CN114677835A - Self-adaptive traffic scheduling system and method based on microcontroller equipment and micro machine learning - Google Patents
Self-adaptive traffic scheduling system and method based on microcontroller equipment and micro machine learning Download PDFInfo
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
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Abstract
The invention discloses a self-adaptive traffic scheduling system and method based on microcontroller equipment and micro machine learning, and belongs to the technical field of traffic scheduling. The invention discloses a self-adaptive traffic scheduling system based on microcontroller equipment and micro-machine learning, which comprises a sensor placing module, a sensor data collecting module, a vehicle detection model, a vehicle classification module and a predicted signal lamp module, wherein the sensor placing module is used for placing a sensor; placing two piezoelectric strips on a sensor placing module, and collecting a voltage peak value of a forklift with a vehicle passing through the piezoelectric strips; the sensor data collection module is used for collecting data, calculating the wheel base of the automobile and identifying the type of the automobile; the vehicle detection module is used for detecting and classifying the vehicle; the vehicle classification module is used for classifying the vehicles according to the wheelbases and the speeds of the vehicles. The self-adaptive traffic scheduling system based on the microcontroller equipment and the micro machine learning can classify vehicles by utilizing data collected by the pressing strips so as to achieve more efficient traffic, and has good popularization and application values.
Description
Technical Field
The invention relates to the technical field of traffic scheduling, and particularly provides a self-adaptive traffic scheduling system and method based on microcontroller equipment and micro machine learning.
Background
Traffic is used as a support for the development of China, and directly influences the health, business and environmental costs of people. But in today's 5G world, traffic congestion has become a serious problem in our daily lives. For example, congested roads are a major cause of air pollution. There is little difference between peak and off-peak hours, even if the specified speed is 80 km/h, the average speed of traffic flow is 50-60 km/h. Long periods of traffic congestion can also lead to health problems for some people who do not have enclosed environmental vehicles. Therefore, it is very urgent to improve the efficiency of traffic signals.
The traffic density and the vehicle classification are calculated by using the data of the piezoelectric sensors on the lane, and then the green light time is predicted according to the identified traffic density through the TinyML algorithm, so that the time setting of the traffic light and the relief of traffic congestion are further improved, and the method has important significance.
Disclosure of Invention
The technical task of the invention is to provide a self-adaptive traffic scheduling system based on microcontroller equipment and micro-machine learning, which can classify vehicles by using data collected by a trim strip so as to achieve more efficient traffic.
A further technical task of the present invention is to provide a method for self-adaptive traffic scheduling based on microcontroller devices and micro-machine learning.
In order to realize the purpose, the invention provides the following technical scheme:
a self-adaptive traffic scheduling system based on microcontroller equipment and micro machine learning comprises a sensor placement module, a sensor data collection module, a vehicle detection model, a vehicle classification module and a predicted signal lamp module;
placing two piezoelectric strips on a sensor placing module, and collecting a voltage peak value of a forklift with a vehicle passing through the piezoelectric strips;
the sensor data collection module is used for collecting data, calculating the wheel base of the automobile and identifying the type of the automobile;
the vehicle detection module is used for detecting and classifying the vehicle;
the vehicle classification module is used for classifying the vehicles according to the wheelbases and the speeds of the vehicles;
the predictive signal module is used to predict the signal duration of the vehicle accumulated during the red signal.
Preferably, the sensor data collection module groups the vehicle types using the calculated vehicle passing speed, wherein the vehicle is classified using a two-point time ratio to calculate an axle base.
Preferably, the classified vehicles are used as the same microcontroller to gather the input of the pre-trained random forest regression model, and then the signal lights are predicted by using the training results.
Preferably, in the vehicle detection module, two pairs of piezoelectric strips are installed on each lane.
Preferably, in the vehicle classification module, the wheel base and the speed of each vehicle are fixed, and are used for vehicle classification.
Preferably, the predictive signal module takes as input the vehicle count for each category and predicts the duration of the green signal required for the vehicle to pass through the intersection.
The invention discloses a self-adaptive traffic scheduling method based on microcontroller equipment and micro machine learning, which is realized based on the self-adaptive traffic scheduling system based on the microcontroller equipment and the micro machine learning.
Preferably, the sensor data collection module groups the vehicle types using the calculated vehicle passing speed, wherein the vehicle is classified using a two-point time ratio to calculate an axle base.
Preferably, the classified vehicles are used as the same microcontroller to gather the input of the pre-trained random forest regression model and then use the training results to predict signal lights.
Preferably, in the vehicle detection module, two pairs of piezoelectric strips are installed on each lane.
Compared with the prior art, the self-adaptive traffic scheduling method based on the microcontroller device and the micro machine learning has the following outstanding beneficial effects: the method of self-adaptive traffic scheduling based on microcontroller device and micro-machine learning detects vehicle conditions by means of piezo-electric sensors embedded on each lane. The method of time ratio between two points is utilized to identify the vehicle by utilizing the sensor data, and the vehicle is classified according to the detected speed, so that the method has good popularization and application values.
Drawings
FIG. 1 is a model schematic diagram of a system for adaptive traffic scheduling based on microcontroller devices and micro-machine learning according to the present invention;
fig. 2 is a schematic diagram of wheelbase and speed calculations of the method for adaptive traffic scheduling based on microcontroller device and micro-machine learning according to the present invention.
Detailed Description
The system and method for adaptive traffic scheduling based on micro-controller device and micro-machine learning according to the present invention will be described in further detail with reference to the accompanying drawings and embodiments.
Examples
The invention discloses a self-adaptive traffic scheduling system based on microcontroller equipment and micro-machine learning.
Two piezoelectric strips are placed on the sensor placing module, and the voltage peak value of the forklift with the piezoelectric strips is collected.
As in FIG. 1, UP and LP represent the installation of two piezoelectric strips, and D represents the distance of each pair of piezoelectric strips. The peak voltage value of the forklift is transmitted to the MCU supporting TinyML by the vehicle through the piezoelectric strip.
The sensor data collection module is used for collecting data, calculating the wheel base of the automobile and identifying the type of the automobile.
The sensor data collection module groups the vehicle types using the calculated vehicle passing speed, wherein the vehicle is classified using a two-point time ratio to calculate a wheel base.
And at a data collection module of the sensor, calculating the wheel base of the automobile through the collected data to identify the type of the automobile, and then grouping the types of the automobiles by using the calculated passing speed. The wheel base is calculated by a two-point time ratio method, and the passing vehicles are classified. The classified vehicle counts are used as input to a pre-trained random forest regression model in the same microcontroller. And finally, predicting the signal lamp by using the training result.
The vehicle detection module is used for detecting and classifying the vehicle.
The classified vehicles are used as the input of a pre-training random forest regression model collected by the same microcontroller, and signal lamps are predicted by using training results.
In the vehicle detection module, each lane is provided with 2 pairs of piezoelectric strips, namely UP and LP, which is helpful for detecting and classifying vehicles. In the vehicle detection module, two pairs of piezoelectric strips are arranged on each lane. In fig. 1, arrows indicate the direction of traffic flow. LP is used to detect entry into the vehicle and UP is used to detect exit from the vehicle. Vehicles approaching the traffic intersection first pass through LP2 and then through LP 1. When a vehicle drives over the belt, a voltage pulse is generated. The single voltage pulses from LP2 and LP1 depict an axis crossing LP. When the same vehicle passes over UP2 and UP1, the time at which UP is activated is recorded. Assuming a vehicle approach signal with "n" axles, n time stamps from each of the 4 strips are recorded. The time stamp of the corresponding voltage pulse is passed to the local microcontroller.
The vehicle classification module is used for classifying the vehicles according to the wheelbases and the speeds of the vehicles.
In the vehicle classification module, the wheel base and the speed of each vehicle are fixed and are used for classifying the vehicles.
And the vehicle classification module is used for classifying the vehicles, wherein the wheel base and the speed of each vehicle are fixed. Consider, for illustrative purposes, the case where a truck arrives at a traffic intersection (fig. 2). The first timestamp (T1) corresponds to the first axis passing through LP 2. The second timestamp (T2) corresponds to the first axis passing through LP 1. The velocity is calculated as V ═ D/(T) 2-T1). By the result of calculationTo classify the vehicle. Meanwhile, in order to classify the vehicles, considering that 2 pairs of sensors are embedded on one single lane road, which will generate 4 multi-timestamp arrays (activation times), the following algorithm is implemented. Let LP1 timestamp a queue named A2 and LP2 timestamp a queue named A1.
a. Start of
b. Creating an empty list to store wheelbase lengths;
c. if A1 is empty, go to step i;
d. using the first inputs of A1 and A2, the speed of the vehicle is calculated. A2 exit column once;
e. if A1 is empty, go to step i;
f. comparing the result with a preset value;
g. if a match is found, then increment the vehicle count for the corresponding category, exit pair once A1, exit queue completely A2, and repeat from step b;
h. if no match is found, repeat from step e;
i. and (5) stopping.
The predictive signal module is used to predict the signal duration of the vehicle accumulated during the red signal.
The predictive signal module takes as input the vehicle count for each category and predicts the duration of the green signal required for the vehicle to pass through the intersection.
In the predictive signal module, a pre-trained Random Forest Regressor (RFR) is used to predict the signal duration of the vehicle accumulated during the red signal. The RFR takes as input the vehicle count for each category and predicts the duration of the green signal required for the vehicle to pass through the intersection. The RFR model is ported to the embedded device using TinyML libraries. Random forest is an integrated technique that can perform regression and classification tasks using multiple decision trees and a technique called guided aggregation (often called bagging). In the random forest approach, Bagging involves training each decision tree on different data samples, where sampling is done by substitution. The basic idea behind this is to combine multiple decision trees to determine the final output, rather than relying on a single decision tree. RFR is the choice of ML model because of its low computational and memory requirements. And finally deploying the model on the MCU. The output of the MCU is the predicted duration delivered to the traffic lights.
The self-adaptive traffic scheduling method based on the micro controller device and the micro machine learning is realized by a self-adaptive traffic scheduling system based on the micro controller device and the micro machine learning, the classes are divided by using the wheelbases and speeds of different vehicles based on the micro controller device and the micro machine learning, the vehicles are classified by using data collected by a piezoelectric bar, and the prediction is carried out by using a random forest according to the conditions of the vehicles.
The sensor data collection module groups the vehicle types using the calculated vehicle passing speed, wherein the vehicle is classified using a two-point time ratio to calculate a wheel base. The classified vehicles are used as the input of a gathering pre-training random forest regression model of the same microcontroller, and signal lamps are predicted by using training results. In the vehicle detection module, two pairs of piezoelectric strips are installed on each lane.
The above-described embodiments are merely preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (10)
1. A system for self-adaptive traffic scheduling based on microcontroller devices and micro-machine learning, characterized by: the system comprises a sensor placement module, a sensor data collection module, a vehicle detection model, a vehicle classification module and a predicted signal lamp module;
Placing two piezoelectric strips on a sensor placing module, and collecting a voltage peak value of a forklift with a vehicle passing through the piezoelectric strips;
the sensor data collection module is used for collecting data, calculating the wheel base of the automobile and identifying the type of the automobile;
the vehicle detection module is used for detecting and classifying the vehicle;
the vehicle classification module is used for classifying the vehicles according to the wheelbases and the speeds of the vehicles;
the predictive signal module is used to predict the signal duration of the vehicle accumulated during the red signal.
2. The system for adaptive traffic scheduling based on microcontroller device and micromachine learning according to claim 1, wherein: the sensor data collection module groups the vehicle types using the calculated vehicle passing speed, wherein the vehicle is classified using a two-point time ratio to calculate a wheel base.
3. The system for adaptive traffic scheduling based on microcontroller device and micromachine learning according to claim 2, wherein: the classified vehicles are used as the input of a pre-training random forest regression model collected by the same microcontroller, and signal lamps are predicted by using training results.
4. The system for adaptive traffic scheduling based on microcontroller device and micromachine learning according to claim 3, wherein: in the vehicle detection module, two pairs of piezoelectric strips are installed on each lane.
5. The system for adaptive traffic scheduling based on microcontroller device and micromachine learning according to claim 4, wherein: in the vehicle classification module, the wheel base and the speed of each vehicle are fixed and are used for classifying the vehicles.
6. The system for adaptive traffic scheduling based on microcontroller device and micromachine learning according to claim 5, wherein: the predictive signal module takes as input the vehicle count for each category and predicts the duration of the green signal required for the vehicle to pass through the intersection.
7. A method for self-adaptive traffic scheduling based on microcontroller device and micro-machine learning, characterized by: the method is realized on the basis of the system for self-adaptive traffic scheduling based on the micro-controller device and the micro-machine learning in any one of claims 1 to 6, classification of classes is carried out by using the wheelbases and speeds of different vehicles based on the micro-controller device and the micro-machine learning, classification of the vehicles is carried out by using data collected by a piezoelectric strip, and prediction is carried out by using a random forest according to the conditions of the vehicles.
8. The method for adaptive traffic scheduling based on microcontroller device and micromachine learning according to claim 7, wherein: the sensor data collection module groups the vehicle types using the calculated vehicle passing speed, wherein the vehicle is classified using a two-point time ratio to calculate a wheel base.
9. The method for adaptive traffic scheduling based on microcontroller devices and micro-machine learning according to claim 8, characterized in that: the classified vehicles are used as the input of a pre-training random forest regression model collected by the same microcontroller, and signal lamps are predicted by using training results.
10. The method for adaptive traffic scheduling based on microcontroller devices and micro-machine learning according to claim 9, characterized in that: in the vehicle detection module, two pairs of piezoelectric strips are arranged on each lane.
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| CN115440046A (en) * | 2022-09-09 | 2022-12-06 | 山东浪潮科学研究院有限公司 | Traffic safety system based on TinyML for assisting pedestrians in passing pedestrian crosswalk |
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