Vehicle control method and device, equipment, storage medium and product

Abstract

The invention discloses a vehicle control method and device, equipment, a storage medium and a product, and relates to the technical field of automatic driving, and the vehicle control method comprises the steps that when a vehicle runs on a road based on speed limit information, whether a target vehicle exceeding the vehicle exists in an adjacent lane or not is detected; if the target vehicle exceeding the own vehicle exists in the adjacent lane, acquiring a first vehicle speed of the target vehicle; and based on the first vehicle speed and the speed limiting information, a vehicle speed increasing value is determined, and based on the vehicle speed increasing value, the second vehicle speed of the vehicle is increased. It can be understood that when a vehicle runs under the road speed limiting condition, if it is detected that a target vehicle exceeding the vehicle exists in an adjacent lane, it is determined that surrounding traffic flow runs over the speed limiting condition, and the current speed of the vehicle and the surrounding traffic flow cannot be kept coordinated, the speed of the vehicle is adjusted based on the speed of the target vehicle and the speed limiting information, and therefore the speed of the vehicle is adjusted. Therefore, the coordination between the vehicle and the surrounding traffic flow is realized, and the continuity of the traffic flow and the driving safety of the vehicle are improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to vehicle control methods, devices, equipment, storage media and products. Background Art

[0002] In the field of autonomous driving, vehicle speed control technology is a key link to ensure driving safety and improve driving experience. With the development of autonomous driving technology, the demand for vehicle speed control is growing, especially in complex traffic environments. How to achieve accurate, safe and efficient vehicle speed control is a hot topic in current research.

[0003] In the related art, the vehicle speed limit system automatically adjusts the vehicle speed according to the road speed limit information or the traffic conditions ahead to ensure driving safety. However, the vehicle speed control system mainly relies on fixed speed limit information. When the surrounding traffic exceeds the speed limit, the vehicle that maintains the road speed limit is relatively slow and cannot keep coordination with the surrounding traffic, thus affecting the continuity of traffic flow and driving safety.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a vehicle control method, which aims to solve the technical problem that the vehicle cannot coordinate with the surrounding traffic, affecting the continuity of traffic flow and driving safety.

[0006] To achieve the above objectives, the present application proposes a vehicle control method, the vehicle control method comprising:

[0007] When the vehicle is driving on the road based on the speed limit information, it detects whether there is a target vehicle that exceeds the vehicle in the adjacent lane;

[0008] If there is a target vehicle that exceeds the vehicle in the adjacent lane, obtaining a first vehicle speed of the target vehicle;

[0009] A vehicle speed increase value is determined based on the first vehicle speed and the speed limit information, and a second vehicle speed of the own vehicle is increased based on the vehicle speed increase value.

[0010] Optionally, the step of detecting whether there is a target vehicle that exceeds the vehicle in the adjacent lane includes:

[0011] Acquire road images;

[0012] Extracting characteristic information of each vehicle in an adjacent lane from the road image, wherein the characteristic information includes position information of the rear end of the vehicle;

[0013] Based on the vehicle rear position information, determine the relative position information between the front of the vehicle and the rear of each vehicle in the adjacent lane;

[0014] If the relative position information satisfies the preset complete overtaking condition, it is determined that there is a target vehicle that exceeds the own vehicle in the adjacent lane.

[0015] Optionally, the step of determining a vehicle speed boost value based on the first vehicle speed and the speed limit information, and increasing the second vehicle speed of the vehicle based on the vehicle speed boost value includes:

[0016] calculating a speed difference between the first vehicle speed and the speed limit information;

[0017] The smallest of the speed difference and the preset limit value is used as the vehicle speed increase value;

[0018] Based on the preset increase amplitude, the second vehicle speed of the vehicle is gradually increased until the difference between the second vehicle speed after the increase and the second vehicle speed before the increase reaches the vehicle speed increase value.

[0019] Optionally, the step of determining a vehicle speed boost value based on the first vehicle speed and the speed limit information, and increasing the second vehicle speed of the vehicle based on the vehicle speed boost value includes:

[0020] Obtaining road condition characteristic information;

[0021] Based on the first vehicle speed, the speed limit information and the road condition feature information, a preset upper-layer controller is used to perform feature fusion and output a vehicle control decision, wherein the vehicle control decision includes a vehicle speed boost value;

[0022] Based on the vehicle speed increase value, the second vehicle speed of the vehicle is increased through a preset lower-level controller, and the vehicle is controlled to travel at the increased second vehicle speed.

[0023] Optionally, the upper-layer controller is composed of a convolutional neural network and a graph neural network, the convolutional neural network is used to process road images, and the graph neural network is used to process graph structure data.

[0024] Optionally, the step of obtaining the first vehicle speed of the target vehicle includes:

[0025] Acquire a corresponding vehicle speed set from continuous image frames of the target vehicle;

[0026] Calculating differences between adjacent vehicle speeds in the vehicle speed set to form a vehicle speed difference matrix;

[0027] Based on a preset observation matrix, the initial difference matrix is ​​subjected to denoising to obtain a vehicle speed difference matrix after denoising;

[0028] Based on the denoised vehicle speed difference matrix, a first vehicle speed of the target vehicle is determined.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle control device, the vehicle control device comprising:

[0030] A detection module, used for detecting whether there is a target vehicle that exceeds the self-vehicle in an adjacent lane when the self-vehicle is traveling on the road based on the speed limit information;

[0031] an acquisition module, configured to acquire a first vehicle speed of a target vehicle if there is a target vehicle that exceeds the vehicle in an adjacent lane;

[0032] The upward adjustment module is used to determine a vehicle speed increase value based on the first vehicle speed and the speed limit information, and to increase the second vehicle speed of the vehicle based on the vehicle speed increase value.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle control method as described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle control method described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the vehicle control method described above are implemented.

[0036] One or more technical solutions proposed in this application have at least the following technical effects:

[0037] Compared with the vehicle speed limit system in the related art, which automatically adjusts the vehicle speed according to the road speed limit information or the traffic conditions ahead to ensure driving safety. However, the vehicle speed control system mainly relies on fixed speed limit information. When the surrounding traffic exceeds the speed limit, the vehicle that maintains the road speed limit has a relatively slow speed and cannot keep coordination with the surrounding traffic, thus affecting the continuity of traffic flow and driving safety. In this application, when the vehicle is driving on the road based on the speed limit information, it detects whether there is a target vehicle that exceeds the vehicle in the adjacent lane; if there is a target vehicle that exceeds the vehicle in the adjacent lane, the first speed of the target vehicle is obtained; based on the first speed and the speed limit information, the speed increase value is determined, and based on the speed increase value, the second speed of the vehicle is increased. It is understandable that when the self-driving vehicle is driving under the speed limit of the road, if a target vehicle that exceeds the self-driving vehicle is detected in the adjacent lane, it is determined that the surrounding traffic is traveling over the speed limit, and the current speed of the self-driving vehicle cannot be coordinated with the surrounding traffic. Then, the speed of the self-driving vehicle is adjusted based on the speed of the target vehicle and the speed limit information to achieve coordination between the self-driving vehicle and the surrounding traffic, thereby improving the continuity of traffic flow and the driving safety of the self-driving vehicle. That is, the present application provides the autonomous driving vehicle with a real-time adaptation function to the actual speed of the surrounding traffic, so as to achieve coordination between the self-driving vehicle and the surrounding traffic, thereby improving the continuity of traffic flow and the driving safety of the self-driving vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A schematic diagram of a flow chart of a first embodiment of a vehicle control method of the present application;

[0041] Figure 2 A schematic diagram of a road scene of the vehicle control method of this application;

[0042] Figure 3 A schematic diagram of a flow chart provided for the second embodiment of the vehicle control method of the present application;

[0043] Figure 4 This is a schematic diagram of the module structure of the vehicle control device according to an embodiment of the present application;

[0044] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the vehicle control method in the embodiment of the present application.

[0045] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0047] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0048] The main solution of the embodiment of the present application is: when the vehicle is traveling on the road based on the speed limit information, detect whether there is a target vehicle that exceeds the vehicle in the adjacent lane; if there is a target vehicle that exceeds the vehicle in the adjacent lane, obtain the first speed of the target vehicle; based on the first speed and the speed limit information, determine the speed increase value, and based on the speed increase value, increase the second speed of the vehicle.

[0049] In this embodiment, the vehicle control device is used as the execution subject. For the convenience of description, it will be described as a "device" hereinafter.

[0050] In the related art, the vehicle speed limit system automatically adjusts the vehicle speed according to the road speed limit information or the traffic conditions ahead to ensure driving safety. However, the vehicle speed control system mainly relies on fixed speed limit information. When the surrounding traffic exceeds the speed limit, the vehicle that maintains the road speed limit has a relatively slow speed and cannot keep coordination with the surrounding traffic, thus affecting the continuity of traffic flow and driving safety.

[0051] The present application provides a solution to achieve vehicle speed adjustment, thereby improving the continuity of traffic flow and the safety of self-driving.

[0052] As can be seen from the above embodiments, when the self-driving vehicle is driving under the speed limit condition of the road, if a target vehicle that exceeds the self-driving vehicle is detected in the adjacent lane, it is determined that the surrounding traffic is traveling over the speed limit, and the current speed of the self-driving vehicle cannot be coordinated with the surrounding traffic. Then, the speed of the self-driving vehicle is adjusted based on the speed of the target vehicle and the speed limit information to achieve coordination between the self-driving vehicle and the surrounding traffic, thereby improving the continuity of traffic flow and the driving safety of the self-driving vehicle. That is, the present application provides the autonomous driving vehicle with a real-time adaptation function to the actual speed of the surrounding traffic, so as to achieve coordination between the self-driving vehicle and the surrounding traffic, thereby improving the continuity of traffic flow and the driving safety of the self-driving vehicle.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a terminal system, etc. that can realize the above functions. The following takes the vehicle control device as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the embodiment of the present application provides a vehicle control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle control method of the present application.

[0055] In this embodiment, the vehicle control method includes steps S100 to S300:

[0056] Step S100, when the vehicle is traveling on the road based on the speed limit information, detecting whether there is a target vehicle that exceeds the vehicle in an adjacent lane;

[0057] It should be noted that the speed limit information refers to the road speed limit sign or built-in speed limit information obtained by the vehicle (the own vehicle). Only when the own vehicle is traveling on the road based on the speed limit information, the device starts to detect whether there is a target vehicle that exceeds the own vehicle in the adjacent lane, wherein the adjacent lane refers to the lane adjacent to the lane where the own vehicle is located, and the target vehicle refers to the vehicle that is in the adjacent lane and exceeds the own vehicle.

[0058] In a specific implementation, the device detects whether there is a target vehicle that has surpassed the own vehicle in an adjacent lane by having the vehicle's sensor system (radar, camera, etc.) monitor the lane next to the vehicle to check whether there are other vehicles (target vehicles) that are traveling at a faster speed than the own vehicle and surpassing the own vehicle.

[0059] In the specific implementation, refer to Figure 2 , the left vehicle is the ego vehicle, and the right vehicle is the vehicle traveling in the adjacent lane of the ego vehicle. If the ego vehicle is currently traveling at the speed limit on the road and it is detected that the right vehicle exceeds the ego vehicle, the right vehicle will be taken as the target vehicle.

[0060] In a specific implementation, the step of the device detecting whether there is a target vehicle that exceeds the vehicle in an adjacent lane includes:

[0061] Acquire a road image; extract characteristic information of each vehicle in an adjacent lane from the road image, wherein the characteristic information includes vehicle rear position information; determine relative position information between the front of the own vehicle and the rear of each vehicle in the adjacent lane based on the vehicle rear position information; if the relative position information satisfies a preset complete overtaking condition, determine that there is a target vehicle in the adjacent lane that is overtaking the own vehicle.

[0062] In the specific implementation, the device first needs to obtain real-time images of the current road through the on-board camera or other sensors, where these images contain information such as vehicles, lane lines, and traffic signs on the road. Further, the device analyzes the acquired road images, identifies vehicles in adjacent lanes, and extracts key feature information of these vehicles. In the specific scenario, the feature information that the device focuses on is the position information of the rear of the vehicle, which is used to determine the relative position between the vehicle and the target vehicle.

[0063] In a specific implementation, the device uses the front position of the vehicle and the rear position information of each vehicle in the adjacent lane to calculate the relative position between the vehicle and these vehicles, wherein the relative position includes the front and rear distances and lateral distances between the vehicle and these vehicles. Furthermore, the device will determine whether a vehicle is overtaking the vehicle based on the relative position information and the preset full overtaking condition (the full overtaking condition includes that the distance between the front of the vehicle and the rear of the vehicle in the adjacent lane is less than a preset threshold). If these conditions are met, the device will determine that there is a target vehicle in the adjacent lane that is overtaking the vehicle.

[0064] Step S200, if there is a target vehicle that exceeds the vehicle in the adjacent lane, obtaining a first vehicle speed of the target vehicle;

[0065] It is understandable that if there is a target vehicle that exceeds the ego vehicle in the adjacent lane, it is determined that the current ego vehicle speed cannot be coordinated with the surrounding traffic, so the ego vehicle speed needs to be adjusted to achieve coordination between the ego vehicle and the surrounding traffic.

[0066] In a specific implementation, when the target vehicle is identified by the device, the device will obtain the current speed of the vehicle (i.e., the first speed). The first speed refers to the speed when the target vehicle exceeds the self-vehicle. The acquisition of the first speed of the target vehicle is achieved through target detection, target tracking, and speed calculation.

[0067] In a specific implementation, the step of obtaining the first speed of the target vehicle by the device includes:

[0068] A corresponding vehicle speed set is obtained from continuous image frames of the target vehicle; adjacent vehicle speeds in the vehicle speed set are subtracted to form a vehicle speed difference matrix; based on a preset observation matrix, the initial difference matrix is ​​subjected to denoising to obtain a denoised vehicle speed difference matrix; based on the denoised vehicle speed difference matrix, a first vehicle speed of the target vehicle is determined.

[0069] In a specific implementation, the device obtains a corresponding vehicle speed set from continuous image frames of the target vehicle, that is, the device calculates the speed of the target vehicle in each frame by analyzing the position change of the target vehicle in the continuous image frames, thereby forming a vehicle speed set. Further, the device subtracts two adjacent vehicle speed values ​​in the vehicle speed set to obtain a vehicle speed difference, and these differences are organized into a matrix to form a vehicle speed difference matrix.

[0070] It is understandable that since there will be noise in the actual measurement, the device uses a preset observation matrix to perform noise reduction on the vehicle speed difference matrix. Specifically, some signal processing technologies are involved, such as Kalman filtering, mean filtering, etc., to reduce measurement errors and obtain a more accurate vehicle speed difference matrix.

[0071] In the specific implementation, the observation matrix is ​​a matrix that maps the system state vector to the observation space. In the intelligent driving system, the observation matrix is ​​used to convert the information in the vehicle speed difference matrix into a form that can be directly measured so that the system can use this information for state estimation and prediction. Specifically, when the device obtains a set of vehicle speeds from continuous image frames and forms a vehicle speed difference matrix, the observation matrix is ​​used to extract useful information from these differences. Through the observation matrix, the device can identify which information is noise and which is real vehicle speed changes, so as to perform noise reduction processing. Among them, the preset observation matrix is ​​constructed based on the prior knowledge of the system and sensor characteristics, and it reflects the relationship between the system state and the observable measurement values.

[0072] Furthermore, during the noise reduction process, the preset observation matrix helps the device understand which measurements are reliable and which are more affected by noise. Specifically, during the noise reduction process, the observation matrix is ​​combined with the speed difference matrix to reduce the impact of noise through mathematical operations (such as Kalman filtering and other algorithms). This process involves operations on the observation matrix and the speed difference matrix to produce a noise-reduced speed difference matrix that is closer to the actual speed change. That is, the noise-reduced speed difference matrix provides more accurate speed change information, and the device can determine the first speed of the target vehicle based on this information.

[0073] Step S300: determining a vehicle speed boost value based on the first vehicle speed and the speed limit information, and increasing a second vehicle speed of the vehicle based on the vehicle speed boost value.

[0074] In the specific implementation, the device will calculate how much speed the vehicle needs to increase in order to achieve a speed that is coordinated with the surrounding traffic based on the first speed of the target vehicle and the speed limit information. This increased value is called the "speed increase value."

[0075] Furthermore, the device adjusts the current second speed of the vehicle according to the calculated speed increase value, so that it increases to a new speed level, and this new speed satisfies the coordination between the vehicle speed and the surrounding traffic flow. That is, the purpose of increasing the speed is to coordinate the vehicle speed with the surrounding traffic flow, thereby improving the continuity of traffic flow and the driving safety of the vehicle.

[0076] In a specific implementation, the device determines a vehicle speed boost value based on the first vehicle speed and the speed limit information, and the step of increasing the second vehicle speed of the vehicle based on the vehicle speed boost value includes:

[0077] Calculate the speed difference between the first vehicle speed and the speed limit information; take the smallest of the speed difference and the preset limit value as the vehicle speed increase value; and gradually increase the second vehicle speed of the vehicle based on the preset increase amplitude until the difference between the second vehicle speed after the increase and the second vehicle speed before the increase reaches the vehicle speed increase value.

[0078] In a specific implementation, the device first calculates the difference between the first speed of the target vehicle and the current road speed limit information, and this difference reflects the extent to which the speed of the target vehicle exceeds the speed limit. Further, the device compares this speed difference with the preset limit value, and selects the smaller one as the speed increase value. The limit value is a maximum speed increase limit preset by the device to ensure that the speed increase of the vehicle itself is not too large.

[0079] For example, the speed difference between the first vehicle speed and the speed limit information is 12kph, and the limit value is 10kph, which means that the speed of the target vehicle is too fast and exceeds the preset limit value. Therefore, the device needs to use the limit value as the speed increase value to prevent the speed of the vehicle from increasing too much.

[0080] Compared with the vehicle speed limit system in the related art, which automatically adjusts the vehicle speed according to the road speed limit information or the traffic conditions ahead to ensure driving safety. However, the vehicle speed control system mainly relies on fixed speed limit information. When the surrounding traffic exceeds the speed limit, the vehicle that maintains the road speed limit has a relatively slow speed and cannot keep coordination with the surrounding traffic, thus affecting the continuity of traffic flow and driving safety. In this application, when the vehicle is driving on the road based on the speed limit information, it detects whether there is a target vehicle that exceeds the vehicle in the adjacent lane; if there is a target vehicle that exceeds the vehicle in the adjacent lane, the first speed of the target vehicle is obtained; based on the first speed and the speed limit information, the speed increase value is determined, and based on the speed increase value, the second speed of the vehicle is increased. It is understandable that when the self-driving vehicle is driving under the speed limit of the road, if a target vehicle that exceeds the self-driving vehicle is detected in the adjacent lane, it is determined that the surrounding traffic is traveling over the speed limit, and the current speed of the self-driving vehicle cannot be coordinated with the surrounding traffic. Then, the speed of the self-driving vehicle is adjusted based on the speed of the target vehicle and the speed limit information to achieve coordination between the self-driving vehicle and the surrounding traffic, thereby improving the continuity of traffic flow and the driving safety of the self-driving vehicle. That is, the present application provides the autonomous driving vehicle with a real-time adaptation function to the actual speed of the surrounding traffic, so as to achieve coordination between the self-driving vehicle and the surrounding traffic, thereby improving the continuity of traffic flow and the driving safety of the self-driving vehicle.

[0081] Based on the above first embodiment, the present application also proposes another embodiment, referring to Figure 3 , the vehicle control method comprises:

[0082] In a specific implementation, the device determines a vehicle speed boost value based on the first vehicle speed and the speed limit information, and the step of increasing the second vehicle speed of the vehicle based on the vehicle speed boost value includes:

[0083] Step A100, obtaining road condition characteristic information;

[0084] In a specific implementation, the device obtains road condition characteristic information through vehicle-mounted sensors (such as cameras, radars, lidars, etc.) or external data sources (such as traffic information centers). The road condition characteristic information includes but is not limited to road conditions, road vehicle driving conditions, traffic congestion, weather conditions, visibility, etc.

[0085] Step A200, based on the first vehicle speed, the speed limit information and the road condition feature information, a preset upper-layer controller performs feature fusion and outputs a vehicle control decision, wherein the vehicle control decision includes a vehicle speed boost value;

[0086] In the specific implementation, the device uses an upper-level controller (the upper-level controller is a decision-making module), which analyzes and processes the input feature information (first vehicle speed, speed limit information, road condition feature information), integrates this information, and outputs a vehicle control decision. This decision includes the vehicle speed increase value, that is, the target speed recommended for the vehicle to increase to. In other words, the device comprehensively considers the speed of the target vehicle (first vehicle speed), the road speed limit, and the road condition feature information to formulate a reasonable vehicle control strategy.

[0087] It should be noted that the upper-layer controller is composed of a convolutional neural network and a graph neural network. The convolutional neural network is used to process road images, and the graph neural network is used to process graph structure data.

[0088] Step A300, based on the vehicle speed increase value, the second vehicle speed of the vehicle is increased through a preset lower-level controller, and the vehicle is controlled to travel at the increased second vehicle speed.

[0089] In the specific implementation, the device uses a lower-level controller (the lower-level controller is an execution module) to adjust the second speed of the vehicle according to the speed boost value provided by the upper-level controller. The lower-level controller is responsible for implementing the specific speed adjustment to ensure that the vehicle can reach the new driving speed smoothly and safely and travel at this speed.

[0090] In the specific implementation, the calculation of the upper controller of the device uses the fused CNN and GNN as the decision model, and the real-time data collected by the vehicle is used as input, and then a decision result is obtained to inform the lower controller. The lower controller controls the vehicle in real time through the model predictive control (MPC) method, and the next moment data of the vehicle is once again transmitted to the upper controller to obtain a decision until the decision changes.

[0091] That is, the upper-level controller is responsible for making strategic decisions, while the lower-level controller is responsible for executing these decisions to ensure that the vehicle drives at the specified speed and route. This hierarchical control structure helps improve the responsiveness and flexibility of the system while ensuring driving safety and compliance.

[0092] In the specific implementation, a deep learning model is pre-built in the vehicle system. The model can comprehensively consider local features (such as the speed and acceleration of the vehicle) and global interactions (such as the relative position and movement between vehicles). The construction process of the model is as follows:

[0093] 1. Data collection and preprocessing, including collecting local feature data of vehicles, such as speed, acceleration, position, etc., which can be obtained through vehicle sensors (such as cameras, radars); collecting global interaction data between vehicles, such as relative position, relative speed, etc., which can be obtained through vehicle communication systems (V2X); preprocessing the data, including normalization, to facilitate model training.

[0094] 2. Feature extraction, including CNN (convolutional neural network) feature extraction, which uses CNN to extract features from local images of vehicles, such as the shape, size, and motion pattern of the vehicle; GNN (graph neural network) feature extraction, which constructs a graph in which nodes represent vehicles and edges represent the relative positions and motion relationships between vehicles, and uses GNN to extract interaction features between vehicles.

[0095] 3. Feature fusion, that is, fusing the features extracted by CNN and GNN, which can be achieved through simple splicing or more complex fusion mechanisms (such as weighted fusion).

[0096] 4. Vehicle speed increase value prediction model. This application designs a fusion model that receives the output of CNN and GNN as input, predicts the vehicle speed increase value, and uses historical data to train the model and optimize the model parameters to minimize the prediction error.

[0097] 6. Training and optimization: The device uses historical traffic data to train the fusion model and optimizes the model parameters to minimize the prediction error. It can also use optimizers such as Adam for training and adjust the learning rate and batch size to improve model performance.

[0098] 7. Real-time adjustment, that is, the device adjusts the model prediction according to real-time traffic data to adapt to the rapid changes in traffic conditions, and dynamically adjusts the vehicle speed increase value according to the difference between the vehicle's actual behavior and the predicted results.

[0099] 8. Maintain the continuity of traffic flow, that is, the device simulates the impact of different speed increase values ​​on the continuity of traffic flow and selects the best speed increase strategy. By optimizing the speed increase value, traffic interruption and congestion can be reduced, and the continuity of the overall traffic flow can be improved.

[0100] In the specific implementation, through the above steps, this application constructs a prediction model that can comprehensively consider local features and global interactions, so as to more accurately predict the speed increase value and maintain the continuity of traffic flow. This method can improve the adaptability and safety of autonomous driving vehicles in complex traffic environments.

[0101] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the vehicle control method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0102] This application also provides a vehicle control device, please refer to Figure 4 , the vehicle control device comprises:

[0103] A detection module 10, used for detecting whether there is a target vehicle that exceeds the vehicle in an adjacent lane when the vehicle is traveling on the road based on the speed limit information;

[0104] An acquisition module 20 is used to acquire a first vehicle speed of a target vehicle if there is a target vehicle that exceeds the vehicle in the adjacent lane;

[0105] The upward adjustment module 30 is used to determine a vehicle speed increase value based on the first vehicle speed and the speed limit information, and to increase the second vehicle speed of the vehicle based on the vehicle speed increase value.

[0106] Optionally, the detection module 10 includes:

[0107] An image acquisition module, used for acquiring road images;

[0108] An extraction module, used to extract characteristic information of each vehicle in an adjacent lane from the road image, wherein the characteristic information includes position information of the rear end of the vehicle;

[0109] A relative position determination module, used to determine the relative position information between the front of the vehicle and the rear of each vehicle in the adjacent lane based on the rear position information of the vehicle;

[0110] The vehicle determination module is used to determine that there is a target vehicle that exceeds the own vehicle in the adjacent lane if the relative position information meets a preset complete overtaking condition.

[0111] Optionally, the upward adjustment module 30 includes:

[0112] A calculation module, used for calculating a speed difference between the first vehicle speed and the speed limit information;

[0113] A vehicle speed boost value determination module, configured to use the smallest of the speed difference and a preset limit value as the vehicle speed boost value;

[0114] The smooth speed increasing module is used to gradually increase the second vehicle speed of the vehicle based on a preset increase amplitude, until the difference between the second vehicle speed after the increase and the second vehicle speed before the increase reaches the vehicle speed increase value.

[0115] Optionally, the upward adjustment module 30 further includes:

[0116] A road condition feature acquisition module is used to acquire road condition feature information;

[0117] a feature fusion module, configured to perform feature fusion based on the first vehicle speed, the speed limit information and the road condition feature information through a preset upper layer controller, and output a vehicle control decision, wherein the vehicle control decision includes a vehicle speed boost value;

[0118] The control module is used to increase the second vehicle speed of the vehicle through a preset lower-level controller based on the vehicle speed increase value, and control the vehicle to travel at the increased second vehicle speed.

[0119] Optionally, the acquisition module 20 includes:

[0120] A vehicle speed set acquisition module, used to acquire a corresponding vehicle speed set from continuous image frames of the target vehicle;

[0121] A forming module, used for performing difference calculation on adjacent vehicle speeds in the vehicle speed set to form a vehicle speed difference matrix;

[0122] A noise reduction module, used for performing noise reduction processing on the initial difference matrix based on a preset observation matrix to obtain a vehicle speed difference matrix after noise reduction;

[0123] The vehicle speed determination module is used to determine a first vehicle speed of the target vehicle based on the noise-reduced vehicle speed difference matrix.

[0124] The vehicle control device provided by the present application adopts the vehicle control method in the above embodiment to solve the technical problem of vehicle control. Compared with the prior art, the beneficial effects of the vehicle control device provided by the present application are the same as the beneficial effects of the vehicle control method provided by the above embodiment, and other technical features in the vehicle control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0125] The present application provides a vehicle control device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle control method in the above-mentioned embodiment one.

[0126] Reference below Figure 5, which shows a schematic diagram of the structure of a vehicle control device suitable for implementing an embodiment of the present application. The vehicle control device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The vehicle control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0127] like Figure 5 As shown, the vehicle control device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle control device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle control device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a vehicle control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0128] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0129] The vehicle control device provided by the present application adopts the vehicle control method in the above embodiment to solve the technical problem of vehicle control. Compared with the prior art, the beneficial effects of the vehicle control device provided by the present application are the same as the beneficial effects of the vehicle control method provided by the above embodiment, and the other technical features in the vehicle control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0130] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0132] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle control method in the above-mentioned embodiment.

[0133] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0134] The computer-readable storage medium may be included in the vehicle control device; or may exist independently without being assembled into the vehicle control device.

[0135] The computer-readable storage medium carries one or more programs, and when the one or more programs are executed by the vehicle control device, the vehicle control device: vehicle control.

[0136] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0137] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0138] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0139] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle control method, and can solve the technical problems of vehicle control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the vehicle control method provided in the above-mentioned embodiment, and will not be described in detail here.

[0140] The present application also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the vehicle control method as described above are implemented.

[0141] The computer program product provided by the present application can solve the technical problem of vehicle control. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the vehicle control method provided by the above embodiment, which will not be repeated here.

[0142] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A vehicle control method, characterized in that: The vehicle control method comprises: When the vehicle is driving on the road based on the speed limit information, it detects whether there is a target vehicle that exceeds the vehicle in the adjacent lane; If there is a target vehicle that exceeds the vehicle in the adjacent lane, obtaining a first vehicle speed of the target vehicle; A vehicle speed increase value is determined based on the first vehicle speed and the speed limit information, and a second vehicle speed of the own vehicle is increased based on the vehicle speed increase value.

2. The vehicle control method according to claim 1, characterized in that: The step of detecting whether there is a target vehicle that exceeds the vehicle in the adjacent lane includes: Acquire road images; Extracting characteristic information of each vehicle in an adjacent lane from the road image, wherein the characteristic information includes position information of the rear end of the vehicle; Based on the vehicle rear position information, determine the relative position information between the front of the vehicle and the rear of each vehicle in the adjacent lane; If the relative position information satisfies the preset complete overtaking condition, it is determined that there is a target vehicle that exceeds the own vehicle in the adjacent lane.

3. The vehicle control method according to claim 1, characterized in that: The step of determining a vehicle speed boost value based on the first vehicle speed and the speed limit information, and increasing the second vehicle speed of the vehicle based on the vehicle speed boost value includes: calculating a speed difference between the first vehicle speed and the speed limit information; The smallest of the speed difference and the preset limit value is used as the vehicle speed increase value; Based on the preset increase amplitude, the second vehicle speed of the vehicle is gradually increased until the difference between the second vehicle speed after the increase and the second vehicle speed before the increase reaches the vehicle speed increase value.

4. The vehicle control method according to claim 1, characterized in that: The step of determining a vehicle speed boost value based on the first vehicle speed and the speed limit information, and increasing the second vehicle speed of the vehicle based on the vehicle speed boost value includes: Obtaining road condition characteristic information; Based on the first vehicle speed, the speed limit information and the road condition feature information, a preset upper-layer controller is used to perform feature fusion and output a vehicle control decision, wherein the vehicle control decision includes a vehicle speed boost value; Based on the vehicle speed increase value, the second vehicle speed of the vehicle is increased through a preset lower-level controller, and the vehicle is controlled to travel at the increased second vehicle speed.

5. The vehicle control method according to claim 4, characterized in that: The upper-layer controller is composed of a convolutional neural network and a graph neural network. The convolutional neural network is used to process road images, and the graph neural network is used to process graph structure data.

6. The vehicle control method according to claim 1, characterized in that: The step of obtaining the first vehicle speed of the target vehicle comprises: Acquire a corresponding vehicle speed set from continuous image frames of the target vehicle; Calculating differences between adjacent vehicle speeds in the vehicle speed set to form a vehicle speed difference matrix; Based on a preset observation matrix, the initial difference matrix is ​​subjected to denoising to obtain a vehicle speed difference matrix after denoising; Based on the denoised vehicle speed difference matrix, a first vehicle speed of the target vehicle is determined.

7. A vehicle control device, characterized in that: The device comprises: A detection module, used for detecting whether there is a target vehicle that exceeds the self-vehicle in an adjacent lane when the self-vehicle is traveling on the road based on the speed limit information; an acquisition module, configured to acquire a first vehicle speed of a target vehicle if there is a target vehicle that exceeds the vehicle in an adjacent lane; The upward adjustment module is used to determine a vehicle speed increase value based on the first vehicle speed and the speed limit information, and to increase the second vehicle speed of the vehicle based on the vehicle speed increase value.

8. A vehicle control device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle control method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle control method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the vehicle control method according to any one of claims 1 to 6 are implemented.

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