CN112927514B - Motor vehicle yellow light running behavior prediction method and system based on 3D laser radar - Google Patents
Motor vehicle yellow light running behavior prediction method and system based on 3D laser radar Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- 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
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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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
- G08G1/0125—Traffic data processing
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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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
Abstract
The invention relates to a method and a system for predicting a yellow light running behavior of a motor vehicle based on a 3D laser radar, wherein the method comprises the following steps: detecting and acquiring vehicle track data by using a 3D laser radar; mapping the vehicle track data into a three-dimensional coordinate system within the range of an entrance lane, and classifying the vehicles according to lanes where the vehicles are located; after receiving a yellow light starting signal, inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model, judging the vehicle track data within the first 1.5s of the yellow light time through the track prediction model, and predicting the vehicle track data within 1.5s after the yellow light time according to a judgment result; and judging the traffic trend of the vehicle after the yellow light is finished according to the vehicle track data 1.5s after the yellow light time. The method can provide a good foundation for solving the traffic safety problem of vehicle intrusion during the phase change of the signal lamp of the urban signal control intersection, and has the advantages of no dependence on the characteristic information of a moving target, accurate, stable and efficient detection, low cost, good adaptability and the like.
Description
Technical Field
The invention relates to the field of intelligent traffic perception, in particular to a method and a system for predicting a yellow light running behavior of a motor vehicle based on a 3D laser radar.
Background
The ultra-high population density and the increasing automobile inventory in the Chinese cities contribute to higher trunk network density in urban road construction. At the intersection of a chinese city with traffic light control, a longer phase transition time is typically used at the end of the green phase, i.e. a flashing green indication of 3 seconds followed by a yellow indication of 3 seconds. Such long phase transition times lead to heterogeneous decisions. Therefore, dangerous driving behaviors, such as red-light driving, sudden stop, aggressive passing, and inconsistent decision making of the leading and trailing vehicles, are more likely to occur at these intersections, possibly resulting in right-angle and rear-end traffic accidents.
For the existing traffic safety problem at the intersection, the traditional solution method is to reduce the occurrence of the events by issuing corresponding traffic safety laws and regulations, increasing law enforcement, severely punishing the illegal behaviors of running red lights and the like, but the punishment after the events is usually carried out on the symptoms and the root causes, and a good effect is difficult to achieve; in recent years, due to the interest of the concept of vehicle-road coordination, some scholars begin to consider that the dangerous behaviors of intersections are predicted and early warned through the coordination control between intelligent road facilities and intelligent vehicles, however, enough equipment foundation is needed, and at the present stage, the traffic system and road facilities in China do not meet the requirements yet.
Therefore, the development of a system which has good adaptability and can actively identify dangerous behaviors can help solve the driving dilemma of the driver and provide a technical basis for further prevention and control and safety early warning.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the yellow light running behavior of a motor vehicle based on a 3D laser radar to overcome the safety problem existing at an intersection. The system fully utilizes data returned by the 3D laser radar, and realizes real-time detection and prediction of vehicles about to enter the intersection within the duration time of yellow light by utilizing the kinematic characteristics and the track prediction of the vehicles, so that powerful technical support is provided for actively identifying and controlling the intrusion behavior of motor vehicles during the phase change of signal lamps of urban road intersections, the detection is accurate, stable and efficient, the cost is low, and the adaptability is good.
The purpose of the invention can be realized by the following technical scheme:
a motor vehicle yellow light running behavior prediction method based on a 3D laser radar comprises the following steps:
step 1, sensing a vehicle about to enter an intersection by using a 3D laser radar installed at an entrance lane of the urban intersection, and detecting and acquiring vehicle track data through the 3D laser radar;
step 2, mapping vehicle track data obtained by the 3D laser radar detection into a three-dimensional coordinate system within the range of an entrance lane, and classifying the vehicles according to lanes where the vehicles are located;
step 3, after receiving a yellow light starting signal, inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model, judging the vehicle track data within the first 1.5s of the yellow light time through the track prediction model, and predicting the vehicle track data within 1.5s after the yellow light time according to a judgment result;
step 4, judging the traffic tendency of the vehicle after the yellow light is finished according to the vehicle track data 1.5s after the yellow light time, namely judging whether the vehicle enters the intersection after the yellow light is finished and the red light is lighted; if yes, outputting the result; if not, returning to the step 1 to predict the next turn.
Preferably, the three-dimensional coordinate system is established before the vehicle trajectory data is acquired, and stop line coordinates of an intersection, coordinates and range of a lane, and information of the lane are input into the three-dimensional coordinate system.
Preferably, the vehicle trajectory data includes: the ID of the vehicle, the speed of the vehicle, the acceleration of the vehicle, the distance of the vehicle from the stop line.
Preferably, the yellow light starting signal specifically includes real-time phase information of the intersection, i.e., the current phase and the duration of the current phase.
Preferably, the track prediction model in step 3 is established according to historical vehicle track data, and the establishment of the track prediction model comprises the following steps:
step 3.1, collecting historical vehicle track data within 3s of yellow light time to form a vehicle track data set A;
step 3.2, carrying out clustering analysis on the vehicle track data set A to obtain clustering center track data; dividing the data of the vehicle track data set A into i types according to the clustering result, taking the i types as i track labels, wherein each type corresponds to one track label;
3.3, dividing the vehicle track data set A into a training set B and a testing set C, taking the track data of the training set B and a track label corresponding to each track as input, and establishing a Convolutional Neural Network (CNN) learning model to learn the historical vehicle track data and the corresponding label;
and 3.4, training the model until the model is tested by using the test set C, and if the test value reaches the expected accuracy, finishing the establishment of the track prediction model.
Preferably, the vehicle trajectory data or historical vehicle trajectory data is divided into a straight-driving vehicle data set, and/or a left-turning vehicle data set, and/or a right-turning vehicle data set.
Preferably, the step 3 specifically includes the following steps:
inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model established in advance, wherein the track prediction model can predict and acquire a track label to which the track data belong; according to the predicted track label, selecting the track data 1.5s behind the yellow light of the clustering center track data (3s) of the category corresponding to the track label, namely the track data 1.5s behind the yellow light of the predicted vehicle, and then judging the passing trend of the vehicle after the yellow light is finished by the track data.
Preferably, the 3D lidar obtains whether a single vehicle passes through or a vehicle queue passes through each lane of the entrance lane at the time by detecting the vehicle at the entrance lane of the intersection, and the prediction in step 3 is divided into two situations, namely single vehicle passing prediction and vehicle queue passing prediction;
the specific prediction process for a single vehicle is as follows: after receiving a yellow light starting signal, judging the phase position of the yellow light at the moment (namely the yellow light at the moment is a left-turn yellow light, a right-turn yellow light or a straight yellow light), wherein different phases need to predict and judge the corresponding lane vehicles; if the yellow light is in the straight-going phase, after the starting signal of the yellow light is received, the system only needs to predict and judge the vehicles on the straight-going lane; if the vehicle is a yellow light with a special left-turn phase, after receiving a starting signal of the yellow light, only the vehicle on the left-turn lane needs to be predicted and judged at the moment;
the specific prediction process for the vehicle queue is as follows: when the 3D laser radar collects vehicle track data, all vehicles are collected integrally; when each vehicle is predicted and judged to break in, the vehicle is predicted and judged one by one from the first vehicle in the vehicle queue; when the vehicles are judged one by one, if a certain vehicle is judged not to break into the intersection when the yellow light is finished, all vehicles in the queue behind the vehicle are judged not to break into the intersection when the yellow light is finished.
The invention also aims to provide a motor vehicle yellow light running behavior prediction system based on the 3D laser radar, which comprises the following modules:
the detection module is used for acquiring vehicle track data at an entrance road of an urban intersection;
the data processing module is used for analyzing and processing the acquired vehicle track data;
the data prediction module is used for predicting the running track of the vehicle and judging whether the vehicle breaks into the intersection or not;
and the signal lamp information module is used for acquiring signal lamp information.
Compared with the prior art, the method and the system for predicting the yellow light running behavior of the motor vehicle based on the 3D laser radar have the following beneficial effects:
the equipment for detecting the vehicle track data is 3D laser radar detection equipment fixed on the side of an entrance road, adopts historical and real-time radar data, and has the advantages of reasonable cost, high accuracy, low operation requirement, adaptability to all-weather road environments and the like; the real-time running track of the motor vehicle with high precision can be obtained, and meanwhile compared with other detection means such as video detection, the information processing of the laser radar is higher in processing efficiency, so that real-time track prediction can be achieved, analysis and processing can be timely carried out, and the driving behavior of the vehicle during the phase change period of the signal control intersection can be accurately, efficiently, stably and all-weather detected.
The invention provides a set of complete vehicle driving trend prediction scheme for the traffic safety problem of vehicle intrusion during the phase change of the signal lamps of the urban signal control intersection. The scheme can stably, accurately and efficiently identify and predict dangerous behaviors during the phase change of the traffic signals, and on the basis, early warning and prevention and control can be performed on the dangerous behaviors in advance to reduce potential safety hazards of vehicles during the phase change of intersections, reduce accidents of the intersections and improve the urban operation safety level.
And thirdly, the continuous track of the vehicle can be accurately acquired only according to the data acquired by the 3D laser radar, and then subsequent analysis and prediction are carried out, and the vehicle-side equipment such as a high-precision GPS is not required to be relied on, so that the required cost is low, and the adaptability to the existing traffic environment is higher.
Drawings
Fig. 1 is a schematic workflow diagram of a method for predicting a yellow light running behavior of a motor vehicle based on a 3D laser radar according to an embodiment of the present invention.
Fig. 2 is a flowchart of establishing a trajectory prediction model according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The invention provides a motor vehicle yellow light running behavior prediction system based on a 3D laser radar, which comprises a detection module, a data processing module, a data prediction module and a signal light information module, wherein the detection module is used for acquiring vehicle track data at an entrance road by using the 3D laser radar, so that the detection is more accurate, stable and efficient; the data processing module is used for analyzing and processing the acquired vehicle track data; the data prediction module is used for predicting the running track of the vehicle and judging whether the vehicle breaks into the intersection or not; the signal lamp information module is used for acquiring signal lamp information.
Based on a general inventive concept, the invention also provides a motor vehicle yellow light running behavior prediction method based on the 3D laser radar, the method fully utilizes data returned by the 3D laser radar, and utilizes the kinematic characteristics and track prediction of the vehicle to realize the real-time prediction of the time required by the vehicle entering the intersection from the current position to the stop line of the vehicle, thereby providing powerful technical support for actively identifying and controlling the motor vehicle running behavior of the urban road intersection during the phase change of the signal light, the detection is accurate, stable and efficient, the cost is low, the adaptability is good, and as shown in figure 1, the method comprises the following specific steps:
step 1, sensing a vehicle about to enter an intersection by using a 3D laser radar installed at an entrance lane of the urban intersection, and detecting and acquiring vehicle track data through the 3D laser radar; specifically, in this embodiment, the vehicle trajectory data includes: information such as the ID of the vehicle, the speed of the vehicle, the acceleration of the vehicle, and the distance from the vehicle to the stop line; the vehicle track data or the historical vehicle track data are divided into a straight-going vehicle data set and/or a left-turning vehicle data set and/or a right-turning vehicle data set;
the 3D laser radar can be installed on the side of an inlet road or a portal frame and a sign rod piece, a certain installation height needs to be guaranteed, and the shielding of green plants, sign boards and the like is avoided by the height requirement. And then the 3D laser radar detects and senses the vehicles in the range of the entrance way, and the function is completed through a detection module of the system.
Step 2, mapping vehicle track data obtained by the detection of the 3D laser radar into a three-dimensional coordinate system within the range of an entrance lane, and classifying the vehicles according to lanes where the vehicles are located; the method comprises the steps that a three-dimensional coordinate system is established before vehicle track data are obtained, and stop line coordinates of an intersection, coordinates and ranges of lanes and information of the lanes are input into the three-dimensional coordinate system;
by establishing a three-dimensional coordinate system within the range of the entrance lane, coordinates of the stop line, coordinates and range of the lane, information of the lane are input in advance in the coordinate system. The 3D laser radar obtains the coordinates of the vehicle relative to the radar by sensing the vehicle, maps the coordinates in the established coordinate system, and classifies the vehicle according to the lane where the vehicle is located. The real-time running speed and acceleration information of the vehicle can be obtained by endowing each vehicle with ID (identity), timestamp information and the moving position of the vehicle in a certain time interval, and the function is completed by a data processing module of the system.
Step 3, after receiving the yellow light starting signal, inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model, judging the vehicle track data within the first 1.5s of the yellow light time through the track prediction model, and predicting the vehicle track data within 1.5s after the yellow light time according to a judgment result;
in this embodiment, the yellow light starting signal specifically includes real-time phase information of the intersection, that is, the current phase and the duration time of the current phase; through the information, the system can selectively predict and judge vehicles of different types;
step 4, judging the traffic tendency of the vehicle after the yellow light is finished according to the vehicle track data 1.5s after the yellow light time, namely judging whether the vehicle enters the intersection after the yellow light is finished and the red light is lighted; if yes, outputting the result; if not, returning to the step 1 to predict the next turn.
Specifically, in this embodiment, the trajectory prediction model in step 3 is established according to historical vehicle trajectory data, and as shown in fig. 2, the establishment of the trajectory prediction model includes the following steps:
step 3.1, collecting historical vehicle track data within 3s of yellow light time to form a vehicle track data set A;
step 3.2, carrying out clustering analysis on the vehicle track data set A (the data volume is large enough, K-Means or DBSCAN) to obtain clustering center track data; dividing the data of the vehicle track data set A into i types according to the clustering result (the specific value of i is based on the clustering result, and each intersection is different), taking the i types as i track labels, and enabling each type to correspond to one track label;
3.3, dividing the vehicle track data set A into a training set B and a testing set C, taking the track data of the training set B and a track label corresponding to each track as input, and establishing a Convolutional Neural Network (CNN) learning model to learn the historical track data and the track label corresponding to the historical track data;
and 3.4, training the model until the model is tested by using the test set C, and establishing the track prediction model when the test value reaches the expected accuracy.
In the building process, vehicle track data within 1.5s before a yellow light of the track data are extracted as input, the track data within the last 1.5s are taken as results, each piece of data is given an explicit label, and a supervised learning process is built.
Based on the above trajectory prediction model, step 3 specifically includes the following steps:
inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model established in advance, wherein the track prediction model can predict and acquire a track label to which the track data belong; according to the predicted track label, selecting the track data 1.5s behind the yellow light of the clustering center track data (3s) of the category corresponding to the track label, namely the track data 1.5s behind the yellow light of the predicted vehicle, and then judging the passing trend of the vehicle after the yellow light is finished by the track data.
Specifically, the 3D laser radar acquires whether a single vehicle passes through or a vehicle queue passes through each lane of the entrance road at the moment through detecting the vehicles at the entrance road of the intersection, and the prediction in the step 3 is divided into two situations of single vehicle passing prediction and vehicle queue passing prediction;
the specific prediction process for a single vehicle is as follows: after receiving a yellow light starting signal, judging the phase position of the yellow light at the moment (namely the yellow light at the moment is a left-turn yellow light, a right-turn yellow light or a straight yellow light), wherein different phases need to predict and judge the corresponding lane vehicles; if the yellow light is in the straight-going phase, after the starting signal of the yellow light is received, the system only needs to predict and judge the vehicles on the straight-going lane; if the vehicle is a yellow light with a special left-turn phase, after receiving a starting signal of the yellow light, only the vehicle on the left-turn lane needs to be predicted and judged at the moment;
the specific prediction process for the vehicle queue is as follows: when the 3D laser radar collects vehicle track data, all vehicles are collected integrally; when each vehicle is predicted and judged to break in, the vehicle is predicted and judged one by one from the first vehicle in the vehicle queue; when the vehicles are judged one by one, if a certain vehicle is judged not to break into the intersection when the yellow light is finished, all vehicles in the queue behind the vehicle are judged not to break into the intersection when the yellow light is finished.
The motor vehicle yellow light running behavior prediction system provided by the embodiment can be matched with an early warning system, an information publishing system and the like subsequently to remind and early warn other vehicles, pedestrians and other road users in the range of the intersection. For example, an acousto-optic and electric early warning facility can be arranged on the road side, and after the system predicts that a vehicle is about to enter the intersection after the green light is finished, the early warning system can be controlled to issue acousto-optic and electric early warning to pedestrians and vehicles in the intersection to remind road users of avoiding in advance or passing the intersection carefully. In addition, in the scene of the occurrence of the future internet connection vehicles and the automatic driving vehicles, the system can directly issue early warning information to the vehicles and assist decision making, so that the potential safety hazards of the vehicles in the phase change period of the intersection are effectively reduced, and the accidents at the intersection are reduced.
The 3D laser radar detection technology and the vehicle track prediction technology based on high accuracy that this embodiment provided help discernment traffic signal phase transition vehicle intrusion action during to carry out real-time prediction to dangerous action like this, on this basis, can have such as mode such as signal lamp adjustment, sound early warning etc. to get and remind the intraoral vehicle pedestrian of cross, with the potential safety hazard of reducing the vehicle during the intersection phase transition, reduce the occurence of failure at intersection, promote the operation safety level of urban road.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A motor vehicle yellow light running behavior prediction method based on a 3D laser radar is characterized by comprising the following steps:
step 1, sensing a vehicle about to enter an intersection by using a 3D laser radar installed at an entrance lane of the urban intersection, and detecting and acquiring vehicle track data through the 3D laser radar;
step 2, mapping vehicle track data obtained by the 3D laser radar detection into a three-dimensional coordinate system within the range of an entrance lane, and classifying the vehicles according to lanes where the vehicles are located;
step 3, after receiving a yellow light starting signal, inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model, judging the vehicle track data within the first 1.5s of the yellow light time through the track prediction model, and predicting the vehicle track data within 1.5s after the yellow light time according to a judgment result;
step 4, judging the traffic tendency of the vehicle after the yellow light is finished according to the vehicle track data 1.5s after the yellow light time, namely judging whether the vehicle enters the intersection after the yellow light is finished and the red light is lighted; if yes, outputting the result; if not, returning to the step 1 to predict the next turn.
2. The method for predicting the yellow light running behavior of the motor vehicle based on the 3D laser radar as claimed in claim 1, wherein the three-dimensional coordinate system is established before the vehicle track data is acquired, and stop line coordinates of an intersection, coordinates and range of a lane and information of the lane are input into the three-dimensional coordinate system.
3. The method for predicting the yellow light running behavior of a motor vehicle based on a 3D laser radar as claimed in claim 1, wherein the vehicle track data comprises: the ID of the vehicle, the speed of the vehicle, the acceleration of the vehicle, the distance of the vehicle from the stop line.
4. The method for predicting the yellow light running behavior of the motor vehicle based on the 3D laser radar as claimed in claim 1, wherein the yellow light starting signal specifically comprises real-time phase information of an intersection, namely a current phase and a duration of the current phase.
5. The method for predicting the yellow light running behavior of the motor vehicle based on the 3D laser radar as claimed in claim 1, wherein the track prediction model in the step 3 is established according to historical vehicle track data, and the establishment of the track prediction model comprises the following steps:
step 3.1, collecting historical vehicle track data within 3s of yellow light time to form a vehicle track data set A;
step 3.2, carrying out clustering analysis on the vehicle track data set A to obtain clustering center track data; dividing the data of the vehicle track data set A into i types according to the clustering result, taking the i types as i track labels, wherein each type corresponds to one track label;
3.3, dividing the vehicle track data set A into a training set B and a testing set C, taking the track data of the training set B and a track label corresponding to each track as input, and establishing a convolutional neural network learning model to learn the historical vehicle track data and the label corresponding to the historical vehicle track data;
and 3.4, training the model until the model is tested by using the test set C, and if the test value reaches the expected accuracy, finishing the establishment of the track prediction model.
6. The method for predicting the yellow light running behavior of a motor vehicle based on a 3D laser radar as claimed in claim 5, wherein the vehicle track data or historical vehicle track data are divided into a straight-going vehicle data set and/or a left-turning vehicle data set and/or a right-turning vehicle data set.
7. The method for predicting the yellow light running behavior of the motor vehicle based on the 3D laser radar as claimed in claim 6, wherein the step 3 specifically comprises the following steps:
inputting vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model established in advance, and predicting by the track prediction model to acquire a track label to which the track data belong; according to the predicted track label, selecting the track data 1.5s behind the yellow light of the clustering center track data of the category corresponding to the track label, namely the track data 1.5s behind the yellow light of the predicted vehicle, and then judging the passing trend of the vehicle after the yellow light is finished by the track data.
8. The method for predicting the yellow light running behavior of the motor vehicle based on the 3D laser radar as claimed in claim 4, wherein the 3D laser radar obtains whether the vehicles pass through each lane of an entrance way or pass through a vehicle queue at the moment through detection of the vehicles at the entrance way of the intersection, and the prediction in the step 3 is divided into two situations of single vehicle passing prediction and vehicle queue passing prediction;
the specific prediction process for a single vehicle is as follows: after receiving a yellow light starting signal, judging the phase position of the yellow light at the moment, wherein different phases need to predict and judge the corresponding lane vehicle; if the yellow light is in the straight-going phase, after the starting signal of the yellow light is received, the system only needs to predict and judge the vehicles on the straight-going lane; if the vehicle is a yellow light with a special left-turn phase, after receiving a starting signal of the yellow light, only the vehicle on the left-turn lane needs to be predicted and judged at the moment;
the specific prediction process for the vehicle queue is as follows: when the 3D laser radar collects vehicle track data, all vehicles are collected integrally; when each vehicle is predicted and judged to break in, the vehicle is predicted and judged one by one from the first vehicle in the vehicle queue; when the vehicles are judged one by one, if a certain vehicle is judged not to break into the intersection when the yellow light is finished, all vehicles in the queue behind the vehicle are judged not to break into the intersection when the yellow light is finished.
9. A motor vehicle yellow light running behavior prediction system based on a 3D laser radar is characterized by comprising the following modules:
the detection module is used for acquiring vehicle track data at an entrance road of an urban intersection;
the data processing module is used for analyzing and processing the acquired vehicle track data;
the data prediction module is used for predicting the running track of the vehicle and judging whether the vehicle breaks into the intersection or not;
the signal lamp information module is used for acquiring signal lamp information;
after receiving a yellow light starting signal, the data prediction module inputs vehicle track data acquired within the first 1.5s of the yellow light time into a track prediction model, judges the vehicle track data within the first 1.5s of the yellow light time through the track prediction model, and predicts the vehicle track data within 1.5s after the yellow light time according to a judgment result;
judging the traffic tendency of the vehicle after the yellow light is finished according to the vehicle track data 1.5s after the yellow light time, namely judging whether the vehicle rushes into the intersection after the yellow light finishes the red light;
the establishment of the track prediction model comprises the following steps:
collecting historical vehicle track data within 3s of yellow light time to form a vehicle track data set A;
performing clustering analysis on the vehicle track data set A to obtain clustering center track data; dividing the data of the vehicle track data set A into i types according to the clustering result, taking the i types as i track labels, wherein each type corresponds to one track label;
dividing the vehicle track data set A into a training set B and a testing set C, taking the track data of the training set B and a track label corresponding to each track as input, and establishing a convolutional neural network learning model to learn historical vehicle track data and the corresponding label;
and training the model until the model is tested by using the test set C, and establishing the track prediction model when the test value reaches the expected accuracy.
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| CN108492601A (en) * | 2018-04-13 | 2018-09-04 | 济南浪潮高新科技投资发展有限公司 | A kind of vehicle DAS (Driver Assistant System) and method |
| CN111724594A (en) * | 2020-06-15 | 2020-09-29 | 青岛海信网络科技股份有限公司 | Safety early warning method and device |
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2021
- 2021-04-09 CN CN202110383518.8A patent/CN112927514B/en active Active
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2022
- 2022-02-21 ZA ZA2022/02145A patent/ZA202202145B/en unknown
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| CN112927514A (en) | 2021-06-08 |
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