Patent Application
20220270477
This application is based upon and claims priority to Chinese Patent Application No. 202110691964.5, filed on Jun. 22, 2021, the entire content of which are incorporated herein by reference.
The present disclosure relates to a field of data processing technologies, in particular to a field of artificial intelligence (AI) technologies such as intelligent transportation, deep learning (DL) and big data processing, and to a method and an apparatus for controlling a variable lane, a device and a storage medium.
With the increase of vehicles owned by users, the smooth traffic becomes a primary problem to be solved, and due to an unreasonable urban layout, a tidal phenomenon and surging traffic easily occur during the peak period. The traffic light at an intersection has pre-stored a specific signal control scheme in each time period, so that a reasonable adjustment cannot be made in time based on the current traffic situation, thus resulting in a traffic imbalance. A variable lane is a main means of planning traffic. It is an urgent problem to be solved how to plan a road network and alleviate traffic pressure by adjusting the variable channel.
According to a first aspect of the disclosure, a method for controlling a variable lane is provided. The method includes: acquiring first traffic data for a first driving direction corresponding to the variable lane and second traffic data for a second driving direction of a lane adjacent to the variable lane; determining a first saturation in the first driving direction based on the first traffic data and determining a second saturation in the second driving direction based on the second traffic data; determining an imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation; and controlling a driving direction of the variable lane based on the imbalance coefficient.
According to a second aspect of the disclosure, an apparatus for controlling a variable lane is provided. The apparatus includes a processor and a memory for storing instructions executable by the processor. The processor is configured to acquire traffic data for a first driving direction corresponding to the variable lane and traffic data for a second driving direction of a lane adjacent to the variable lane; determine a first saturation in the first driving direction based on the first traffic data and determine a second saturation in the second driving direction based on the second traffic data; determine an imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation; and control a driving direction of the variable lane based on the imbalance coefficient.
According to a third aspect of the disclosure, a non-transitory computer readable storage medium stored with a computer program thereon. When the computer program is executed by the processor, the method as described in the first aspect is implemented.
It should be understood that, the content described in the part is not intended to identify key or important features of embodiments of the disclosure, nor intended to limit the scope of the disclosure. Other features of the disclosure will be easy to understand through the following specification.
The drawings are used to better understand the solution, and do not constitute a limitation to the disclosure.
Embodiments of the present disclosure are described as below with reference to the accompanying drawings, which include various details of embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following descriptions.
The embodiments of the disclosure relate to a field of artificial intelligence (AI) technologies such as deep learning, intelligent transportation and big data processing.
Artificial Intelligence (AI) is a new science of technology that studies and develops theories, methods, technologies and application systems configured to simulate, extend and expand human intelligence.
Deep learning (DL) learns inherent law and representation hierarchy of sample data, and information obtained in the learning process is of great help in interpretation of data such as texts, images and sounds. The final goal of DL is that the machine may have analytic learning ability like human beings, which may recognize data such as words, images, sounds, etc.
Intelligent transportation is a real-time, accurate and efficient comprehensive traffic transportation management system established by integrating and applying advanced information technology, data communication transmission technology, electronic sensing technology, control technology, computer technology and the like to a whole ground traffic management system, which plays a role in a wide range and all directions and includes a traffic information service system and a traffic management system.
Big data processing refers to a process of analyzing and processing large-scale data by means of artificial intelligence, and big data may be summarized as 5 Vs, that is, volume, velocity, variety, value, veracity.
The method for controlling a variable lane may be performed by an apparatus for controlling a variable lane according to the disclosure, and may also be performed by an electronic device according to the disclosure. The electronic device may include but not limited to a terminal, such as a desktop computer and a tablet computer, and may also be a server. The apparatus for controlling a variable lane (which is referred to as an “apparatus” below) is configured to perform a method for controlling a variable lane according to the disclosure, and does not constitute a limitation of the disclosure.
The method and the apparatus for controlling a variable lane, the computer device and the storage medium according to the disclosure are described as below referring to accompanying drawings.
As illustrated in
At 101, traffic data for a first driving direction corresponding to a variable lane and traffic data for a second driving direction adjacent to the variable lane are acquired.
It should be noted that, since a traffic flow is relatively concentrated during peak hours while there are few lanes, a variable lane is generally set at an intersection, which flexibly regulates a flow direction based on the flow direction characteristics of the traffic flow in different time periods, thus relieving the traffic pressure. The variable lane refers to a lane with multiple oblique lines drawn on its inner side of the lane, which can change an indication direction according to the traffic flow at any time. For example, the vehicle entering the variable lane needs to be driven along a direction indicated by a guide arrow in an indication sign mounted above the variable lane. When a red fork light above the variable lane turns on, the vehicle is not allowed to drive into the variable lane. The driving direction for the variable lane is determined based on the traffic flow of vehicles. For example, when the traffic flow in a left one of the variable lane is large, the sign of the variable lane may indicate a left turn arrow to relieve the pressure of the left lane. Similarly, when the pressure in a right lane of the variable lane is large, the sign of the variable lane may indicate a right turn arrow.
The first driving direction may be a traffic flow direction corresponding to the current variable lane, such as a straight direction, a left turn direction, which is not limited herein. The second driving direction may be a traffic flow direction corresponding to a lane adjacent to the variable lane.
Alternatively, there may be a plurality of methods for acquiring traffic data. For example, the traffic data may be acquired by performing image recognition on video stream information; alternatively, the traffic data may be acquired by sensing vehicles through geomagnetism buried under both sides of the variable lane; alternatively, the traffic data may be acquired by analyzing trajectory data information in the Internet of vehicles. In addition, the traffic data may be also acquired by performing detection and sense through a sensor, a communication device, a radar, or the like, which is not limited herein.
Alternatively, the traffic data may be acquired by combining two or more of the above methods for acquiring traffic data. Therefore, reliable data support may be provided for overall analysis of a traffic network, and conditions are provided for flexibly regulating the variable lane, which improves the feasibility and universality of the embodiments of the disclosure.
Traffic data may be any data that may represent driving states for the first driving direction and the second driving direction. Alternatively, the traffic data may be a green light duration, a waste duration, a red light queue length, and a saturated time headway in each signal period, which is not limited herein.
In the disclosure, a time difference between the front end of two vehicles (e.g., a front vehicle and a rear vehicle) passing through the same location may be taken as a time headway. It may be understood that, in actual applications, when the green light is on, a difference for the time headway between vehicles may gradually be changed from relatively large to stable with the increase of passing vehicles. In the disclosure, the gradually stable time headway may be taken as the saturated time headway for any lane.
The waste duration may be a duration when there may be no vehicle to pass through the lane in both directions due to visual delay and vehicle start time.
The red light queue length may be a vehicle queue length during a time period when the red light is on, for example, a distance from a head of the vehicle at an intersection of any lane to a tail of the last vehicle at the queue tail, which is not limited herein.
At 102, a first saturation in the first driving direction and a second saturation in the second driving direction are determined based on the traffic data.
It may be understood that, the saturation may be configured to represent a ratio of an actual traffic flow of a lane to the saturated traffic flow of the lane. The disclosure may first calculate a first saturation in a first driving direction corresponding to the variable lane and a second saturation in a second driving direction adjacent to the variable lane, and determine a solution for controlling a variable lane based on the first saturation and the second saturation.
The first saturation may be a saturation of a variable lane corresponding to a first driving direction, and the second saturation may be a saturation of a lane adjacent to the variable lane corresponding to a second driving direction.
It should be noted that, the traffic flow direction of vehicles in each lane is controlled in nature by a traffic light. As a possible implementation, the disclosure may calculate a saturation corresponding to the lane based on the green light duration, the waste duration, the saturated time headway and the red light queue length.
Alternatively, the apparatus may determine a first saturation in the first driving direction based on the green light duration, the waste duration, the red light queue length, and the saturated time headway within each signal period in the first driving direction, and determine a second saturation in the second driving direction based on the green light duration, the waste duration, the red light queue length, and the saturated time headway within each signal period in the second driving direction.
For convenience of description, taking traffic data corresponding to the first driving direction for an example, denoting the green light duration as green(Lane_1), the waste duration as waste(Lane_1), the red light queue length as Queue_Red(Lane_1), and the saturated time headway as H(Lane_1), the first saturation DS(Lane_1) may be calculated by the following formula:
DS(Lane_1)=[green(Lane_1)−waste(Lane_1)+Queue_Red(Lane_1)*H(Lane_1)]/green(Lane_1).
It should be noted that the second saturation DS(Lane_2) also may be calculated referring to the above.
As a possible implementation, the disclosure may further calculate the saturation based on the green light duration and the waste duration. For example, the waste duration is subtracted from the green light duration to acquire effective green light duration, and then the ratio of the effective green light duration to the green light duration is calculated as a degree of saturation. Therefore, a first saturation in the first driving direction and a second saturation in the second driving direction are calculated respectively based on the traffic data for the first driving direction corresponding to the variable lane and the traffic data for the second driving direction adjacent to the variable lane, which are not limited herein.
At 103, an imbalance coefficient between the first driving direction and the second driving direction is determined based on the first saturation and the second saturation.
It should be noted that, after the first saturation and the second saturation are acquired, the apparatus may determine an imbalance coefficient between the first driving direction and the second driving direction accordingly. The imbalance coefficient may be configured to represent an imbalance situation of a first saturation corresponding to the first driving direction and a second saturation corresponding to the second driving direction, so as to provide a basis for a suitable solution for controlling a variable lane.
For convenience of description, denoting the first saturation as DS(Lane_1), and the second saturation as DS(Lane_2), an imbalance coefficient DS_Diff_Rate may be calculated by the following formula:
DS_Diff_Rate=[DS(Lane_1)−DS(Lane_2)]/DS(Lane_1).
At 104, a driving direction of the variable lane may be controlled based on the imbalance coefficient.
It should be noted that, after determining the imbalance coefficient, the apparatus may control a driving direction of the variable lane based on the imbalance coefficient. For example, the variable lane is controlled to maintain the original first driving direction, or the variable lane is changed to a second driving direction.
For example, a reference range of the imbalance coefficient may be preset. In response to the imbalance coefficient being within the reference range, the variable lane may be controlled to maintain the original first driving direction unchanged, and in response to the imbalance coefficient being not within the reference range, the first driving direction of the current variable lane may be changed to the second driving direction, which is not limited herein. It should be noted that, the reference range may be preset based on previous historical data, and may be set based on actual road conditions of the current variable lane, which is not limited herein.
In the embodiments of the disclosure, the apparatus first acquires the traffic data for the first driving direction corresponding to the variable lane and the traffic data for the second driving direction adjacent to the variable lane, determines the first saturation in the first driving direction and the second saturation in the second driving direction based on the traffic data, determines the imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation, and controls the driving direction of the variable lane based on the imbalance coefficient. Therefore, the imbalance coefficient may be taken as an indicator to finally determine whether the direction of the variable lane needs to be changed. By calculating the saturation corresponding to the variable lane and the saturation corresponding to the lane adjacent to the variable lane, it may be determined whether the saturations are unbalanced, thus providing a reliable basis for a solution of controlling a variable lane. As such, the traffic capacity in various directions may be improved as much as possible through reliable control of the variable lane and the road network may be effectively planned.
As illustrated in
At 201, traffic data for a first driving direction corresponding to a variable lane and traffic data for a second driving direction adjacent to the variable lane are acquired.
At 202, a first saturation in the first driving direction and a second saturation in the second driving direction are determined based on the traffic data.
At 203, an imbalance coefficient between the first driving direction and the second driving direction is determined based on the first saturation and the second saturation.
It should be noted that, the specific implementations at 201, 202 and 203 may refer to the above embodiments, which are not repeated here.
At 204, it is acquired attribute information of an intersection where the variable lane is located.
The attribute information may be a number of lanes and lane types at the intersection where the variable lane is located, construction situation around the intersection, a slope of the intersection, etc., which are not limited here.
As a possible implementation, the apparatus may first acquire video information of the intersection where the variable lane is located, and acquire the situation of the intersection where the variable lane is located by performing image processing on the video images, for example, whether construction is carried out, whether a roadblock is set and the slope and width information of the intersection, which are not limited here.
At 205, a reference range between the first driving direction and the second driving direction may be determined based on the attribute information.
It should be noted that, since information such as the slope of the intersection, signs, and the construction situation may affect traffic conditions of each lane at the current intersection, such as a vehicle queue length, residence time and passing time. Therefore, the apparatus may determine a reference range corresponding to the first driving direction and the second driving direction at the intersection where the current variable lane is located by determining the reference range between the first driving direction and the second driving direction based on the attribute information, thus facilitating to make a reasonable lane adjustment and effectively avoiding a traffic imbalance.
At 206, in response to the imbalance coefficient being within the reference range, a first driving direction of the variable lane keeps unchanged.
It should be noted that, in response to the imbalance coefficient being within the reference range, it indicates that the pressure of the current variable lane in the first direction is relatively large. Therefore, the first driving direction of the variable lane may keep unchanged, so as to relieve traffic pressure in the first driving direction.
As a possible implementation, when the imbalance coefficient is within a reference range and the current variable lane or the lane adjacent to the variable lane satisfies other necessary conditions, the apparatus needs to adjust a driving direction of the variable lane. For example, in response to the red light queue length in the first driving direction being greater than a first threshold of red light queue, or in response to the red light queue length in the second driving direction being greater than a second threshold of red light queue, the driving direction of the variable lane may be adjusted to a second driving direction. It may be described as below in the disclosure.
Alternatively, a first threshold of red light queue corresponding to the first driving direction and a second threshold of red light queue corresponding to the second driving direction are acquired first.
The threshold of red light queue may be a threshold of a red light queue length. The threshold may be a preset threshold determined based on the previous historical data information, or, and further may be a threshold determined based on current traffic data and attribute information of the intersection, which is not limited here.
The first threshold of red light queue corresponds to a first driving direction, and may be determined based on the traffic data of the variable lane. The second threshold of red light queue corresponds to a second driving direction, and may be determined based on the traffic data of the lane adjacent to the variable lane.
Further, in response to the red light queue length in the first driving direction being greater than the first threshold of red light queue, or in response to the red light queue length in the second driving direction being greater than the second threshold of red light queue, the driving direction of the variable lane may be adjusted to a second driving direction.
For convenience of description, a red light queue length of the variable lane in the first driving direction, that is, a queue value at the moment when the red light is on is denoted as Queue1_Red(lanei), and the threshold of red light queue corresponding to the variable lane is denoted as Queue1_Red_min. When Queue1_Red(lanei) >Queue1_Red_min, the apparatus needs to change a driving direction of the current variable lane to a second driving direction, thus relieving the traffic pressure.
At 207, the driving direction of the variable lane may be adjusted to a second driving direction in response to the imbalance coefficient being not within the reference range.
It should be noted that, when the imbalance coefficient is not within a reference range, which indicates that the traffic pressure of the variable lane is less than that of the adjacent lane, the apparatus may adjust the driving direction of the variable lane to a second driving direction. For example, the straight direction is changed to a left turn direction, thus reducing the stranded vehicles in the second driving direction, decreasing a vehicle queue length and a driving delay, and improving a traffic capability.
In embodiments of the disclosure, the apparatus first acquires the traffic data for the first driving direction corresponding to the variable lane and the traffic data for the second driving direction adjacent to the variable lane, determines the first saturation in the first driving direction and the second saturation in the second driving direction based on the traffic data, determines the imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation, acquires attribute information of the intersection where the variable lane is located, determines a reference range between the first driving direction and the second driving direction and based on the attribute information, keeps a first driving direction of the variable lane unchanged in response to the imbalance coefficient being within the reference range, and adjusts the driving direction of the variable lane to a second driving direction in response to the imbalance coefficient being not within the reference range. As such, the reference range is determined based on the attribute information of the intersection, and the reference range of the imbalance coefficient may be determined based on the actual situations of the intersection, which provides a basis for determining whether the variable lane needs to be changed in real time, and further may reduce the stranded vehicles, the vehicle queue length and the driving delay.
As illustrated in
At 301, traffic data for a first driving direction corresponding to a variable lane and traffic data for a second driving direction adjacent to the variable lane are acquired.
At 302, a first saturation in the first driving direction and a second saturation in the second driving direction are determined based on the traffic data.
At 303, an imbalance coefficient between the first driving direction and the second driving direction is determined based on the first saturation and the second saturation.
At 304, a driving direction of the variable lane may be controlled based on the imbalance coefficient.
It should be noted that, the specific implementations at 301, 302, 303 and 304 may refer to the above embodiments, which are not repeated here.
At 305, in response to adjusting the driving direction of the variable lane to the second driving direction, the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction are determined.
It should be noted that, in case that the driving direction of the variable lane is adjusted to the second driving direction, the traffic data respectively corresponding to the first and second driving directions may change as their respective lane numbers change. In the disclosure, in order to avoid an abnormal situation in the first driving direction caused due to the driving direction change of the variable lane, traffic data and vehicle number in the first driving direction and traffic data and vehicle number in the second driving direction may continue to be monitored, and the variable lane and the traffic light timing may be timely and effectively controlled based on the actual needs.
In the disclosure, the specific implementation of acquiring the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction may refer to the relevant description of the above embodiments, which is not repeated here.
At 306, traffic light states respectively corresponding to the first driving direction and the second driving direction are controlled based on the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction.
As a possible implementation, after the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction are acquired, a green light signal ratio may be calculated based on each of the acquired traffic data and the vehicle number information. For example, a first green signal ratio corresponding to the first driving direction and a second green signal ratio corresponding to the second driving direction may be determined based on a period duration of a traffic light, a total waste duration in each signal period, a red light duration within each signal period in each driving direction, and a traffic flow in each driving direction from the traffic data.
It should be noted that, the green signal ratio may be generally a ratio of effective green light duration in any signal period to a signal period duration. A time loss may occur inevitably when the traffic light performs signal switching. For example, when the green light is about to end, the driver is stopped at a slow speed to wait and any vehicle cannot be driven in the loss time. Therefore, the green signal ratio may be calculated based on the actual passing time of the vehicle, that is, the effective green light duration.
In the disclosure, in order to determine the green signal ratio in each driving direction more accurately, it may be calculated based on the period duration of the traffic light, the total waste duration in each signal period, the red light duration within each signal period in each driving direction, and the traffic flow in each driving direction from the traffic data.
Alternatively, the green signal ratio may be calculated by the following formula:
where C0 is a period duration of a traffic light, L is a total waste duration in each signal period, Y is a sum of traffic flows in various driving directions, A is a red light duration in each signal duration, yi is a traffic flow in any driving direction i, li is an initial waste duration in the driving direction i, and gi is a green signal ratio corresponding to any driving direction i.
It should be noted that, a first green signal ratio g1 corresponding to the first driving direction and a second green signal ratio g2 corresponding to the second driving direction may be calculated in the above formula based on the period duration of the traffic light, the total waste duration in each signal period, the red light duration within each signal period in each driving direction and the traffic flow in each driving direction, from the traffic data respectively corresponding to the first driving direction and the second driving direction.
Further, a first predicted traffic volume corresponding to the first driving direction and a second predicted traffic volume corresponding to the second driving direction may be determined based on the adjusted lane number corresponding to each driving direction, a historical lane number and a historical traffic volume associated with the current moment.
Alternatively, the first or second predicted traffic volume may be calculated by the following formula:
where Ri is a predicted traffic volume in any driving direction i, ri is a historical traffic volume corresponding to the driving direction i, Li is an adjusted lane number corresponding to the driving direction i, and li is a historical lane number associated with the current moment.
It should be noted that, the apparatus may determine the first predicted traffic volume corresponding to the first driving direction based on an adjusted lane number corresponding to the first driving direction, a first historical lane number and a first historical traffic volume associated with the current moment in the first driving direction; and the apparatus may determine the second predicted traffic volume corresponding to the second driving direction based on an adjusted lane number corresponding to the second driving direction, and a second historical lane number and a second historical traffic volume associated with the current moment in the second driving direction.
The first historical lane number may be a lane number in the first driving direction of the intersection at a historical moment corresponding to the current moment, and the second historical lane number may be a lane number in the second driving direction of the intersection at a historical moment corresponding to the current moment. The first historical traffic volume may be a number of historical vehicles passing in the first driving direction, and the second historical traffic volume may be a number of historical vehicles passing in the second driving direction.
That is, the predicted traffic volume corresponding to any driving direction may be determined by calculating the ratio of the adjusted lane number in any driving direction to the historical lane number associated with the current moment, and multiplying the ratio by the corresponding historical traffic volume.
Further, a sum of green light durations in each signal period corresponding to the first driving direction is determined based on the first green signal ratio and the first predicted traffic volume, and a sum of green light durations in each signal period corresponding to the second driving direction is determined based on the second green signal ratio and the second predicted traffic volume.
Alternatively, a sum of green light durations in each signal period corresponding to the first driving direction and a sum of green light durations in each signal period corresponding to the second driving direction may be determined by the following formula G1:
where gi is a green signal ratio in any driving direction i, and Ri is a predicted traffic flow in any driving direction i.
Based on the above formula, a sum of green light durations corresponding to the first driving direction may be calculated based on the first green light ratio and the first predicted traffic volume, and a sum of green light durations corresponding to the second driving direction may be calculated based on the second green light ratio and the second predicted traffic volume.
Further, based on a preset green light duration difference range and the sum of green light durations, a first green light duration in each signal period corresponding to the first driving direction and a second green light duration in each signal period corresponding to the second driving direction may be determined.
The first green light duration may be green time corresponding to the first driving direction after switching the variable lane, and the second green time may be green time corresponding to the second driving direction after switching the variable lane.
Alternatively, it may be calculated by the following formula:
G
vlan
>
g
vlan+Δvlan
where taking vlan as a driving direction after a variable lane is switched, Δvlan is a preset difference range of green light duration, gvlan is a green light duration corresponding to the driving direction vlan before a variable lane is switched, and Gvlan is a green light duration corresponding to the driving direction vlan after a variable lane is switched.
It should be noted that, in response to the sum of green light durations corresponding to any driving direction, Gvlan, satisfying the condition in the above formula (which is greater than the preset difference range of green light duration and the green light duration corresponding to the driving direction before the variable lane is switched), a traffic light does not need to be adjusted. When Gvlan does not satisfy the condition in the above formula, the traffic light may be reconfigured based on the calculated green light duration.
Therefore, a first green light duration in each signal period corresponding to the first driving direction and a second green light duration in each signal period corresponding to the second driving direction may be calculated respectively from the above formula.
In the embodiments of the disclosure, the apparatus first acquires the traffic data for the first driving direction corresponding to the variable lane and the traffic data for the second driving direction adjacent to the variable lane, determines the first saturation in the first driving direction and the second saturation in the second driving direction based on the traffic data, and determines the imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation. In case that the driving direction of the variable lane is adjusted to the second driving direction, the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction are determined. Finally, the traffic light states respectively corresponding to the first driving direction and the second driving direction are controlled based on the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction. As such, the traffic capability may be adjusted on time by controlling the traffic light states, so as to obtain an optimal allocation duration of the traffic light. Therefore, dynamic combination optimization may be achieved from time and space, which avoids a poor effect caused by a single control, with a good feasibility and universality.
In order to achieve the above embodiments, the disclosure further provides an apparatus for controlling a variable lane.
As illustrated in
The first acquiring module 410 is configured to acquire traffic data for a first driving direction corresponding to the variable lane and traffic data for a second driving direction adjacent to the variable lane; the first determining module 420 is configured to determine a first saturation in the first driving direction and a second saturation in the second driving direction based on the traffic data; the second determining module 430 is configured to determine an imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation; and the controlling module 440 is configured to control a driving direction of the variable lane based on the imbalance coefficient.
As a possible implementation, the traffic data includes a green light duration, a waste duration, a red light queue length, and a saturated time headway in each signal period, the first determining module is specifically configured to: determine a first saturation in the first driving direction based on the green light duration, the waste duration, the red light queue length, and the saturated time headway in each signal period in the first driving direction; and determine a second saturation in the second driving direction based on the green light duration, the waste duration, the red light queue length, and the saturated time headway in each signal period in the second driving direction.
As a possible implementation, the controlling module is specifically configured to: acquire attribute information of an intersection where the variable lane is located; determine a reference range between the first driving direction and the second driving direction based on the attribute information; keep a first driving direction of the variable lane unchanged in response to the imbalance coefficient being within the reference range; and adjust the driving direction of the variable lane to a second driving direction in response to the imbalance coefficient being not within the reference range.
As a possible implementation, the traffic data includes a red light queue length. The apparatus further includes a second acquiring module and an adjusting module.
The second acquiring module is configured to acquire a first threshold of red light queue corresponding to the first driving direction and a second threshold of red light queue corresponding to the second driving direction; and the adjusting module is configured to in response to the red light queue length in the first driving direction being greater than the first threshold of red light queue, or in response to the red light queue length in the second driving direction being greater than the second threshold of red light queue, adjust the driving direction of the variable lane to the second driving direction.
As a possible implementation, the controlling module further includes a determining unit and a determining unit.
The determining unit is configured to in response to adjusting the driving direction of the variable lane to the second driving direction, determine the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction; and the controlling unit is configured to control traffic light states respectively corresponding to the first driving direction and the second driving direction based on the adjusted traffic data and lane numbers respectively corresponding to the first driving direction and the second driving direction.
As a possible implementation, the controlling module includes a first determining subunit, a second determining subunit, and a third determining subunit.
The first determining subunit is configured to determine a first green signal ratio corresponding to the first driving direction and a second green signal ratio corresponding to the second driving direction based on a period duration of a traffic light, a total waste duration in each signal period, a red light duration within each signal period in each driving direction and a traffic flow in each driving direction from the traffic data; the second determining subunit is configured to determine a first predicted traffic volume corresponding to the first driving direction and a second predicted traffic volume corresponding to the second driving direction based on the adjusted lane number corresponding to each driving direction, a historical lane number and a historical traffic volume associated with the current moment; and the third determining subunit is configured to determine a sum of green light durations in each signal period corresponding to the first driving direction and the second driving direction, based on the first green signal ratio, the second green signal ratio, the first predicted traffic volume and the second predicted traffic volume.
As a possible implementation, the second determining subunit is specifically configured to: determine a first predicted traffic volume corresponding to the first driving direction based on the adjusted lane number corresponding to the first driving direction, a first historical lane number and a first historical traffic volume associated with the current moment in the first driving direction; and determine a second predicted traffic volume corresponding to the first driving direction based on the adjusted lane number corresponding to the second driving direction, a second historical lane number and a second historical traffic volume associated with the current moment in the second driving direction.
As a possible implementation, the third determining subunit is further configured to: determine a first green light duration in each signal period corresponding to the first driving direction and a second green light duration in each signal period corresponding to the second driving direction based on a preset green light duration difference range and the sum of green light durations.
In embodiments of the disclosure, the apparatus first acquires traffic data for a first driving direction corresponding to the variable lane and traffic data for a second driving direction adjacent to the variable lane, then determines a first saturation in the first driving direction and a second saturation in the second driving direction based on the traffic data, and determines an imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation, and controls a driving direction of the variable lane based on the imbalance coefficient. Therefore, the imbalance coefficient may be taken as an indicator, to finally determine whether the direction of the variable lane needs to be changed. By calculating saturations corresponding to the variable lane and the lane adjacent to the variable lane, it may be determined whether the saturations are unbalanced, thereby providing a reliable basis for providing a solution for controlling a variable lane, so that through reliable control of the variable lane, the traffic capacity in all directions may be improved as much as possible and a road network may be effectively planned.
According to the embodiments of the disclosure, the disclosure further provides an electronic device, a readable storage medium and a computer program product.
As illustrated in
Several components in the device 500 are connected to the I/O interface 505, and include: an input unit 506, for example, a keyboard, a mouse, etc.; an output unit 507, for example, various types of displays, speakers, etc.; a storage unit 508, for example, a magnetic disk, an optical disk, etc.; and a communication unit 509, for example, a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices over a computer network such as the Internet and/or various telecommunication networks.
A computing unit 501 may be various types of general and/or dedicated processing components with processing and computing ability. Some examples of a computing unit 501 include but not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running a machine learning model algorithm, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processings described above, for example, a method for controlling a variable lane. For example, in some embodiments, a method for controlling a variable lane may be achieved as a computer software program tangibly included in a machine-readable medium, such as a storage unit 508. In some embodiments, a part or all of computer programs may be loaded and/or installed on the device 500 through a ROM 502 and/or a communication unit 509. When the computer programs are loaded in a RAM 503 and performed by a computing unit 501, one or more blocks of the above method for controlling a variable lane may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method for controlling a variable lane by any other suitable means (for example, by means of a firmware).
Various implementation modes of the systems and technologies described above may be implemented in a digital electronic circuit system, a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), an application specific standard product (ASSP), a system-on-chip (SOC) system, a complex programmable logic device, a computer hardware, a firmware, a software, and/or combinations thereof. The various implementation modes may include: being implemented in one or more computer programs, and the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, and the programmable processor may be a dedicated or a general-purpose programmable processor that may receive data and instructions from a storage system, at least one input apparatus, and at least one output apparatus, and transmit the data and instructions to the storage system, the at least one input apparatus, and the at least one output apparatus.
A computer code configured to execute a method in the present disclosure may be written with one or any combination of a plurality of programming languages. The programming languages may be provided to a processor or a controller of a general purpose computer, a dedicated computer, or other apparatuses for programmable data processing so that the function/operation specified in the flowchart and/or block diagram may be performed when the program code is executed by the processor or controller. A computer code may be performed completely or partly on the machine, performed partly on the machine as an independent software package and performed partly or completely on the remote machine or server.
In the context of the disclosure, a machine-readable medium may be a tangible medium that may contain or store a program intended for use in or in conjunction with an instruction execution system, apparatus, or device. A machine readable medium may be a machine readable signal medium or a machine readable storage medium. A machine readable storage medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any appropriate combination thereof. A more specific example of a machine readable storage medium includes an electronic connector with one or more cables, a portable computer disk, a hardware, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (an EPROM or a flash memory), an optical fiber device, and a portable optical disk read-only memory (CDROM), an optical storage device, a magnetic storage device, or any appropriate combination of the above.
In order to provide interaction with the user, the systems and technologies described here may be implemented on a computer, and the computer has: a display apparatus for displaying information to the user (for example, a CRT (cathode ray tube) or a LCD (liquid crystal display) monitor); and a keyboard and a pointing apparatus (for example, a mouse or a trackball) through which the user may provide input to the computer. Other types of apparatuses may further be configured to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (for example, visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including an acoustic input, a voice input, or a tactile input).
The systems and technologies described herein may be implemented in a computing system including back-end components (for example, as a data server), or a computing system including middleware components (for example, an application server), or a computing system including front-end components (for example, a user computer with a graphical user interface or a web browser through which the user may interact with the implementation mode of the system and technology described herein), or a computing system including any combination of such back-end components, middleware components or front-end components. The system components may be connected to each other through any form or medium of digital data communication (for example, a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), an internet and a blockchain network.
The computer system may include a client and a server. The client and server are generally far away from each other and generally interact with each other through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computer and having a client-server relationship with each other. A server may be a cloud server, also known as a cloud computing server or a cloud host, is a host product in a cloud computing service system, to solve the shortcomings of large management difficulty and weak business expansibility existed in the conventional physical host and Virtual Private Server (VPS) service. A server further may be a server with a distributed system, or a server in combination with a blockchain.
In the embodiments of the disclosure, the apparatus first acquires the traffic data for the first driving direction corresponding to the variable lane and the traffic data for the second driving direction adjacent to the variable lane, determines the first saturation in the first driving direction and the second saturation in the second driving direction based on the traffic data, determines the imbalance coefficient between the first driving direction and the second driving direction based on the first saturation and the second saturation, and controls the driving direction of the variable lane based on the imbalance coefficient. Therefore, the imbalance coefficient may be taken as an indicator to finally determine whether the direction of the variable lane needs to be changed. By calculating the saturation corresponding to the variable lane and the saturation corresponding to the lane adjacent to the variable lane, it may be determined whether the saturations are unbalanced, thus providing a reliable basis for a solution of controlling a variable lane. As such, the traffic capacity in various directions may be improved as much as possible through reliable control of the variable lane and the road network may be effectively planned.
It should be understood that, various forms of procedures shown above may be configured to reorder, add or delete blocks. For example, blocks described in the disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in the present disclosure may be achieved, which will not be limited herein.
The above specific implementations do not constitute a limitation on the protection scope of the disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions may be made according to design requirements and other factors. Any modification, equivalent replacement, improvement, etc., made within the spirit and principle of embodiments of the present disclosure shall be included within the protection scope of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202110691964.5 | Jun 2021 | CN | national |
