Road condition recognition method and device and electronic equipment

Abstract

The invention belongs to the field of road condition recognition, and discloses a road condition recognition method and device and electronic equipment, and the method comprises the steps: calculating a yaw velocity actual value according to a lateral sensor signal; calculating a yaw velocity reference value according to the vehicle information and the driving information; calculating an accumulated deviation value, and judging whether the accumulated deviation value is greater than a first threshold value or not; and if yes, setting first identification information for the current road section. According to the method, the actual value and the reference value of the yaw velocity are calculated in real time by using the lateral sensor signal and the vehicle driving data, and the road condition is accurately judged by comparing the deviation value of the two values, so that the road condition information is provided for a driver or an automatic driving system in time. When the bad road is detected, the identification information is set, so that early warning is facilitated, a driver can take proper measures, potential driving risks are avoided, and driving safety and comfort are ensured. And meanwhile, the method does not depend on a visual system which is easily influenced by the environment, so that the robustness and adaptability of the road condition recognition system are enhanced.

Description

[Technical field]

[0001] The present invention relates to the field of road condition recognition, and in particular to a method, device, electronic device and computer-readable storage medium for road condition recognition. [Background technology]

[0002] Road conditions have a significant impact on the safety and comfort of vehicle driving. Bad roads (such as potholes and damaged roads) can cause unstable vehicle driving and increase the risk of traffic accidents. Current road recognition technology mainly relies on image processing technology, which uses cameras and other devices to obtain road images and processes them through computer vision technology. However, this method is greatly affected by weather and lighting conditions, and the accuracy and real-time performance of recognition need to be improved.

[0003] Existing road condition recognition technologies mainly include visual recognition technology and laser radar recognition technology. Visual recognition technology obtains road images through cameras and processes them using computer vision algorithms to identify information such as road signs, lanes and obstacles. However, visual recognition technology performs poorly in low light and bad weather conditions. Laser radar recognition technology generates point cloud images through laser beam reflection, which has high accuracy and high stability, but is costly and has high environmental requirements.

[0004] Therefore, how to improve the effect of road condition recognition and reduce the cost is a technical problem that those skilled in the art need to solve. [Summary of the invention]

[0005] In order to solve the problem of high cost and poor effect of road condition recognition in the prior art, the present invention provides a method for road condition recognition.

[0006] A method for road condition recognition, comprising:

[0007] Acquire a lateral sensor signal, and calculate an actual value of the yaw angular velocity according to the lateral sensor signal;

[0008] Acquiring vehicle information and driving information, and calculating a yaw rate reference value according to the vehicle information and the driving information;

[0009] calculating a cumulative deviation amount according to the actual yaw rate value and the reference yaw rate value, and determining whether the cumulative deviation amount is greater than a first threshold;

[0010] If so, the first identification information is set for the current road section.

[0011] Optionally, the calculating the accumulated deviation according to the actual yaw rate value and the reference yaw rate value includes:

[0012] Get the historical cumulative deviation;

[0013] determining whether the vehicle is in a turning state according to the actual value of the yaw angular velocity;

[0014] If not, calculating a first deviation between the actual yaw rate value and the reference yaw rate value;

[0015] The sum of the square value of the first deviation and the historical cumulative deviation is determined as the cumulative deviation.

[0016] Optionally, before calculating the accumulated deviation amount according to the actual yaw rate value and the yaw rate reference value, the method further includes:

[0017] determining an actual value of lateral acceleration according to the lateral sensor signal;

[0018] Calculating a lateral acceleration reference value according to the yaw angular velocity reference value, and calculating a second deviation between the lateral acceleration actual value and the lateral acceleration reference value;

[0019] Determine the driving direction and real-time vehicle speed according to the driving information;

[0020] Determining whether the vehicle is in a normal driving state according to the second deviation, the driving direction and the real-time vehicle speed;

[0021] If not, the historical accumulated deviation is cleared to zero, and the process returns to the step of acquiring the lateral sensor signal;

[0022] If yes, the step of calculating the accumulated deviation according to the actual yaw rate value and the reference yaw rate value is performed.

[0023] Optionally, when it is determined according to the actual value of the yaw angular velocity that the vehicle is in a turning state, the method further includes:

[0024] The difference between the historical accumulated deviation amount and a first preset value is determined as the accumulated deviation amount.

[0025] Optionally, calculating a yaw rate reference value according to the vehicle information and the driving information includes:

[0026] Determine characteristic vehicle speed and wheelbase according to the vehicle information, and determine real-time vehicle speed and front wheel turning angle according to the driving information;

[0027] The yaw rate reference value is calculated according to the formula:

[0028]

[0029] Among them, TargetYawrate ackermannis the yaw rate reference value, V is the real-time vehicle speed, L is the wheelbase, δ is the front wheel turning angle, V ch is the characteristic vehicle speed.

[0030] Optionally, after setting the first identification information for the current road section, the method further includes:

[0031] Obtaining the wheel acceleration of each wheel and the count value corresponding to each wheel;

[0032] For each of all the wheels, updating the count value according to the magnitude and direction of the wheel acceleration;

[0033] Sum all the updated count values, and determine whether the summation result is greater than a second threshold;

[0034] If yes, set the second identification information for the current road section;

[0035] The road segment having the first identification information and the second identification information is marked as a bad road.

[0036] Optionally, updating the count value according to the magnitude and direction of the wheel acceleration includes:

[0037] determining whether the direction of the wheel acceleration changes;

[0038] If not, reducing the count value by a second preset value;

[0039] If yes, determining whether the wheel acceleration exceeds a third threshold;

[0040] If the wheel acceleration exceeds the third threshold, increasing the count value by a third preset value;

[0041] If the wheel acceleration does not exceed the third threshold, the count value is reduced by the second preset value.

[0042] A device for road condition recognition, comprising:

[0043] A first acquisition module, used for acquiring a lateral sensor signal and calculating an actual value of a yaw angular velocity according to the lateral sensor signal;

[0044] a second acquisition module, configured to acquire vehicle information and driving information, and calculate a yaw rate reference value according to the vehicle information and the driving information;

[0045] a calculation module, configured to calculate a cumulative deviation according to the actual yaw rate value and the reference yaw rate value, and determine whether the cumulative deviation is greater than a first threshold;

[0046] The first setting module is used to set first identification information for the current road section when the accumulated deviation is greater than the first threshold.

[0047] An electronic device, comprising:

[0048] A processor and a memory, wherein the memory is used to store at least one instruction, and when the instruction is loaded and executed by the processor, the method for road condition identification as described in any one of the above is implemented.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for road condition identification as described in any one of the above.

[0050] The method for road condition recognition provided by an embodiment of the present invention calculates the actual value of the yaw angular velocity based on the lateral sensor signal, then calculates the yaw angular velocity reference value based on the vehicle information and driving information, and then calculates the cumulative deviation, and determines whether the cumulative deviation is greater than a first threshold value; if so, sets the first identification information for the current road section. The present invention calculates the actual value and reference value of the yaw angular velocity in real time by using the lateral sensor signal and vehicle driving data, and accurately determines the road condition by comparing the deviation between the two, thereby providing road condition information to the driver or the automatic driving system in a timely manner. When a bad road is detected, the system will set identification information, which helps to provide early warning, allowing the driver to take appropriate measures to avoid potential driving risks and ensure driving safety and comfort. At the same time, it does not rely on the visual system that is easily affected by the environment, thereby enhancing the robustness and adaptability of the road condition recognition system.

Brief Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 A flow chart of a method for road condition identification provided by an embodiment of the present invention;

[0053] Figure 2 for Figure 1 A flowchart of a practical expression of S03 in a method for road condition identification is provided;

[0054] Figure 3 A flowchart of another method for road condition identification provided by an embodiment of the present invention;

[0055] Figure 4 A flowchart of another method for road condition identification provided by an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of the structure of a road condition recognition device provided in an embodiment of the present invention. [Specific implementation method]

[0057] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0060] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0061] Road conditions have a significant impact on the safety and comfort of vehicle driving. Bad roads (such as potholes and damaged roads) can cause unstable vehicle driving and increase the risk of traffic accidents. Current road recognition technology mainly relies on image processing technology, which uses cameras and other devices to obtain road images and processes them through computer vision technology. However, this method is greatly affected by weather and lighting conditions, and the accuracy and real-time performance of recognition need to be improved.

[0062] Existing road condition recognition technologies mainly include visual recognition technology and laser radar recognition technology. Visual recognition technology obtains road images through cameras and processes them using computer vision algorithms to identify information such as road signs, lanes and obstacles. However, visual recognition technology performs poorly in low light and bad weather conditions. Laser radar recognition technology generates point cloud images through laser beam reflection, which has high accuracy and high stability, but is costly and has high environmental requirements.

[0063] Therefore, the present invention provides a method for road condition identification to solve the above problems.

[0064] Please refer to Figure 1, is a flow chart of a method for road condition identification provided by an embodiment of the present invention, comprising the following steps:

[0065] Step S01, acquiring a lateral sensor signal, and calculating an actual yaw angular velocity value according to the lateral sensor signal.

[0066] In this embodiment, the yaw rate refers to the rotation rate of the vehicle around its vertical axis, and is an important parameter for evaluating the vehicle's handling stability and identifying road conditions. By analyzing the actual value of the yaw rate, it can be determined whether the vehicle is in an unstable road condition, such as slipping or oversteering, thereby helping the system identify potential bad road conditions.

[0067] This embodiment uses the lateral sensor on the vehicle to monitor and analyze the lateral dynamic behavior of the vehicle during driving. The lateral sensor can capture the lateral acceleration of the vehicle when it turns or is affected by lateral force. After processing, these data can be used to calculate the actual value of the vehicle's yaw rate. Its purpose is to provide dynamic stability information of the vehicle in the lateral direction, help identify whether the vehicle is driving on an uneven or damaged road surface, and thus accurately evaluate the road conditions.

[0068] In some embodiments, the lateral sensor may include a yaw rate gyroscope or a lateral accelerometer to capture the lateral dynamics of the vehicle during driving. The lateral sensor can sense the lateral acceleration of the vehicle, and the actual value of the yaw angular velocity of the vehicle can be calculated based on the lateral acceleration. For example, the actual value of the yaw angular velocity can be calculated according to the formula:

[0069]

[0070] Among them, Yawrate Ay is the actual value of the yaw rate, V is the real-time vehicle speed, and Ay is the lateral acceleration.

[0071] Step S02: acquiring vehicle information and driving information, and calculating a yaw rate reference value according to the vehicle information and driving information.

[0072] In this step, the yaw rate reference value is calculated based on the vehicle information and driving information in order to obtain a theoretical yaw rate value, which reflects the expected yaw rate of the vehicle under ideal road conditions based on its dynamic characteristics and current driving state (such as vehicle speed, steering angle, etc.). This reference value is calculated through the physical model and driving parameters of the vehicle, and can be compared with the actually measured yaw rate to evaluate the driving stability and road conditions of the vehicle. For example, the expected yaw rate of the vehicle under normal driving conditions can be calculated by combining the vehicle dynamics equation with the vehicle wheelbase, speed, front wheel steering angle and other parameters, providing a benchmark for judging whether there are abnormalities in the actual driving process.

[0073] In some embodiments, as mentioned in step S02, the yaw rate reference value is calculated according to the vehicle information and the driving information, which can be specifically:

[0074] Determine characteristic vehicle speed and wheelbase based on vehicle information, and determine real-time vehicle speed and front wheel steering angle based on driving information;

[0075] Calculate the yaw rate reference value according to the formula:

[0076]

[0077] Among them, TargetYawrate ackermann is the yaw rate reference value, V is the real-time vehicle speed, L is the wheelbase, δ is the front wheel turning angle, V ch is the characteristic speed.

[0078] Step S03: calculating the accumulated deviation according to the actual yaw rate value and the yaw rate reference value, and determining whether the accumulated deviation is greater than a first threshold.

[0079] If yes, execute step S04.

[0080] In this embodiment, the actual yaw rate value obtained by the lateral sensor is compared with the yaw rate reference value calculated based on the vehicle information and the driving information. The actual yaw rate value reflects the dynamic response of the vehicle under actual driving conditions, while the yaw rate reference value is the dynamic performance of the vehicle under ideal or expected road conditions. By calculating the deviation between the two values ​​and accumulating them, the difference between the actual driving state of the vehicle and the expected state can be quantified. When the accumulated deviation exceeds the preset first threshold, the system will determine that there may be a road condition problem on the current road section, and then execute step S04 to set the first identification information for the current road section to prompt the driver or the vehicle control system to take corresponding measures.

[0081] In some embodiments, the first threshold value can be set based on data analysis of tests on different bad roads (common bad roads such as Belgian roads, damaged cement roads, and cement cracked roads). For example, on a bad road, at each time of a large deviation, the first deviation between the actual yaw rate value and the reference yaw rate value contributes approximately 0.03 rad / s 2 The accumulated value of the vehicle can be considered to be on a bad road when the vehicle experiences 5-10 large deviations. Therefore, the range of the first threshold can be set to 0.15rad / s 2 -0.3rad / s 2 .

[0082] In some embodiments, when the accumulated deviation does not exceed the preset first threshold, the system will determine that there is no road condition problem on the current road section. At this time, the system can return to execute step S01, continue to obtain the lateral sensor signal, and calculate the actual value of the yaw angular velocity based on the lateral sensor signal.

[0083] Please refer to Figure 2 ,for Figure 1 A flowchart of an actual performance of S03 in a method for road condition recognition is provided. In some embodiments, in order to accurately reflect the road condition changes of the vehicle in a straight-line driving state and reduce the interference caused by normal vehicle operation, it is also possible to determine whether the vehicle is in a turning state, and to exclude the yaw rate change caused by normal turning operation, thereby avoiding misjudgment. That is, as mentioned in step S03, the cumulative deviation is calculated based on the actual yaw rate value and the yaw rate reference value, which can be specifically as follows Figure 2 Steps shown:

[0084] Step S031, obtaining the historical accumulated deviation.

[0085] In this step, the system stores and updates the cumulative sum of the deviations between the actual yaw rate and the reference value calculated previously. This historical cumulative deviation reflects the overall deviation between the actual yaw rate and the expected value during the vehicle's driving over a period of time. By obtaining the historical cumulative deviation, the system can more comprehensively evaluate the vehicle's driving state, rather than just based on a single measurement at the current moment. The purpose of this is to take into account the vehicle's continuous driving behavior when judging the current road condition, thereby improving the accuracy and robustness of road condition recognition. For example, if the vehicle continues to have large deviations over a period of time, the system may believe that the vehicle is driving on a bad road even if the deviation at the current moment is not large. This method helps to avoid misjudgments caused by instantaneous measurement errors or normal vehicle operations (such as turning), thereby providing more accurate road condition information to the driver or vehicle control system.

[0086] Step S032, judging whether the vehicle is in a turning state according to the actual value of the yaw angular velocity.

[0087] If yes, then execute step S033. If no, then execute step S034.

[0088] In this step, before calculating the cumulative deviation, the driving state of the vehicle is first determined to exclude the influence of normal turning operation on the yaw rate. Specifically, when the vehicle turns, the yaw rate will increase significantly. At this time, the yaw rate value measured by the sensor mainly reflects the turning action, rather than the bumpy condition of the road surface. Therefore, in order to ensure the accuracy of road condition identification, it is necessary to exclude data interference during turning. If it is determined that the vehicle is in a turning state, that is, the yaw rate exceeds the preset threshold value e (which can be set at 0.0348 to 0.087 rad / s, equivalent to 2-5 degrees per second), step S033 is executed. The purpose of setting this judgment condition is to more accurately identify bad roads through the deviation of the yaw rate when the vehicle is approximately traveling in a straight line, thereby improving the accuracy and reliability of road condition identification.

[0089] Step S033: determine the difference between the historical accumulated deviation and the first preset value as the accumulated deviation.

[0090] In this embodiment, when it is determined that the vehicle is in a turning state, the system will compare the historical cumulative deviation with a preset value and use the difference between the two as a new cumulative deviation. The purpose of this is to maintain or reduce the cumulative deviation instead of increasing it when the vehicle is turning, thereby avoiding misjudging the yaw rate change caused by turning as a road condition problem.

[0091] Specifically, when the vehicle turns, the yaw rate increases, which usually leads to an increase in the cumulative deviation. However, this increase does not reflect the actual road condition, but is simply the result of the normal turning of the vehicle. Therefore, by executing step S033, the system subtracts the first preset value from the historical cumulative deviation instead of adding the deviation. The first preset value is usually set to a small value to ensure that the impact of the turning action on the cumulative deviation is minimized. For example, the first preset value can be selected as the square value of the cumulative contribution value of the deviation at the moment of large deviation, that is, about 0.0009.

[0092] In some embodiments, each time a turning operation is performed, the accumulated deviation amount decreases by a first preset value. The first preset value should be set as small as possible to reduce the impact of the turning action on the accumulated deviation amount. In this way, the system can more accurately identify the yaw rate changes caused by road bumps rather than changes caused by normal turning operations of the vehicle, thereby improving the accuracy of road condition recognition.

[0093] Step S034: calculating a first deviation between the actual yaw rate value and the reference yaw rate value.

[0094] In this embodiment, if the vehicle is not turning (ie, traveling approximately in a straight line), a first deviation between the actual value of the yaw rate and the reference value is calculated.

[0095] The first deviation reflects the difference between the actual driving state of the vehicle and the expected state. The yaw rate reference value is calculated based on the vehicle dynamics model and cannot fully reflect the yaw rate oscillation caused by road bumps. On bad roads, due to body shaking or steering wheel shaking, the actual yaw rate value will fluctuate greatly. Therefore, the difference between the actual yaw rate value and the yaw rate reference value (i.e., the first deviation) can be used as a judgment condition for entering a bad road.

[0096] Step S035: determine the sum of the square value of the first deviation and the historical cumulative deviation as the cumulative deviation.

[0097] In this embodiment, the square value of the first deviation is added to the historical cumulative deviation to obtain a new cumulative deviation. The square deviation is used to emphasize the influence of a larger deviation and weaken the cumulative amount generated when the deviation is smaller, so that the cumulative amount calculation result is mainly generated by the larger deviation moment, that is, when the yaw rate measurement value has a small deviation from the reference value, it will not have a significant impact on the deviation cumulative value. In this way, the cumulative deviation can better reflect the significant changes when the vehicle is driving on a bad road.

[0098] In some embodiments, the cumulative deviation amount may be calculated according to the formula:

[0099] Accumultion_Deviation+=Deviation 2

[0100] Among them, Accumultion_Deviation is the cumulative deviation, and Deviation is the first deviation.

[0101] Assuming that when the vehicle is driving normally, the first deviation between the actual yaw rate value and the reference yaw rate value is 0.001, then the accumulated amount every 40 milliseconds is only 0.000001, and the accumulated amount generated by the first deviation of 0.001 accounts for only 10% of the total accumulated amount A. -6 / A.

[0102] If the deviation between the yaw rate measurement and the reference value is 0.1, the accumulated amount every 40 milliseconds is 0.01, and the accumulated amount generated by the first deviation of 0.1 accounts for 10% of the total accumulated amount A. -2 / A.

[0103] That is, the weight of the accumulated amount produced by the first deviation of 0.001 in the total accumulated amount A is only 0.01% (1% without square) of the weight of the accumulated amount produced by the first deviation of 0.1 in the total accumulated amount A. Therefore, the weight of the accumulated amount produced by the squared smaller deviation in the total accumulated amount is weakened compared with the accumulated amount produced without square.

[0104] Based on the above technical solution, this embodiment can evaluate based on the vehicle's past driving data, thereby providing more comprehensive road condition information. Secondly, by judging whether the vehicle is in a turning state, the normal yaw rate change generated when turning can be excluded to avoid misjudging it as bumps caused by bad roads. Finally, only when the vehicle is driving in a straight line will the deviation between the actual yaw rate and the reference value be calculated, and the square of this deviation is added to the historical cumulative deviation to form a new cumulative deviation. The influence of larger deviations is emphasized, while the influence of small deviations is weakened, so that the cumulative deviation can better reflect the actual driving conditions of the vehicle on bad roads, improving the accuracy and robustness of road condition recognition.

[0105] Step S04, setting first identification information for the current road section.

[0106] In this embodiment, when the accumulated deviation exceeds a preset first threshold, the system will determine that there may be road condition problems on the current road section. At this time, first identification information is set for the current road section to prompt the driver or vehicle control system to take corresponding measures.

[0107] Based on the above technical solution, the method for road condition identification provided by the embodiment of the present invention calculates the actual value of the yaw angular velocity according to the lateral sensor signal, then calculates the yaw angular velocity reference value according to the vehicle information and driving information, and then calculates the cumulative deviation, and determines whether the cumulative deviation is greater than the first threshold value; if so, the first identification information is set for the current road section. The present invention calculates the actual value and reference value of the yaw angular velocity in real time by using the lateral sensor signal and vehicle driving data, and accurately determines the road condition by comparing the deviation between the two, thereby providing road condition information to the driver or the automatic driving system in a timely manner. When a bad road is detected, the system will set identification information, which helps to provide early warning, so that the driver can take appropriate measures to avoid potential driving risks and ensure driving safety and comfort. At the same time, it does not rely on the visual system that is easily affected by the environment, which enhances the robustness and adaptability of the road condition identification system.

[0108] Please refer to Figure 3 , which is a flow chart of another method for identifying road conditions provided by an embodiment of the present invention. Based on the above embodiments, in some embodiments, in order to eliminate interference factors in normal driving, reduce misjudgment, and improve the accuracy of bad road identification, the following can also be performed: Figure 3 Steps shown:

[0109] Step S11, acquiring a lateral sensor signal, and calculating an actual yaw angular velocity value according to the lateral sensor signal.

[0110] Step S12, acquiring vehicle information and driving information, and calculating a yaw rate reference value according to the vehicle information and driving information.

[0111] Step S13, determining the actual value of the lateral acceleration according to the lateral sensor signal.

[0112] In this embodiment, the lateral acceleration experienced by the vehicle during driving is measured and determined by using a lateral sensor installed on the vehicle, such as a lateral accelerometer. The lateral acceleration refers to the acceleration of the vehicle in the lateral direction, which can reflect the lateral force generated by the vehicle due to the inclination of the road surface. By comparing the actually measured lateral acceleration with the reference value calculated based on the vehicle dynamics model, it can be determined whether the vehicle is on an inclined road surface, and then whether the vehicle is in a normal driving state, so as to improve the accuracy of road condition recognition.

[0113] Step S14: calculating a lateral acceleration reference value according to the yaw angular velocity reference value, and calculating a second deviation between the lateral acceleration actual value and the lateral acceleration reference value.

[0114] In this embodiment, in order to more accurately determine whether the vehicle is in a normal driving state, especially whether the road surface is inclined, the vehicle dynamics model and the yaw rate reference value are first used to calculate the expected value of the lateral acceleration, that is, the lateral acceleration reference value. Then, the actual lateral acceleration value of the vehicle is obtained through the lateral sensor, that is, the actual lateral acceleration value. Next, the deviation between the actual value and the reference value is calculated, that is, the second deviation. This deviation helps to identify whether the vehicle has abnormal lateral acceleration due to an inclined or uneven road surface. If the second deviation exceeds the normal range, it indicates that the vehicle is on an inclined road surface.

[0115] In some embodiments, the lateral acceleration reference value may be calculated according to the formula:

[0116]

[0117] Among them, Ay' is the lateral acceleration reference value, V is the real-time vehicle speed, L is the wheelbase, δ is the front wheel turning angle, V ch is the characteristic speed.

[0118] Step S15, determining the driving direction and real-time vehicle speed according to the driving information.

[0119] In this embodiment, the driving direction and speed of the vehicle may be determined through information obtained by the vehicle's navigation system or other sensors.

[0120] Step S16, judging whether the vehicle is in a normal driving state according to the second deviation, the driving direction and the real-time vehicle speed.

[0121] If not, execute step S17, and if so, execute step S18.

[0122] In this embodiment, whether the vehicle is in a normal driving state is determined based on the second deviation, the driving direction and the real-time vehicle speed, and whether to perform bad road identification is determined accordingly. If the vehicle is not in a normal driving state, such as when the road surface is inclined, the vehicle speed is too low or the vehicle is reversing, the system will execute step S17, clear the historical accumulated deviation, and return to step S11 to reacquire the lateral sensor signal to avoid bad road identification in an abnormal driving state, thereby reducing misjudgment. On the contrary, if the vehicle is in a normal driving state, the system will continue to execute step S18, calculate the accumulated deviation between the actual value of the yaw angular velocity and the reference value, and determine whether there is a bad road based on the comparison result with the first threshold. Such a design ensures the accuracy of bad road identification. The system will only perform corresponding identification and processing when the vehicle is driving normally and encounters a potential bad road.

[0123] Step S17, clearing the historical accumulated deviation.

[0124] After executing step S17, the process returns to executing step S11 to obtain the lateral sensor signal.

[0125] Step S18, calculating the accumulated deviation according to the actual yaw rate value and the yaw rate reference value, and determining whether the accumulated deviation is greater than a first threshold.

[0126] If yes, execute step S19.

[0127] Step S19, setting first identification information for the current road section.

[0128] Based on the above technical solution, this embodiment adds multiple steps before calculating the cumulative deviation, and these steps together constitute a comprehensive judgment mechanism to ensure that bad road identification is performed only when the vehicle is in a normal driving state. First, the actual value of the lateral acceleration is determined by the lateral sensor signal, and compared with the lateral acceleration reference value calculated based on the yaw angular velocity reference value to obtain a second deviation. Then, combined with the vehicle's driving direction and real-time vehicle speed, the system can more accurately determine whether the vehicle is driving normally. If the vehicle is not in a normal driving state, such as turning, reversing or driving on an inclined road, the system will clear the historical cumulative deviation and start collecting lateral sensor signals again to avoid misjudgment. Only when the vehicle is driving normally will the cumulative deviation between the actual value and the reference value of the yaw angular velocity continue to be calculated, and the road condition is judged accordingly. This embodiment improves the accuracy and reliability of bad road identification by eliminating interference factors in abnormal driving conditions, thereby providing more accurate road condition information for the driver or the automatic driving system and enhancing driving safety.

[0129] Please refer to Figure 4, which is a flow chart of another method for identifying road conditions provided by an embodiment of the present invention. Based on the above embodiments, in some embodiments, in order to improve the accuracy and reliability of bad road identification, thereby providing more accurate road condition information to the driver or the automatic driving system, a comprehensive determination of bad roads can also be achieved through dual identification information. That is, after executing step S04 to set the first identification information for the current road section, the following can also be executed: Figure 4 Steps shown:

[0130] Step S21, obtaining the wheel acceleration of each wheel and the count value corresponding to each wheel.

[0131] In this step, wheel acceleration refers to the acceleration of the wheel around its rotation axis, which can reflect the change in force when the wheel contacts the road surface, thereby providing information about the smoothness of the road surface. The count value is used to record and quantify the performance of the wheel under specific conditions. By analyzing the change in the count value, the system can determine whether the vehicle is experiencing a bad road. The purpose of this step is to more comprehensively evaluate the driving status of the vehicle, especially when identifying a bad road. By considering the data of all wheels, the accuracy and reliability of identification can be improved.

[0132] In some embodiments, the wheel speed sensor installed on the wheel can collect the vehicle wheel speed signal in real time, and perform pre-processing such as filtering and denoising, data smoothing, etc. on the signal to eliminate the interference caused by random noise and non-road factors in the signal. The collected wheel speed signal is calculated and processed to obtain the real-time wheel acceleration signal of the wheel.

[0133] Step S22: for each wheel among all the wheels, the count value is updated according to the magnitude and direction of the wheel acceleration.

[0134] In this embodiment, the system checks the magnitude and direction of the wheel acceleration of each wheel to determine whether the wheel encounters a bumpy or uneven road surface. According to the characteristics of the wheel acceleration, the system updates the count value corresponding to each wheel accordingly. These count values ​​accumulate all the bump events encountered by the wheel during driving, which helps the system determine whether the vehicle is driving on a bad road. If the count value exceeds a preset threshold, the system can set a second identification information for the current road section, so as to take corresponding measures, such as adjusting the vehicle suspension system or reminding the driver to pay attention.

[0135] In some embodiments, as mentioned in step S22, updating the count value according to the magnitude and direction of the wheel acceleration may specifically include the following steps:

[0136] Step S221, determining whether the direction of the wheel acceleration changes.

[0137] If not, execute step S222; if so, execute step S223.

[0138] In this step, by continuously monitoring the direction of wheel acceleration, it is identified whether the wheel experiences frequent contact force changes and direction changes due to driving on a bad road. When the wheel passes through a bad road, due to the unevenness of the road surface, the contact force between the wheel and the ground will fluctuate, resulting in frequent changes in the direction and magnitude of the wheel acceleration. If the direction of the wheel acceleration does not change, it means that the wheel is driving on a relatively smooth road surface. At this time, step S222 is executed to reduce the count value by the second preset value, which helps to avoid erroneous identification of a bad road under non-bad road conditions.

[0139] On the contrary, if the direction of the wheel acceleration changes, this may be a sign of a bad road, and the system will execute step S223 and enter the next judgment condition for further analysis to determine whether it is a bad road. This method helps to improve the accuracy of bad road identification and ensure that the system will only respond accordingly when the wheel actually experiences bumps caused by bad roads.

[0140] Step S222, reducing the count value by a second preset value.

[0141] Step S223, determining whether the wheel acceleration exceeds a third threshold.

[0142] If the wheel acceleration exceeds the third threshold, step S224 is executed; if the wheel acceleration does not exceed the third threshold, the process returns to step S222 to reduce the count value by the second preset value.

[0143] Step S224, increasing the count value by a third preset value.

[0144] In this embodiment, the system monitors the change in direction of wheel acceleration, which is usually caused by a sudden change in the contact force between the wheel and the ground when driving on a bad road. If a change in direction is detected, the system will further determine whether the amplitude of the wheel acceleration exceeds a preset third threshold value, which is carefully selected based on vehicle characteristics and test data to distinguish between normal road surface fluctuations and violent fluctuations caused by bad roads. If the wheel acceleration exceeds the third threshold value, it indicates that the wheel may have encountered a large bump, and the count value will increase by the third preset value to reflect this situation. This design helps the system to more accurately identify bad roads, avoid misjudgment under non-bad road conditions, and ensure timely response when actually encountering bad roads. In this way, the system can more effectively monitor and respond to different road conditions, improving the safety and comfort of vehicle driving.

[0145] Based on the above technical solution, this embodiment first determines whether the direction of the wheel acceleration has changed. If it has not changed, the count value will be reduced by the second preset value, which means that the vehicle is driving on a relatively smooth road. If the direction of the wheel acceleration has changed, the system further determines whether the wheel acceleration exceeds the third threshold, which usually means that the vehicle has encountered a large bump or uneven road surface. If it exceeds the threshold, the count value will increase by the third preset value, which helps to quickly identify bad road conditions and can dynamically adjust the count value according to the actual driving conditions of the vehicle, so as to more accurately reflect the road conditions, improve the timeliness and reliability of bad road identification, and ultimately help improve the driving safety and ride comfort of the vehicle.

[0146] Step S23, summing up all updated count values, and determining whether the summation result is greater than a second threshold.

[0147] If yes, execute step S24; if no, return to execute step S21 to continue monitoring the wheel acceleration of each wheel.

[0148] Step S24, setting second identification information for the current road section.

[0149] In this embodiment, the updated count values ​​of all wheels are summarized and summed, and it is evaluated whether this sum exceeds a preset second threshold. This second threshold is set according to the driving characteristics of the vehicle and the road condition test data, and is used to determine whether the vehicle as a whole may be traveling on a bad road. If the sum of the count values ​​of all wheels exceeds this threshold, it indicates that the vehicle is likely to have encountered a bad road, so the system will execute step S24 to set the second identification information for the current road section. If the sum of the count values ​​does not exceed the second threshold, it means that although the wheels may have encountered some bumps, the vehicle as a whole may still be traveling on a normal road surface, so the system will return to step S21 and continue to monitor the wheel acceleration of each wheel to continuously evaluate the road condition.

[0150] Step S25: Mark the road section having the first identification information and the second identification information as a bad road.

[0151] In this embodiment, whether a specific road section is a bad road is determined by combining the previous analysis results. The first identification information comes from the deviation analysis of the yaw angular velocity, and the second identification information comes from the monitoring of the wheel acceleration and the evaluation of the count value. When two independent identification information both indicate that the vehicle has encountered a bad road condition, the system marks the road section as a bad road. The purpose of this is to provide a more accurate and reliable bad road identification result, thereby helping the driver or the automatic driving system to take appropriate countermeasures, such as adjusting the driving route or driving style, to improve driving safety and comfort. By combining multiple sensor data and set thresholds, this method can reduce false alarms and ensure that bad roads are only marked when the vehicle is actually driving on a bad road.

[0152] Based on the above technical solution, this embodiment improves the accuracy and reliability of bad road identification by setting dual identification information, thereby providing the driver or the automatic driving system with more accurate road condition information and enhancing driving safety.

[0153] Please refer to Figure 5 , is a schematic diagram of the structure of a road condition recognition device provided by an embodiment of the present invention, and the road condition recognition device may include:

[0154] A first acquisition module 100 is used to acquire a lateral sensor signal and calculate an actual value of a yaw angular velocity according to the lateral sensor signal;

[0155] The second acquisition module 200 is used to acquire vehicle information and driving information, and calculate a yaw rate reference value according to the vehicle information and driving information;

[0156] The calculation module 300 is used to calculate the accumulated deviation according to the actual value of the yaw angular velocity and the reference value of the yaw angular velocity, and determine whether the accumulated deviation is greater than a first threshold;

[0157] The first setting module 400 is used to set first identification information for the current road section when the accumulated deviation is greater than a first threshold.

[0158] Based on the above embodiments, in a specific embodiment, the calculation module 300 can be specifically used for:

[0159] Get the historical cumulative deviation;

[0160] Judging whether the vehicle is in a turning state according to the actual value of the yaw angular velocity;

[0161] If not, calculating a first deviation between the actual yaw rate value and the reference yaw rate value;

[0162] The sum of the square value of the first deviation and the historical cumulative deviation is determined as the cumulative deviation.

[0163] Based on the above embodiment, in a specific embodiment, the calculation module 300 can also be used for:

[0164] determining an actual value of lateral acceleration according to a lateral sensor signal;

[0165] Calculating a lateral acceleration reference value according to the yaw angular velocity reference value, and calculating a second deviation between the lateral acceleration actual value and the lateral acceleration reference value;

[0166] Determine the driving direction and real-time vehicle speed based on driving information;

[0167] Determining whether the vehicle is in a normal driving state according to the second deviation, the driving direction and the real-time vehicle speed;

[0168] If not, the historical accumulated deviation is cleared to zero, and the process returns to the step of obtaining the lateral sensor signal;

[0169] If yes, the step of calculating the accumulated deviation amount according to the actual yaw rate value and the reference yaw rate value is executed.

[0170] Based on the above embodiment, in a specific embodiment, the calculation module 300 can also be used for:

[0171] The difference between the historical accumulated deviation amount and the first preset value is determined as the accumulated deviation amount.

[0172] Based on the above embodiment, in a specific embodiment, the second acquisition module 200 can be specifically used for:

[0173] Determine characteristic vehicle speed and wheelbase based on vehicle information, and determine real-time vehicle speed and front wheel steering angle based on driving information;

[0174] Calculate the yaw rate reference value according to the formula:

[0175]

[0176] Among them, TargetYawrate ackermann is the yaw rate reference value, V is the real-time vehicle speed, L is the wheelbase, δ is the front wheel turning angle, V ch is the characteristic speed.

[0177] Based on the above embodiment, in a specific embodiment, the device may further include:

[0178] A third acquisition module is used to acquire the wheel acceleration of each wheel and the count value corresponding to each wheel;

[0179] An updating module, for updating the count value for each wheel among all wheels according to the magnitude and direction of the wheel acceleration;

[0180] A judgment module, used to sum all updated count values ​​and judge whether the sum calculation result is greater than a second threshold;

[0181] A second setting module, used for setting second identification information for the current road section when the sum calculation result is greater than a second threshold;

[0182] The marking module is used to mark a road section having the first identification information and the second identification information as a bad road.

[0183] Based on the above embodiment, in a specific embodiment, the update module can be specifically used for:

[0184] Determine whether the direction of wheel acceleration has changed;

[0185] If not, the count value is reduced by a second preset value;

[0186] If yes, determining whether the wheel acceleration exceeds a third threshold;

[0187] If the wheel acceleration exceeds a third threshold, the count value is increased by a third preset value;

[0188] If the wheel acceleration does not exceed the third threshold, the count value is reduced by a second preset value.

[0189] This embodiment provides an electronic device, including a processor and a memory, the memory is used to store at least one instruction, and when the instruction is loaded and executed by the processor, the method for road condition recognition described above is implemented. Its execution method and beneficial effects are similar and will not be repeated here.

[0190] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned road condition identification method is implemented. Its execution method and beneficial effects are similar and will not be repeated here.

[0191] It should be noted that although the above describes the various steps in a specific order, it does not mean that the various steps must be executed in the above specific order. In fact, some of these steps can be executed concurrently or even in a different order as long as the required functions can be achieved.

[0192] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for road condition recognition, characterized in that: include: Acquire a lateral sensor signal, and calculate an actual value of the yaw angular velocity according to the lateral sensor signal; Acquiring vehicle information and driving information, and calculating a yaw rate reference value according to the vehicle information and the driving information; calculating a cumulative deviation amount according to the actual yaw rate value and the reference yaw rate value, and determining whether the cumulative deviation amount is greater than a first threshold; If so, the first identification information is set for the current road section.

2. The method according to claim 1, characterized in that The calculating the accumulated deviation according to the actual yaw rate value and the reference yaw rate value comprises: Get the historical cumulative deviation; determining whether the vehicle is in a turning state according to the actual value of the yaw angular velocity; If not, calculating a first deviation between the actual yaw rate value and the reference yaw rate value; The sum of the square value of the first deviation and the historical cumulative deviation is determined as the cumulative deviation.

3. The method according to claim 2, characterized in that Before calculating the accumulated deviation amount according to the actual yaw rate value and the reference yaw rate value, the method further includes: determining an actual value of lateral acceleration according to the lateral sensor signal; Calculating a lateral acceleration reference value according to the yaw angular velocity reference value, and calculating a second deviation between the lateral acceleration actual value and the lateral acceleration reference value; Determine the driving direction and real-time vehicle speed according to the driving information; determining whether the vehicle is in a normal driving state according to the second deviation, the driving direction and the real-time vehicle speed; If not, the historical accumulated deviation is cleared to zero, and the process returns to the step of acquiring the lateral sensor signal; If yes, the step of calculating the accumulated deviation according to the actual yaw rate value and the reference yaw rate value is performed.

4. The method according to claim 2, characterized in that: When it is determined according to the actual value of the yaw angular velocity that the vehicle is in a turning state, the method further includes: The difference between the historical accumulated deviation amount and a first preset value is determined as the accumulated deviation amount.

5. The method according to claim 1, characterized in that: The calculating the yaw rate reference value according to the vehicle information and the driving information includes: Determine characteristic vehicle speed and wheelbase according to the vehicle information, and determine real-time vehicle speed and front wheel turning angle according to the driving information; The yaw rate reference value is calculated according to the formula: Among them, TargetYawrate ackermann is the yaw rate reference value, V is the real-time vehicle speed, L is the wheelbase, δ is the front wheel turning angle, V ch is the characteristic vehicle speed.

6. The method according to any one of claims 1 to 5, characterized in that: After setting the first identification information for the current road section, the method further includes: Obtaining the wheel acceleration of each wheel and the count value corresponding to each wheel; For each of all the wheels, updating the count value according to the magnitude and direction of the wheel acceleration; Sum all the updated count values, and determine whether the summation result is greater than a second threshold; If yes, set the second identification information for the current road section; The road segment having the first identification information and the second identification information is marked as a bad road.

7. The method according to claim 6, characterized in that The updating of the count value according to the magnitude and direction of the wheel acceleration includes: determining whether the direction of the wheel acceleration changes; If not, reducing the count value by a second preset value; If yes, determining whether the wheel acceleration exceeds a third threshold; If the wheel acceleration exceeds the third threshold, increasing the count value by a third preset value; If the wheel acceleration does not exceed the third threshold, the count value is reduced by the second preset value.

8. A device for road condition recognition, characterized in that: include: A first acquisition module, used for acquiring a lateral sensor signal and calculating an actual value of a yaw angular velocity according to the lateral sensor signal; a second acquisition module, configured to acquire vehicle information and driving information, and calculate a yaw rate reference value according to the vehicle information and the driving information; a calculation module, configured to calculate a cumulative deviation according to the actual yaw rate value and the reference yaw rate value, and determine whether the cumulative deviation is greater than a first threshold; The first setting module is used to set first identification information for the current road section when the accumulated deviation is greater than the first threshold.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store at least one instruction, and when the instruction is loaded and executed by the processor, the method for road condition identification as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for road condition identification as described in any one of claims 1 to 7 is implemented.

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