CN110952427A - Modularized intelligent road sensing equipment and system based on driving feeling - Google Patents
Modularized intelligent road sensing equipment and system based on driving feeling Download PDFInfo
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Abstract
The invention provides a modularized intelligent road sensing device and system based on driving feeling, comprising a sensing unit, a positioning unit and a speed fitting unit; the sensing unit, the positioning unit and the speed fitting unit are respectively connected with the data processing unit; the sensing unit is used for acquiring pavement acoustic signals and tire pressure change signals; the positioning unit is used for providing positioning information; the speed fitting unit is used for providing the driving speed; the data processing unit is used for carrying out data fusion and analysis on the collected road acoustic signal information, the tire pressure change signal information, the positioning information and the driving speed information. According to the sensing unit, the positioning unit and the speed fitting unit, the driving feeling of 'ear listening' and 'body feeling' is simulated, and the road surface information is real and effective. And the data processing unit analyzes and outputs a simulation information result, so that the road driving comfort and the road formation safety can be accurately evaluated, and data information service is provided for public trips.
Description
Technical Field
The invention relates to the technical field of road engineering sensing and measurement, in particular to modular road surface intelligent sensing equipment and system based on driving feeling.
Background
Road traffic is an important ring in the traffic and transportation industry, the total mileage of the highway in China reaches 484.65 kilometers by 2018, the total mileage of the highway reaches 14.26 kilometers by 14 kilometers, and the highway is the first in the world. Such a huge road traffic system provides a great challenge to the management of roads in China. However, the conventional manual detection method has long detection time, high labor intensity, low detection speed and influences normal traffic order.
Patent CN110516564A discloses a road surface detection method and device, relating to the technical field of image processing, the method includes: acquiring the advancing direction of the motor vehicle and point cloud data on two sides of the advancing direction in real time through a laser radar as initial point cloud data; preprocessing the initial point cloud data to obtain point cloud data in a preset space range as target point cloud data; and carrying out data processing on the target point cloud data by using a plane detection algorithm to obtain road surface data. By the method, the condition of the road surface can be detected in real time during automatic driving, and the driving path is planned for the automatic driving.
Patent CN104878680B discloses a road surface detection device, which comprises a detection vehicle, a cross bar mounted at the lower part of the front end of the detection vehicle, a plurality of laser displacement sensors, an a/D conversion module, a microprocessor module, a storage module, a wireless transmission module and a GPS module; a plurality of laser displacement sensors are arranged on the cross rod at equal intervals; the output end of the laser displacement sensor is connected with the microprocessor module and the storage module respectively through the A/D conversion module; the output end of the microprocessor module is connected with the wireless transmission module; and the data transmission end of the GPS module is connected with the data transmission end of the microprocessor module. The scheme has high precision, transmits data to the remote center in real time for processing, has good real-time performance, can conveniently adjust the use structure according to actual needs, and has good popularization and application prospects.
However, in the prior art, automatic detection is generally performed on the road surface quality by adopting measurement modes such as laser and image, but the laser is greatly influenced by weather and the detection condition is limited; image recognition mostly relies on manual assistance for making decisions. The evaluation result obtained by the measuring mode of laser and image is not completely consistent with the real quality condition of the road surface and the road surface driving experience of the driver and the passengers; therefore, the detection mode cannot truly reflect the driving comfort, safety and road service life of the vehicle, and cannot provide a real and effective data support for a decision maker.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the modularized intelligent road sensing equipment and system based on the driving feeling, and solves the problem that the detection result cannot truly reflect the driving comfort and safety of the vehicle.
The invention provides modular intelligent road sensing equipment based on driving feeling, which comprises a sensing unit, a positioning unit and a speed fitting unit, wherein the sensing unit is used for sensing the driving feeling of a driver; the sensing unit, the positioning unit and the speed fitting unit are respectively connected with the data processing unit; the sensing unit is used for acquiring pavement acoustic signals and tire pressure change signals; the positioning unit is used for providing positioning information; the speed fitting unit is used for providing the driving speed; the data processing unit is used for carrying out data fusion and analysis on the collected road surface tire noise information, road surface vibration information, positioning information and driving speed information.
Furthermore, the sensing unit comprises a microphone sensor and a dynamic tire pressure sensor, wherein the microphone sensor is arranged on the vehicle and positioned at the position of a rear tire of the vehicle and is used for acquiring a road surface acoustic signal generated by friction between the tire and a road surface in the driving process; the dynamic tire pressure sensor is arranged on a rear tire of the vehicle and used for collecting a tire pressure change signal in the tire in the driving process of the vehicle; the microphone sensor and the dynamic tire pressure sensor are respectively connected with the signal conditioning circuit, the output end of the signal conditioning circuit is connected with the AD collector, and the output end of the AD collector is connected with the data processor unit.
Furthermore, the positioning unit comprises a GNSS receiver, the GNSS receiver is respectively connected to the data processing unit, and the GNSS receiver is configured to acquire time, position, and traveling speed information.
Furthermore, the positioning unit also comprises a nine-axis sensor and a binocular camera, and the nine-axis sensor and the binocular camera are respectively connected with the data processing unit; acquiring acceleration information, rotation angular velocity information and magnetic field direction information of a vehicle by a nine-axis sensor to determine an initial inertial navigation initial coordinate; the binocular camera is used for shooting the position information of the nearest corner point in the advancing direction of the vehicle in the driving process.
Further, the speed fitting unit comprises a wheel encoder and a CAN bus protocol analysis circuit, and the wheel encoder is connected with the data processing unit; the CAN bus protocol analysis circuit is respectively connected with the automobile OBD interface and the data processing unit, and is used for reading the driving speed, the engine speed and the mileage information of the automobile and sending the read information to the data processing unit.
Further, the wheel encoder is a photoelectric rotary pulse encoder, the photoelectric rotary pulse encoder is installed on a rear wheel shaft of the vehicle, and a trigger pulse generated by the wheel encoder is used for generating running distance information of the vehicle and transmitting the running distance information to the data processing unit; the trigger pulse generated by the wheel encoder is also used for triggering the sensing unit to acquire road surface data.
The modularized intelligent road sensing system based on driving feeling comprises a data processing unit, an RAM (random access memory) storage unit, an acoustic signal processing algorithm unit, a tire pressure signal processing algorithm unit, a positioning algorithm unit and a driving speed fitting algorithm unit, wherein the RAM storage unit, the acoustic signal processing algorithm unit, the tire pressure signal processing algorithm unit, the positioning algorithm unit and the driving speed fitting algorithm unit are connected with a processor; the RAM storage unit is respectively connected with the acoustic signal processing algorithm unit, the tire pressure signal processing algorithm unit, the positioning algorithm unit and the driving speed fitting algorithm unit and is used for caching the digital signals of the sensing unit, the positioning unit and the speed fitting unit; the acoustic signal processing algorithm unit calculates the road surface structure depth according to the collected road surface acoustic signals; the tire pressure signal processing algorithm unit calculates the international flatness of the road surface according to the tire pressure change signals acquired by the dynamic tire pressure sensor in the vehicle advancing process; the positioning algorithm unit calculates the position information of the vehicle according to the positioning information provided by the positioning unit; and the running speed fitting algorithm unit calculates the current running speed information by using the output signal of the speed fitting unit and the output speed signal of the GNSS receiver.
Further, the calculation of the road surface structure depth by the acoustic signal processing algorithm unit specifically comprises: weighting and filtering the sensed acoustic signals in a frequency range of 0-2 KHZ so as to gain or attenuate the acoustic signals of each frequency point in sound pressure level; then, performing wavelet transformation on the weighted and filtered acoustic signals to a frequency domain, and passing the acoustic signals through a low-pass filter with the cut-off frequency of 700 HZ; windowing is carried out on the filtered frequency domain acoustic signals, first principal component signals are extracted through a principal component analysis method, and finally the peri signals are predicted through probability radial basis functions based on Markov Monte Carlo to obtain the value of the pavement microstructure depth.
Further, the calculation of the international road surface flatness by the tire pressure signal processing algorithm unit specifically comprises the following steps: when the vehicle is in a static state, the data processing unit measures the acceleration and the direction of the current vibration of the vehicle through the nine-axis sensor, measures the tire pressure change in the current tire of the vehicle through the dynamic tire pressure sensor, and calculates the conversion coefficient of the vibration of the vehicle according to the acceleration and the tire pressure change information; acquiring dynamic tire pressure signals on a road surface with a known international flatness index, and obtaining a conversion coefficient of the road surface flatness by combining a conversion system of the acceleration of a vehicle and the vibration of the vehicle; and finally, calculating the international road surface flatness index through the dynamic tire pressure sensor and the nine-axis sensor.
Further, the specific method for positioning by the positioning algorithm unit is as follows: in the area with stronger GPS signals, the GNSS receiver CAN obtain the effective position information and speed information of the vehicle in the running process, and simultaneously, the CAN bus protocol analysis circuit reads the real-time speed of the vehicle from the OBD interface of the vehicle; carrying out error correction and Kalman filtering processing on the speed data obtained by analyzing the CAN bus by using the speed data obtained by the GNSS receiver; in a region with weak GPS signals, a nine-axis sensor collects acceleration information, rotation angular velocity information and magnetic field direction information of a vehicle to determine initial inertial navigation initial coordinates; the binocular camera is used for shooting the position information of a nearest corner point in the advancing direction of the vehicle in the driving process, extracting the position information of the corner point through image features and establishing a three-dimensional coordinate; the processor updates the inertial navigation position of the vehicle by converting the coordinate into the difference value of the vehicle positions at the adjacent moments through the same reference point position information acquired by the vehicle at different positions at different moments.
According to the technical scheme, the invention has the beneficial effects that:
1. the invention provides modular intelligent road sensing equipment based on driving feeling, which comprises a sensing unit, a positioning unit and a speed fitting unit, wherein the sensing unit is used for sensing the driving feeling of a driver; the sensing unit, the positioning unit and the speed fitting unit are respectively connected with the data processing unit; the sensing unit is used for acquiring pavement acoustic signals and tire pressure change signals; the positioning unit is used for providing positioning information; the speed fitting unit is used for providing the driving speed; the data processing unit is used for carrying out data fusion and analysis on the collected road surface tire noise information, road surface vibration information, positioning information and driving speed information. According to the sensing unit, the positioning unit and the speed fitting unit, the driving feeling of 'ear listening' and 'body feeling' is simulated, and the road surface information is real and effective. And the data processing unit analyzes and outputs a simulation information result so as to accurately evaluate the road driving comfort and the road formation safety and provide data information service for public trips.
2. The modularized intelligent road sensing system based on the driving feeling can obtain effective position information and speed information of a vehicle in the driving process through the GNSS receiver in an area with stronger GPS signals; when the vehicle runs to an area with weak GPS signals, the position information of the same reference point acquired by the vehicle at different positions at different moments is obtained through the nine-axis sensor and the binocular camera, and the position information is converted into the difference value of the vehicle positions at adjacent moments through coordinates, so that the inertial navigation position of the vehicle is updated, the integral error of inertial navigation is reduced, and high-precision positioning is achieved.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a circuit structure block diagram of a modular road surface intelligent sensing device based on driving feeling.
Fig. 2 is a schematic view of an application scenario of the modular road surface intelligent sensing device based on driving feeling.
Fig. 3 is a schematic physical structure diagram of the modular road surface intelligent sensing device based on the driving feeling.
Fig. 4 is a circuit structure block diagram of a modular road surface intelligent sensing system based on driving feeling.
Reference numerals:
the system comprises a wheel encoder 1, a binocular camera 2, a microphone sensor 3, a dynamic tire pressure sensor 4, a shell 5, a first module 51, a second module 52, a third module 53 and a fourth module 54.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Example 1
As shown in fig. 1, the modular intelligent road sensing device based on driving feeling comprises a sensing unit, a positioning unit and a speed fitting unit; the sensing unit, the positioning unit and the speed fitting unit are respectively connected with the data processing unit; the sensing unit is used for acquiring pavement acoustic signals and tire pressure change signals; the positioning unit is used for providing positioning information; the speed fitting unit is used for providing the driving speed; the data processing unit is used for carrying out data fusion and analysis on the collected road surface tire noise information, road surface vibration information, positioning information and driving speed information. The driving feeling of 'ear listening' and 'body feeling' is simulated according to the sensing unit, the positioning unit and the speed fitting unit, and the simulation information result is analyzed and output through the data processing unit so as to be used for evaluating the road driving comfort degree and the road forming safety degree and provide data information service for public trips.
The sensing unit comprises a microphone sensor and a dynamic tire pressure sensor, wherein the microphone sensor is arranged on the vehicle and positioned at the position of a rear tire of the vehicle, and is used for acquiring a road surface acoustic signal generated by friction between the tire and a road surface in the driving process, such as: acoustic signals such as road noise, wind noise, engine noise, and traffic noise. The dynamic tire pressure sensor is arranged on a rear tire of the vehicle and used for collecting a tire pressure change signal in the tire in the driving process of the vehicle; the microphone sensor and the dynamic tire pressure sensor are respectively connected with the signal conditioning circuit, and the signal conditioning circuit isolates, filters and amplifies received signals of the microphone sensor and the dynamic tire pressure sensor to form a conditioning signal suitable for transmission and signal processing; the output end of the signal conditioning circuit is connected with an AD collector, and the AD collector converts the analog signal quantity transmitted by the signal conditioning circuit into a digital signal; the output end of the AD collector is connected with the data processor unit.
The speed fitting unit comprises a wheel encoder and a CAN bus protocol analysis circuit, and the wheel encoder is connected with the data processing unit; the CAN bus protocol analysis circuit is respectively connected with the automobile OBD interface and the data processing unit and is used for reading the information of the driving speed, the engine speed and the mileage of the automobile and sending the read information to the data processing unit. And analyzing the speed data obtained by the CAN bus, and performing Kalman filtering and fitting treatment to obtain accurate speed information.
The wheel encoder is a photoelectric rotary pulse encoder which is arranged on a rear wheel shaft of the vehicle, and a trigger pulse generated by the wheel encoder is used for generating the running distance information of the vehicle and transmitting the running distance information to the data processing unit; the trigger pulse generated by the wheel encoder is also used for triggering the sensing unit to acquire road surface data.
The positioning unit comprises a GNSS receiver which is respectively connected with the data processing unit and is used for acquiring time, position and driving speed information. In a region with stronger GPS signals, the effective position information and speed information of the vehicle running process CAN be obtained through the GNSS receiver, meanwhile, the real-time speed of the vehicle is read from the vehicle OBD interface through the CAN bus protocol analysis circuit, and the data processor unit carries out vehicle positioning according to the effective position information and speed information of the vehicle running process and the real-time speed calculation of the vehicle read from the vehicle OBD interface.
The positioning unit further comprises a nine-axis sensor and a binocular camera, and the nine-axis sensor and the binocular camera are respectively connected with the data processing unit. The nine-axis sensor consists of a three-axis acceleration sensor, a three-axis gyroscope and a three-axis magnetometer and is used for sensing motion information of a vehicle body in the driving process in real time, wherein the motion information comprises a course angle, a pitch angle, an inclination angle and a three-axis angular velocity signal of the vehicle, X, Y, Z three-axis acceleration signals and included angle direction signals in four directions of south, east, west and north. When the vehicle travels to an area where the GPS signal is weak, the speed information cannot be acquired by the GNSS receiver. The nine-axis sensor collects acceleration information, rotation angular velocity information and magnetic field direction information of the vehicle to determine an initial inertial navigation starting coordinate, and the binocular camera is used for shooting position information of a nearest corner point in the advancing direction of the vehicle in the driving process. The data processor unit is used for processing according to the vehicle information acquired by the nine-axis sensor and the position information of the corner points shot by the binocular camera, and obtaining the positioning information.
The modularized intelligent road sensing equipment based on the driving feeling further comprises a wireless control unit, a wireless transmission unit, a power supply protection unit, an HDMI high-definition interface, a USB interface and an OBD interface. The wireless control unit, the wireless transmission unit, the power supply protection unit, the HDMI high-definition interface, the USB interface and the OBD interface are respectively connected with the data processing unit. The HDMI high definition interface, the USB interface and the OBD interface are used for being externally connected with other equipment. The power supply protection unit is used for performing voltage stabilization, current stabilization, reverse connection prevention and filtering protection on an external power supply.
The wireless control unit is used for performing wireless operation and control on equipment in a close range, and is provided with a WLAN wireless control circuit, wherein the WLAN wireless control circuit is a wireless data communication module supporting IEEE 802.11a/b/g/n and 802.11ac and is used for receiving and transmitting control instruction data in a local wireless local area network, so that the instruction control is performed on the sensing unit. The WLAN wireless control circuit is connected with the data processing unit and transmits the control instruction signal to the data processing unit.
The wireless transmission unit comprises an LTE wireless transmission circuit, the LTE wireless transmission circuit is a mobile communication module supporting 4G/5G and is used for transmitting the road surface acoustic digital signal sensed by the sensing unit, the tire pressure change digital signal and the digital signal output by the data processing unit back to the cloud server through an air interface. The LTE wireless transmission circuit is connected with the data processing unit and transmits the digital signals stored in the data processing unit to a cloud server.
As shown in fig. 2 to 3, the wheel encoder is installed at the rear wheel of the vehicle, the binocular camera is disposed at the front windshield of the vehicle, the microphone sensor is disposed on the vehicle at the rear tire position of the vehicle, and the dynamic tire pressure sensor is disposed on the rear tire of the vehicle. The signal conditioning circuit and the AD collector are integrated in the first module; the nine-axis sensor and the GNSS receiver are integrated in the second module; the wireless control unit, the wireless transmission unit and the power supply protection unit are integrated in the third module; HDMI high definition interface, USB interface and OBD interface integration are in the fourth module, and first module, second module, third module and fourth module are connected with data processing unit respectively to set up in a box body jointly with data processing unit, the seat below is placed in the vehicle to the box body.
Example 2
As shown in fig. 4, the driving feeling-based modular road surface intelligent perception system comprises the driving feeling-based modular road surface intelligent perception device and the data processing unit in embodiment 1. The data processing unit comprises a processor, and an RAM storage unit, an acoustic signal processing algorithm unit, a tire pressure signal processing algorithm unit, a positioning algorithm unit and a driving speed fitting algorithm unit which are connected with the processor; the RAM storage unit is respectively connected with the acoustic signal processing algorithm unit, the tire pressure signal processing algorithm unit, the positioning algorithm unit and the driving speed fitting algorithm unit and is used for caching the digital signals of the sensing unit, the positioning unit and the speed fitting unit; the acoustic signal processing algorithm unit calculates the road surface structure depth according to the collected road surface acoustic signals; the tire pressure signal processing algorithm unit calculates the international flatness of the road surface according to the tire pressure change signals acquired by the dynamic tire pressure sensor in the vehicle advancing process; the positioning algorithm unit calculates the position information of the vehicle according to the positioning information provided by the positioning unit; and the running speed fitting algorithm unit calculates the current running speed information by using the output signal of the speed fitting unit and the output speed signal of the GNSS receiver.
The calculation of the road surface structure depth by the acoustic signal processing algorithm unit specifically comprises the following steps: weighting and filtering the sensed acoustic signals in a frequency range of 0-2 KHZ so as to gain or attenuate the acoustic signals of each frequency point in sound pressure level; then, performing wavelet transformation on the weighted and filtered acoustic signals to a frequency domain, and passing the acoustic signals through a low-pass filter with the cut-off frequency of 700 HZ; windowing is carried out on the filtered frequency domain acoustic signals, first principal component signals are extracted through a principal component analysis method, and finally the peri signals are predicted through probability radial basis functions based on Markov Monte Carlo to obtain the value of the pavement microstructure depth.
The tire pressure signal processing algorithm unit specifically calculates the international flatness of the road surface as follows: when the vehicle is in a static state, the data processing unit measures the acceleration and the direction of the current vibration of the vehicle through the nine-axis sensor, measures the tire pressure change in the current tire of the vehicle through the dynamic tire pressure sensor, and calculates the conversion coefficient of the vibration of the vehicle according to the acceleration and the tire pressure change information; acquiring dynamic tire pressure signals on a road surface with a known international flatness index, and obtaining a conversion coefficient of the road surface flatness by combining a conversion system of the acceleration of a vehicle and the vibration of the vehicle; and finally, calculating the international road surface flatness index through the dynamic tire pressure sensor and the nine-axis sensor.
Because: the variation of the pressure inside the tyre during the running of the vehicle is mainly caused by the vibration of the vehicle itself and the flatness of the road surface, i.e. Vdtps＝Hvibration·avibration+Hpavement·apavementIn which V isdtpsIndicating the international flatness index, H, of the pavementvibrationConversion coefficient representing vibration of vehicle, avibrationRepresenting acceleration due to vibration, HpavementIndicating that the road surface is levelConversion coefficient of degree, apavementIndicating the acceleration due to the unevenness of the road surface.
When the vehicle is in a static state, the vehicle is given vibration excitation, and the change of the tire pressure in the tire is completely caused by the vibration of the vehicle, namely Vdtps＝Hvibration·avibration. The acceleration and direction of the current vibration are measured by a nine-axis sensor, the tire pressure change in the tire of the current vehicle is measured by a dynamic tire pressure sensor, and the conversion coefficient H of the vehicle vibration is obtained by the current acceleration and tire pressure change information through a function conversion methodvibration. Acquiring dynamic tire pressure signals on a large number of test pavements with known pavement international flatness indexes, and combining acceleration data and vehicle vibration conversion coefficients to obtain a conversion coefficient H of the pavement flatnesspavement. Therefore, the international road flatness index can be calculated through the dynamic tire pressure sensor and the nine-axis sensor.
The specific method for positioning by the positioning algorithm unit comprises the following steps:
in the area with stronger GPS signals, the GNSS receiver CAN obtain the effective position information and speed information of the vehicle in the running process, and simultaneously, the CAN bus protocol analysis circuit reads the real-time speed of the vehicle from the OBD interface of the vehicle. And carrying out error correction and Kalman filtering processing on the speed data obtained by analyzing the CAN bus by using the speed data obtained by the GNSS receiver so as to ensure that the accuracy of the speed information obtained in the two modes is consistent.
When the vehicle travels to an area where the GPS signal is weak, the speed information cannot be acquired by the GNSS receiver. Acquiring acceleration information, rotation angular velocity information and magnetic field direction information of a vehicle by a nine-axis sensor to determine an initial inertial navigation initial coordinate; the binocular camera is used for shooting the position information of a nearest corner point in the advancing direction of the vehicle in the driving process, extracting the position information of the corner point through image features and establishing a three-dimensional coordinate; the data processing unit updates the inertial navigation position of the vehicle through the position information of the same reference point acquired by the vehicle at different positions at different moments and the difference value of the vehicle position converted to the adjacent moment through coordinates. And analyzing the speed data obtained by the CAN bus, and performing Kalman filtering and fitting treatment to obtain accurate speed information.
The specific method for carrying out speed fitting by the driving speed fitting algorithm unit comprises the following steps:
in the area with stronger GPS signals, effective speed information is obtained through a GNSS receiver, and meanwhile the real-time speed of the vehicle is read from the OBD interface of the vehicle through a CAN bus protocol analysis circuit. And carrying out error correction and Kalman filtering processing on the speed data obtained by analyzing the CAN bus by using the speed data obtained by the GNSS receiver so as to ensure that the accuracy of the speed information obtained in the two modes is consistent. When the vehicle runs to an area with weak GPS signals, the speed information cannot be acquired through the GNSS receiver, and the accurate speed information is acquired through the speed data acquired through CAN bus analysis and Kalman filtering and fitting processing.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. Modularization road surface intelligence perception equipment based on drive and take experience, its characterized in that: the device comprises a sensing unit, a positioning unit and a speed fitting unit; the sensing unit, the positioning unit and the speed fitting unit are respectively connected with the data processing unit;
the sensing unit is used for acquiring pavement acoustic signals and tire pressure change signals;
the positioning unit is used for providing positioning information;
the speed fitting unit is used for providing the driving speed;
the data processing unit is used for carrying out data fusion and analysis on the collected road acoustic signal information, the tire pressure change signal information, the positioning information and the driving speed information.
2. The modular roadway intelligent sensing device based on riding experience of claim 1, wherein: the sensing unit comprises a microphone sensor and a dynamic tire pressure sensor, wherein the microphone sensor is arranged on the vehicle, is positioned at the position of a rear tire of the vehicle and is used for acquiring a road surface acoustic signal generated by friction between the tire and a road surface in the driving process; the dynamic tire pressure sensor is arranged on a rear tire of the vehicle and is used for collecting a tire pressure change signal in the tire in the driving process of the vehicle;
the microphone sensor and the dynamic tire pressure sensor are respectively connected with a signal conditioning circuit, the output end of the signal conditioning circuit is connected with an AD collector, and the output end of the AD collector is connected with the data processor unit.
3. The modular roadway intelligent sensing device based on riding experience of claim 1, wherein: the positioning unit comprises a GNSS receiver, the GNSS receiver is respectively connected with the data processing unit, and the GNSS receiver is used for acquiring time, position and driving speed information.
4. The modular roadway intelligent sensing device based on riding experience of claim 1, wherein: the positioning unit further comprises a nine-axis sensor and a binocular camera, and the nine-axis sensor and the binocular camera are respectively connected with the data processing unit; the nine-axis sensor acquires acceleration information, rotation angular velocity information and magnetic field direction information of a vehicle to determine an initial inertial navigation initial coordinate; the binocular camera is used for shooting the position information of the nearest corner point in the advancing direction of the vehicle in the driving process.
5. The modular roadway intelligent sensing device based on riding experience of claim 3, wherein: the speed fitting unit comprises a wheel encoder and a CAN bus protocol analysis circuit, and the wheel encoder is connected with the data processing unit;
CAN bus protocol analytic circuit respectively with car OBD interface with the data processing unit is connected, CAN bus protocol analytic circuit is used for reading the driving speed of vehicle, engine speed and mileage information to the information transmission who will read sends data processing unit.
6. The modular roadway intelligent sensing device based on riding experience of claim 5, wherein: the wheel encoder is a photoelectric rotary pulse encoder, the photoelectric rotary pulse encoder is arranged on a rear wheel shaft of the vehicle, and a trigger pulse generated by the wheel encoder is used for generating running distance information of the vehicle and transmitting the running distance information to the data processing unit; the trigger pulse generated by the wheel encoder is also used for triggering the sensing unit to acquire road surface data.
7. The modular road surface intelligent perception system based on riding experience of any one of claims 1-6, wherein: the data processing unit comprises a processor, and an RAM storage unit, an acoustic signal processing algorithm unit, a tire pressure signal processing algorithm unit, a positioning algorithm unit and a driving speed fitting algorithm unit which are connected with the processor;
the RAM storage unit is respectively connected with the acoustic signal processing algorithm unit, the tire pressure signal processing algorithm unit, the positioning algorithm unit and the driving speed fitting algorithm unit and is used for caching the digital signals of the sensing unit, the positioning unit and the speed fitting unit;
the acoustic signal processing algorithm unit calculates the road surface structure depth according to the collected road surface acoustic signals;
the tire pressure signal processing algorithm unit calculates the international flatness of the road surface according to tire pressure change signals acquired by the dynamic tire pressure sensor in the vehicle advancing process;
the positioning algorithm unit calculates the position information of the vehicle according to the positioning information provided by the positioning unit;
and the running speed fitting algorithm unit calculates the current running speed information by using the output signal of the speed fitting unit and the output speed signal of the GNSS receiver.
8. The modular roadway intelligent perception system based on riding experience of claim 7, wherein: the acoustic signal processing algorithm unit specifically calculates the road surface structure depth as follows: weighting and filtering the sensed acoustic signals in a frequency range of 0-2 KHZ so as to gain or attenuate the acoustic signals of each frequency point in sound pressure level; then, performing wavelet transformation on the weighted and filtered acoustic signals to a frequency domain, and passing the acoustic signals through a low-pass filter with the cut-off frequency of 700 HZ; windowing is carried out on the filtered frequency domain acoustic signals, first principal component signals are extracted through a principal component analysis method, and finally the peri signals are predicted through probability radial basis functions based on Markov Monte Carlo to obtain the value of the pavement microstructure depth.
9. The modular roadway intelligent perception system based on riding experience of claim 7, wherein: the tire pressure signal processing algorithm unit specifically calculates the international flatness of the road surface as follows: when the vehicle is in a static state, the data processing unit measures the acceleration and the direction of the current vibration of the vehicle through the nine-axis sensor, measures the tire pressure change in the current tire of the vehicle through the dynamic tire pressure sensor, and calculates the conversion coefficient of the vibration of the vehicle according to the acceleration and the tire pressure change information;
acquiring dynamic tire pressure signals on a road surface with a known international flatness index, and obtaining a conversion coefficient of the road surface flatness by combining a conversion system of the acceleration of a vehicle and the vibration of the vehicle;
and finally, calculating the international road surface flatness index through the dynamic tire pressure sensor and the nine-axis sensor.
10. The modular roadway intelligent perception system based on riding experience of claim 7, wherein: the specific method for positioning by the positioning algorithm unit comprises the following steps:
in the area with stronger GPS signals, the effective position information and speed information in the running process of the vehicle are obtained through a GNSS receiver, and the real-time speed of the vehicle is read from an OBD interface of the vehicle through a CAN bus protocol analysis circuit; carrying out error correction and Kalman filtering processing on the speed data obtained by analyzing the CAN bus by using the speed data obtained by the GNSS receiver;
in a region with weak GPS signals, a nine-axis sensor collects acceleration information, rotation angular velocity information and magnetic field direction information of a vehicle to determine initial inertial navigation initial coordinates; the binocular camera is used for shooting the position information of a nearest corner point in the advancing direction of the vehicle in the driving process, extracting the position information of the corner point through image features and establishing a three-dimensional coordinate; the processor updates the inertial navigation position of the vehicle by converting the coordinate into the difference value of the vehicle positions at the adjacent moments through the same reference point position information acquired by the vehicle at different positions at different moments.
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