A vehicle lane processing method and device, electronic equipment and storage medium

Abstract

The application provides a vehicle merging method and device, electronic equipment and storage medium, comprising determining a first prediction merging index of a host vehicle based on each host vehicle candidate driving data in a host vehicle candidate driving data set according to lane data, first position data, second position data and labeled driving data, predicting the prediction driving data of an interactive vehicle based on the historical driving intention parameters in the labeled driving data, determining the target driving intention parameters of the interactive vehicle according to the error of the labeled driving data and the prediction driving data, determining the target prediction merging index of the host vehicle based on each host vehicle candidate driving data according to the first prediction merging index, the second prediction merging index of the interactive vehicle based on the interactive target driving data and the target driving intention parameters, and determining the target driving data of the host vehicle from the host vehicle candidate driving data set according to the target prediction merging index and the combination probability. The application can improve the robustness of vehicle merging processing, improve the merging efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a vehicle merging processing method, device, electronic equipment and storage medium. Background Art

[0002] Merging from two lanes into a single lane is a common traffic scenario on existing urban roads. For example, at a highway on-ramp, vehicles on the ramp can accelerate to find a suitable gap to merge into the main lane. This method poses significant safety risks. Alternatively, vehicles on the ramp can stop and wait for a suitable gap to merge into the main lane. This method reduces road efficiency and increases vehicle energy consumption. Summary of the Invention

[0003] In order to solve the problems of low lane merging safety performance, low traffic efficiency and high vehicle energy consumption in the prior art, the present application provides a vehicle lane merging processing method, device, electronic device and storage medium:

[0004] According to a first aspect of the present application, a method for handling vehicle merging is provided, comprising:

[0005] Acquire lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle during a historical pre-merge time, and a target gap; the interacting vehicle is the vehicle traveling behind the adjacent surrounding vehicles corresponding to the target gap;

[0006] determining, based on the lane data, the first position data, the second position data, and the annotated driving data, a first predicted lane merging index of the vehicle based on each candidate driving data of the vehicle in the candidate driving data set;

[0007] Predicting predicted driving data of the interacting vehicle based on historical driving intention parameters in the annotated driving data, and determining target driving intention parameters of the interacting vehicle based on an error between the annotated driving data and the predicted driving data;

[0008] determining a target predicted lane merging index for the host vehicle based on each candidate driving data of the host vehicle according to the first predicted lane merging index, the second predicted lane merging index of the interacting vehicle based on the interactive target driving data, and the target driving intention parameter;

[0009] Based on the target predicted merging indicator and combined probability, the vehicle's target driving data is determined from the candidate driving dataset. Based on the vehicle's target driving data, the target gap in the main lane from the lane to be merged into is indicated. The combined probability represents the probability of determining the indicator based on both the vehicle's target state data and the interactive target state data.

[0010] According to a second aspect of the present application, a vehicle merging processing device is provided, comprising:

[0011] An acquisition module is configured to acquire lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle during a historical pre-merge time, and a target gap; the interacting vehicle is the vehicle traveling behind the adjacent surrounding vehicles corresponding to the target gap;

[0012] a first determining module, configured to determine, based on the lane data, the first position data, the second position data, and the annotated driving data, a first predicted lane merging index of the vehicle based on each candidate driving data of the vehicle in the candidate driving data set;

[0013] a second determination module, configured to predict predicted driving data of the interacting vehicle based on historical driving intention parameters in the annotated driving data, and determine target driving intention parameters of the interacting vehicle based on an error between the annotated driving data and the predicted driving data;

[0014] a third determining module, configured to determine a target predicted lane merging index for the host vehicle based on each candidate driving data of the host vehicle according to the first predicted lane merging index, the second predicted lane merging index based on the interactive target driving data of the interacting vehicle, and the target driving intention parameter;

[0015] The fourth determination module is used to determine the vehicle's target driving data from the vehicle's candidate driving data set based on the target predicted merging indicator and the combined probability, and to indicate the target gap in the main lane from the lane to be merged to the lane to be merged based on the vehicle's target driving data; the combined probability represents the probability of determining the indicator based on both the vehicle's target state data and the interactive target state data.

[0016] On the other hand, the above-mentioned vehicle lane merging processing device further includes:

[0017] The construction module is used to preset a set of candidate merging times for the vehicle before determining a first predicted merging index for the vehicle based on each candidate driving data of the vehicle in the candidate driving data set of the vehicle, and to construct the candidate driving data of the vehicle corresponding to each candidate merging time to obtain the candidate driving data set of the vehicle.

[0018] On the other hand, a first determination module is configured to determine, for each candidate driving data of the own vehicle, first relative distance data between the own vehicle and the interacting vehicle and second relative distance data between the own vehicle and the target object after the own vehicle has traveled for a preset driving time based on each candidate driving data of the own vehicle and the interacting vehicle has traveled for a preset driving time based on the interactive target driving data; the target object being an end point of the lane to be merged;

[0019] determining a predicted safe lane merging index based on the first relative distance data, the second relative distance data, and the driving data of each candidate vehicle;

[0020] determine a prediction efficiency index according to the traffic flow speed data of the main lane, the second relative distance data and each candidate driving data of the ego vehicle in the lane data;

[0021] determine a prediction stability index according to the length data of the to-merge lane, the second relative distance data, each candidate merging time and the driving distance data corresponding to each candidate merging time in the lane data;

[0022] determine a prediction priority driving index according to the second relative distance data and each candidate driving data of the ego vehicle;

[0023] determine a first prediction merging index of the ego vehicle based on the candidate driving data of the ego vehicle according to a sum value of the safety prediction merging index, the efficiency prediction index, the stability prediction index and the priority driving prediction index.

[0024] On the other hand, the second determining module is configured to determine, from the labeled driving data, labeled driving sub-data of the interactive vehicle at each historical prediction merging sampling moment within a historical prediction merging time; each labeled driving sub-data includes a historical driving intention parameter;

[0025] predict, based on the historical driving intention parameter in each labeled driving sub-data, prediction driving sub-data of the interactive vehicle at each historical prediction merging sampling moment;

[0026] determine a minimum difference value of the labeled driving sub-data and the prediction driving sub-data in the plurality of historical prediction merging sampling moments, and take the historical driving intention parameter in the labeled driving sub-data corresponding to the minimum difference value as a target driving intention parameter of the interactive vehicle.

[0027] On the other hand, the third determining module is configured to determine a first target prediction merging sub-index according to a product of the first prediction merging index and the target driving intention parameter;

[0028] determine a second target prediction merging sub-index according to a product of the second prediction merging index and the target driving intention parameter;

[0029] determine a target prediction merging index of the ego vehicle based on each candidate driving data of the ego vehicle according to a sum value of the first target prediction merging sub-index and the second target prediction merging sub-index.

[0030] On the other hand, the fourth determining module is configured to take, as a target driving data of the ego vehicle, the candidate driving data of the ego vehicle corresponding to a maximum product according to a product of the target prediction merging index of the ego vehicle based on each candidate driving data of the ego vehicle and the combination probability.

[0031] On the other hand, the acquisition module is configured to acquire a plurality of candidate gaps; the candidate gap is a longitudinal space between adjacent surrounding vehicles in the main lane;

[0032] Determining a first indicator corresponding to each candidate gap according to the multi-order derivatives of the longitudinal distance data of each candidate gap in unit time;

[0033] Determining a second indicator corresponding to each candidate gap based on multiple derivatives of the lateral distance data between the vehicle and each candidate gap in unit time;

[0034] A target gap is determined from a plurality of candidate gaps according to a difference between the first indicator and the second indicator.

[0035] According to a third aspect of the present application, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the vehicle lane merging processing method of the first aspect of the present application.

[0036] According to a fourth aspect of the present application, a computer storage medium is provided, in which at least one instruction or at least one program is stored. The at least one instruction or at least one program is loaded and executed by a processor to implement the vehicle lane merging method of the first aspect of the present application.

[0037] According to the fifth aspect of the present application, a computer program product is provided, which includes at least one instruction or at least one program segment, and the at least one instruction or at least one program segment is loaded and executed by a processor to implement the vehicle lane merging processing method of the first aspect of the present application.

[0038] The embodiments of the present application provide a method, device, electronic device, and storage medium for processing a vehicle lane change, which have the following technical effects:

[0039] The system obtains lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle within a historical pre-merge time, and a target gap. The interacting vehicle is the vehicle behind the adjacent surrounding vehicles corresponding to the target gap. Based on the lane data, first position data, second position data, and annotated driving data, the system determines a first predicted merging index for the host vehicle based on each candidate driving data of the host vehicle in the candidate driving data set. The system predicts the predicted driving data of the interacting vehicle based on the historical driving intention parameters in the annotated driving data, and determines the target driving intention parameter of the interacting vehicle based on the error between the annotated driving data and the predicted driving data. Based on the first predicted merging index, the second predicted merging index of the interacting vehicle based on the interactive target driving data, and the target driving intention parameter, the system determines a target predicted merging index for each candidate driving data of the host vehicle. Based on the target predicted merging index and a combined probability, the system determines the target driving data of the host vehicle from the candidate driving data set. Based on the target predicted merging index and a combined probability, the system indicates the target gap in the main lane for the host vehicle merging from the lane to be merged into the lane. The combined probability represents the probability of determining the index based on both the host vehicle's target state data and the interacting target state data. Based on the embodiments of the present application, by introducing the tentative behavior of the vehicle, obtaining the driving data of the interactive vehicle in response to the tentative behavior of the vehicle, and determining the time-varying driving intention parameters of the interactive vehicle to dynamically describe the driving characteristics of traffic participants, the robustness of the vehicle lane merging process can be improved, and the efficiency and safety of the lane merging can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 is a schematic diagram of an application environment provided by an embodiment of the present application;

[0042] Figure 2 This is a system structure diagram of a vehicle lane merging processing method provided by an embodiment of the present application;

[0043] Figure 3 1 is a flow chart of a vehicle lane merging processing method provided in an embodiment of the present application;

[0044] Figure 4 This is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0045] Figure 5 This is a schematic diagram of a vehicle's exploratory behavior provided in an embodiment of the present application;

[0046] Figure 6 is a schematic diagram of a candidate driving data set of the vehicle provided by an embodiment of the present application;

[0047] Figure 7 is a schematic diagram of another candidate driving data set of the vehicle provided by an embodiment of the present application;

[0048] Figure 8 is a structural schematic diagram of a vehicle merging processing apparatus provided by an embodiment of the present application;

[0049] Figure 9 is a hardware structural schematic diagram of an electronic device for implementing the vehicle merging processing method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0051] The term "embodiment" referred to herein means a specific feature, structure or characteristic that can be included in at least one implementation of the present application. In the description of the embodiments of the present application, it should be understood that the terms "first", "second" and "third" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" and "third" can be explicitly or implicitly included one or more of the features. Moreover, the terms "first", "second" and "third" are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include", "have" and "be" and any variations thereof are intended to cover non-exclusive inclusion.

[0052] It can be understood that in the detailed description of the present application, the data related to position data and driving data, when the above embodiments of the present application are applied to specific products or technologies, need to obtain user permission or consent, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0053] The embodiments of the present application can be applied to various scenarios, including but not limited to intelligent transportation systems, intelligent vehicle-road cooperative systems, etc.

[0054] Among them, Intelligent Traffic System (ITS), also known as Intelligent Transportation System (ITS), effectively integrates advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) into transportation, service control and vehicle manufacturing, strengthens the connection between vehicles, roads and users, and thus forms a comprehensive transportation system that ensures safety, improves efficiency, improves the environment and saves energy.

[0055] Intelligent Vehicle Infrastructure Cooperative Systems (IVICS), also known as VICS, are a development direction of Intelligent Transportation Systems (ITS). VICS utilizes advanced wireless communications and next-generation internet technologies to implement dynamic, real-time information exchange between vehicles and roads. Based on the collection and integration of dynamic traffic information across time and space, VICS conducts active vehicle safety control and collaborative road management. This fully realizes effective coordination between people, vehicles, and roads, ensuring traffic safety and improving traffic efficiency, resulting in a safe, efficient, and environmentally friendly road transportation system.

[0056] See also Figure 1 , Figure 1 1 is a schematic diagram of an application environment provided by an embodiment of the present application, which may include a terminal 10 and a server 20. The terminal 10 and the server 20 may be directly or indirectly connected via wired or wireless communication.

[0057] In some possible embodiments, the terminal 10 can send road information data to the server 20, and the server can provide a vehicle merging processing service. For traffic participants with different driving styles, the target driving data of the research vehicle can be determined based on the time-varying driving intention parameters of the traffic participants under the exploratory behavior of the research vehicle to indicate the target gap in the main lane from the lane to be merged.

[0058] The terminal 10 may be, but is not limited to, a smartphone, tablet computer, laptop computer, or desktop computer. The terminal 10 may be installed with client software, such as an application (App), that provides human-computer interaction functionality. The application may be a standalone application or a subroutine within the application.

[0059] Server 20 can be a standalone physical server, a service cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The server may include a network communication unit, a processor, and memory, among other components.

[0060] In some possible implementations, both the terminal 10 and the server 20 may be node devices in a blockchain system, capable of sharing acquired and generated information with other node devices in the blockchain system, thereby enabling information sharing among multiple node devices. Multiple node devices in a blockchain system may be configured with the same blockchain, which is composed of multiple blocks, and where adjacent blocks are associated, such that any tampering with data in any block can be detected by the next block, thereby preventing tampering with data in the blockchain and ensuring the security and reliability of the data in the blockchain.

[0061] Figure 2 This is a system architecture diagram of a vehicle lane merging processing method provided by an embodiment of the present application. The collected perception information of the ego vehicle is filtered by an unscented Kalman filter module and then input into an upper-level decision module of a hierarchical decision module for selecting an interacting vehicle and a target gap. Furthermore, in a lower-level decision module of the hierarchical decision module, a first predicted lane merging index for the ego vehicle based on each candidate driving data of the ego vehicle in the candidate driving data set of the ego vehicle is determined based on the lane data, the first position data, the second position data, and the annotated driving data. The predicted driving data of the interacting vehicle is predicted based on the historical driving intention parameters in the annotated driving data. The target driving intention parameter of the interacting vehicle is determined based on the error between the annotated driving data and the predicted driving data. The target predicted lane merging index for the ego vehicle based on each candidate driving data of the ego vehicle is determined based on the first predicted lane merging index, the second predicted lane merging index of the interacting vehicle based on the interactive target driving data, and the target driving intention parameter. The target driving data of the ego vehicle is determined from the candidate driving data set of the ego vehicle based on the target predicted lane merging index and the combined probability. Based on the target predicted lane merging index and the combined probability, the target driving data of the ego vehicle is determined. The target gap in the main lane for the ego vehicle to merge from the lane to be merged into is indicated based on the target driving data of the ego vehicle.

[0062] The following describes a specific embodiment of a vehicle lane merging processing method of the present application. Figure 3The flowchart of a vehicle merging method provided in an embodiment of the present application is shown. This specification provides the method steps shown in the embodiment or flowchart, but more or fewer steps may be included based on routine or non-inventive work. The order of steps listed in the embodiment is only one of many possible execution sequences and does not represent the only execution sequence. In actual execution, the method steps may be executed in the order shown in the embodiment or the figure, or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0063] In the embodiment of the present application, the vehicle merging processing method can be applied to ramp merging sections.

[0064] Specific as Figure 3 As shown, the vehicle lane merging processing method may include:

[0065] S301: Acquire lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle within a historical pre-merge time, and a target gap.

[0066] In the embodiment of this application, Figure 4 This is a schematic diagram of an application scenario provided by an embodiment of the present application, including the main lane, the lane to be merged, i.e., the ramp, candidate gaps (gap i-1), candidate gaps (gap i), candidate gaps (gap i+1), the ego vehicle (EV), the following vehicle (FV), the leading vehicle (FV), and the target object, i.e., the ramp endpoint A. Assume that the EV is traveling at a constant acceleration with noise, which has no effect on the EV's merging. Therefore, ramp merging can be modeled as a two-vehicle static game problem, i.e., the set of traffic participants participating in the game, P = (EV, FV). The static game can mean that each traffic participant simultaneously and independently determines target driving data and then exchanges target driving data. Therefore, the interacting vehicle can be the vehicle traveling behind the adjacent surrounding vehicles corresponding to the target gap.

[0067] In some possible implementations, Figure 5is a schematic diagram of a tentative behavior of the vehicle provided by an embodiment of the present application. For the scene of ramp merging, considering that the vehicle often expresses its merging intention through lateral movement or turn signal when the traffic flow is high, that is, "tentative". Specifically, when the EV is located on the merging lane, that is, the ramp, when "tentative" is selected, the vehicle turns on the turn signal and slightly moves laterally with a smaller amplitude than normal lane changing to reduce the distance to the lane line between the lane and the merging lane, but does not cross the lane line between the lane and the merging lane. In the initial stage of ramp merging, the main lane data, the merging lane data, that is, the ramp data, the xy coordinates of the vehicle, the xy coordinates of the surrounding vehicles, and the annotated driving data of the longitudinal speed, longitudinal acceleration, heading angle and yaw angle of the FV during the "tentative" process of the EV can be collected. By lateral movement of the vehicle, the interactivity with the interactive vehicle can be enhanced, and the success rate of the candidate lane changing can be improved.

[0068] In some possible implementations, a kinematic model (Constsnt Turn Rate and Acceleration, CTRA) can be used to describe the motion of the vehicle and the interactive vehicle. As shown in the following formula:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] wherein x(t) can represent the longitudinal coordinate of the vehicle, y(t) can represent the lateral coordinate of the vehicle, v(t) can represent the speed along the longitudinal axis of the vehicle body, a(t) can represent the acceleration along the longitudinal axis of the vehicle body, ϕ(t) can represent the heading angle of the vehicle, and ω(t) can represent the yaw rate of the vehicle. Considering the nonlinearity of the kinematic model, an unscented Kalman filter (UKF) can be used to estimate the current driving state of the vehicle. Specifically, x(t) and y(t) can be used as the observation of the model, and in the actual driving scene, the relative position relationship can be obtained more accurately through the laser radar. In the state observation of the real vehicle, the observation equation is as follows:

[0076]

[0077] Where x(t) and y(t) represent the vehicle's initial position data, v(t) represents the vehicle's velocity along its longitudinal axis, a(t) represents the vehicle's acceleration along its longitudinal axis, ϕ(t) represents the vehicle's heading angle, and ω(t) represents the vehicle's yaw rate.

[0078] In the embodiment of the present application, a set of candidate merging times for the vehicle can be preset, and candidate driving data corresponding to each candidate merging time can be constructed to obtain a candidate driving dataset for the vehicle. The candidate merging time can be referred to as the decision preview time. The candidate driving dataset for the vehicle can include a longitudinal driving dataset and a lateral driving dataset, i.e., EV ∈{acceleration, constant speed, deceleration}*{lane change, trial, keep / give up}. The interactive candidate driving dataset can include the longitudinal driving dataset, i.e., π FV ∈{-2,-1,0,1,2}m / s 2 In the quantitative explanation, "2m / s 2 " can indicate EV acceleration, "-2m / s 2 " can indicate EV deceleration, "0m / s 2 " can indicate that the EV is moving at a constant speed, "Lane Change" can indicate that the EV moves to the center of the main lane after the candidate merging time ts, "Exploration" can indicate that the EV closely follows the lane line between the main lane and the lane to be merged, but remains completely in the lane to be merged after the candidate merging time ts, and "Hold / Abandon" can indicate that the EV maintains its current lateral position (if the EV has not crossed the lane line between the main lane and the lane to be merged) or returns to the center line of the lane to be merged (if the EV has crossed the lane line between the main lane and the lane to be merged) after the candidate merging time ts. Figure 6 is a schematic diagram of a candidate driving data set of the vehicle provided in an embodiment of the present application. Figure 7 This is a schematic diagram of another candidate driving data set provided by the embodiment of the present application. Among them, t1 can be a candidate merging time, 0m / s 2 Uniform speed, 2m / s 2 Acceleration, -2m / s 2Deceleration can represent different longitudinal driving data corresponding to the candidate merging time. A shorter candidate merging time indicates that, under the same conditions, the vehicle would need less time to complete the merging task, and the corresponding lateral movement would be more aggressive, meaning the vehicle would have greater lateral acceleration, giving the interacting vehicle a shorter reaction time. Conversely, a longer candidate merging time indicates that, under the same conditions, the vehicle would need more time to complete the merging task, and the corresponding lateral movement would be gentler, with the entire process from initiation to completion taking longer. This would provide the interacting vehicle with more time to react and would lower the signal strength conveying the merging situation to the interacting vehicle. By using different candidate merging times to describe different driving styles, stylized driving can be achieved.

[0079] In some possible implementations, multiple candidate gaps may be obtained. These candidate gaps may be the longitudinal spaces between adjacent surrounding vehicles in the main lane. A first index corresponding to each candidate gap may then be determined based on the multiple-order derivatives of the longitudinal distance data per unit time for each candidate gap. Furthermore, a second index corresponding to each candidate gap may be determined based on the multiple-order derivatives of the lateral distance data per unit time between the vehicle and each candidate gap. A target gap may then be determined from the multiple candidate gaps based on the difference between the first and second indexes.

[0080] Considering that the interaction between the vehicle behind the vehicle FV or the vehicle in front of the vehicle LV and the EV is low due to factors such as occlusion or distance, the embodiment of the application only analyzes the longitudinal space between the surrounding vehicles adjacent to the vehicle in the main lane. In actual application, the following formula can be used to determine the target gap:

[0081]

[0082] Among them, ln can represent the logarithm with natural exponential as the base, c i It can represent the first indicator of the vehicle merging into the candidate gap gap i, v i It can represent the second indicator of the vehicle merging into the candidate gap gap i, u j It can be the difference between the first index and the second index corresponding to the previous candidate gap gap j of the candidate gap gap i, This is to avoid switching candidate gaps too frequently. It can represent the lateral distance data of the candidate gap gap i and its first-order and second-order derivatives with respect to time, It can represent the longitudinal distance of the candidate gap gap i and its first-order and second-order derivatives with respect to time, ~ After determining the first and second indicators corresponding to each candidate gap, the candidate gap corresponding to the maximum difference between the first and second indicators among the plurality of candidate gaps may be used as the target gap.

[0083] S303: Determine, based on the lane data, the first position data, the second position data, and the annotated driving data, a first predicted lane merging index of the vehicle based on each candidate driving data of the vehicle in the candidate driving data set of the vehicle.

[0084] In an embodiment of the present application, for each candidate driving data set of the host vehicle, first relative distance data between the host vehicle and the interacting vehicle, as well as second relative distance data between the host vehicle and the target object, can be determined based on the first position data and the second position data after the host vehicle has traveled for a preset time based on the candidate driving data of the host vehicle and the interacting vehicle has traveled for a preset time based on the interacting target driving data. The target object can be the end point of the lane to be merged. A predicted safe merging index can then be determined based on the first relative distance data, the second relative distance data, and each candidate driving data set of the host vehicle. A predicted efficiency index can be determined based on the traffic speed data of the main lane in the lane data, the second relative distance data, and each candidate driving data set of the host vehicle. A predicted stability index can be determined based on the length data of the lane to be merged in the lane data, the second relative distance data, each candidate merging time, and the driving distance data corresponding to each candidate merging time. A predicted priority driving index can be determined based on the second relative distance data and each candidate driving data set of the host vehicle. A first predicted merging index for the host vehicle based on the candidate driving data of the host vehicle can be determined based on the sum of the predicted safe merging index, the predicted efficiency index, the predicted stability index, and the predicted priority driving index.

[0085] The foundation of merging is safety (SA). Ramp merging differs from free lane changes in that there is a risk of collision between the vehicle and the target object. Furthermore, while weighing the safety of merging, traffic efficiency, driving stability, and road priority can also be considered. These four aspects are comprehensively considered in the first predicted merging indicator for each candidate vehicle's driving data.

[0086] In some possible implementations, when the interactive target driving data of the interactive vehicle j is π j When the candidate driving data π of the vehicle i, the absolute longitudinal distance and absolute lateral distance between the vehicle and the interactive vehicle after a preset driving time of 1s can be used as indicators to measure the safety between the vehicles. The larger the distance, the larger the predicted safe lane merging index, and the safer the lane merging of the vehicle. Similarly, the longitudinal distance and lateral distance between the vehicle i and the target object A can be used as indicators to measure the safety between the vehicle and the target object. It is worth noting that when the lateral distance between the vehicle i and the target object A is greater than 0, it can be said that the vehicle i has completed the lane merging, and there is no need to consider the safety between the vehicle and the target object. In order to avoid the inability to determine the predicted safe lane merging index when the longitudinal speed in the candidate driving data of the vehicle is 0, it is necessary to travel to the minimum value of the speed in the candidate driving data of the vehicle. Specifically, the following formula can be used to determine the safe predicted lane merging index:

[0087]

[0088] Among them, SA can represent the weight corresponding to the safety prediction merging index, Δx ij It can represent the absolute longitudinal distance between the host vehicle and the interacting vehicle, Δy ij It can represent the absolute lateral distance between the host vehicle and the interacting vehicle, Δx i It can represent the longitudinal distance between the vehicle and the target object, Δy i It can represent the lateral distance between the vehicle and the target object, v i It can represent the longitudinal speed of the vehicle in the candidate driving data, ϕ i It can represent the heading angle in the candidate driving data of the vehicle, p i It can represent the total number of perceived traffic participants, I can represent a logical judgment function, and the output result is 0 or 1, L i It can express the length of the vehicle, W i It can indicate the width of the vehicle.

[0089] In some possible implementations, considering traffic efficiency, the smaller the lateral distance between the vehicle and the centerline of the main lane, the higher the overall speed of the traffic flow, and the higher the predicted efficiency index of the vehicle. Specifically, the predicted efficiency index can be determined using the following formula:

[0090]

[0091] Among them, TC can be expressed as an efficiency prediction index, v i It can represent the longitudinal speed of the vehicle in the candidate driving data, v tra It can indicate the traffic speed in the main lane.

[0092] In some possible implementation, the longer the time ti spent by the ego vehicle on the ramp, the higher the ratio of the distance traveled compared to the total length Lr of the ramp, the worse the driving stability of the ego vehicle, in view of the driving stability. Specifically, the predicted stability index can be determined by the following formula:

[0093]

[0094] wherein REmay represent the stability prediction index, t i may represent the time spent by the ego vehicle on the ramp, d i may represent the distance traveled by the ego vehicle on the ramp, and Lrmay represent the length data of the ramp lane.

[0095] In some possible implementation, in view of the road driving priority, when the ego vehicle has the right of way, the behavior of accelerating or approaching the center line of the main lane is worth encouraging, and vice versa. Here, if the right of way of the ego vehicle is not clear, the time required by the ego vehicle to uniformly travel to the end of the ramp can be determined according to the time required by the ego vehicle and the interactive vehicle, and the ego vehicle has the right of way if the time required by the ego vehicle is longer than the time required by the interactive vehicle, i.e., Row=1, otherwise Row=0.

[0096]

[0097] wherein SCmay represent the priority driving prediction index, a i may represent the acceleration in the candidate driving data of the ego vehicle, a j acceleration in the target driving data of the interactive vehicle.

[0098] After obtaining the safety prediction merging index, the efficiency prediction index, the stability prediction index and the priority driving prediction index, the first prediction merging index of the ego vehicle based on the candidate driving data of the ego vehicle can be determined by the following formula:

[0099]

[0100] wherein W SA may represent the weight corresponding to the safety prediction merging index, W TC may represent the weight corresponding to the efficiency prediction index, W RE may represent the weight corresponding to the stability prediction index, and W SC may represent the weight corresponding to the priority driving prediction index.

[0101] S305: predicting the prediction driving data of the interactive vehicle based on the historical driving intention parameters in the labeled driving data, and determining the target driving intention parameters of the interactive vehicle according to the error between the labeled driving data and the prediction driving data.

[0102] In an embodiment of the present application, after determining the first predicted merging index of the vehicle based on each candidate driving data of the vehicle in the candidate driving data set of the vehicle, the labeled driving sub-data of the interactive vehicle at each historical pre-merge sampling moment during the historical pre-merge time can be determined from the labeled driving data, and each labeled driving sub-data can include the historical driving intention parameters at each historical pre-merge sampling moment. Then, the predicted driving sub-data of the interactive vehicle at each historical pre-merge sampling moment can be predicted based on the historical driving intention parameters in each labeled driving sub-data, and the minimum difference between the labeled driving sub-data and the predicted driving sub-data at multiple historical pre-merge sampling moments can be determined, and the historical driving intention parameters in the labeled driving sub-data corresponding to the minimum difference can be used as the target driving intention parameters of the interactive vehicle. In actual application, the target driving intention parameters can be determined using the following formula:

[0103]

[0104] in, It can represent the target driving intention parameter, n can represent the historical pre-merge sampling time, It can represent the annotated driving sub-data of interacting vehicles at the historical pre-merge sampling moment. It can represent the predicted driving sub-data of the interacting vehicles at the historical pre-merge sampling moment. can represent historical driving intention parameters, where .

[0105] S307: Determine a target predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle according to the first predicted lane merging index, the second predicted lane merging index of the interacting vehicle based on the interactive target driving data, and the target driving intention parameter.

[0106] In some possible implementations, a first target predicted merging sub-index can be determined based on the product of the first predicted merging index and the target driving intention parameter, and a second target predicted merging sub-index can be determined based on the product of the second predicted merging index and the target driving intention parameter. Then, the target predicted merging index for the vehicle based on each candidate driving data of the vehicle can be determined based on the sum of the first target predicted merging sub-index and the second target predicted merging sub-index. Specifically, the target predicted merging index for the vehicle based on the candidate driving data of the vehicle can be determined using the following formula:

[0107]

[0108] in, It can represent the target driving intention parameter, R i It can represent the first prediction index, R j It can represent the second prediction merging index.

[0109] S209: Determine the target driving data of the vehicle from the candidate driving data set based on the target predicted merging indicator and the combined probability. Indicate the target gap in the main lane from the lane to be merged to the lane to be merged based on the target driving data. The combined probability represents the probability of determining the indicator based on both the vehicle's target state data and the interactive target state data.

[0110] In an embodiment of the present application, after obtaining the target predicted lane merging index of each candidate driving data of the vehicle, the candidate driving data of the vehicle corresponding to the maximum product can be used as the target driving data of the vehicle based on the product of the target predicted lane merging index of each candidate driving data of the vehicle and the combination probability.

[0111] The vehicle merging method provided in the embodiments of the present application improves the robustness of vehicle merging, efficiency, and safety by introducing the host vehicle's tentative behavior and obtaining driving data from the interacting vehicle in response to the host vehicle's tentative behavior. This allows the interaction vehicle's time-varying driving intention parameters to be determined to dynamically describe the driving characteristics of traffic participants. By using different candidate merging times to describe different driving styles, stylized driving can be achieved. While weighing the safety of vehicle merging, traffic efficiency, driving stability, and road priority can be considered, and the first predicted merging indicator for each host vehicle's candidate driving data can be comprehensively considered from these four aspects.

[0112] The embodiment of the present application also provides a vehicle lane merging processing device, Figure 8 : is a structural diagram of a vehicle lane-merging processing device provided in an embodiment of the present application, such as Figure 8 As shown, the vehicle lane merging processing device may include:

[0113] An acquisition module 801 is configured to acquire lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle during a historical pre-merge time, and a target gap; the interacting vehicle is the vehicle behind the adjacent surrounding vehicles corresponding to the target gap;

[0114] A first determining module 803 is configured to determine, based on the lane data, the first position data, the second position data, and the annotated driving data, a first predicted lane merging index of the vehicle based on each candidate driving data of the vehicle in the candidate driving data set;

[0115] A second determination module 805 is configured to predict the predicted driving data of the interacting vehicle based on the historical driving intention parameters in the annotated driving data, and determine the target driving intention parameters of the interacting vehicle according to the error between the annotated driving data and the predicted driving data;

[0116] a third determining module 807 for determining a target predicted lane merging index for the host vehicle based on each candidate vehicle driving data according to the first predicted lane merging index, the second predicted lane merging index based on the interactive target driving data of the interacting vehicle, and the target driving intention parameter;

[0117] The fourth determination module 809 is used to determine the target driving data of the vehicle from the candidate driving data set of the vehicle based on the target predicted merging indicator and the combined probability, and indicate the target gap in the main lane for the vehicle to merge from the lane to be merged based on the target driving data of the vehicle; the combined probability represents the probability of determining the indicator based on both the target state data of the vehicle and the interactive target state data.

[0118] In some possible implementations, the above-mentioned vehicle lane merging processing device further includes:

[0119] The construction module is used to preset a set of candidate merging times for the vehicle before determining a first predicted merging index for the vehicle based on each candidate driving data of the vehicle in the candidate driving data set of the vehicle, and to construct the candidate driving data of the vehicle corresponding to each candidate merging time to obtain the candidate driving data set of the vehicle.

[0120] In some possible implementations, the first determining module 803 is configured to determine, for each candidate driving data of the host vehicle, based on the first position data and the second position data, first relative distance data between the host vehicle and the interacting vehicle, and second relative distance data between the host vehicle and the target object, after the host vehicle has traveled for a preset driving time based on each candidate driving data of the host vehicle and the interacting vehicle has traveled for a preset driving time based on the interactive target driving data; the target object being the end point of the lane to be merged;

[0121] determining a predicted safe lane merging index based on the first relative distance data, the second relative distance data, and the driving data of each candidate vehicle;

[0122] Determine a prediction efficiency index based on the traffic speed data of the main lane, the second relative distance data, and the driving data of each candidate vehicle in the lane data;

[0123] Determine a prediction stability index based on the length data of the lane to be merged, the second relative distance data, each candidate merging time, and the driving distance data corresponding to each candidate merging time in the lane data;

[0124] determining a predicted priority driving index based on the second relative position data and each candidate driving data of the host vehicle;

[0125] A first predicted lane merging index of the vehicle based on the candidate driving data of the vehicle is determined according to the sum of the safety prediction lane merging index, the efficiency prediction index, the stability prediction index and the priority driving prediction index.

[0126] In some possible implementations, the second determining module 805 is configured to determine, from the labeled driving data, labeled driving sub-data of the interacting vehicle at each historical pre-merge sampling moment within the historical pre-merge time; each labeled driving sub-data includes a historical driving intention parameter;

[0127] Predicting the predicted driving sub-data of the interacting vehicle at each historical pre-merge sampling moment based on the historical driving intention parameters in each annotated driving sub-data;

[0128] The minimum difference between the labeled driving sub-data and the predicted driving sub-data at multiple historical pre-merge sampling moments is determined, and the historical driving intention parameter in the labeled driving sub-data corresponding to the minimum difference is used as the target driving intention parameter of the interacting vehicle.

[0129] In some possible implementations, the third determining module 807 is configured to determine a first target predicted lane merging sub-index based on a product of the first predicted lane merging index and the target driving intention parameter;

[0130] determining a second target predicted lane merging sub-index according to a product of the second predicted lane merging index and the target driving intention parameter;

[0131] The target predicted lane merging index of the vehicle based on each candidate driving data of the vehicle is determined according to the sum of the first target predicted lane merging sub-index and the second target predicted lane merging sub-index.

[0132] In some possible implementations, the fourth determination module 809 is configured to use the candidate driving data of the vehicle corresponding to the maximum product as the target driving data of the vehicle based on the product of the target predicted merging index and the combined probability of each candidate driving data of the vehicle.

[0133] In some possible implementations, the acquisition module is configured to acquire a plurality of candidate gaps; the candidate gaps are longitudinal spaces between adjacent surrounding vehicles in the main lane;

[0134] Determining a first indicator corresponding to each candidate gap according to the multi-order derivatives of the longitudinal distance data of each candidate gap in unit time;

[0135] Determining a second indicator corresponding to each candidate gap based on multiple derivatives of the lateral distance data between the vehicle and each candidate gap in unit time;

[0136] A target gap is determined from a plurality of candidate gaps according to a difference between the first indicator and the second indicator.

[0137] The device and method embodiments in the embodiments of this application are based on the same application concept.

[0138] An embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the vehicle lane merging processing method provided in the above-mentioned method embodiment.

[0139] Figure 9 This is a hardware structure diagram of an electronic device provided in an embodiment of the present application for implementing the vehicle lane merging processing method provided in an embodiment of the present application. The electronic device may participate in or include the vehicle lane merging processing device provided in an embodiment of the present application. Figure 9 As shown, the electronic device may include one or more processors 901 (illustrated as 901a and 901b in the figure) (the processor 901 may include, but is not limited to, a microprocessor 901 MCU or a programmable logic device FPGA, etc.), a memory 903 for storing data, and a transmission device 905 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and / or a power supply. Those skilled in the art will understand that Figure 9 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 9 More or fewer components than shown, or with Figure 9 Different configurations shown.

[0140] It should be noted that the one or more processors 901 and / or other data processing circuits described above may generally be referred to as "data processing circuitry" in this application. The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of an electronic device (or mobile device). As described in the embodiments of this application, the data processing circuitry functions as a processor 901 control (e.g., selection of a variable resistor terminal path connected to an interface).

[0141] The memory 903 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the vehicle lane merging processing method in the embodiments of the present application. The processor 901 executes the software programs and modules stored in the memory 903 without executing various functional applications and data processing, thereby implementing the aforementioned vehicle lane merging processing method. The memory 903 may include high-speed random access memory (RAM) and may also include non-volatile random access memory 903, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory 903. In some possible embodiments, the memory 903 may further include a memory 903 remotely located relative to the processing unit. These remote memories 903 may be connected to electronic devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0142] Transmission device 905 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the electronic device's communications provider. In one embodiment, transmission device 905 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 905 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0143] The display may be, for example, a touch screen liquid crystal display (LED) that enables a user to interact with a user interface of the electronic device (or mobile device).

[0144] An embodiment of the present application provides a computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a vehicle lane merging processing method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the vehicle lane merging processing method provided by the above method embodiment.

[0145] Optionally, in this embodiment, the storage medium may be located in at least one of a plurality of network servers in the computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0146] It should be noted that the order of the embodiments of the present application described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. The above description describes specific embodiments, and other embodiments are also within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in the order of different embodiments and can achieve the expected results. In addition, the processes depicted in the accompanying drawings do not necessarily require a specific order or a connection order to achieve the desired results. In some embodiments, multi-tasking parallel processing is also possible or may be advantageous.

[0147] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the embodiments of the apparatus and electronic device are described more simply because they are based on similarities to the method embodiments. For relevant portions, refer to the description of the method embodiments.

[0148] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for handling vehicle merging, characterized in that: include: Acquire lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle during a historical pre-merge time, and a target gap; the interacting vehicle is the vehicle traveling behind the adjacent surrounding vehicles corresponding to the target gap; determining, based on the lane data, the first position data, the second position data, and the annotated driving data, a first predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle in the candidate driving data set; predicting predicted driving data of the interacting vehicle based on historical driving intention parameters in the annotated driving data, and determining target driving intention parameters of the interacting vehicle according to an error between the annotated driving data and the predicted driving data; determining a target predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle according to the first predicted lane merging index, the second predicted lane merging index of the interacting vehicle based on the interactive target driving data, and the target driving intention parameter; Based on the target predicted merging indicator and the combined probability, the target driving data of the vehicle is determined from the candidate driving data set of the vehicle, and the target gap in the main lane from the lane to be merged into the lane is indicated based on the target driving data of the vehicle. The combined probability represents the probability of determining the indicator based on both the target state data of the vehicle and the interactive target state data.

2. The method according to claim 1, characterized in that Before determining the first predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle in the candidate driving data set, the method further includes: A set of candidate merging times for the vehicle is preset, and candidate driving data of the vehicle corresponding to each candidate merging time is constructed to obtain the candidate driving data set of the vehicle.

3. The method according to claim 2, characterized in that The determining the first predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle includes: For each candidate driving data of the host vehicle, determining, based on the first position data and the second position data, first relative distance data between the host vehicle and the interacting vehicle and second relative distance data between the host vehicle and a target object after the host vehicle has traveled for a preset driving time based on each candidate driving data of the host vehicle and the interacting vehicle has traveled for the preset driving time based on the interacting target driving data; the target object being an end point of the lane to be merged; determining a predicted safe lane merging index based on the first relative distance data, the second relative distance data, and the driving data of each candidate vehicle; determining a prediction efficiency index based on the traffic speed data of the main lane in the lane data, the second relative distance data, and each candidate driving data of the host vehicle; determining a prediction stability index based on the length data of the lane to be merging in the lane data, the second relative distance data, each of the candidate merging times, and the travel distance data corresponding to each of the candidate merging times; determining a predicted priority driving index based on the second relative distance data and each candidate driving data of the host vehicle; The first predicted lane merging index of the host vehicle based on the candidate driving data of the host vehicle is determined according to the sum of the predicted safe lane merging index, the predicted efficiency index, the predicted stability index and the predicted priority driving index.

4. The method according to claim 1, wherein The determining, based on an error between the labeled driving data and the predicted driving data, a target driving intention parameter of the interactive vehicle includes: Determining, from the labeled driving data, labeled driving sub-data of the interacting vehicle at each historical pre-merge sampling moment within the historical pre-merge time; each labeled driving sub-data includes a historical driving intention parameter; Predicting the predicted driving sub-data of the interacting vehicle at each of the historical pre-merge sampling moments based on the historical driving intention parameters in each of the annotated driving sub-data; Determine a minimum difference between the labeled driving sub-data and the predicted driving sub-data at a plurality of the historical pre-merge sampling moments, and use the historical driving intention parameter in the labeled driving sub-data corresponding to the minimum difference as the target driving intention parameter of the interacting vehicle.

5. The method according to claim 1, wherein The determining of the target predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle includes: determining a first target predicted lane merging sub-index according to a product of the first predicted lane merging index and the target driving intention parameter; determining a second target predicted lane merging sub-index according to a product of the second predicted lane merging index and the target driving intention parameter; The target predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle is determined according to the sum of the first target predicted lane merging sub-index and the second target predicted lane merging sub-index.

6. The method according to claim 1, characterized in that Determining the target driving data of the vehicle from the candidate driving data set of the vehicle according to the target predicted merging index and the combined probability includes: According to the product of the target predicted lane merging index of the host vehicle based on each of the candidate driving data of the host vehicle and the combination probability, the host vehicle candidate driving data corresponding to the maximum product is used as the target driving data of the host vehicle.

7. The method according to any one of claims 1 to 6, characterized in that: The obtaining of the target gap includes: Acquire a plurality of candidate gaps; the candidate gaps being longitudinal spaces between adjacent surrounding vehicles in the main lane; Determining a first indicator corresponding to each candidate gap according to a multi-order derivative of the longitudinal distance data of each candidate gap in unit time; determining a second indicator corresponding to each candidate gap according to a multi-order derivative of the lateral distance data between the host vehicle and each candidate gap in unit time; The target gap is determined from a plurality of candidate gaps according to a difference between the first indicator and the second indicator.

8. A vehicle merging processing device, characterized in that: include: an acquisition module configured to acquire lane data, first position data of the host vehicle, second position data of the interacting vehicle, annotated driving data of the interacting vehicle during a historical pre-merge time, and a target gap; the interacting vehicle being the vehicle behind the adjacent surrounding vehicles corresponding to the target gap; a first determining module, configured to determine, based on the lane data, the first position data, the second position data, and the annotated driving data, a first predicted merging index of the host vehicle based on each candidate driving data of the host vehicle in the candidate driving data set; a second determining module, configured to predict predicted driving data of the interacting vehicle based on historical driving intention parameters in the annotated driving data, and determine target driving intention parameters of the interacting vehicle according to an error between the annotated driving data and the predicted driving data; a third determining module, configured to determine a target predicted lane merging index of the host vehicle based on each candidate driving data of the host vehicle according to the first predicted lane merging index, the second predicted lane merging index of the interacting vehicle based on the interactive target driving data, and the target driving intention parameter; an indication module for determining target driving data of the host vehicle from the host vehicle candidate driving data set based on the target predicted merging indicator and the combined probability, and indicating, based on the host vehicle target driving data, when to merge from the lane to be merged to the target gap in the main lane; wherein the combined probability represents the probability of determining the indicator based on both the host vehicle target state data and the interactive target state data.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the vehicle lane merging processing method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the vehicle lane merging processing method according to any one of claims 1 to 7.

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