Intelligent connected vehicle lane-borrowing control method, device and electronic equipment

Abstract

The present invention provides a method, device and electronic device for controlling lane borrowing of an intelligent connected vehicle. The method comprises: obtaining the remaining time of the traffic light of the road section where the intelligent connected vehicle is located in the most recent traffic state; determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane; if the intelligent connected vehicle cannot pass through the intersection in the current lane and can pass through the intersection after borrowing the lane, determining the bus data of the road section where the intelligent connected vehicle is located; determining, based on the bus data, the real-time clearance distance of the buses on the road section where the intelligent connected vehicle is located; and determining, based on the real-time clearance distance and the vehicle position of the intelligent connected vehicle, whether the intelligent connected vehicle should borrow the lane. The present invention can clarify whether the intelligent connected vehicle needs to borrow the lane during lane borrowing control, thereby ensuring the driving advantage of the intelligent connected vehicle and the priority of buses in the bus lane.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method, device and electronic equipment for controlling lane borrowing of an intelligent connected vehicle. Background Art

[0002] In recent years, advances in internet, sensor, and communication technologies have enabled the gradual adoption of intelligent connected vehicles (ICVs). Compared to standard vehicles, ICVs possess superior sensory capabilities, relying on their own sensors to perceive their surroundings. ICVs enable vehicle-road and vehicle-vehicle collaboration, significantly improving the sensing range and accuracy of ICVs. However, when ICVs and standard vehicles mix on the road, collisions between the two types of vehicles can easily occur, hindering the full potential of ICVs.

[0003] Some related technologies involve adding dedicated lanes for ICVs, essentially creating an additional lane within the existing road layout for ICVs. However, this approach is often tied to large-scale road infrastructure construction projects. Given the spatial limitations and high construction costs of urban areas, its feasibility in densely populated city centers is limited. Other technologies involve the transformation and upgrading of existing lanes, allowing ICVs to use bus lanes.

[0004] Therefore, how to control intelligent connected vehicles to use bus lanes while ensuring the driving advantages of intelligent connected vehicles and the priority of buses in bus lanes has become an urgent problem that needs to be solved. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, and electronic device for controlling lane borrowing of an intelligent connected vehicle, so as to clarify whether an intelligent connected vehicle needs to borrow lanes during lane borrowing control, thereby ensuring the driving advantages of the intelligent connected vehicle and the priority of buses in bus lanes.

[0006] In a first aspect, an embodiment of the present invention provides a method for controlling lane borrowing of an intelligent connected vehicle, comprising:

[0007] Obtain the remaining time of the traffic light in the nearest traffic state on the road section where the intelligent connected vehicle is located;

[0008] Determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane;

[0009] If the intelligent connected vehicle cannot pass through the intersection in the current lane, but can pass through the intersection after borrowing the lane, then determine the public transportation data of the road section where the intelligent connected vehicle is located;

[0010] Determine the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data;

[0011] Determine whether the intelligent connected vehicle is traveling in a borrowed lane based on the real-time clearing distance and the vehicle position of the intelligent connected vehicle.

[0012] In one possible implementation, determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane includes:

[0013] Determine the length of the queue of vehicles preceding the intelligent connected vehicle in the current lane based on the position of the intelligent connected vehicle, calculate the queue-clearing time required for the intelligent connected vehicle based on the queue length, and calculate the lane-changing waiting time for the intelligent connected vehicle to pass through the bus lane from the current lane based on the position of the intelligent connected vehicle;

[0014] It is determined whether the intelligent connected vehicle can pass through the intersection in the current lane based on the queue dissipation time and the remaining time, and it is determined whether the intelligent connected vehicle can pass through the intersection after borrowing the lane based on the lane change waiting time and the remaining time.

[0015] In a possible implementation, after determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane, the method further includes:

[0016] If the intelligent connected vehicle can pass through the intersection in the current lane, or the intelligent connected vehicle cannot pass through the intersection after borrowing the lane, it is determined that the intelligent connected vehicle does not need to borrow the lane, and the intelligent connected vehicle is controlled to continue driving in the current lane.

[0017] In one possible implementation, the bus data includes the free length of the bus lane on the road section where the intelligent connected vehicle is located, the driving type of the bus driver on the road section where the intelligent connected vehicle is located, and the real-time speed of the bus;

[0018] Determining the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data includes:

[0019] Obtaining the average speed of the intelligent connected vehicle, and calculating the difference between the average speed and the real-time speed;

[0020] Calculating an initial clearance distance for buses on the road section where the intelligent connected vehicle is located based on the free road length and the difference;

[0021] Based on the initial clearing distance and the driving type, a real-time clearing distance of buses on the road section where the intelligent connected vehicle is located is calculated.

[0022] In a possible implementation, before determining the public transportation data of the road section where the intelligent connected vehicle is located, the method further includes:

[0023] Obtain the occupancy data of bus lanes and bus operation trajectories in the previous historical period before the current period;

[0024] Inputting the occupancy data and the running trajectory into a preset idleness prediction model to obtain a predicted idle length of the bus lane on each road section output by the idleness prediction model; wherein the idleness prediction model is trained based on pre-acquired historical occupancy data and historical running trajectories, as well as the corresponding historical idle lengths of the bus lane;

[0025] Obtain real-time positioning data of multiple buses on each road section;

[0026] The predicted idle length is corrected according to the real-time positioning data to obtain the idle length of the bus lane on each road section.

[0027] In one possible implementation, determining whether the intelligent connected vehicle is traveling in a borrowed lane according to the real-time clearing distance and the vehicle position of the intelligent connected vehicle includes:

[0028] Determining a location for the intelligent connected vehicle to borrow lanes for bus lanes based on the vehicle location of the intelligent connected vehicle;

[0029] Determine whether the intelligent connected vehicle is traveling in the borrowed lane according to the borrowed lane position and the real-time clearing distance.

[0030] In a possible implementation, calculating the queue-clearing time required for the intelligent connected vehicle based on the queue length includes:

[0031] According to the expression: Calculating the queue dissipation time required for the intelligent connected vehicles;

[0032] Where t represents the queue dissipation time, L det Indicates the queue length of vehicles in front of the intelligent connected vehicle in the current lane, represents the upper limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located, and v represents the lower limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located.

[0033] In a possible implementation, calculating the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the idle length of the road, the driving type, and the difference includes:

[0034] According to the expression: Calculate the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located;

[0035] Where, d clear represents the real-time clearing distance, d represents the idle length of the road, v e Indicates the real-time speed of the bus. represents the average speed of the intelligent connected vehicle, and a represents the weight of the driving type corresponding to the bus driver.

[0036] In a second aspect, an embodiment of the present invention provides a lane-borrowing control device for an intelligent connected vehicle, comprising:

[0037] An acquisition module is used to obtain the remaining time of the traffic light in the nearest traffic state on the road section where the intelligent connected vehicle is located;

[0038] a judgment module, configured to determine, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane;

[0039] a determination module, configured to determine the public transportation data of the road section where the intelligent connected vehicle is located if the intelligent connected vehicle cannot pass through the intersection in the current lane but can pass through the intersection after borrowing the lane;

[0040] A calculation module, configured to determine, based on the bus data, a real-time clearance distance of buses on the road section where the intelligent connected vehicle is located;

[0041] The control module determines whether the intelligent connected vehicle is traveling in a borrowed lane according to the real-time clearing distance and the vehicle position of the intelligent connected vehicle.

[0042] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0044] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0045] The embodiment of the present invention determines whether the intelligent connected vehicle can pass through the intersection in the current lane and after borrowing the lane through the remaining time of the traffic light in the most recent traffic state. The necessity of the intelligent connected vehicle to borrow the lane can be judged first; if the intelligent connected vehicle cannot pass through the intersection in the current lane, but can pass through the intersection after borrowing the lane, it means that it is beneficial for the intelligent connected vehicle to borrow the bus lane at this time, and borrowing the lane can be considered at this time; then, through the real-time clearance distance of the buses on the road section where the intelligent connected vehicle is located and the vehicle position of the intelligent connected vehicle, it can be further determined whether the intelligent connected vehicle can borrow the lane, and then the driving of the intelligent connected vehicle can be controlled. The intelligent connected vehicle can borrow the lane without affecting the normal operation of the buses, thereby making full use of the bus lane and giving play to the driving advantages of the intelligent connected vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 This is a diagram of an application scenario of the intelligent connected vehicle lane borrowing control method provided by an embodiment of the present invention;

[0048] Figure 2 This is a flowchart of an implementation method of a lane borrowing control method for an intelligent connected vehicle provided by an embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the remaining time of a traffic light passing state provided by an embodiment of the present invention;

[0050] Figure 4 is a schematic diagram of the real-time clearing distance of a bus provided by an embodiment of the present invention;

[0051] Figure 5 This is a flowchart of another method for controlling lane borrowing of an intelligent connected vehicle provided by an embodiment of the present invention;

[0052] Figure 6 2 is a schematic structural diagram of a lane borrowing control device for an intelligent connected vehicle provided by an embodiment of the present invention;

[0053] Figure 7 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0055] The inventors have discovered that among the technologies for controlling the use of bus lanes by intelligent connected vehicles, some directly control the use of bus lanes by intelligent connected vehicles, some allow the use of bus lanes during periods of the day when bus departure frequency is low, and some determine whether the intelligent connected vehicle can use the bus lane at this time based on the positional relationship between the platform, the intelligent connected vehicle, and the bus. However, these methods only consider the use of bus lanes by intelligent connected vehicles, making it difficult to simultaneously ensure the driving advantages of intelligent connected vehicles and the principle of bus priority in bus lanes. Moreover, these methods only consider whether intelligent connected vehicles can enter bus lanes, but do not consider whether it is necessary for intelligent connected vehicles to enter bus lanes. Therefore, it is necessary to consider a new method for controlling the use of bus lanes by intelligent connected vehicles.

[0056] In order to consider whether the intelligent connected vehicle needs to borrow lanes when it is controlled, and to ensure the driving advantages of the intelligent connected vehicle and the principle of priority for buses in bus lanes, in the implementation mode of this application, the necessity of the intelligent connected vehicle borrowing lanes is judged, and it is determined whether the intelligent connected vehicle can pass through the intersection in the current lane and after borrowing lanes within the remaining time of the traffic light in the most recent traffic state, so as to judge whether it is more advantageous for the intelligent connected vehicle to borrow lanes than in ordinary lanes; if borrowing lanes is advantageous, a judgment is made on whether it is possible to borrow lanes, and by real-time clearing distance and the vehicle position of the intelligent connected vehicle, it is determined whether the intelligent connected vehicle borrowing lanes will affect the normal operation of buses, and finally the intelligent connected vehicle is controlled to drive in the corresponding lanes, so that the intelligent connected vehicle can borrow lanes without affecting the normal operation of buses, fully utilize bus lanes, and give play to the driving advantages of intelligent connected vehicles.

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0058] like Figure 1 The application scenario diagram of the intelligent connected vehicle lane borrowing control method shown in the figure, Figure 1 The figure shows an intersection and a road section with multiple lanes, where the road section only shows a one-way section, which includes ordinary lanes and bus lanes. Intelligent connected vehicles and other ordinary vehicles usually travel on ordinary lanes. In some cases, intelligent connected vehicles can use the bus lanes.

[0059] Figure 2 The following is a flowchart of the implementation of the lane borrowing control method for an intelligent connected vehicle provided in an embodiment of the present invention:

[0060] Step S201: Obtain the remaining time of the traffic light on the road section where the intelligent connected vehicle is located in the most recent traffic state.

[0061] In this embodiment, the traffic state refers to the green state when the traffic light is in a traffic light or red-yellow-green state, and the flashing yellow state when the traffic light is in a continuously flashing yellow state. When the traffic light is in a traffic light or red-yellow-green state, if the traffic light is currently green, the most recent traffic state refers to the current green light; if the traffic light is currently red, the most recent traffic state refers to the next green light, that is, the green light after the current red light. When the traffic light is in a continuously flashing yellow state, indicating that the traffic signal has been temporarily lifted and vehicles can pass through the intersection, the most recent traffic state refers to the current continuously flashing yellow light.

[0062] like Figure 3 The schematic diagram of the remaining time of the traffic light's passage state is shown. When the current time is a, the traffic light is currently green, and the remaining time of the traffic light in the most recent passage state is A; when the current time is b, the traffic light is currently red, and the remaining time of the traffic light in the most recent passage state is B.

[0063] In addition, if the traffic light is a continuously flashing yellow light, the remaining time of the traffic light in the most recent traffic state can be considered to be infinite.

[0064] Step S202: Determine, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane.

[0065] In this embodiment, whether the intelligent connected vehicle can travel to the intersection within the remaining time can be used to determine whether the intelligent connected vehicle can pass through the intersection when the traffic light is in the nearest traffic state.

[0066] Here, the main control principle is to give priority to the intelligent connected vehicle driving in the current lane. Therefore, it is first judged whether the intelligent connected vehicle can pass through the intersection in the current lane within the remaining time, and whether it can pass through the intersection within the remaining time after borrowing the lane. Only when borrowing the lane is advantageous, the intelligent connected vehicle is controlled to borrow the lane. This can reduce the situation of intelligent connected vehicles changing lanes and reduce the impact on the driving of vehicles on the road. In addition, when driving in the current lane, the intelligent connected vehicle passes the intersection within the remaining time, which can simultaneously ensure the driving advantages of the intelligent connected vehicle and the priority of buses in the bus lane.

[0067] Among them, the lane where the intelligent connected vehicle is currently located is the current lane, which is generally an ordinary lane other than the bus lane.

[0068] In step S203, if the intelligent connected vehicle cannot pass through the intersection in the current lane and can pass through the intersection after borrowing the lane, the public transportation data of the road section where the intelligent connected vehicle is located is determined.

[0069] In this embodiment, if the intelligent connected vehicle cannot pass through the intersection in the current lane, but can pass through the intersection after borrowing the lane, it means that it is advantageous for the intelligent connected vehicle to choose to borrow the lane at this time. In this case, the bus data of the road section at this time can be obtained to determine whether the intelligent connected vehicle's borrowing the lane will affect the driving of the bus, thereby clarifying whether the intelligent connected vehicle can borrow the lane.

[0070] Here, the bus data may include the real-time location, real-time speed, and corresponding driver of the bus on the road section where the intelligent connected vehicle is located, and other bus-related data.

[0071] Step S204: Determine the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data.

[0072] In this embodiment, when the bus lane is idle, non-public vehicles are allowed to enter. When a bus arrives, the bus lane is cleared to ensure that the bus is given priority. The range of the bus lane clearing is the real-time clearing distance of the bus. The real-time clearing distance can be set before and after the bus.

[0073] Here, based on the bus data, the buses near the intelligent connected vehicle and the real-time clearance distance of the buses can be determined.

[0074] Step S205: Determine whether the intelligent connected vehicle is traveling in a borrowed lane based on the real-time clearing distance and the vehicle position of the intelligent connected vehicle.

[0075] In this embodiment, by using the real-time clearing distance and the vehicle position of the intelligent connected vehicle, it can be determined whether the intelligent connected vehicle will enter the clearing distance of the bus if it wants to use the lane, and whether it will affect the normal operation of the bus, thereby controlling whether the intelligent connected vehicle uses the lane.

[0076] Optionally, this embodiment determines whether the intelligent connected vehicle is traveling in the bus lane based on the real-time clearing distance and the vehicle position of the intelligent connected vehicle. It can first determine the borrowing position of the intelligent connected vehicle to the bus lane based on the vehicle position of the intelligent connected vehicle; and then determine whether the intelligent connected vehicle is traveling in the bus lane based on the borrowing position and the real-time clearing distance.

[0077] In this embodiment, when determining whether the intelligent connected vehicle is traveling in a borrowed lane, the borrowed lane position of the intelligent connected vehicle when changing lanes from the current vehicle position to the bus lane can be calculated based on the vehicle position of the intelligent connected vehicle. Figure 4The schematic diagram of the real-time clearing distance of the bus shown in the figure is as follows. Assuming that the intelligent connected vehicle changes lanes from lane 1 in the ordinary lane to the bus lane, the corresponding lane-borrowing position is Figure 4 Position P in.

[0078] Then change the borrowing position (such as Figure 4 The position P in the figure is compared with the real-time clearing distance of the bus, so as to determine whether the intelligent connected vehicle will affect the normal operation of the bus. Figure 4 If the real-time clearing distance A in the position P overlaps and intersects with the real-time clearing distance A, that is, the intelligent connected vehicle using the bus lane will affect the normal operation of the bus, then it is determined that the intelligent connected vehicle does not use the lane and continues to drive in the current lane. If the real-time clearing distance of the bus is Figure 4 If the real-time clearing distance B in the bus lane is 0, then there is no overlap or intersection between the position P and the real-time clearing distance B, that is, the intelligent connected vehicle using the bus lane does not affect the normal operation of the bus, then it can be determined that the intelligent connected vehicle is using the lane.

[0079] The above example only illustrates the real-time clearance distance in front of a bus. If there are multiple buses on the road section where the intelligent connected vehicle is located, the real-time clearance distance in front and behind each bus can be considered, and the borrowing position can be compared with each real-time clearance distance of each bus.

[0080] In addition, this embodiment determines whether the intelligent connected vehicle is traveling in the bus lane based on the real-time clearing distance and the vehicle position of the intelligent connected vehicle. Alternatively, it may first determine the clearing range corresponding to the real-time clearing position on the current lane of the intelligent connected vehicle, and then determine whether the vehicle position of the intelligent connected vehicle is within the above-mentioned clearing range. If the vehicle position is within the clearing range, it means that the intelligent connected vehicle's use of the bus lane will affect the normal operation of the bus, and it is determined that the intelligent connected vehicle does not use the bus lane and continues to travel in the current lane. If the vehicle position is not within the clearing range, it means that the intelligent connected vehicle's use of the bus lane will not affect the normal operation of the bus, and it can be determined that the intelligent connected vehicle is traveling in the bus lane.

[0081] The embodiment of the present invention determines whether the intelligent connected vehicle can pass through the intersection in the current lane and after borrowing the lane through the remaining time of the traffic light in the most recent traffic state. The necessity of the intelligent connected vehicle to borrow the lane can be judged first; if the intelligent connected vehicle cannot pass through the intersection in the current lane, but can pass through the intersection after borrowing the lane, it means that it is beneficial for the intelligent connected vehicle to borrow the bus lane at this time, and borrowing the lane can be considered at this time; then, through the real-time clearance distance of the buses on the road section where the intelligent connected vehicle is located and the vehicle position of the intelligent connected vehicle, it can be further determined whether the intelligent connected vehicle can borrow the lane, and then the driving of the intelligent connected vehicle can be controlled. The intelligent connected vehicle can borrow the lane without affecting the normal operation of the buses, thereby making full use of the bus lane and giving play to the driving advantages of the intelligent connected vehicle.

[0082] The above describes the overall process of lane-borrowing control for intelligent connected vehicles. The following details the process of determining whether an intelligent connected vehicle can pass through an intersection in its current lane and whether it can pass through the intersection after borrowing lanes.

[0083] In some embodiments, based on the remaining time, it is determined whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane. The method can be to first determine the queue length of vehicles before the intelligent connected vehicle in the current lane based on the vehicle position of the intelligent connected vehicle, calculate the queue dissipation time required for the intelligent connected vehicle based on the queue length, and calculate the lane change waiting time for the intelligent connected vehicle to borrow the lane from the current lane to the bus lane based on the vehicle position of the intelligent connected vehicle; then, based on the queue dissipation time and the remaining time, determine whether the intelligent connected vehicle can pass through the intersection in the current lane, and based on the lane change waiting time and the remaining time, determine whether the intelligent connected vehicle can pass through the intersection after borrowing the lane.

[0084] In this embodiment, the queue length of the vehicles in front can be analyzed according to the vehicle position of the intelligent connected vehicle. The queue length can be used to analyze the queue dissipation time required by the intelligent connected vehicle in real time, thereby judging whether the intelligent connected vehicle can pass through the intersection in the current lane.

[0085] Optionally, this embodiment calculates the queue-clearing time required for the intelligent connected vehicle based on the queue length, which can be based on the expression: Calculate the queue dissipation time required for intelligent connected vehicles; where t represents the queue dissipation time, L det Indicates the queue length of vehicles in front of the intelligent connected vehicle in the current lane, It represents the upper limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located, and v represents the lower limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located.

[0086] In this embodiment, the lane change waiting time of the intelligent connected vehicle, which is used to transfer to the bus lane and pass through the intersection in the bus lane, can also be analyzed based on the vehicle's position, thereby determining whether the intelligent connected vehicle can pass through the intersection in the bus lane. Here, the lane change waiting time includes the lane change time of the intelligent connected vehicle from the current lane to the bus lane, as well as the queue time at the intersection when transferring to the bus lane.

[0087] If the queue dissipation time is less than the remaining time, it means that the intelligent connected vehicle can pass through the intersection in the current lane within the most recent traffic state of the traffic light; if the queue dissipation time is greater than or equal to the remaining time, it means that the intelligent connected vehicle will find it difficult to pass through the intersection in the current lane within the most recent traffic state of the traffic light.

[0088] Correspondingly, if the lane change waiting time is less than the remaining time, it means that within the most recent traffic state of the traffic light, the intelligent connected vehicle can pass through the intersection by using the bus lane; if the queue dissipation time is greater than or equal to the remaining time, it means that within the most recent traffic state of the traffic light, even if the intelligent connected vehicle uses the bus lane, it will be difficult to pass through the intersection.

[0089] At this point, it should be noted that in the above process of determining whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane, there is no restriction on the order of determining whether the intelligent connected vehicle can pass through the intersection in the current lane (step one) and determining whether the intelligent connected vehicle can pass through the intersection after borrowing the lane (step two). Step one can be executed first, then step two, or step two can be executed first, then step one, or step one and step two can be executed at the same time.

[0090] After determining the intersection using the method described above, if the connected vehicle cannot pass through the intersection in its current lane but can pass through the intersection after using the new lane, it will consider using the new lane. The feasibility of using the new lane must be determined, and the real-time clearance distance for the bus must be calculated during this process. The following details the calculation process for the real-time clearance distance for the bus.

[0091] In some embodiments, the bus data may include the free length of the bus lane on the road section where the intelligent connected vehicle is located, the driving type of the bus driver on the road section where the intelligent connected vehicle is located, and the real-time speed of the bus.

[0092] Here, the road free length is the distance between all two adjacent buses after removing the real-time clearance distance. The road free length of the bus lane on the road section where the intelligent connected vehicle is located can be the road free length between the two buses in front and behind the intelligent connected vehicle.

[0093] The driving type represents the driving characteristics of the bus driver, which can include aggressive, general and conservative. The specific information can be obtained from the driver scheduling information of the bus company.

[0094] This embodiment determines the real-time clearing distance of buses on the road section where the intelligent connected vehicle is located based on public transportation data. The method may be to first obtain the average speed of the intelligent connected vehicle and calculate the difference between the average speed and the real-time speed; then, based on the idle length of the road and the difference, calculate the initial clearing distance of buses on the road section where the intelligent connected vehicle is located; finally, based on the initial clearing distance and the driving type, calculate the real-time clearing distance of buses on the road section where the intelligent connected vehicle is located.

[0095] In this embodiment, when calculating the real-time clearing distance of each bus, the initial clearing distance of the bus on the road section where the intelligent connected vehicle is located can be calculated based on the difference between the average speed and the real-time speed of the intelligent connected vehicle and the idle length of the road.

[0096] Considering that each bus driver has different driving characteristics, the traffic impact on the bus lane is also different. Therefore, the driving type corresponding to the bus driver can be used to correct the initial clearing distance of the corresponding bus to accurately obtain the real-time clearing distance corresponding to each bus.

[0097] Here, different weights may be set for different driving types. For example, the weight corresponding to the aggressive type may be 1.2, the weight corresponding to the general type may be 1.0, and the weight corresponding to the conservative type may be 0.8.

[0098] Alternatively, a driver's driving type can be determined using a driving type recognition model. For example, driving data for each driver, including steering wheel angle and speed, brake system pressure, and accelerator pedal depression, is obtained. This driving data is then fed into the driving type recognition model, which then outputs the driver's driving type. The driving type recognition model is trained based on historical driving data and corresponding driving types for different types of drivers.

[0099] Here, the driving type recognition model can be trained using a neural network model such as an LSTM model.

[0100] The following is a detailed description of how to obtain the idle length of the road in the above embodiment.

[0101] In some embodiments, before determining the bus data of the road section where the intelligent connected vehicle is located, the occupancy data of the bus lane and the operation trajectory of the bus in the previous historical period of the current period can be obtained first; then the occupancy data and the operation trajectory are input into a preset idle prediction model to obtain the predicted idle length of the bus lane of each road section output by the idle prediction model; wherein, the idle prediction model is trained based on the pre-acquired historical occupancy data and historical operation trajectory, as well as the corresponding historical idle length of the bus lane; then the real-time positioning data of multiple buses on each road section is obtained; finally, based on the real-time positioning data, the predicted idle length is corrected to obtain the road idle length of the bus lane on each road section.

[0102] In this embodiment, considering that the running trajectory and running process of the bus have strong time characteristics, the idle length of the road on the bus lane will also have strong time correlation. Therefore, the idle prediction model can be used to make a prediction to obtain the predicted idle length of the bus lane.

[0103] Because unexpected situations can occur on bus lanes, such as buses colliding with each other, real-time bus positioning data can be obtained and used to correct the predicted free length on the bus lane. For example, if the difference between the bus's real-time positioning data and the bus's position corresponding to the predicted free length is less than a preset deviation, the predicted free length is used as the free length of the road. If the difference is greater than or equal to the preset deviation, the free length of the road is recalculated based on the bus's real-time positioning data.

[0104] Here, the idle prediction model can also be trained using a neural network model such as an LSTM model.

[0105] In addition, in some embodiments, real-time bus location data and speed data can be obtained through a roadside information collection system. This roadside information collection system can be deployed on the road and can have image acquisition and radar detection capabilities. It can capture environmental impacts and track vehicle dynamic trajectories in real time, thereby obtaining the running trajectories, speeds, and occupancy status of buses and intelligent connected vehicles.

[0106] In addition, considering that the real-time positioning data of the bus may have problems such as signal delay and signal loss, which may lead to the problem of not being able to obtain accurate real-time positioning data of the bus in a timely manner, therefore, in the above embodiment, when the predicted idle length is corrected according to the real-time positioning data to obtain the road idle length of the bus lane on each road section, if the signal delay of the real-time positioning data is greater than the preset delay, or the signal of the real-time positioning data is lost, the predicted idle length can be used as the road idle length of the bus lane on each road section, so as to timely determine the road idle length of the bus lane.

[0107] In some embodiments, as Figure 5 The implementation flow chart of another intelligent connected vehicle lane borrowing control method is shown in FIG, and is detailed as follows:

[0108] Step S501: Obtain the remaining time of the traffic light on the road section where the intelligent connected vehicle is located in the nearest traffic state.

[0109] Step S502: Determine whether the intelligent connected vehicle can pass through the intersection in the current lane based on the remaining time. If the intelligent connected vehicle can pass through the intersection in the current lane, it is determined that the intelligent connected vehicle does not need to use the lane, and the intelligent connected vehicle is controlled to continue driving in the current lane.

[0110] Here, the main control principle can be to give priority to the intelligent connected vehicle driving in the current lane. It can be judged first whether the intelligent connected vehicle can pass through the intersection in the current lane. If the intelligent connected vehicle can pass through the intersection in the current lane, no subsequent judgment can be made, and the intelligent connected vehicle can be directly controlled to continue driving in the current lane, reducing the frequency of intelligent connected vehicles using bus lanes.

[0111] Step S503: If the intelligent connected vehicle cannot pass through the intersection in the current lane, it is determined whether the intelligent connected vehicle can pass through the intersection after borrowing the lane. If the intelligent connected vehicle cannot pass through the intersection after borrowing the lane, it is determined that the intelligent connected vehicle does not need to borrow the lane, and the intelligent connected vehicle is controlled to continue driving in the current lane.

[0112] In this embodiment, when it is determined that the intelligent connected vehicle cannot pass through the intersection in the current lane, consideration is given to borrowing the lane, that is, determining whether the intelligent connected vehicle can pass through the intersection after borrowing the lane. If the intelligent connected vehicle still cannot pass through the intersection within the remaining time after borrowing the lane, the intelligent connected vehicle can be controlled to continue driving in the current lane, reducing the frequency of intelligent connected vehicles borrowing the bus lane and reducing the impact of vehicle lane changes on traffic flow.

[0113] Step S504: If the intelligent connected vehicle can pass through the intersection after borrowing the lane, the public transportation data of the road section where the intelligent connected vehicle is located is determined.

[0114] Step S505: Determine the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data.

[0115] Optionally, this embodiment calculates the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the idle length of the road, the driving type, and the difference, which can be based on the expression: Calculate the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located; where d clear represents the real-time clearing distance, d represents the idle length of the road, v e Indicates the real-time speed of the bus. represents the average speed of the intelligent connected vehicle, and a represents the weight of the driving type corresponding to the bus driver.

[0116] Step S506: Determine whether the intelligent connected vehicle is within the real-time clearing distance after borrowing the lane based on the real-time clearing distance and the vehicle position of the intelligent connected vehicle.

[0117] Step S507: If the intelligent connected vehicle is within the real-time clearing distance after borrowing the lane, it is determined that the intelligent connected vehicle does not borrow the lane, and the intelligent connected vehicle is controlled to continue driving in the current lane.

[0118] Step S508: If the intelligent connected vehicle is not within the real-time clearing distance after borrowing the lane, it is determined that the intelligent connected vehicle is borrowing the lane, and the intelligent connected vehicle is controlled to borrow the lane and drive on the bus lane.

[0119] Here, the implementation of steps S501, S504, S506 to S508 can be found in Figure 2 The relevant description in the embodiments will not be repeated here.

[0120] In an embodiment of the present invention, the remaining time of the signal light in the most recent traffic state is used to determine whether the intelligent connected vehicle can pass through the intersection in the current lane and after borrowing the lane. The necessity of the intelligent connected vehicle to borrow the lane can be judged first. If the intelligent connected vehicle cannot pass through the intersection in the current lane, but can pass through the intersection after borrowing the lane, it indicates that it is advantageous for the intelligent connected vehicle to borrow the bus lane at this time, and borrowing the lane can be considered at this time. Then, based on the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located and the vehicle position of the intelligent connected vehicle, it can be further determined whether the intelligent connected vehicle can borrow the lane. Then, the driving of the intelligent connected vehicle can be controlled, so that the intelligent connected vehicle can borrow the lane without affecting the normal operation of the buses, fully utilizing the bus lane and giving full play to the driving advantages of the intelligent connected vehicle. Among them, if the intelligent connected vehicle can pass through the intersection in the current lane, but cannot pass through the intersection after borrowing the lane, it indicates that it is not advantageous to use the bus lane at this time. In this case, the intelligent connected vehicle is controlled to continue driving in the current lane to reduce the impact of the change on the traffic flow of the current road section. When calculating the real-time clearing distance of a bus, the initial clearing distance of the bus can be calculated first, and then the initial clearing distance can be corrected using the driving type of the bus driver, so that the obtained real-time clearing distance is more accurate and more consistent with the actual situation of the bus, reducing the impact of smart connected vehicles on buses when occupying bus lanes and ensuring that buses have priority.

[0121] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0122] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0123] Figure 6 The following is a schematic diagram showing the structure of a lane-borrowing control device for an intelligent connected vehicle provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows:

[0124] like Figure 6 As shown, the intelligent connected vehicle lane borrowing control device 60 includes:

[0125] An acquisition module 61 is configured to acquire the remaining time of the traffic light in the nearest traffic state of the road section where the intelligent connected vehicle is located;

[0126] A judgment module 62 is used to determine whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane based on the remaining time;

[0127] A determination module 63 is configured to determine the public transportation data for the road section where the intelligent connected vehicle is located if the intelligent connected vehicle cannot pass through the intersection in the current lane but can pass through the intersection after borrowing the lane;

[0128] The calculation module 64 is used to determine the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data;

[0129] The control module 65 determines whether the intelligent network-connected vehicle should travel in the borrowed lane according to the real-time clearing distance and the vehicle position of the intelligent network-connected vehicle.

[0130] In a possible implementation, the determination module 62 is specifically configured to:

[0131] Based on the position of the ICV, the length of the queue of vehicles ahead of the ICV in the current lane is determined. Based on the queue length, the queue-clearing time required for the ICV is calculated. Furthermore, based on the position of the ICV, the lane-changing waiting time for the ICV to pass from the current lane to the bus lane is calculated.

[0132] Based on the queue dissipation time and the remaining time, it is determined whether the intelligent connected vehicle can pass through the intersection in the current lane, and based on the lane change waiting time and the remaining time, it is determined whether the intelligent connected vehicle can pass through the intersection after borrowing the lane.

[0133] In a possible implementation, the control module 65 is further configured to:

[0134] If the intelligent connected vehicle can pass through the intersection in the current lane, or the intelligent connected vehicle cannot pass through the intersection after borrowing the lane, it is determined that the intelligent connected vehicle does not need to borrow the lane, and the intelligent connected vehicle is controlled to continue driving in the current lane.

[0135] In one possible implementation, the bus data includes the free length of the bus lane on the road section where the intelligent connected vehicle is located, the driving type of the bus driver on the road section where the intelligent connected vehicle is located, and the real-time speed of the bus;

[0136] The calculation module 64 is specifically used for:

[0137] Obtain the average speed of the intelligent connected vehicle and calculate the difference between the average speed and the real-time speed;

[0138] Based on the free length of the road and the difference, the initial clearance distance of the bus on the road section where the intelligent connected vehicle is located is calculated;

[0139] Based on the initial clearing distance and driving type, the real-time clearing distance of the bus on the road section where the intelligent connected vehicle is located is calculated.

[0140] In a possible implementation, the determining module 63 is further configured to:

[0141] Obtain the occupancy data of bus lanes and bus operation trajectories in the previous historical period before the current period;

[0142] Inputting the occupancy data and running trajectories into a preset idleness prediction model, the idleness prediction model outputs the predicted idle length of the bus lanes on each road section. The idleness prediction model is trained based on pre-acquired historical occupancy data and running trajectories, as well as the corresponding historical idle lengths of the bus lanes.

[0143] Obtain real-time positioning data of multiple buses on each road section;

[0144] According to the real-time positioning data, the predicted idle length is corrected to obtain the idle length of the bus lane on each road section.

[0145] In a possible implementation, the determination module 65 is specifically configured to:

[0146] According to the position of the intelligent connected vehicle, the position where the intelligent connected vehicle borrows the lane to the bus lane is determined;

[0147] Determine whether the intelligent connected vehicle should use the lane according to the location of the lane and the real-time clearance distance.

[0148] In a possible implementation, the determination module 62 is specifically configured to:

[0149] According to the expression: Calculate the queue dissipation time required for intelligent connected vehicles;

[0150] Where t represents the queue dissipation time, L det Indicates the queue length of vehicles in front of the intelligent connected vehicle in the current lane, It represents the upper limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located, and v represents the lower limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located.

[0151] In one possible implementation, the calculation module 64 is specifically configured to:

[0152] According to the expression: Calculate the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located;

[0153] Where, d clear represents the real-time clearing distance, d represents the idle length of the road, v e Indicates the real-time speed of the bus. represents the average speed of the intelligent connected vehicle, and a represents the weight of the driving type corresponding to the bus driver.

[0154] Figure 7Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 7 As shown, the electronic device 70 of this embodiment includes: a processor 71, a memory 72, and a computer program 73 stored in the memory 72 and executable on the processor 71. When the processor 71 executes the computer program 73, the steps in the above-mentioned embodiments of the intelligent connected vehicle lane borrowing control method are implemented, such as Figure 2 Alternatively, when the processor 71 executes the computer program 73, the functions of the modules in the above-mentioned device embodiments are realized, for example, Figure 6 The functions of modules 61 to 65 are shown.

[0155] For example, the computer program 73 may be divided into one or more modules / units, one or more modules / units are stored in the memory 72 and executed by the processor 71 to implement the present invention. One or more modules / units may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 73 in the electronic device 70. For example, the computer program 73 may be divided into Figure 6 Modules 61 to 65 are shown.

[0156] The electronic device 70 may include, but is not limited to, a processor 71 and a memory 72. Those skilled in the art will appreciate that Figure 7 This is merely an example of the electronic device 70 and does not constitute a limitation of the electronic device 70 . The electronic device 70 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0157] The processor 71 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0158] The memory 72 can be an internal storage unit of the electronic device 70, such as a hard drive or memory of the electronic device 70. The memory 72 can also be an external storage device of the electronic device 70, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 70. Furthermore, the memory 72 can include both an internal storage unit of the electronic device 70 and an external storage device. The memory 72 is used to store computer programs and other programs and data required by the electronic device. The memory 72 can also be used to temporarily store data that has been output or is about to be output.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0160] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0161] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0162] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0163] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0164] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0165] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0166] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for controlling lane borrowing of an intelligent connected vehicle, characterized in that: include: Obtain the remaining time of the traffic light in the nearest traffic state on the road section where the intelligent connected vehicle is located; Determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane; If the intelligent connected vehicle cannot pass through the intersection in the current lane, but can pass through the intersection after borrowing the lane, then determine the public transportation data of the road section where the intelligent connected vehicle is located; Determine the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data; Determining whether the intelligent connected vehicle should travel in a borrowed lane based on the real-time clearing distance and the vehicle position of the intelligent connected vehicle; The public transportation data includes the free length of the bus lane on the road section where the intelligent connected vehicle is located, the driving type of the bus driver on the road section where the intelligent connected vehicle is located, and the real-time speed of the bus; Determining the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located based on the bus data includes: Obtaining the average speed of the intelligent connected vehicle, and calculating the difference between the average speed and the real-time speed; Calculating an initial clearance distance for buses on the road section where the intelligent connected vehicle is located based on the free road length and the difference; Calculating a real-time clearance distance for buses on the road section where the intelligent connected vehicle is located based on the initial clearance distance and the driving type; Before determining the public transportation data of the road section where the intelligent connected vehicle is located, the method further includes: Obtain the occupancy data of bus lanes and bus operation trajectories in the previous historical period before the current period; Inputting the occupancy data and the running trajectory into a preset idleness prediction model to obtain a predicted idle length of the bus lane on each road section output by the idleness prediction model; wherein the idleness prediction model is trained based on pre-acquired historical occupancy data and historical running trajectories, as well as the corresponding historical idle lengths of the bus lane; Obtain real-time positioning data of multiple buses on each road section; Correcting the predicted idle length according to the real-time positioning data to obtain the idle length of the bus lane on each road section; Determining whether the intelligent connected vehicle should travel in a borrowed lane according to the real-time clearing distance and the vehicle position of the intelligent connected vehicle includes: Determining a location for the intelligent connected vehicle to borrow lanes for bus lanes based on the vehicle location of the intelligent connected vehicle; Determine whether the intelligent connected vehicle is traveling in the borrowed lane according to the borrowed lane position and the real-time clearing distance.

2. The intelligent connected vehicle lane borrowing control method according to claim 1, characterized in that: Determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane includes: Determine the length of the queue of vehicles preceding the intelligent connected vehicle in the current lane based on the position of the intelligent connected vehicle, calculate the queue-clearing time required for the intelligent connected vehicle based on the queue length, and calculate the lane-changing waiting time for the intelligent connected vehicle to pass through the bus lane from the current lane based on the position of the intelligent connected vehicle; It is determined whether the intelligent connected vehicle can pass through the intersection in the current lane based on the queue dissipation time and the remaining time, and it is determined whether the intelligent connected vehicle can pass through the intersection after borrowing the lane based on the lane change waiting time and the remaining time.

3. The intelligent connected vehicle lane borrowing control method according to claim 1, characterized in that: After determining, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane, the method further includes: If the intelligent connected vehicle can pass through the intersection in the current lane, or the intelligent connected vehicle cannot pass through the intersection after borrowing the lane, it is determined that the intelligent connected vehicle does not need to borrow the lane, and the intelligent connected vehicle is controlled to continue driving in the current lane.

4. The intelligent connected vehicle lane borrowing control method according to claim 2, characterized in that: The calculating, based on the queue length, the queue-clearing time required for the intelligent connected vehicle includes: According to the expression: Calculating the queue dissipation time required for the intelligent connected vehicles; Where t represents the queue dissipation time, L det Indicates the queue length of vehicles in front of the intelligent connected vehicle in the current lane, Indicates the upper limit of the speed of vehicles on the ordinary lane of the road section where the intelligent connected vehicle is located, Indicates the lower limit of the speed of vehicles in the ordinary lane of the road section where the intelligent connected vehicle is located.

5. The method for controlling lane borrowing of an intelligent connected vehicle according to any one of claims 1 to 3, characterized in that: The calculating, based on the bus data, the real-time clearance distance of the buses on the road section where the intelligent connected vehicle is located, includes: According to the expression: Calculate the real-time clearance distance of buses on the road section where the intelligent connected vehicle is located; Where, d clear represents the real-time clearing distance, d represents the idle length of the road, v e Indicates the real-time speed of the bus. represents the average speed of the intelligent connected vehicle, and a represents the weight of the driving type corresponding to the bus driver.

6. A lane borrowing control device for an intelligent connected vehicle, characterized in that: include: An acquisition module is used to obtain the remaining time of the traffic light in the nearest traffic state on the road section where the intelligent connected vehicle is located; a judgment module, configured to determine, based on the remaining time, whether the intelligent connected vehicle can pass through the intersection in the current lane and whether it can pass through the intersection after borrowing the lane; a determination module, configured to determine the public transportation data of the road section where the intelligent connected vehicle is located if the intelligent connected vehicle cannot pass through the intersection in the current lane but can pass through the intersection after borrowing the lane; A calculation module, configured to determine, based on the bus data, a real-time clearance distance of buses on the road section where the intelligent connected vehicle is located; A control module, which determines whether the intelligent network-connected vehicle should travel in a borrowed lane according to the real-time clearing distance and the vehicle position of the intelligent network-connected vehicle; The public transportation data includes the free length of the bus lane on the road section where the intelligent connected vehicle is located, the driving type of the bus driver on the road section where the intelligent connected vehicle is located, and the real-time speed of the bus; The calculation module is specifically used for: Obtaining the average speed of the intelligent connected vehicle, and calculating the difference between the average speed and the real-time speed; Calculating an initial clearance distance for buses on the road section where the intelligent connected vehicle is located based on the free road length and the difference; Calculating a real-time clearance distance for buses on the road section where the intelligent connected vehicle is located based on the initial clearance distance and the driving type; The Determine module is also used to: Obtain the occupancy data of bus lanes and bus operation trajectories in the previous historical period before the current period; Inputting the occupancy data and the running trajectory into a preset idleness prediction model to obtain a predicted idle length of the bus lane on each road section output by the idleness prediction model; wherein the idleness prediction model is trained based on pre-acquired historical occupancy data and historical running trajectories, as well as the corresponding historical idle lengths of the bus lane; Obtain real-time positioning data of multiple buses on each road section; Correcting the predicted idle length according to the real-time positioning data to obtain the idle length of the bus lane on each road section; The control module is specifically used to: Determining a location for the intelligent connected vehicle to borrow lanes for bus lanes based on the vehicle location of the intelligent connected vehicle; Determine whether the intelligent connected vehicle is traveling in the borrowed lane according to the borrowed lane position and the real-time clearing distance.

7. An electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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