. Thus, the recognition of the road surface uneven state under different vehicle vibration conditions can be satisfied.

E2: if the vertical acceleration of the vehicle meets a first preset condition in the estimated rear wheel vibration time interval, the first acceleration sequence and the second acceleration sequence form a target acceleration sequence.

As can be seen from S201 to S207, in the estimated rear wheel vibration time interval, the vertical acceleration of the vehicle satisfies the first preset condition and the correlation degree between the first acceleration sequence and the second acceleration sequence satisfies the preset range, which indicates that the front and rear wheels of the vehicle vibrate and are caused by uneven road surfaces.

At this time, the first acceleration sequence and the second acceleration sequence can be acquired, and the first acceleration sequence and the second acceleration sequence are combined into the target acceleration sequence. A specific type of road surface unevenness is identified based on a plurality of preselected acceleration sequences and a target acceleration sequence composed of the first acceleration sequence and the second acceleration sequence.

For example, the first acceleration sequence is S ₁ The second acceleration sequence is S ₂ The target acceleration sequence is S ₃ ，S ₃ ＝[s _a-d ,…,s _a+d ,s _c-d ,…,s _c+d ]. Further, S is ₃ And a plurality of pre-selected acceleration sequences stored in advance.

E3: based on a dynamic time warping algorithm, the warping path distance between the target acceleration sequence and each pre-selected acceleration sequence is calculated.

The dynamic time warping algorithm has good detection effect on measuring the similarity of two time sequences, and in addition, because the lengths of pits, the lengths of bulges and the parameters of a vehicle body in an actual road are different, the lengths of a plurality of preselected acceleration sequences are inconsistent, and even if the lengths of a target acceleration sequence and the preselected acceleration sequences are different, the dynamic time warping algorithm can still calculate the warping path distance between the target acceleration sequence and each preselected acceleration sequence. Based on this, as an alternative example, a dynamic time warping algorithm is used to calculate the warped path distance between the target acceleration sequence and each of the preselected acceleration sequences.

For example, the preselected acceleration sequence is x= [ X ₁ ,x ₂ ,…,x _m ]The sample length is m. The target acceleration sequence is y=s ₃ ＝[y ₁ ,y ₂ ,…,y _q ]The sample length is q. The calculation formula of the regular path distance D (i, j) is as follows:

D(i,j)＝Dist(i,j)+min{D(i-1,j),D(i,j-1),D(i-1,j-1)}

Wherein Dist (i, j) represents the Euclidean distance between the i-th acceleration in the X sequence and the j-th acceleration in the Y sequence. D (i, j) represents the euclidean distance between the total first i accelerations of the X-sequence and the first j accelerations of the Y-sequence. So that i=m, j=q, the normalized path distance D (m, q) between the target acceleration sequence and each of the preselected acceleration sequences is calculated and solved using the dynamic time warping algorithm formula described above.

E4: and determining the label corresponding to the preselected acceleration sequence with the regular path distance meeting the third preset condition as the label of the road surface at the position of the target vehicle.

As an alternative example, the third preset condition is that the regular path distance is minimum. For example, the normal path distance between the target acceleration sequence and the preselected acceleration sequence 1 is minimal. And if the label corresponding to the preselected acceleration sequence 1 is in a hollow state, determining that the uneven road surface at the position of the target vehicle is in the hollow state.

Based on E1-E4, the specific type of the road surface unevenness state at the target vehicle position can be accurately determined by sampling the preselected acceleration sequence and the dynamic time warping algorithm.

In addition, based on the foregoing, in a second vehicle vibration situation, in one possible implementation manner, the embodiment of the present application provides a specific implementation manner for identifying a vehicle uneven state, including:

F1: acquiring a plurality of preselected acceleration sequences; the preselected acceleration sequence corresponds to a label; the label includes a depressed state or a raised state.

F2: if the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle meet the second preset condition in the estimated rear wheel vibration time interval, the first acceleration sequence is used as a target acceleration sequence.

It will be appreciated that in the case of the second vehicle vibration of D1-D2, the vehicle vertical acceleration does not meet the first preset condition and the vehicle steering wheel angle data meets the second preset condition during the estimated rear wheel vibration time interval, indicating that the front wheels of the vehicle are vibrated and are caused by uneven road surfaces.

At this time, only the first acceleration sequence is acquired, and the first acceleration sequence is taken as the target acceleration sequence.

F3: based on a dynamic time warping algorithm, the warping path distance between the target acceleration sequence and each pre-selected acceleration sequence is calculated.

F4: and determining the label corresponding to the preselected acceleration sequence with the regular path distance meeting the third preset condition as the label of the road surface at the position of the target vehicle.

It will be appreciated that technical details of F1, F3-F4 are similar to those of the above embodiments E1, E3-E4, and reference may be made to the above embodiments, which are not repeated here.

Referring to fig. 3, fig. 3 is a flowchart of another pavement detection method according to an embodiment of the present application. As shown in fig. 3, the vertical acceleration and the vehicle position of the vehicle are collected in real time, and the vertical acceleration of the vehicle is monitored to determine whether the vertical acceleration of the vehicle meets a first preset condition.

And if the vertical acceleration of the vehicle does not meet the first preset condition, ending the process. If the vertical acceleration of the vehicle meets the first preset condition, acquiring the front wheel vibration moment, the first vertical acceleration, the target vehicle position and a first acceleration sequence comprising the first vertical acceleration when the first preset condition is met.

Further, it is determined whether the vertical acceleration of the vehicle satisfies a first preset condition within the estimated rear wheel shake time period.

If the vertical acceleration of the vehicle meets the first preset condition in the estimated rear wheel vibration time interval, acquiring a second vertical acceleration and a second acceleration sequence comprising the second vertical acceleration. And calculating the correlation coefficient of the first acceleration sequence and the second acceleration sequence. And when the correlation coefficient does not meet the preset range, ending the flow. And when the correlation coefficient meets a preset range, acquiring a target acceleration sequence from the first acceleration sequence and the second acceleration sequence. Based on the target acceleration sequence and the preselected acceleration sequence, a dynamic time warping algorithm is adopted to identify the pavement pothole state or the bulge state, and the detection result and the position information are uploaded to a cloud platform and the like.

If the vertical acceleration of the vehicle does not meet the first preset condition in the estimated rear wheel vibration time interval, further judging whether the steering wheel angle in the estimated rear wheel vibration time interval meets the second preset condition. If the steering wheel rotation angle in the estimated rear wheel vibration time interval does not meet the second preset condition, ending the flow. And if the steering wheel rotation angle in the estimated rear wheel vibration time interval meets a second preset condition, taking the first acceleration sequence as a target acceleration sequence. Based on the target acceleration sequence and the preselected acceleration sequence, a dynamic time warping algorithm is adopted to identify the pavement pothole state or the bulge state, and the detection result and the position information are uploaded to a cloud platform and the like.

As can be seen from the above, in the road surface detection method provided by the embodiment of the present application, it is determined whether the vehicle vibrates due to whether the vehicle passes over the uneven road surface, and when it is determined that the vehicle vibrates due to the vehicle passing over the uneven road surface, it is determined whether the uneven road surface is a pothole road surface or a bump road surface. Further, in determining that the vehicle has passed over an uneven road surface, a case in which both the front and rear wheels of the vehicle vibrate or only the front wheels of the vehicle vibrate and the rear wheels of the vehicle are avoided is considered. The method not only can overcome detection misjudgment caused by vibration noise of the vehicle body, but also can be suitable for different vehicle parameters and can identify various scenes such as a hollow road surface, a raised road surface and the like.

Based on the road surface detection method provided by the embodiment of the method, the embodiment of the application also provides a road surface detection device, and the road surface detection device is described below with reference to the accompanying drawings.

Referring to fig. 4, a schematic diagram of a road surface detection device according to an embodiment of the present application is shown. As shown in fig. 4, the road surface detection device includes:

a first acquisition unit 401 for acquiring the vertical acceleration of the vehicle and the vehicle position in real time;

a first acquiring unit 402, configured to acquire a front wheel vibration moment when a vertical acceleration of the vehicle satisfies a first preset condition, a first vertical acceleration, and a target vehicle position;

a second acquisition unit 403 configured to acquire a first acceleration sequence including the first vertical acceleration;

a first determining unit 404, configured to determine an estimated rear wheel vibration time interval according to the front wheel vibration time;

a third obtaining unit 405, configured to obtain, in the estimated rear wheel vibration time interval, a second vertical acceleration when the first preset condition is satisfied when the vertical acceleration of the vehicle satisfies the first preset condition;

a fourth acquisition unit 406 configured to acquire a second acceleration sequence including the second vertical-direction acceleration;

The first calculating unit 407 is configured to calculate a degree of correlation between the first acceleration sequence and the second acceleration sequence, and determine that the road surface at the target vehicle position is in an uneven state if the degree of correlation satisfies a preset range.

In one possible implementation, the uneven state includes a depressed state or a raised state, and the apparatus further includes:

a fifth acquisition unit for acquiring a plurality of preselected acceleration sequences; the preselected acceleration sequence corresponds to a label; the label comprises a hollow state or a convex state;

the first composition unit is used for composing the first acceleration sequence and the second acceleration sequence into a target acceleration sequence if the vertical acceleration of the vehicle meets the first preset condition in the estimated rear wheel vibration time interval;

a second calculation unit for calculating a regular path distance between the target acceleration sequence and each of the preselected acceleration sequences, respectively, based on a dynamic time warping algorithm;

and the second determining unit is used for determining the label corresponding to the preselected acceleration sequence, the regular path distance of which meets a third preset condition, as the label of the road surface at the position of the target vehicle.

In one possible implementation, the apparatus further includes:

the second acquisition unit is used for acquiring steering wheel angle data of the vehicle in real time;

and a third determining unit, configured to determine, in the estimated rear wheel vibration time interval, that the road surface at the target vehicle position is in an uneven state when the vertical acceleration of the vehicle does not satisfy the first preset condition and the steering wheel angle data of the vehicle satisfies a second preset condition.

a sixth acquisition unit for acquiring a plurality of preselected acceleration sequences; the preselected acceleration sequence corresponds to a label; the label comprises a hollow state or a convex state;

a fourth determining unit, configured to take the first acceleration sequence as a target acceleration sequence if the vertical acceleration of the vehicle does not meet the first preset condition and the steering wheel angle data of the vehicle meets the second preset condition in the estimated rear wheel vibration time interval;

a third calculation unit for calculating a regular path distance between the target acceleration sequence and each of the preselected acceleration sequences, respectively, based on a dynamic time warping algorithm;

And a fifth determining unit, configured to determine a tag corresponding to a preselected acceleration sequence in which the regular path distance satisfies a third preset condition as a tag of a road surface at the target vehicle position.

In one possible implementation, the apparatus further includes:

and a sixth determining unit, configured to determine, in the estimated rear wheel vibration time interval, that the road surface at the target vehicle position is in a flat state when the vertical acceleration of the vehicle does not satisfy the first preset condition and the steering wheel angle data of the vehicle does not satisfy the second preset condition.

In one possible implementation manner, the second obtaining unit 403 includes:

a first acquisition subunit, configured to acquire an adjacent first acceleration sub-sequence before the first vertical acceleration; the length of the adjacent first acceleration sub-sequence is the first acceleration sampling length;

a second acquisition subunit, configured to acquire an adjacent second acceleration sub-sequence after the first vertical acceleration; the adjacent second acceleration subsequence is a second acceleration sampling length;

a first composing sub-unit for composing the adjacent first acceleration sub-sequence, the first vertical acceleration and the adjacent second acceleration sub-sequence into a first acceleration sequence;

The fourth obtaining unit 406 includes:

a third acquisition subunit configured to acquire an adjacent third acceleration sub-sequence before the second vertical acceleration; the length of the adjacent third acceleration sub-sequence is the third acceleration sampling length;

a fourth acquisition subunit configured to acquire an adjacent fourth acceleration sub-sequence after the second vertical acceleration; the adjacent fourth acceleration sub-sequence is a fourth acceleration sampling length;

and the second component subunit is used for combining the adjacent third acceleration subsequence, the second vertical acceleration subsequence and the adjacent fourth acceleration subsequence into a second acceleration sequence.

In one possible implementation manner, the first determining unit 404 includes:

a calculating subunit for calculating an interval time period according to the vehicle wheelbase and the vehicle speed;

A determining subunit configured to determine, as a target time, a time when the front wheel vibration time passes the interval period;

a fifth obtaining subunit, configured to obtain an adjacent first estimated rear wheel vibration time subinterval before the target time and an adjacent second estimated rear wheel vibration time subinterval after the target time;

and the third component subunit is used for forming the adjacent first estimated rear wheel vibration time subinterval, the target time and the adjacent second estimated rear wheel vibration time subinterval into an estimated rear wheel vibration time interval.

In addition, an embodiment of the present application further provides a road surface detection apparatus, including: the road surface detection method according to any one of the embodiments, wherein the processor executes the computer program.

In addition, an embodiment of the present application further provides a computer readable storage medium, where an instruction is stored, where the instruction when executed on a terminal device causes the terminal device to execute the pavement detection method according to any one of the foregoing embodiments.

The embodiment of the application provides a road surface detection device and equipment, which are used for collecting the vertical acceleration of a vehicle and the position of the vehicle in real time. When the vertical acceleration of the vehicle satisfies a first preset condition, it indicates that the front wheels of the vehicle are vibrated. The front wheel shake timing, the first vertical direction acceleration, the target vehicle position, and the first acceleration sequence including the first vertical direction acceleration at this time are acquired. And determining the estimated vibration time interval of the rear wheel according to the vibration time of the front wheel. And in the estimated vibration time interval of the rear wheels, when the vertical acceleration of the vehicle meets a first preset condition, indicating that the rear wheels of the vehicle vibrate. The second vertical-direction acceleration at this time and a second acceleration sequence including the second vertical-direction acceleration are acquired. When the front and rear wheels of the vehicle pass through the uneven road surface at the same position to cause the vibration of the wheels, the correlation degree of the first acceleration sequence and the second acceleration sequence can meet the preset range. Thus, the degree of correlation of the first acceleration sequence and the second acceleration sequence is calculated. When the degree of correlation satisfies a preset range, it is determined that the vibration of the front and rear wheels of the vehicle is vibration caused by passing over an uneven road surface at the same position, that is, a road surface at the position of the target vehicle. The device is applicable to different vehicle parameters for the detection accuracy on uneven road surface is high.

It should be noted that, in the present description, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system or device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant points refer to the description of the method section.

It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of pavement detection, the method comprising:

calculating the correlation degree of the first acceleration sequence and the second acceleration sequence, and if the correlation degree meets a preset range, determining that the road surface at the position of the target vehicle is in an uneven state;

the acquiring a first acceleration sequence including the first vertical acceleration includes:

acquiring adjacent first acceleration subsequences before the first vertical acceleration; the length of the adjacent first acceleration sub-sequence is the first acceleration sampling length;

acquiring adjacent second acceleration subsequences after the first vertical acceleration; the adjacent second acceleration subsequence is a second acceleration sampling length;

2. The method of claim 1, wherein the uneven condition comprises a pitted condition or a raised condition, the method further comprising:

3. The method according to claim 1, wherein the method further comprises:

collecting steering wheel rotation angle data of a vehicle in real time;

4. A method according to claim 3, wherein the uneven condition comprises a pitted condition or a raised condition, the method further comprising:

5. A method according to claim 3, characterized in that the method further comprises:

6. The method of claim 1, wherein the first acceleration sample length, the second acceleration sample length, the third acceleration sample length, and the fourth acceleration sample length are each determined based on a preset rough road length, a vehicle speed, a vehicle acceleration sample rate, and a sample point selection coefficient.

7. The method of claim 1, wherein determining the estimated rear wheel shake time interval from the front wheel shake time comprises:

8. A pavement detection apparatus, the apparatus comprising:

A first calculation unit, configured to calculate a degree of correlation between the first acceleration sequence and the second acceleration sequence, and determine that a road surface at the target vehicle position is in an uneven state if the degree of correlation satisfies a preset range;

the second acquisition unit includes:

the fourth acquisition unit includes:

a third acquisition subunit, configured to acquire an adjacent third acceleration sub-sequence before the second vertical acceleration; the length of the adjacent third acceleration sub-sequence is the third acceleration sampling length;

a fourth acquisition subunit, configured to acquire an adjacent fourth acceleration sub-sequence after the second vertical acceleration; the adjacent fourth acceleration sub-sequence is a fourth acceleration sampling length;

9. A pavement inspection apparatus, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed, implements the road surface detection method as claimed in any one of claims 1-7.

10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein instructions, which when run on a terminal device, cause the terminal device to perform the road surface detection method according to any one of claims 1-7.