Vehicle driving auxiliary system based on digital twinning and control method

Abstract

The invention discloses a vehicle driving assistance system based on digital twinning and a control method.The vehicle driving assistance system comprises a vehicle following subsystem, a vehicle scanning subsystem and a control subsystem, and when the vehicle following subsystem detects that a target vehicle approaches, the vehicle scanning subsystem can scan the target vehicle through a guide rail and a driving mechanism; the control subsystem controls the vehicle sensing device to follow a target vehicle to run along a target road section, realizes data tracking and acquisition of the target vehicle, ensures stable acquisition of target vehicle information, synchronizes the acquired vehicle state data and vehicle contour space data acquired by the vehicle scanning subsystem to the control subsystem, and controls the vehicle sensing device to follow the target vehicle to run along the target road section. The control subsystem updates the traffic digital twin model corresponding to the target road section, and the twin model generates vehicle driving auxiliary information and feeds back the vehicle driving auxiliary information to the target vehicle, so that higher-accuracy and more stable vehicle driving auxiliary service is provided for the target vehicle, and the technical problem that an existing mode is prone to misalignment when facing complex road conditions is solved.

Description

Technical Field

[0001] The present application relates to the field of digital transportation technology, and in particular to a vehicle driving assistance system and control method based on digital twins. Background Art

[0002] Compared with other modes of transportation, road traffic is more complex. Road traffic usually includes various types of vehicles, and the parallel, lane-changing and overtaking behaviors of external vehicles will affect the normal driving of vehicles. At the same time, road conditions will also have a great impact on the normal operation of vehicles. Factors such as road conditions, quality and capacity will increase the complexity of road traffic.

[0003] Currently, vehicle data on the road can be collected by sensing devices such as cameras and radars, and the operating status of vehicles on the road can be simulated using intelligent algorithms or digital twin models to generate driving assistance information to assist drivers in making more appropriate driving decisions. However, the existing methods are prone to technical problems such as inaccurate driving assistance information when faced with complex road conditions with heavy traffic. Summary of the invention

[0004] The present application provides a vehicle driving assistance system and control method based on digital twins, which are used to solve the technical problem that the existing method is prone to inaccurate driving assistance information when facing complex road conditions with heavy traffic.

[0005] In order to solve the above technical problems, the first aspect of the present application provides a vehicle driving assistance system based on digital twins, including: a vehicle following subsystem, a vehicle scanning subsystem and a control subsystem;

[0006] The vehicle following subsystem comprises: a vehicle sensing device, a guide rail and a driving mechanism, wherein the vehicle sensing device is used to wirelessly communicate with a target vehicle to collect vehicle status information of the target vehicle, and the driving mechanism is used to drive the vehicle sensing device to move along the guide rail to follow the target vehicle;

[0007] The vehicle scanning subsystem is used to scan the vehicle contour space data of the vehicle traveling on the target road section, wherein the vehicle contour space data includes the shape of the vehicle contour and the road space occupied by the vehicle contour;

[0008] The control subsystem is used to update the road section digital twin model of the target road section according to the received vehicle status information and the vehicle contour spatial data, and generate vehicle driving assistance information based on the road section digital twin model and feed it back to the target vehicle.

[0009] Preferably, the guide rail comprises: a longitudinal guide rail and a transverse guide rail, wherein the longitudinal guide rail is a guide rail laid along the target road section, and the driving mechanism comprises: a first driving structure and a second driving mechanism;

[0010] The vehicle sensing device is mounted on the transverse guide rail and is movably connected to the transverse guide rail;

[0011] The transverse guide rail is assembled on the longitudinal guide rail and is movably connected to the transverse guide rail;

[0012] The first driving mechanism is used to drive the transverse guide rail to move on the longitudinal guide rail, and the second driving mechanism is used to drive the vehicle sensing device to move on the transverse guide rail.

[0013] Preferably, the longitudinal guide rail is an annular guide rail laid on a vertical plane.

[0014] Preferably, the longitudinal guide rail is an annular guide rail laid on a transverse plane.

[0015] Preferably, it further comprises: a road obstacle recognition subsystem, the road obstacle recognition subsystem comprising: an image acquisition module and an image recognition module;

[0016] The image recognition module is used to obtain the obstacle recognition result of the target road section based on the road surface image captured by the image acquisition module in combination with a preset obstacle image recognition model, and then upload the obstacle recognition result to the control subsystem, so that the control subsystem is used to update the road section digital twin model of the target road section according to the received obstacle recognition result.

[0017] Preferably, the vehicle following subsystem is buried under the road surface of the target road section.

[0018] Preferably, the vehicle sensing device further includes: a wireless charging transmitting module, used for wirelessly charging the target vehicle.

[0019] Preferably, it also includes: a strengthened glass strip;

[0020] A road surface through hole is arranged on the road surface of the target road section, and the tempered glass strip is embedded in and fixed in the road surface through hole.

[0021] At the same time, the second aspect of the present application provides a control method for a vehicle driving assistance system based on digital twins, which is applied to the vehicle driving assistance system based on digital twins provided in the first aspect of the present application, and the control method includes:

[0022] The vehicle scanning subsystem collects the vehicle profile spatial data of the vehicles traveling on the target road section in real time;

[0023] When a vehicle following assistance request is received, the vehicle identification information in the vehicle following assistance request is extracted, and the vehicle identification information is then sent to the vehicle following subsystem, so that when the vehicle following subsystem detects a target vehicle corresponding to the vehicle identification information, it performs a following task for the target vehicle and collects vehicle status information of the target vehicle in real time;

[0024] According to the received vehicle status information and the vehicle contour spatial data, the road section digital twin model of the target road section is updated, and vehicle driving assistance information is generated based on the road section digital twin model and fed back to the target vehicle.

[0025] Preferably, after updating the road section digital twin model of the target road section, the method further includes:

[0026] According to the real-time target vehicle status and real-time traffic status in the current digital twin model of the road section, path optimization is performed through a driving path optimization model constructed based on a preset genetic algorithm and a random simulation algorithm to obtain a reference driving path for the target vehicle.

[0027] It can be seen from the above technical solutions that this application has the following advantages:

[0028] The vehicle driving assistance system provided by the present application includes: a vehicle following subsystem, a vehicle scanning subsystem and a control subsystem. When the vehicle following subsystem detects that a target vehicle is approaching, the vehicle sensing device can be controlled through a guide rail and a driving mechanism to follow the target vehicle along a target section of road, thereby realizing data tracking and collection of the target vehicle, ensuring that the target vehicle information can be stably collected, and synchronizing the collected vehicle status data and the vehicle contour space data collected by the vehicle scanning subsystem to the control subsystem. The control subsystem updates the traffic digital twin model corresponding to the target section, and the twin model generates vehicle driving assistance information and feeds it back to the target vehicle, thereby providing the target vehicle with a more accurate and stable vehicle driving assistance service, solving the technical problem that the existing method is prone to inaccurate driving assistance information when facing complex road conditions with heavy traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] 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, 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 paying creative labor.

[0030] Figure 1 A schematic diagram of the system architecture of a vehicle driving assistance system based on digital twins provided in this application.

[0031] Figure 2 A schematic diagram of the effect of the burial method of the vehicle following subsystem provided in this application.

[0032] Figure 3 This is a schematic diagram of the effect of the overhead laying method of the vehicle following subsystem provided in this application.

[0033] Figure 4 A schematic diagram of a laying path for the longitudinal guide rail of the vehicle following subsystem provided in the present application and the moving trajectory of the vehicle sensing device.

[0034] Figure 5 A schematic diagram of another laying path for the longitudinal guide rail of the vehicle following subsystem and the moving trajectory of the vehicle sensing device provided in this application.

[0035] Figure 6 A schematic diagram of the cross-sectional structure of a vehicle driving assistance system and a road surface structure based on digital twins provided in this application.

[0036] Figure 7 A schematic diagram of a road surface gap structure in a vehicle driving assistance system based on digital twins provided in this application.

[0037] Figure 8 A flow chart of a control method for a vehicle driving assistance system based on digital twins provided in this application.

[0038] The reference numerals are as follows:

[0039] A. Vehicle following subsystem; B. Vehicle scanning subsystem; C. Control subsystem; D. Road obstacle identification subsystem; S. Server; 1. Vehicle sensing device; 11. Horizontal guide rail; 12. Longitudinal guide rail; 13. Guide rail support frame; 2. Scanning equipment; 3. Road surface; 31. Strengthened glass strip. DETAILED DESCRIPTION

[0040] After analysis and research on the technical defects of the existing technology, it was found that the cause of the problem is that the existing vehicle sensing devices mostly adopt a fixed setting method. When the road traffic volume is heavy, it is easy for the target to be lost due to obstruction of the vehicle, thereby affecting the subsequent use of intelligent algorithms or digital twin models to simulate the operating status of vehicles on the road. The accuracy of the model.

[0041] In view of this, the present application provides a vehicle driving assistance system and control method based on digital twins, which is used to solve the technical problem that the existing method is prone to inaccurate driving assistance information when facing complex road conditions with heavy traffic.

[0042] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0044] Unless otherwise specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0045] First, a detailed description of an embodiment of a vehicle driving assistance system based on digital twins provided by this application is as follows:

[0046] See also Figure 1 , a vehicle driving assistance system based on digital twins provided in this embodiment includes: a vehicle following subsystem A, a vehicle scanning subsystem B and a control subsystem C;

[0047] The vehicle following subsystem A comprises: a vehicle sensing device 1, a guide rail and a driving mechanism, wherein the vehicle sensing device 1 is used to wirelessly communicate with the target vehicle and collect vehicle status information of the target vehicle, and the driving mechanism is used to drive the vehicle sensing device 1 along the guide rail to follow the target vehicle to move;

[0048] The vehicle scanning subsystem B is used to scan the vehicle contour space data of the vehicle traveling on the target road section, and the vehicle contour space data includes the shape of the vehicle contour and the road space occupied by the vehicle contour;

[0049] The control subsystem C is used to update the road section digital twin model of the target road section according to the received vehicle status information and vehicle profile spatial data, and to generate vehicle driving assistance information based on the road section digital twin model and feed it back to the target vehicle.

[0050] It should be noted that the vehicle driving assistance system provided in this embodiment includes: a vehicle following subsystem A, a vehicle scanning subsystem B and a control subsystem C.

[0051] The vehicle following subsystem A is used to follow and communicate with vehicles that need to provide driving assistance services. When the vehicle following subsystem A detects that a target vehicle is approaching, it controls the vehicle sensing device 1 to establish a wireless communication connection with the target vehicle, and follows the target vehicle along the target road section through the guide rail and the driving mechanism to achieve data tracking and collection of the target vehicle, ensuring that the target vehicle information can be stably collected. The vehicle following subsystem A can be buried under the road surface 3, as shown in the following example. Figure 2 As shown, it can also be set above the road surface 3, for example, on the ceiling of the tunnel. If it is an open road scene, in addition to the buried setting method, it can also be used as shown in FIG. Figure 3 Overhead setup shown.

[0052] The moving vehicles are equipped with on-board communication equipment, such as on-board communication modules or on-board communication systems. These devices can receive external data through wireless communication technologies, such as Wi-Fi, Bluetooth, or mobile networks. Roadside equipment, roadside equipment is installed beside the road or under the road surface 3. These devices can be communication base stations, roadside sensors or road traffic management systems. These devices can communicate with moving vehicles through wireless networks covering the entire road network. Data transmission, the driving guarantee system transmits the prediction result data to the roadside equipment, which is then sent to the moving vehicle by the roadside equipment through wireless communication technology. Data transmission can use short-range wireless communication technologies, such as Wi-Fi or Bluetooth, or can be transmitted remotely through mobile networks.

[0053] In this process, the signal of the vehicle following subsystem A is generally transmitted to the digital twin model through a wireless network link, and the signal characteristics of the vehicle following subsystem A are identified and compared through the driving guarantee model in the digital twin model, thereby obtaining the real-time status of the vehicle. The signal of the vehicle following subsystem A here can be processed and characterized in the digital twin model, or it can be processed before entering the digital twin model, which can effectively reduce the amount of data transmission and improve the update efficiency of the digital twin model.

[0054] The vehicle scanning subsystem B is set on the road surface 3, and can be set on both sides of the road of the target section or above the road in an overhead manner. It generally includes multiple scanning devices 2, and each scanning device 2 is evenly distributed in the target section. It is used to scan the contour data of the vehicle traveling on the target section, specifically including the contour shape of the vehicle and the space occupied by the contour. When the vehicle following subsystem A adopts the overhead laying mode, the scanning device 2 of the vehicle scanning subsystem B can be installed on the guide rail support frame 13.

[0055] The control subsystem C is the main control and dispatching unit. It can collect the vehicle contour spatial data of the vehicles traveling on the target section in real time through the vehicle scanning subsystem B on the target section. It can respond to the service request sent directly by the user or forwarded via the server S, and dispatch the vehicle following subsystem A to perform the following task to collect the vehicle status data of the target vehicle, including but not limited to the speed, acceleration, wheel steering angle, vehicle inclination, etc., and then summarize the collected vehicle status data and vehicle contour spatial data to the control subsystem C. The control subsystem C updates the digital twin model of the target section it carries in real time, and generates vehicle driving assistance information based on the digital twin model of the section and feeds it back to the target vehicle through the vehicle sensing device 1 or other wireless communication links for reference by the target vehicle, so as to facilitate the driver of the target vehicle to have a more comprehensive understanding of the overall road conditions of the section, so as to make more appropriate driving decisions. The feedback vehicle driving assistance information generally includes the following information: the contour and spatial position of the vehicle, the contours of other vehicles, and the spatial distribution on the target section.

[0056] In some embodiments, the vehicle driving assistance system provided in this embodiment may further include: a road 3 obstacle recognition subsystem D, the road 3 obstacle recognition subsystem D includes: an image acquisition module and an image recognition module;

[0057] The image recognition module is used to obtain the obstacle recognition result of the target road section based on the road surface 3 image collected by the image acquisition module and the preset obstacle image recognition model, and then upload the obstacle recognition result to the control subsystem C, so that the control subsystem C is used to update the road section digital twin model of the target road section according to the received obstacle recognition result.

[0058] It should be noted that the road surface 3 obstacle recognition subsystem D collects road surface 3 images through image acquisition devices such as cameras, inputs them into a pre-trained obstacle image recognition model, obtains the obstacle recognition result of the target road section, and then uploads the obstacle recognition result to the control subsystem C, so that the control subsystem C is used to update the road section digital twin model of the target road section according to the received obstacle recognition result, and can provide the target vehicle with driving assistance information containing road obstacle information.

[0059] Further, the guide rail includes: a longitudinal guide rail 12 and a transverse guide rail 11, wherein the longitudinal guide rail 12 is a guide rail laid along the target road section, and the driving mechanism includes: a first driving structure and a second driving mechanism;

[0060] The vehicle sensing device 1 is mounted on the transverse guide rail 11 and is movably connected to the transverse guide rail 11;

[0061] The transverse guide rail 11 is assembled on the longitudinal guide rail 12 and is movably connected to the transverse guide rail 11;

[0062] The first driving mechanism is used to drive the transverse guide rail 11 to move on the longitudinal guide rail 12 , and the second driving mechanism is used to drive the vehicle sensing device 1 to move on the transverse guide rail 11 .

[0063] It should be noted that when the vehicle following subsystem A starts the following task, the first driving mechanism drives the transverse guide rail 11 and the vehicle sensing device 1 along the longitudinal guide rail 12 and moves forward following the target vehicle. When the target vehicle changes lanes, the second driving mechanism can be used to drive the vehicle sensing device 1 to move on the transverse guide rail 11 so that the vehicle sensing device 1 can be offset in the direction of the target vehicle's lane change, thereby reducing the impact of changes in the interval distance on the communication quality.

[0064] In some special scenarios, such as on a one-way single lane or a two-way single lane, that is, a road section with only one lane in the same direction, the transverse guide rail 11 and the second driving mechanism can also be removed, and the vehicle sensing device 1 can be directly mounted on the longitudinal guide rail 12 and directly driven by the first driving mechanism.

[0065] Furthermore, the longitudinal guide rail 12 mentioned in the present embodiment can specifically take the structure of a circular guide rail. Regardless of whether the circular guide rail is installed in an buried or overhead manner, there are two laying methods for the circular guide rail in terms of the laying direction: one is a laying method based on the vertical plane direction, and the other is a laying method based on the horizontal plane direction.

[0066] The laying path of the longitudinal guide rail 12 and the moving track of the vehicle sensing device 1 in the first laying method can be referred to in Figure 4 It is generally used in one-way road sections, but can also be used in two-way road sections. Its operating principle is that when the vehicle sensing device 1 completes the following task and reaches the end of the road section, the vehicle sensing device 1 continues to move forward driven by the driving mechanism, enters the return loop, returns to the starting point of the road section through the return loop, and then waits to receive the next following task.

[0067] The laying path of the longitudinal guide rail 12 and the moving track of the vehicle sensing device 1 in the second laying method can be referred to Figure 5Its operating principle is similar to that of the first paving method, the difference being that the return loop of this paving method is located in the opposite lane and is suitable for two-way lane sections. When the vehicle sensing device 1 completes the following task and reaches the end of the section, the vehicle sensing device 1 continues to move forward driven by the driving mechanism, and after passing through the buffer area, it moves to the starting point of the section in the opposite lane, and then waits to receive the next following task in that direction.

[0068] If the vehicle following subsystem A is buried, the vehicle sensing device 1 may further include: a wireless charging transmitter module, which can be used to wirelessly charge a target vehicle with wireless charging conditions during the execution of the following task.

[0069] like Figure 6 and Figure 7 As shown, if the vehicle following subsystem A is buried, a through hole in the road surface 3 of the target section can be provided, and a tempered glass strip 31 is embedded and fixed in the through hole in the road surface 3. The strip has high reflectivity and wear resistance, so that it can withstand frequent traffic while providing visual guidance. The glass strip can be embedded in or attached to the road surface, and fixed by special adhesives or mechanical fixing means to ensure its long-term stability. At the same time, by setting a glass strip on the road surface 3, the attenuation effect of concrete on wireless signals and charging electromagnetic waves can also be reduced. Moreover, the light transmittance of glass is strong. When the vehicle sensing device 1 and the target vehicle use optical signal communication methods such as infrared, their communication quality can be ensured.

[0070] The above is a detailed description of an embodiment of a vehicle driving assistance system based on digital twins provided in the present application. The following is a detailed description of an embodiment of a control method for a vehicle driving assistance system based on digital twins provided in the present application.

[0071] See also Figure 8 In a second aspect, the present application provides a control method for a vehicle driving assistance system based on digital twins, which is applied to the vehicle driving assistance system based on digital twins provided in the above embodiment. The control method includes:

[0072] Step 101: collecting vehicle profile spatial data of vehicles traveling on a target road section in real time through a vehicle scanning subsystem;

[0073] Step 102: when a vehicle following assistance request is received, extract the vehicle identification information in the vehicle following assistance request, and then send the vehicle identification information to the vehicle following subsystem, so that when the vehicle following subsystem detects a target vehicle corresponding to the vehicle identification information, it executes a following task for the target vehicle and collects vehicle status information of the target vehicle in real time;

[0074] Step 103: Update the road section digital twin model of the target road section according to the received vehicle status information and vehicle profile spatial data, and generate vehicle driving assistance information based on the road section digital twin model and feed it back to the target vehicle.

[0075] It should be noted that when building a digital twin model, a geometric model is first established, and the assembly connection relationship of each component in the geometric model is consistent with the physical space. Assume that there are two point cloud data sets: one is the unprocessed initial scan point cloud data, which represents the original shape and position of the car; the other is the target point cloud data, which represents the shape and position of the desired car after being scanned. The goal is to align the point cloud data obtained from the initial scan to the target point cloud data in order to build an accurate digital twin model. Taking the ICP algorithm as an example, the following steps can be applied:

[0076] Assume that the unknown number to be solved is the transformation matrix T, which includes the rotation matrix R and the translation vector t. The steps of the ICP algorithm can be redescribed as follows: Initialization, select an initial transformation matrix T0. Here, T0 is an unknown number, representing the transformation in the initial state. Corresponding point matching, for each source point pi (point in the initial scan point cloud), find the nearest point qi in the target point cloud data and establish the correspondence between the point pairs. Calculate the transformation, by matching the point pairs, calculate the optimal transformation T, so that the distance between the initial scan point cloud data and the target point cloud data is minimized. This can be achieved by minimizing the distance between the point pairs, that is, minimizing the error function:

[0077]

[0078] Where E(T) is the error function, which represents the distance error between point cloud data. N is the number of point pairs. represents the Euclidean distance. i is a point in the source point cloud. i is the target point cloud with p i Corresponding point. T(p i ) is to convert point p i Transformed points through transformation T. The distance here can use Euclidean distance or other distance measurement methods. Update the point cloud, apply the optimal transformation T obtained, and transform the point cloud data obtained from the initial scan to align it with the target point cloud data. Repeat the iteration and repeat the above steps until the stop condition is met, such as reaching the maximum number of iterations or reaching the convergence condition. In each iteration, the transformation matrix T is updated until the error function E(T) reaches the minimum value or converges to a certain threshold. In this scenario, the ICP algorithm helps to achieve accurate alignment of the car, so that an accurate digital twin model can be built. With this model, various simulation, analysis, and design work can be performed without the actual physical car.

[0079] For the convenience of description, the physical space here refers to the actual space, and the virtual space refers to the space where the digital twin model is located. Sometimes, in order to simplify the digital twin device model, certain restrictions are set for the digital twin device model in the virtual space so that it meets certain production and operation functions and constitutes an integrated digital twin device model with topological characteristics. According to the established geometric model, physical properties are added to the digital twin model in the virtual space, such as some component process requirements, constraints on connecting parts, material property information, etc., to make it more consistent with the equipment in the physical space. The outer wall of the digital twin model fully reflects the state of the physical space and is used for the simulation calculation of the 3D geometric scanner. In the digital twin model, the impact of the environment can be fully considered, thereby realizing the interactivity of the environment and equipment in the digital twin model.

[0080] Furthermore, after updating the road section digital twin model of the target road section, the following steps are further included:

[0081] Step 104: According to the real-time target vehicle status and real-time traffic status in the digital twin model of the current road section, a path optimization model constructed based on a preset genetic algorithm and a random simulation algorithm is used to perform path optimization to obtain a reference driving path for the target vehicle.

[0082] Among them, the random simulation algorithms mentioned in this embodiment mainly include: decision tree, random forest, Bayesian algorithm, time series prediction algorithm, etc.

[0083] It should be noted that genetic algorithms are used to optimize the parameters of each component in the digital twin model to ensure that the model can accurately reflect the behavior and performance of the physical system.

[0084]

[0085] Among them: P (model parameter set), F (fitness function), P optimal It is a set of parameters that maximizes the fitness function F, which is defined based on the prediction accuracy and efficiency of the model.

[0086] For example, the fitness function: ,in, , , is the weight coefficient, which reflects the relative importance of safety, comfort and energy consumption in driving strategy.

[0087] The general process is as follows: Initialize the population and randomly generate a population with an initial parameter set. Each individual represents a parameter set. Evaluate the fitness and use the fitness function F(P) to evaluate each individual in the population and calculate its fitness value. Select and use the selection operator to select excellent individuals as parents based on the fitness value of each individual for crossover and mutation. Crossover and combine the genes of parent individuals to generate new offspring individuals through crossover operations. Mutation and perform mutation operations on the newly generated offspring individuals to introduce some randomness to increase the diversity of the population. Evaluate new individuals: Apply the fitness function to the newly generated individuals and calculate their fitness values. Update the population and use the selection operator to select the best individuals from the parents and offspring to form the next generation population.

[0088] Then, crossover, mutation, and selection are repeated until the termination condition is reached (such as reaching the maximum number of iterations or finding a satisfactory solution). Through this process, the parameter set of the model can be gradually optimized to achieve the best prediction accuracy and efficiency of the model, while considering the relative importance of safety, comfort, and energy consumption, thereby ensuring that the model can accurately reflect the behavior and performance of the physical system.

[0089] Decision trees and random forests are used to identify key features and patterns from imported data for deeper analysis and prediction. Decision trees are used to classify the driving state of the vehicle (e.g., "normal driving", "emergency braking", "obstacle avoidance") in real time based on real-time sensor data. Let X (feature vector) contain real-time sensor data, and Y (target variable) be the driving state of the vehicle (e.g., "normal driving", "emergency braking", "obstacle avoidance"). Then Y=f(X), where f is the function learned by the decision tree or random forest, used to predict or classify the vehicle state.

[0090] The digital twin model simulates and trains the input data of the vehicle following subsystem to construct the following driving assurance objective function.

[0091]

[0092] In the formula, y t is the vehicle state predicted by the time series model, It's white noise. are the parameters of the autoregressive model. θ1,…,θ q are the parameters of the moving average model

[0093] During the operation of the equipment, the digital twin model is updated in real time, and some of its operating status can be directly collected, such as temperature, speed and power consumption. Its motion status is fed back through the signal of the follow-up vehicle following subsystem. Through the signal feedback of the follow-up vehicle following subsystem, we can get the real-time status of the vehicle in a timely and comprehensive manner.

[0094] Bayesian networks are used to update the probability prediction of vehicle status in real time and process real-time data with high uncertainty. The posterior probability is calculated by P(X|Y)=P(Y)*P(Y|X) to update the uncertainty assessment of vehicle status. Bayesian networks are used to process and reason about situations with high uncertainty, such as driving under poor visibility conditions, and calculate the success probability of various driving strategies based on historical data and current perception data.

[0095] In this embodiment, the simulation operation data is obtained by simulating the target digital twin model, which can reduce the time cost of data acquisition compared with the method of obtaining data based on actual physical space experiments. Time series analysis, such as the autoregressive moving average (ARMA) model, is used to process and analyze the time series data of vehicle driving status and optimize the prediction accuracy of the digital twin model.

[0096] In one possible implementation, the model is trained based on experimental data and simulation data, including:

[0097] Compare the experimental data with the simulation data to obtain error data;

[0098] Correct the target digital twin model based on the error data;

[0099] Construct a sample set based on experimental data and simulation data;

[0100] The calibrated target digital twin model is trained based on the sample set.

[0101] In this embodiment, the target digital twin model is first calibrated during the model training process to ensure that the digital twin model is consistent with the functional characteristics of the equipment and environment in the physical space, and to ensure the correct association between the sensing device and the vehicle in the digital twin model, which is beneficial to improving the accuracy of training.

[0102] In model training, an efficient and applicable intelligent prediction model can be obtained based on a combination of judgment model and generation model. This refers to performing evolutionary calculations on a suitable model based on operating data and simulation data to obtain suitable model parameters. The judgment model can be a combination of one or more of linear regression, logarithmic regression, linear discriminant analysis, support vector machine, boosting, conditional random field, neural network, etc. The generation model can be a collection of one or more models, such as Gaussian mixture model, hidden Markov model and naive Bayes model.

[0103] The generated driving assurance model can give the vehicle's driving status based on the signal from the vehicle following subsystem. It can usually give the driving status results and perform predictive analysis on future safe driving, thereby helping relevant personnel to discover problems in advance and take measures to respond, so as to reduce or minimize losses and casualties.

[0104] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0108] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle driving assistance system based on digital twins, characterized in that: include: A vehicle following subsystem, a vehicle scanning subsystem and a control subsystem, wherein the vehicle following subsystem and the vehicle scanning subsystem are both communicatively connected with the control subsystem; The vehicle following subsystem comprises: a vehicle sensing device, a guide rail and a driving mechanism, wherein the vehicle sensing device is used to wirelessly communicate with a target vehicle to collect vehicle status information of the target vehicle, and the driving mechanism is used to drive the vehicle sensing device to move along the guide rail to follow the target vehicle; The vehicle scanning subsystem is used to scan the vehicle contour space data of the vehicle traveling on the target road section, wherein the vehicle scanning subsystem is used to scan the vehicle contour space data of the vehicle traveling on the target road section, and the vehicle contour space data includes the shape of the vehicle contour and the road space occupied by the vehicle contour; The control subsystem is used to update the road section digital twin model of the target road section according to the received vehicle status information and the vehicle contour spatial data, and generate vehicle driving assistance information based on the road section digital twin model and feed it back to the target vehicle.

2. The vehicle driving assistance system based on digital twin according to claim 1, characterized in that: The guide rail comprises: a longitudinal guide rail and a transverse guide rail, wherein the longitudinal guide rail is a guide rail laid along the target road section, and the driving mechanism comprises: a first driving structure and a second driving mechanism; The vehicle sensing device is mounted on the transverse guide rail and is movably connected to the transverse guide rail; The transverse guide rail is assembled on the longitudinal guide rail and is movably connected to the transverse guide rail; The first driving mechanism is used to drive the transverse guide rail to move on the longitudinal guide rail, and the second driving mechanism is used to drive the vehicle sensing device to move on the transverse guide rail.

3. The vehicle driving assistance system based on digital twin according to claim 2, characterized in that: The longitudinal guide rail is an annular guide rail laid on a vertical plane.

4. The vehicle driving assistance system based on digital twin according to claim 2, characterized in that: The longitudinal guide rail is an annular guide rail laid on the basis of a transverse plane.

5. The vehicle driving assistance system based on digital twin according to claim 1, characterized in that: Also includes: A road obstacle recognition subsystem, the road obstacle recognition subsystem comprising: an image acquisition module and an image recognition module; The image recognition module is used to obtain the obstacle recognition result of the target road section based on the road surface image captured by the image acquisition module in combination with a preset obstacle image recognition model, and then upload the obstacle recognition result to the control subsystem, so that the control subsystem is used to update the road section digital twin model of the target road section according to the received obstacle recognition result.

6. The vehicle driving assistance system based on digital twin according to claim 1, characterized in that: The vehicle following subsystem is buried under the road surface of the target road section.

7. The vehicle driving assistance system based on digital twin according to claim 6, characterized in that: The vehicle sensing device also includes: a wireless charging transmitting module, which is used to wirelessly charge the target vehicle.

8. The vehicle driving assistance system based on digital twin according to claim 6, characterized in that: Also includes: tempered glass strips; A road surface through hole is arranged on the road surface of the target road section, and the tempered glass strip is embedded in and fixed in the road surface through hole.

9. A control method for a vehicle driving assistance system based on digital twins, applied to a vehicle driving assistance system based on digital twins as claimed in any one of claims 1 to 8, characterized in that: The control method comprises: The vehicle scanning subsystem collects the vehicle profile spatial data of the vehicles traveling on the target road section in real time; When a vehicle following assistance request is received, the vehicle identification information in the vehicle following assistance request is extracted, and the vehicle identification information is then sent to the vehicle following subsystem, so that when the vehicle following subsystem detects a target vehicle corresponding to the vehicle identification information, it performs a following task for the target vehicle and collects vehicle status information of the target vehicle in real time; According to the received vehicle status information and the vehicle contour spatial data, the road section digital twin model of the target road section is updated, and vehicle driving assistance information is generated based on the road section digital twin model and fed back to the target vehicle.

10. The control method of a vehicle driving assistance system based on digital twin according to claim 9, characterized in that: After updating the road segment digital twin model of the target road segment, the method further includes: According to the real-time target vehicle status and real-time traffic status in the current digital twin model of the road section, path optimization is performed through a driving path optimization model constructed based on a preset genetic algorithm and a random simulation algorithm to obtain a reference driving path for the target vehicle.

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