CN117198063B - Method, system and medium for monitoring vehicle superelevation - Google Patents

Abstract

The embodiment of the specification provides a vehicle ultrahigh monitoring method, a system and a medium, wherein the method comprises the following steps: acquiring running state information of a vehicle to be detected, initial detection height of the vehicle to be detected and environment images of a detection road section; determining a first reliability of the initial detection height based on the driving state information, the environment image, the vehicle information to be detected, the weather information and the detection road section information; determining a reference height based on the initial height and the first reliability; and responding to the fact that the reference height meets the preset condition, and sending out early warning to the vehicle to be tested.

Description

Method, system and medium for monitoring vehicle superelevation

Technical Field

The present disclosure relates to the field of vehicle monitoring, and in particular, to a method, a system, and a medium for vehicle ultrahigh monitoring.

Background

During the running process of the vehicle, the vehicle often passes through special road sections, such as bridges, tunnels and other areas with height limitation, and the height limitation road sections have certain requirements on the height of the passing vehicle, so that the road facilities and the vehicle can be damaged by the forced passing of the ultrahigh vehicle, and even the life safety of personnel in the vehicle is influenced. The height limiting monitoring device arranged on the height limiting road section can be used for early warning of the ultrahigh vehicle, but when the running speed of the vehicle to be measured is too high, the running state of the vehicle to be measured is unstable (such as lane changing everywhere) or the running environment of the vehicle to be measured is bad in weather conditions, inaccurate calculation of the height of the vehicle to be measured can be caused, and the accuracy of final early warning is affected.

Therefore, it is desirable to provide a method, a system and a medium for monitoring the vehicle over-height, which can accurately calculate the height of the vehicle to be tested on the height-limited road section and improve the accuracy of final early warning.

Disclosure of Invention

One or more embodiments of the present disclosure provide a vehicle over-head monitoring method. The vehicle superelevation monitoring method comprises the following steps: acquiring running state information of a vehicle to be detected, initial detection height of the vehicle to be detected and environment images of a detection road section; determining a first reliability of the initial detection height based on the driving state information, the environment image, the vehicle information to be detected, the weather information and the detection road section information; determining a reference height based on the initial height and the first reliability; and responding to the fact that the reference height meets the preset condition, and sending out early warning to the vehicle to be tested.

One or more embodiments of the present disclosure provide a vehicle superelevation monitoring system including an acquisition module, a first determination module, a second determination module, and an early warning module. The acquisition module is configured to acquire running state information of a vehicle to be detected, initial height of the vehicle to be detected and an environment image of a detection road section; the first determining module is configured to determine a first reliability of the initial height based on the driving state information, the environment image, the vehicle information to be detected, weather information, and detected road section information; the second determination module is configured to determine a reference height based on the initial height and the first reliability; and the early warning module is configured to send early warning to the vehicle to be tested in response to the reference height meeting a preset condition.

One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform a vehicle over-height monitoring method.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a system block diagram of a vehicle superelevation monitoring system according to some embodiments of the present description;

FIG. 2 is an exemplary flow chart of a vehicle over-head monitoring method according to some embodiments of the present disclosure;

FIG. 3 is an exemplary flow chart of determining a first reliability of an initial height of height according to some embodiments of the present description;

FIG. 4 is an exemplary flow chart for determining a reference height according to some embodiments of the present description;

FIG. 5 is an exemplary flow chart for providing an early warning to a vehicle under test according to some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is a block diagram of a vehicle superelevation monitoring system according to some embodiments of the present description.

As shown in fig. 1, the vehicle superelevation monitoring system 100 may include an acquisition module 110, a first determination module 120, a second determination module 130, and an early warning module 140.

In some embodiments, the acquisition module 110 may be configured to acquire driving state information of the vehicle under test, an initial height of the vehicle under test, and an environmental image of the detected road segment.

In some embodiments, the first determination module 120 may be configured to determine the first reliability of the initial height of the test road based on the driving state information, the environment image, the vehicle information to be tested, the weather information, and the test road segment information.

In some embodiments, the first determination module 120 is further configured to determine the first reliability of the initial height of the vehicle by a reliability model based on the driving state information, the environment image, the vehicle information to be tested, the weather information, and the detected road segment information. The reliability model may be a machine learning model.

In some embodiments, the second determination module 130 may be configured to determine the reference height based on the initial height and the first reliability.

In some embodiments, the second determination module 130 is further configured to detect the height with the initial height as a reference in response to the first reliability meeting the reliability condition.

In some embodiments, the second determination module 130 is further configured to generate a vehicle adjustment instruction based on the first reliability and send the vehicle adjustment instruction to the vehicle under test in response to the first reliability not meeting the reliability condition, the vehicle adjustment instruction for controlling the vehicle under test to perform the vehicle adjustment parameter.

In some embodiments, the second determining module 130 is further configured to obtain an updated initial detection height for the vehicle under test after performing the vehicle adjustment parameters; determining a second reliability of the updated initial height; and stopping generating the vehicle adjustment instruction and taking the updated initial detection height as a reference detection height in response to the second reliability satisfying the reliability condition.

In some embodiments, the pre-warning module 140 may be configured to issue a pre-warning to the vehicle under test in response to the reference altitude meeting a preset condition.

In some embodiments, the preset conditions include a first preset condition and a second preset condition, the pre-warning includes a first pre-warning and a second pre-warning, and the pre-warning module 140 is further configured to determine the height difference based on the reference height and the road segment limit height criteria.

In some embodiments, the pre-warning module 140 is further configured to send a first pre-warning to the vehicle under test in response to the height difference meeting a first preset condition, the first pre-warning for prompting the user to control the vehicle speed; and responding to the height difference meeting a second preset condition, and sending a second early warning to the vehicle to be tested, wherein the second early warning is used for prompting a user to replace the travel route of the vehicle to be tested.

In some embodiments, the pre-warning module 140 is further configured to determine a difficulty factor of the vehicle under test passing the height-limited road segment based on the height difference, the height-limited road segment information, and the vehicle under test information in response to issuing the first pre-warning to the vehicle under test; and determining a reference vehicle speed based on the difficulty coefficient, and transmitting the reference vehicle speed to the vehicle to be tested, wherein the reference vehicle speed is used for the vehicle to be tested to pass through the height-limiting road section.

For more description of the content of the acquisition module 110, the first determination module 120, the second determination module 130, and the pre-warning module 140, see in particular the description of fig. 2-5.

It should be noted that the above description of the vehicle ultrahigh monitoring system and its modules is for convenience of description only and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the acquiring module, the first determining module, the second determining module and the early warning module disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

FIG. 2 is an exemplary flow chart of a vehicle over-head monitoring method according to some embodiments of the present description. In some embodiments, the process 200 may be performed by the vehicle superelevation monitoring system 100 (hereinafter system 100). As shown in fig. 2, the process 200 includes steps 210-240 described below.

Step 210, acquiring running state information of the vehicle to be detected, initial height of the vehicle to be detected and environment images of the detection road section.

The vehicle to be tested refers to a vehicle which is about to pass through a height-limited road section. The height-limited road section refers to a road section through which only vehicles having a height lower than a preset height threshold value are allowed to pass. The preset height threshold may be preset empirically by those skilled in the art.

The detection link is a link for detecting a traveling vehicle. The detection road section may be a road section on which the vehicle to be measured travels before traveling to the height-limit road section.

The running state information refers to information about the running state of the vehicle to be measured on the detected link.

In some embodiments, the travel state information may include one or more of a travel path (e.g., a travel lane, etc.), a travel speed, etc. of the vehicle under test on the detected road segment.

In some embodiments, the system 100 may obtain the driving state information in a variety of manners, for example, the system 100 may obtain the driving state information through a vehicle recorder on the vehicle to be tested, and so on.

The initial height refers to the vehicle height of the vehicle to be detected obtained by detection in the initial state. The initial state refers to a state when the vehicle to be tested first enters the detection road section. In some embodiments, the initial height may be the height of the vehicle obtained by the first detection after the vehicle to be tested travels into the detection section.

In some embodiments, the system 100 may obtain an initial height of the vehicle under test via a height monitoring module. The height monitoring module is configured to monitor an actual height of the passing vehicle in real time. The height monitoring module may continuously acquire the actual height of the passing vehicle. The height monitoring module may obtain the initial height of the passing vehicle by one or more of infrared correlation, laser scanning, ultrasonic scanning, etc.

The environmental image refers to an image capable of reflecting the environmental information of the detected link. Detecting link context information may include detecting one or more of the topography of the link, obstructions, etc. Detecting road segment environmental information may affect the accuracy of the height monitoring device in monitoring the actual height of the passing vehicle, e.g., the passing vehicle is blocked by an obstacle, and may affect the accuracy of the height monitoring device in monitoring the actual height of the passing vehicle.

In some embodiments, the system 100 may acquire an ambient image of the detected road segment through a camera device (e.g., a camera, etc.). The camera device may be part of the system 100 or a stand-alone device.

Step 220, determining a first reliability of the initial detection altitude based on the driving status information, the environment image, the vehicle information to be detected, the weather information, and the detected link information.

The vehicle information to be measured refers to information related to the vehicle itself to be measured, such as the complexity of the appearance of the vehicle to be measured, and the like. In some embodiments, the vehicle appearance complexity to be measured may be represented based on a plurality of levels.

In some embodiments, the system 100 may acquire an image of the vehicle under test via a camera device (e.g., a camera) and determine the complexity of the appearance of the vehicle under test via vector matching based on the image of the vehicle under test.

In some embodiments, the system 100 may determine the first target feature vector based on an image of the vehicle under test; determining, by the vector database, a first associated feature vector based on the first target feature vector; and determining the appearance complexity of the reference vehicle corresponding to the first associated feature vector as the appearance complexity of the vehicle to be detected.

The vector database contains a plurality of first reference feature vectors, wherein each first reference feature vector has a corresponding reference vehicle appearance complexity. The first reference feature vector is a feature vector constructed based on images of the historical vehicle.

In some embodiments, the system 100 may determine a first reference feature vector that meets the target preset condition in the vector database based on the first target feature vector, and determine the first reference feature vector that meets the target preset condition as the first associated feature vector. In some embodiments, the target preset condition may include a minimum vector distance from the first target feature vector, and the like.

In some embodiments, the system 100 may determine the vehicle appearance complexity to be measured based on the reference vehicle appearance complexity corresponding to the determined first associated feature vector.

The weather information refers to information related to the weather of the area where the vehicle to be measured is located. For example, the weather information may include one or more of rainfall, rainfall level, temperature, humidity, etc. in the area where the vehicle under test is located.

In some embodiments, the system 100 may obtain weather information via a third party platform (e.g., weather bureau, etc.).

The detected link information refers to information related to the detected link itself, such as a detected link length, a flatness distribution of the detected link, and the like. The flatness distribution of the detection road section may be characterized based on the distribution of the road surface flatness corresponding to the plurality of first sub-areas divided on the detection road section. Road surface flatness refers to the degree of height of the road surface. In some embodiments, the system 100 may divide the detection road segment into a plurality of first sub-regions in a plurality of ways, for example, divide the image of the detection road segment into a plurality of first sub-regions (for example, square regions with a side length of 1 m) according to a preset shape or size. In some embodiments, the detected road segment flatness profile may be characterized based on a vector comprised of road surface flatness of a plurality of first sub-regions on the detected road segment.

In some embodiments, the system 100 may acquire an image of the detected road segment through a camera device (e.g., a camera); dividing the detected road section image into a plurality of first subareas (for example, square areas with side length of 1 m) according to the preset shape and size; acquiring the flatness of each sub-area based on technologies such as image recognition and the like; and determining the flatness distribution of the detected road section based on the flatness of each first sub-area.

The first reliability refers to the accuracy of the initial detection height of the vehicle to be detected obtained by the detection.

In some embodiments, the system 100 may determine the first reliability of the initial height of the vehicle by using a first preset lookup table based on the driving state information, the environment image, the vehicle information to be measured, the weather information, and the detected link information. The first preset comparison table comprises corresponding relations of reference running state information, reference environment images, reference vehicle information, reference weather information and reference detection road section information and reference first reliability of reference detection height. The first preset comparison table can be constructed according to priori knowledge or historical data, for example, the more the number of variable passes and the faster the running speed of the vehicle to be tested in the running process of the detected road section, the lower the first reliability of the reference corresponding to the preset reference height.

In some embodiments, the system 100 may also determine the first reliability of the initial height using the method illustrated in FIG. 3, with particular reference to the description in FIG. 3.

Step 230, determining a reference height of the test based on the initial height of the test and the first reliability.

The reference height refers to the final measured height of the vehicle to be measured obtained by the detection.

In some embodiments, the system 100 may also determine the reference height of the test by a second predetermined look-up table based on the initial height of the test and the first reliability. The second preset comparison table comprises the corresponding relation between the reference initial detection height and the reference first reliability and the reference detection height. The second preset lookup table may be constructed based on a priori knowledge or historical data.

In some embodiments, the system 100 may also determine the reference height using the method illustrated in FIG. 4, with particular reference to the description in FIG. 4.

And step 240, responding to the reference height to detect that the reference height meets the preset condition, and sending out early warning to the vehicle to be tested.

The preset condition refers to a condition that the preset reference height is required to meet. For example, the preset condition may be that a height difference between the road section height limit standard and the reference height is smaller than a preset threshold value. The road section height limit standard refers to the maximum height of the height limit road section allowing the vehicles to pass through, for example, the height limit standard is 2.5m, and the vehicles with the height exceeding 2.5m cannot enter the height limit road section. The road section height limiting standard is the actual height limiting obtained by measuring the road section with the height. The preset threshold may be empirically preset by one skilled in the art, e.g., 0.3m.

In some embodiments, the preset conditions may include a first preset condition and a second preset condition. For descriptions of the first preset condition and the second preset condition, please refer to the related description in fig. 5.

The early warning is to early warn the vehicles to be detected which possibly have the road section which can not pass through the height limit or the road section which can not pass through the height limit with the maximum probability. The pre-warning may include one or more of voice pre-warning, video pre-warning, text pre-warning, and the like.

In some embodiments, the pre-warning may include a first pre-warning and a second pre-warning. For a description of the first and second warnings, please refer to the description of fig. 5.

In some embodiments, the system 100 may send an early warning to the vehicle under test in response to the road segment height limit criteria and the reference height difference being less than a preset threshold.

In some embodiments, the system 100 may also employ the method of fig. 5to provide an early warning to the vehicle under test, see in particular the description of fig. 5.

In some embodiments of the present disclosure, the reliability of the detected height is evaluated by acquiring the driving state information, the environmental image, the vehicle information to be detected, the weather information and the detected road section information of the vehicle to be detected, and the detected height with the reliability meeting the requirement is used as the reference detected height, so as to improve the accuracy of the calculated height of the vehicle to be detected, further accurately judge whether the vehicle to be detected can pass through the height-limited road section smoothly, and realize accurate early warning on the vehicle to be detected with risk failing to pass through the height-limited road section, thereby improving the accuracy of early warning.

It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.

FIG. 3 is an exemplary flow chart for determining a first reliability of an initial height of height according to some embodiments of the present description. In some embodiments, the process illustrated in FIG. 3 may be performed by the vehicle ultrahigh monitoring system 100 (hereinafter referred to as system 100).

In some embodiments, the system 100 may determine the first reliability 341 of the initial height by the reliability model 320 based on the driving state information 311, the environment image 316, the vehicle information under test 312, the weather information 313, and the detected link information 314.

For descriptions of the driving state information 311, the environment image 316, the vehicle information to be measured 312, the weather information 313, and the detected link information 314, please refer to the related descriptions in step 220 of fig. 2.

In some embodiments, reliability model 320 may be a machine learning model. In some embodiments, the types of reliability models may include neural network models (Neural Networks, NN) and deep neural networks (Deep Neural Networks, DNN).

In some embodiments, reliability model 320 may include an environmental feature extraction layer 330 and a reliability prediction layer 340. The environmental feature extraction layer 330 may be a deep neural network and the reliability prediction layer 340 may be a neural network model.

In some embodiments, the environmental feature extraction layer 330 may be used to process the environmental image 316 to determine the environmental feature vector 331. The environmental feature vector 331 is a vector constructed by the environmental feature extraction layer 330 after feature extraction based on an input environmental image. Elements of the environmental feature vector 331 may include detecting terrain and obstacles of the road segment, and the like.

In some embodiments, the reliability prediction layer 340 may be configured to process the environmental feature vector 331, the driving state information 311, the vehicle information 312 to be tested, the weather information 313, and the detected link information 314, and determine the first reliability 341 of the initial height.

In some embodiments, the input to reliability prediction layer 340 may also include detecting traffic 315 for the road segment. The accuracy of the height monitoring module in the system 100 to obtain the detected height of the vehicle to be detected may be affected by the traffic flow of the detected road section, for example, the traffic flow of the detected road section is excessive, and there may be a shielding, which results in inaccurate detected height of the vehicle to be detected obtained by the height monitoring module.

In some embodiments, the system 100 may obtain the traffic flow of the detected road segment through a traffic flow detection device provided at the detected road segment.

In some embodiments of the present disclosure, since the traffic flow of the detected road section is excessive, there may be a blocking that is unfavorable for the vehicle height detection, and the traffic flow of the detected road section is also considered in the input of the reliability prediction layer, the accuracy of the first reliability of the initial detection height output by the reliability prediction layer can be further improved.

In some embodiments, the reliability model may be based on a number of first training samples, and derived by a combined training of the environmental feature extraction layer and the reliability prediction layer.

Each set of first training samples in the first training samples may include historical sample travel state information of a historical sample vehicle, a historical sample environmental image, historical sample vehicle information, historical sample weather information, and historical sample detection section information. The first training tag may be a historical sample first reliability of an initial height of the historical sample vehicle.

In some embodiments, the first training sample may be obtained from historical data. In some embodiments, the system 100 may obtain an initial height of the historical sample vehicle via the height monitoring module; acquiring the parking height of a manually detected historical vehicle; and obtaining a first reliability of the history sample by inversely correlating an absolute value of a difference between the initial detection height and the parking detection height of the history sample vehicle with the first reliability of the history sample based on the initial detection height and the parking detection height of the history sample vehicle, e.g.,

Wherein a is an adjustment coefficient set by a person skilled in the art.

The parking height is the actual height of the history vehicle obtained by manually detecting the history vehicle after the history vehicle is parked.

In some embodiments, the adjustment coefficient may be a dynamic coefficient. In some embodiments, the system 100 may determine the adjustment factor through a third preset lookup table based on a difference between the parking height of the historical sample vehicle and the road segment limit height standard. The third preset comparison table comprises the corresponding relation between the parking height of the reference history sample vehicle and the difference of the road section height limiting standard and the reference adjustment coefficient.

The third predetermined look-up table may be constructed based on a priori knowledge or historical data. The third preset lookup table is shown in the following chart:

Wherein the values of a1 > a2 > a3 > a4, a1, a2, a3, a4, c1, c2, c3, c4 may be preset empirically by a person skilled in the art.

In some embodiments of the present disclosure, the system may increase the accuracy of the first reliability of the obtained historical sample by determining an adjustment coefficient based on a difference between the parking height and a road segment height limit standard to adjust a difference between the parking height and the initial height. For example, assuming that the initial height is 3m and the parking height is 3.1m, the difference between the parking height and the initial height is 10cm, and if the link limit height standard is 5m, the height difference of 10cm is completely acceptable (equivalent to considering the initial height to be reliable); if the road section height limit standard is 3.3m, a 10cm difference is likely to cause a serious collision accident (i.e., the difference is unacceptable and unreliable), and therefore, the accuracy of the first reliability of the acquired history sample can be improved by determining different adjustment coefficients based on the difference between the parking height and the road section height limit standard to adjust the difference between the parking height and the initial height.

In some embodiments, the processor may input a historical sample environmental image in the first training sample with the first tag into the initial environmental feature extraction layer, then input the historical sample driving state information, the historical sample environmental image, the historical sample vehicle information, the historical sample weather information, the historical sample detection road section information and the historical sample environmental feature vector output by the initial environmental feature extraction layer into the initial reliability prediction layer, construct a loss function according to the prediction results of the first tag and the initial reliability prediction layer, and iteratively update parameters of the initial environmental feature extraction layer and the initial reliability prediction layer based on the loss function until the loss function converges, the number of iterations reaches a threshold value, and the like, and after the training is finished, obtain the trained reliability model.

In some embodiments of the present disclosure, the influence of various information such as driving state information, environmental image, vehicle information to be tested, weather information, and detection section information of the vehicle to be tested on the reliability of the detection height of the vehicle to be tested is considered in the training sample of the reliability model, so that the accuracy of the reliability model prediction result obtained by final training is improved, and the first reliability of the initial height can be rapidly and accurately predicted through the trained reliability model. Meanwhile, the detection accuracy of the radar in the height monitoring module also depends on attenuation and interference of signals in the propagation process, and the environmental characteristic vector is obtained by inputting the environmental image into the environmental characteristic extraction layer so as to reflect the influence of factors such as atmospheric conditions, terrains, obstacles, electromagnetic interference and the like on radar signal propagation and detection, so that the accuracy of the first reliability of the initial height of final prediction is improved.

FIG. 4 is an exemplary flow chart of a method of determining a reference height according to some embodiments of the present description. In some embodiments, the process 400 may be performed by the vehicle superelevation monitoring system 100 (hereinafter system 100). As shown in fig. 4, the process 400 includes steps 410-450 described below.

In step 410, in response to the first reliability satisfying the reliability condition, the initial height is detected as a reference height.

For a description of the first reliability, initial height of detection, reference height of detection, see fig. 2 and its associated description.

The reliability condition refers to a condition that the reliability needs to satisfy, for example, the reliability is higher than a reliability threshold.

The reliability threshold refers to a critical value of reliability. In some embodiments, the reliability threshold may be obtained by manual preset.

In some embodiments, the system 100 may detect the initial height as a reference height in response to the first reliability being above a reliability threshold.

And step 420, generating a vehicle adjustment instruction based on the first reliability and transmitting the vehicle adjustment instruction to be tested in response to the first reliability not meeting the reliability condition.

The vehicle adjustment instruction refers to an instruction for adjusting the running state of the vehicle.

In some embodiments, the vehicle adjustment instructions may be used to control the vehicle under test to perform the vehicle adjustment parameters.

The vehicle adjustment parameter refers to a parameter for adjusting the running state of the vehicle. In some embodiments, the vehicle adjustment parameters may include a target vehicle speed, a target travel lane, and the like. The target vehicle speed is the vehicle speed of the vehicle to be measured after being adjusted based on the adjustment instruction. The target driving lane is a lane in which the vehicle to be measured finally runs after being adjusted based on the adjustment instruction.

In some embodiments, the system 100 may determine the target vehicle speed in a variety of ways.

In some embodiments, the target vehicle speed may be empirically preset by one skilled in the art.

In some embodiments, the system 100 may also determine a vehicle speed reduction from the first reliability of the initial detection height, through a fourth preset lookup table, and determine the target vehicle speed based on the vehicle speed reduction. The fourth preset comparison table comprises a corresponding relation between the first reliability of the reference initial height and the reference vehicle speed reduction. The fourth preset map may be constructed based on a priori knowledge or historical data, e.g., the lower the first reliability of the reference initial height, the greater the vehicle speed reduction.

In some embodiments, the system 100 may calculate the target vehicle speed by positively correlating the difference between the current vehicle speed and the vehicle speed reduction of the vehicle under test with the target vehicle speed, e.g., target vehicle speed = current vehicle speed of the vehicle under test-vehicle speed reduction. By performing deceleration processing on the vehicle to be detected, the accuracy of the detection height of the obtained vehicle to be detected can be improved.

In some embodiments, the system 100 may determine the target lane based on the traffic flow of the currently detected road segment. The traffic flow refers to the number of vehicles passing through the currently detected road section per unit time. For a manner of acquiring the vehicle flow, please refer to the related description in fig. 3.

In some embodiments, the system 100 may take the current driving lane as the target lane, i.e., without lane change, in response to the traffic flow of the current detected road segment meeting the traffic flow condition. The traffic flow condition may be that the traffic flow of the currently detected road segment is greater than or equal to a traffic flow threshold. The vehicle flow threshold may be preset empirically by those skilled in the art. By the arrangement, lane change under the condition of large traffic flow of the current driving lane can be avoided, and the risk of traffic accidents is reduced.

In some embodiments, the system 100 may further take the preset driving lane as the target lane if the traffic flow in response to the currently detected road segment does not satisfy the traffic flow condition. The preset driving lane refers to a lane with a good effect of detecting the height of the vehicle to be detected, for example, a middle lane.

Step 430, obtaining updated initial height of the detected vehicle after the vehicle adjustment parameters are executed.

The updated initial height is the detection height obtained by the height monitoring module through re-detection of the vehicle to be detected, and the vehicle to be detected is the vehicle adjusted based on the vehicle adjustment parameters. In some embodiments, the system 100 may obtain the updated initial height through a height monitoring module on the system 100. For more explanation of the height monitoring module, see the associated description in step 210 of FIG. 2.

Step 440, determining a second reliability of the updated initial height.

The second reliability refers to the accuracy of detecting the obtained updated initial detection height.

The second reliability determination method is similar to the first reliability determination method, and is specifically described with reference to step 220 of fig. 2.

And step 450, stopping generating the vehicle adjustment instruction and taking the updated initial detection height as a reference detection height in response to the second reliability meeting the reliability condition.

In some embodiments, the system 100 may stop generating the vehicle adjustment instruction and take the updated initial height as the reference height in response to the second reliability meeting the reliability condition (e.g., the second reliability being above a reliability threshold).

In some embodiments, the system 100 may further generate a vehicle adjustment instruction based on the second reliability and send the vehicle under test in response to the second reliability not meeting the reliability condition (e.g., the second reliability does not exceed the reliability threshold), and continuously control the vehicle under test to execute the vehicle adjustment parameter, and obtain an updated detection height for the vehicle under test after executing the vehicle adjustment parameter; and determining whether the reliability of the updated detection height meets the reliability condition until the detection height is obtained, for which the reliability meets the reliability condition (e.g., the second reliability is higher than the reliability threshold), as the reference detection height.

In some embodiments of the present description, the detected height of the vehicle to be tested is directly used as the reference detected height of the vehicle to be tested in response to the detected height of the vehicle to be tested meeting the reliability condition by comparing the reliability of the detected height of the vehicle to be tested with the reliability condition; and responding to the detection height of which the reliability does not meet the reliability condition, generating a vehicle adjustment instruction and sending the vehicle adjustment instruction to the vehicle to be detected so as to control the vehicle to be detected to execute the vehicle adjustment parameters until the detection height of which the reliability meets the reliability condition is obtained, and finally obtaining an accurate and reliable reference detection height for later judgment.

FIG. 5 is an exemplary flow chart for providing an early warning to a vehicle under test according to some embodiments of the present disclosure. In some embodiments, the process 500 may be performed by the vehicle superelevation monitoring system 100 (hereinafter system 100). As shown in fig. 5, the process 500 includes steps 510-550 described below.

Step 510, determining the height difference based on the reference height and the road section height limit standard.

For a description of reference to the height detection, road section height limit criteria, please refer to the related description in fig. 2.

The height difference refers to the difference between the road segment height limit standard and the reference height.

In some embodiments, the system 100 may calculate the difference between the road segment height limit criteria and the reference height as the height difference.

And step 520, responding to the height difference meeting a first preset condition, and sending a first early warning to the vehicle to be tested.

The first preset condition refers to a condition that a preset height difference needs to be satisfied.

In some embodiments, the first preset condition may be a first variance threshold < a height difference < a second variance threshold.

In some embodiments, the first variance threshold and the second variance threshold may be empirically preset by one skilled in the art, e.g., the first variance threshold is 0.2m and the second variance threshold is 0.8m.

In some embodiments, the first variance threshold may be a dynamic threshold. In some embodiments, the first discrepancy threshold value may be related to a road segment height limit criterion, e.g., the road segment height limit criterion is inversely related to the first discrepancy threshold value.

In some embodiments of the present disclosure, since the road segment height limit standard itself reflects the degree of control of the road on the vehicle height, when the road segment height limit standard is smaller, the road segment height limit standard is stricter, the vehicle height allowed to pass is lower, and the corresponding warning should be stricter (for example, the first difference threshold is increased), the rationality of the set first difference threshold may be further improved by setting the first difference threshold as a dynamic threshold, and the first difference threshold is related to the road segment height limit standard.

The first early warning refers to risk early warning of a vehicle to be tested, which possibly has a section incapable of passing through the height limit road. In some embodiments, the first warning may be used to prompt the user to control the vehicle speed, e.g., alert the vehicle under test to the speed of travel, etc. The first early warning can avoid the collision of the vehicle to be detected on the height-limiting road section so as to safely pass through the height-limiting road section.

In some embodiments, the system 100 may send a first warning to the vehicle under test in response to the difference in altitude satisfying a first difference threshold < difference in altitude < a second difference threshold.

And step 530, responding to the height difference meeting a second preset condition, and sending a second early warning to the vehicle to be tested.

The second preset condition refers to another condition different from the first preset condition that the preset height difference needs to satisfy.

In some embodiments, the second preset condition may be a height difference < a first difference threshold.

The second early warning refers to risk early warning on the vehicle to be tested which cannot pass through the height-limited road section with the maximum probability. In some embodiments, the second warning may be used to prompt the user to change the route of travel of the vehicle under test, e.g., to alert the vehicle under test to change routes, not to drive into a highway section, etc. The first early warning can avoid that the vehicle to be tested which cannot pass through the height-limiting road section drives into the height-limiting road section and collides.

In some embodiments, the system 100 may send a second warning to the vehicle under test in response to the difference in altitude satisfying the difference in altitude < the first difference threshold.

In some embodiments, in response to issuing the first warning to the vehicle under test, the method 500 of issuing the warning to the vehicle under test may further include the following steps 540-550.

Step 540, determining a difficulty coefficient of the vehicle to be tested passing through the height-limited road section based on the height difference, the height-limited road section information and the vehicle to be tested information.

The height-restricted section information refers to information related to the height-restricted section itself. In some embodiments, the height-limited road segment information may include one or more of a length of the height-limited road segment, a flatness profile of the height-limited road segment, and the like.

The flatness distribution of the height-limited road section can be characterized based on the distribution of the road surface flatness corresponding to the plurality of second sub-areas divided on the height-limited road section. In some embodiments, the system 100 may divide the height-restricted section into a plurality of second sub-areas in a variety of ways, such as dividing the image of the height-restricted section into a plurality of second sub-areas (e.g., square areas with a side length of 1 m) according to a preset shape or size. In some embodiments, the evenness profile of the height-restricted road segment may be characterized based on a vector consisting of road surface evenness of the plurality of second sub-regions on the height-restricted road segment. The method of obtaining the flatness profile of the distance-restricted road segment by the system 100 is similar to the method of obtaining the flatness profile of the detected road segment, see in particular the description of step 220 of fig. 2.

For a description of the vehicle information to be tested, please refer to the related description in step 220 of fig. 2.

The difficulty coefficient can be used for representing the difficulty level of the vehicle to be tested in the safety passing of the height-limiting road section. In some embodiments, the greater the difficulty factor, the less likely the vehicle under test will safely pass the height-restricted road segment.

In some embodiments, the system 100 may determine the height-limited road segment coefficients based on the height-limited road segment information; determining a vehicle coefficient to be tested based on the vehicle information to be tested; and determining the difficulty coefficient of the vehicle to be tested passing through the height-limiting road section through a difficulty coefficient algorithm based on the height-limiting road section coefficient, the vehicle coefficient to be tested and the height difference. The difficulty coefficient algorithm may be any feasible algorithm for determining a difficulty coefficient of the vehicle to be tested passing through the height-limited road section, for example, the difficulty coefficient algorithm may be the following formula:

the difficulty coefficient= (a×the height limit road section coefficient+b×the vehicle coefficient to be measured)/(height difference), wherein a and b are preset coefficient values which can be preset according to experience of a person skilled in the art.

The height-limiting road section coefficient can be used for representing the influence degree of the road condition of the height-limiting road section on the passing of the vehicle to be tested through the height-limiting road section.

In some embodiments, the system 100 may determine the height-limited road segment coefficients through a fifth preset lookup table based on the height-limited road segment information. The fifth preset comparison table comprises the corresponding relation between the reference height limit road section information and the reference height limit road section coefficient. The fifth preset comparison table can be constructed according to priori knowledge or historical data, for example, the longer the length of the reference height-limiting road section is, the worse the evenness of the flatness distribution of the reference height-limiting road section is, and the larger the coefficient of the preset reference height-limiting road section is.

In some embodiments, the system 100 may calculate the standard deviation based on the flatness of each second sub-area on the reference elevation-limiting road segment, and take the standard deviation as the reference elevation-limiting road segment flatness distribution uniformity.

The vehicle coefficient to be measured can be used for representing the influence degree of the request condition of the vehicle to be measured on the road section where the vehicle to be measured passes through the height limit.

In some embodiments, the system 100 may determine the vehicle coefficient to be measured based on the vehicle information to be measured through a sixth preset lookup table. The sixth preset comparison table includes a correspondence between the reference vehicle information and the reference vehicle coefficient. The sixth preset reference table may be constructed based on a priori knowledge or historical data, e.g., the higher the complexity of the appearance of the reference vehicle, the greater the preset reference vehicle coefficient.

And step 550, determining a reference vehicle speed based on the difficulty coefficient and sending the reference vehicle speed to the vehicle to be tested.

The reference vehicle speed refers to a vehicle speed at which the vehicle under test is recommended to pass through the height-restricted road section. In some embodiments, the reference vehicle speed may be used for the vehicle under test to pass the height-limited road segment.

In some embodiments, the system 100 may determine the reference vehicle speed based on the difficulty coefficient via a seventh preset lookup table. The seventh preset comparison table comprises the corresponding relation between the reference difficulty coefficient and the reference vehicle speed. The seventh preset comparison table may be constructed according to priori knowledge or historical data, for example, the reference difficulty coefficient is inversely related to the reference vehicle speed, and the correspondence between the reference difficulty coefficient and the reference vehicle speed is preset.

In some embodiments, the determination of the reference vehicle speed is also related to weather information, e.g., the weather information is displayed as a rainy day, and the reference vehicle speed in the rainy day may be set lower than the reference vehicle speed in a sunny day.

For a description of weather information, please refer to the description of step 220 of fig. 2.

In some embodiments of the present specification, the accuracy of the set reference vehicle speed may be further improved by setting the determination of the reference vehicle speed to be correlated with weather information.

In some embodiments, the determination of the reference vehicle speed is also related to the vehicle flow of the high road section, e.g., the reference vehicle speed of the high road section where the vehicle flow is large is slower than the reference vehicle speed of the low road section where the vehicle flow is small.

In some embodiments of the present description, the greater the vehicle flow in the current period of the high-limit road section, the lower the reference vehicle speed, so as to reduce the risk of traffic accidents.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (6)

1. A vehicle superelevation monitoring method, comprising:

acquiring running state information of a vehicle to be detected, initial detection height of the vehicle to be detected and environment images of a detection road section;

determining a first reliability of the initial detection height based on the driving state information, the environment image, the vehicle information to be detected, weather information and the detection road section information;

determining a reference height of detection based on the initial height of detection and the first reliability; and

Responding to the reference height to meet a preset condition, and sending out early warning to the vehicle to be tested;

The determining the first reliability of the initial detection height based on the driving state information, the environment image, the vehicle information to be detected, the weather information and the detection road section information includes:

Determining a first reliability of the initial detection height through a reliability model based on the driving state information, the environment image, the vehicle information to be detected, the weather information and the detection road section information, wherein the reliability model is a machine learning model; the first reliability refers to the accuracy of the initial height of the detected vehicle, the driving state information comprises the driving path and the driving speed of the detected vehicle on the detected road section, the detected vehicle information comprises the appearance complexity of the detected vehicle, and the detected road section environment information comprises the topography and the obstacle of the detected road section;

the reliability model comprises an environment characteristic extraction layer and a reliability prediction layer;

The environment feature extraction layer is used for processing the environment image and determining an environment feature vector, and elements of the environment feature vector comprise the topography and the obstacle of the detection road section;

the reliability prediction layer is used for processing the environment feature vector, the driving state information, the vehicle information to be detected, the weather information, the detection road section information and the traffic flow of the detection road section, and determining the first reliability of the initial height;

The determining a reference height based on the initial height and the first reliability comprises:

In response to the first reliability satisfying a reliability condition, taking the initial height as a reference height;

Generating a vehicle adjustment instruction based on the first reliability and sending the vehicle adjustment instruction to be tested to the vehicle to be tested, wherein the vehicle adjustment instruction is used for controlling the vehicle to be tested to execute vehicle adjustment parameters;

acquiring updated initial height of the vehicle to be detected after the vehicle adjustment parameters are executed;

determining a second reliability of the updated initial height; and

And stopping generating the vehicle adjustment instruction and taking the updated initial detection height as the reference detection height in response to the second reliability meeting the reliability condition.

2. The method of claim 1, wherein the preset conditions include a first preset condition and a second preset condition, the pre-warning includes a first pre-warning and a second pre-warning, and the issuing the pre-warning to the vehicle under test in response to the reference height meeting the preset conditions includes:

determining a height difference based on the reference height and a road section height limiting standard;

responding to the height difference meeting the first preset condition, and sending out the first early warning to the vehicle to be tested, wherein the first early warning is used for prompting a user to control the vehicle speed; and

And responding to the height difference meeting the second preset condition, sending out the second early warning to the vehicle to be tested, wherein the second early warning is used for prompting a user to replace the travelling route of the vehicle to be tested.

3. The method according to claim 2, wherein the method further comprises:

Responding to the first early warning sent to the vehicle to be tested, and determining a difficulty coefficient of the vehicle to be tested passing through a height limiting section based on the height difference, the height limiting section information and the vehicle to be tested; and

And determining a reference vehicle speed based on the difficulty coefficient and sending the reference vehicle speed to the vehicle to be tested, wherein the reference vehicle speed is used for the vehicle to be tested to pass through the height-limiting road section.

4. The vehicle ultrahigh monitoring system is characterized by comprising an acquisition module, a first determination module, a second determination module and an early warning module;

The acquisition module is configured to acquire running state information of a vehicle to be detected, initial height of the vehicle to be detected and an environment image of a detection road section;

The first determining module is configured to determine a first reliability of the initial height based on the driving state information, the environment image, the vehicle information to be detected, weather information, and detected road section information;

the second determination module is configured to determine a reference height based on the initial height and the first reliability; and

The early warning module is configured to send early warning to the vehicle to be tested in response to the reference height meeting a preset condition;

The first determining module is further configured to determine a first reliability of the initial height through a reliability model based on the driving state information, the environment image, the vehicle information to be detected, the weather information, and the detected road section information, the reliability model being a machine learning model; the first reliability refers to the accuracy of the initial height of the detected vehicle, the driving state information comprises the driving path and the driving speed of the detected vehicle on the detected road section, the detected vehicle information comprises the appearance complexity of the detected vehicle, and the detected road section environment information comprises the topography and the obstacle of the detected road section;

the reliability model comprises an environment characteristic extraction layer and a reliability prediction layer;

The environment feature extraction layer is used for processing the environment image and determining an environment feature vector, and elements of the environment feature vector comprise the topography and the obstacle of the detection road section;

the reliability prediction layer is used for processing the environment feature vector, the driving state information, the vehicle information to be detected, the weather information, the detection road section information and the traffic flow of the detection road section, and determining the first reliability of the initial height;

the second determination module is further configured to:

In response to the first reliability satisfying a reliability condition, taking the initial height as a reference height;

Generating a vehicle adjustment instruction based on the first reliability and sending the vehicle adjustment instruction to be tested to the vehicle to be tested, wherein the vehicle adjustment instruction is used for controlling the vehicle to be tested to execute vehicle adjustment parameters;

acquiring updated initial height of the vehicle to be detected after the vehicle adjustment parameters are executed;

determining a second reliability of the updated initial height; and

And stopping generating the vehicle adjustment instruction and taking the updated initial detection height as the reference detection height in response to the second reliability meeting the reliability condition.

5. The system of claim 4, wherein the preset conditions comprise a first preset condition and a second preset condition, the pre-warning comprises a first pre-warning and a second pre-warning, the pre-warning module is further configured to:

determining a height difference based on the reference height and a road section height limiting standard;

responding to the height difference meeting the first preset condition, and sending out the first early warning to the vehicle to be tested, wherein the first early warning is used for prompting a user to control the vehicle speed; and

And responding to the height difference meeting the second preset condition, sending out the second early warning to the vehicle to be tested, wherein the second early warning is used for prompting a user to replace the travelling route of the vehicle to be tested.

6. A computer-readable storage medium storing computer instructions, wherein when the computer reads the computer instructions in the storage medium, the computer performs the vehicle superelevation monitoring method according to any one of claims 1 to 3.