Vehicle speed prediction method and system under dynamic vehicle following scene considering traffic environment

Abstract

The invention discloses a vehicle speed prediction method and system in a dynamic vehicle following scene considering a traffic environment, and the method comprises the steps: extracting vehicle following case data through setting a standard and a threshold value of a vehicle following process in a natural driving data set; using a target detection algorithm to obtain traffic environment characteristics related to the vehicle following case data, extracting vehicle dynamics parameters in the vehicle following case data, fusing the vehicle dynamics parameters and the traffic environment characteristics, and performing normalization processing to obtain fusion characteristics; removing redundant variables in the fused features by adopting a combined feature selection method; and dividing the fusion features from which the redundant variables are removed into a data set, training a pre-established Informer-FDR vehicle speed prediction model, and obtaining a vehicle speed prediction result by using the trained vehicle speed prediction model. The method effectively reflects the dynamic interaction characteristics of the driver and the traffic environment and potential traffic conflicts, enhances the understanding of the model for the complex driving environment, and improves the real-time performance and accuracy of vehicle speed prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle speed prediction, and in particular to a vehicle speed prediction method and system in a dynamic following vehicle scenario taking into account a traffic environment. Background Art

[0002] Driven by the rapid development of driver assistance systems, automobile intelligence has become an important solution to reduce traffic accidents and ease traffic congestion. Relevant studies have shown that if the driver assistance system can warn the driver in time, the traffic accident rate can be reduced by 60%, which emphasizes the key role of driver assistance systems in improving road safety. The vehicle speed prediction model is particularly important in the driver assistance system. Accurately predicting vehicle speed is one of the keys to achieving efficient driver assistance systems and intelligent driving technology. At the same time, rear-end collisions account for 60%-70% of traffic accidents. By accurately predicting vehicle speed and behavior, the driver assistance system can better avoid potential collision risks, thereby reducing the incidence of rear-end collisions. Therefore, accurately predicting vehicle speed in the following vehicle scenario has important research significance and practical value for improving the effectiveness of the driver assistance system, enhancing human-like driving capabilities, and reducing rear-end collisions.

[0003] However, achieving accurate vehicle speed prediction is a complex task. In the following vehicle scenario, the driver is not only affected by the driving behavior of the vehicle in front, but also by the complex interactions of motor vehicles, non-motor vehicles and pedestrians in the surrounding lanes, causing the speed of the following vehicle to show obvious fluctuation characteristics during the following cycle.

[0004] Existing methods rarely consider the impact of these dynamic environmental factors on the driver's speed choice during the following process. At the same time, when predicting vehicle speed, existing neural networks can mostly only trace back linear historical information, resulting in limited receptive field of the model, computational redundancy, and uncertainty about the impact of future information on time series prediction. At the same time, increasing the network depth may lead to slower model training and reduced model performance. Summary of the invention

[0005] The purpose of the present invention is to address the above-mentioned problems in the prior art and provide a vehicle speed prediction method and system in a dynamic following scenario taking into account the traffic environment, so as to improve the real-time and accuracy of vehicle speed prediction in an actual complex driving environment.

[0006] In order to achieve the above object, the present invention has the following technical solutions:

[0007] In a first aspect, a vehicle speed prediction method in a dynamic following vehicle scenario considering a traffic environment is provided, comprising the following steps:

[0008] By setting the standards and thresholds of the car-following process in the natural driving dataset, the car-following case data is extracted;

[0009] Use the target detection algorithm to obtain the traffic environment characteristics related to the car following case data, extract the vehicle dynamics parameters in the car following case data, fuse the vehicle dynamics parameters with the traffic environment characteristics, and obtain the fusion features after normalization;

[0010] Combined feature selection method is used to remove redundant variables in fusion features;

[0011] The fused features after removing redundant variables are divided into data sets, the pre-established Informer-FDR vehicle speed prediction model is trained, and the trained Informer-FDR vehicle speed prediction model is used to obtain the vehicle speed prediction result.

[0012] As a preferred solution, in the step of extracting the car-following case data by setting the standards and thresholds of the car-following process in the natural driving data set, the standards and thresholds of the car-following process are as follows, and the car-following case data is selected when the following conditions are met:

[0013] The relative speed of the front and rear vehicles is less than 2.5m / s;

[0014] The lateral offset between the front and rear vehicles is less than 2.5m;

[0015] The following time is greater than 10 seconds;

[0016] The extracted car-following case data are divided into two road types: urban roads and highways.

[0017] As a preferred solution, the step of using the target detection algorithm to obtain traffic environment characteristics related to the vehicle following case data includes:

[0018] The yolov5 target detection algorithm is used to detect other vehicles and traffic participants in adjacent lanes. The traffic density is defined according to the number of vehicles traveling in the same direction as the vehicle captured by the camera. The situation where the number of vehicles traveling in the same direction is 1-2 is defined as low traffic density, the situation where the number of vehicles traveling in the same direction is 3-4 is defined as medium traffic density, and the situation where the number of vehicles traveling in the same direction is greater than 4 is defined as high traffic density. The pedestrians and cyclists identified by the target detection algorithm are merged into a unified category, defined as vulnerable traffic participants, which are used as input features of the Informer-FDR vehicle speed prediction model.

[0019] As a preferred solution, when extracting the vehicle dynamics parameters in the vehicle following case data, the vehicle's own driving information is read through the CAN bus, and the vehicle's own driving information includes speed, acceleration, relative distance, accelerator pedal opening and headway.

[0020] As a preferred solution, the method of removing redundant variables in fusion features by using a combined feature selection method includes:

[0021] Use message integrity check MIC, classification and regression tree CART, random forest RF and gradient boosting ensemble learning XGBoost algorithm to calculate the correlation between candidate input features and vehicle speed;

[0022] The average value of each candidate variable is calculated by adding the relevant results of the four different algorithms and dividing it by the total number of algorithms. All variables are calculated and ranked in turn. Based on the ranking, the first few variables with the highest importance are selected as the input of the model.

[0023] As a preferred solution, the Informer-FDR vehicle speed prediction model establishes the Informer deep learning model in the following manner:

[0024] In the rolling forecast setting with a fixed window size, the input at time point t is:

[0025]

[0026] Where L is the length of the sample sequence, D is the feature dimension, and in X, a single data sample is:

[0027]

[0028] In the formula, Represents the data of the input of the i-th model at the j-th time step;

[0029] Using the vehicle speed as the output indicator:

[0030]

[0031] In the formula, y i represents the output of the i-th model at each time step;

[0032] The encoder of the Informer deep learning model is composed of a multi-head sparse probabilistic self-attention mechanism and a stack of distillation operations:

[0033]

[0034] in, is a sparse matrix containing sparsity measurements; Q and K generated during encoder training represent query vectors and key vectors, respectively, V represents the value vector, and d represents the input dimension; the sparse probabilistic self-attention mechanism is defined as:

[0035]

[0036] Where, LK 1 / L K =q(k j |q i );Define the i queries of sparsity measurement as q i ;q i and k j from Q and K respectively;

[0037] The Informer deep learning model adds a convolutional layer and a max pooling layer between each pair of self-attention layers to reduce the input length; after the previous self-attention layer, a convolutional layer with a kernel size of 3 and a stride of 1 is used to enhance the perception of contextual information features; then, a max pooling layer with a kernel size of 3 and a stride of 2 is used to prioritize the main features, thereby generating smaller but more focused feature maps for the subsequent self-attention layer:

[0038]

[0039] In the formula, the sparse probabilistic self-attention is contained in [·] AB In the figure, 1-D convolution filtering is implemented by Conv(·) with ELU(·) as the activation function. The encoder combines sparse probabilistic self-attention and distillation operations to generate feature maps.

[0040] Time series input to the decoder of the Informer deep learning model It is divided into two parts, namely the known sequence before the predicted point and forecast series that require masking future data

[0041]

[0042] Where, L token is the length of the start marker sequence; L y is the length of the predicted sequence; d model is the model dimension;

[0043] The position of each input information in the sequence is encoded as follows:

[0044]

[0045] In the formula, pos represents the position of the feature vector, d model Represents the dimension of the feature vector. Given the importance of time information for time series prediction, the time information in the time series is encoded, and the location information and time information are integrated into the input model.

[0046] As a preferred solution, the Informer-FDR vehicle speed prediction model replaces the maximum pooling layer of the distillation layer in the decoder of the Informer deep learning model with an average pooling layer, and changes the convolution layer to an expanded causal convolution; the expanded causal convolution consists of two parts: causal convolution and expanded convolution; the causal convolution is subject to time constraints, and the output of time t is convolved only with the time t and earlier elements of the previous layer to ensure causality in time series prediction, so that the output information of the network is only affected by past input information;

[0047] The dilated convolution operation is as follows:

[0048]

[0049] Where K is the convolution kernel size, s is the element of X, and F i is the weight of the convolution kernel, t is the time step, and td·i represents the past direction.

[0050] As a preferred solution, the Informer-FDR vehicle speed prediction model introduces PRELU as the activation function of the distillation layer. PRELU pushes the output mean to zero. The improved distillation operation is as follows:

[0051]

[0052] In the formula, the sparse probabilistic self-attention is contained in [·] AB In the figure, the dilated causal convolution is implemented by DCConv(·), the activation function is PReLU(·), and the final output is generated by AvgPool(·).

[0053] As a preferred solution, the Informer-FDR vehicle speed prediction model adds a residual connection module, which consists of three sparse probabilistic self-attention layers and two distillation layers. In order to ensure that the feature maps output by these two layers have the same shape when added together, a 1*1 convolution is used to merge the output of the first sparse probabilistic self-attention layer in the main encoder with the output of the third sparse probabilistic self-attention layer to ensure the effective transmission of feature information.

[0054] In a second aspect, a vehicle speed prediction system is provided in a dynamic following vehicle scenario taking into account a traffic environment, including:

[0055] A vehicle following case data extraction module is used to extract vehicle following case data by setting standards and thresholds for vehicle following processes in a natural driving data set;

[0056] The feature fusion and normalization processing module is used to obtain the traffic environment features related to the car following case data using the target detection algorithm, extract the vehicle dynamics parameters in the car following case data, fuse the vehicle dynamics parameters with the traffic environment features, and obtain the fusion features after normalization processing;

[0057] A redundant variable removal module is used to remove redundant variables in fusion features by using a combined feature selection method;

[0058] The vehicle speed prediction model training and prediction output module is used to divide the fused features after removing redundant variables into data sets, train the pre-established Informer-FDR vehicle speed prediction model, and obtain the vehicle speed prediction results using the trained Informer-FDR vehicle speed prediction model.

[0059] Compared with the prior art, the present invention has at least the following beneficial effects:

[0060] By setting the standards and thresholds of the following process in the natural driving data set, the following case data is effectively extracted, and the targeted mining of the following data is realized, so that the following dynamic behavior in the driving environment can be more comprehensively understood and analyzed. The target detection algorithm is used to obtain the traffic environment characteristics related to the following case data, and the vehicle dynamics parameters in the following case data are extracted, and the vehicle dynamics parameters are fused with the traffic environment characteristics. By fusing the traffic environment characteristics and the vehicle dynamics parameter characteristics, the dynamic interaction characteristics and potential traffic conflicts between the driver and the traffic environment are effectively reflected, and the model's understanding of the complex driving environment is enhanced. The fused features after removing the redundant variables are divided into data sets, and the pre-established Informer-FDR speed prediction model is trained, and the speed prediction results are obtained using the trained Informer-FDR speed prediction model. Compared with other models, the Informer-FDR speed prediction model adopted by the present invention has the best prediction effect, can more accurately capture the fluctuation trend of the data sequence, and has a high degree of fit with the actual data. In particular, the prediction deviation of the Informer-FDR speed prediction model at the extreme point is small, indicating that it can more accurately capture the random situation under traffic conditions.

[0061] Furthermore, the present invention reduces the high computational complexity of the Informer-FDR vehicle speed prediction model by using a sparse probabilistic self-attention mechanism, which helps to solve the difficulty of applying Transformer-type deep learning models to vehicle-mounted platforms.

[0062] Furthermore, the Informer-FDR vehicle speed prediction model established in the present invention takes into account the impact of the dynamic traffic environment on vehicle speed fluctuations, realizes global feature extraction for long following cycles, and solves the degradation problem of deep networks.

[0063] Furthermore, the present invention introduces dilated causal convolution, which expands the receptive field of the Informer-FDR vehicle speed prediction model, ensures the causality of time prediction, and avoids the loss of extreme position information and information leakage.

[0064] Furthermore, the Informer-FDR vehicle speed prediction model of the present invention adds a residual connection module, and the residual connection accelerates the convergence of the Informer-FDR vehicle speed prediction model and enhances the stability of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 This is a flow chart of a vehicle speed prediction method in a dynamic following vehicle scenario in a traffic environment according to an embodiment of the present invention;

[0067] Figure 2 The network structure diagram of the Informer-FDR vehicle speed prediction model established in the embodiment of the present invention;

[0068] Figure 3 This is a diagram of the dilated causal convolution structure of an embodiment of the present invention;

[0069] Figure 4 This is a test effect diagram of a vehicle speed prediction method in a dynamic following vehicle scenario in a traffic environment according to an embodiment of the present invention;

[0070] Figure 5 This is a comparison chart of the prediction performance of the Informer-FDR vehicle speed prediction model of an embodiment of the present invention and other algorithm models. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, ordinary technicians in this field can also obtain other embodiments without making creative work.

[0072] like Figure 1As shown, the embodiment of the present invention proposes a vehicle speed prediction method in a dynamic following scenario taking into account the traffic environment, which mainly includes: first, by setting the standards and thresholds of the following process in the natural driving data set, the following case data is effectively extracted, and then the target detection algorithm is used to obtain relevant environmental features, and the vehicle dynamics parameters under the following segment are extracted from the CAN bus, and integrated with the environmental features, and then the combined feature selection method is used to eliminate redundant variables, improve the model prediction accuracy and perform normalization processing, and finally divide the data set into a training set, a test set, and a validation set, and input the pre-established Informer-FDR vehicle speed prediction model, perform training and testing, and finally obtain the vehicle speed prediction result. The implementation of the complete vehicle speed prediction method of the implementation case of the present invention is mainly divided into four major steps: acquisition of following data, acquisition of traffic environment information based on the target detection algorithm, feature fusion and elimination of redundant variables, and establishment of the Informer-FDR vehicle speed prediction model for vehicle speed prediction.

[0073] Step 1: Acquisition of vehicle following data, including the following steps:

[0074] Step 1.1: Install a comprehensive data acquisition system consisting of four cameras on the ego vehicle, including: facial recognition camera, pedal camera, front camera and wide-angle front camera.

[0075] Step 1.2: Video data captured by the front camera is used to identify traffic participants, while data related to the actual movement of the vehicle, such as speed, acceleration, steering wheel angle and mileage, are extracted from the CAN-BUS system.

[0076] Step 1.3: Extract the car following case data by setting the standards and thresholds for the car following process. All car following case data must meet the specified thresholds. If any of these thresholds are not met, the corresponding event will not be included in the initial car following case database. The thresholds set are:

[0077] 1) The relative speed of the front and rear vehicles is less than 2.5m / s;

[0078] 2) The lateral offset between the front and rear vehicles is less than 2.5m;

[0079] 3) The following vehicle time is greater than 10s.

[0080] Furthermore, since there are significant differences in traffic conditions and driving behaviors between urban roads and highways, which lead to differences in car following behaviors, the embodiment of the present invention divides the car following cases into two road types: urban roads and highways, which are used as input features of the model so that the model can better understand and learn the car following behaviors under different road backgrounds.

[0081] Step 2: Obtaining traffic environment information based on the target detection algorithm, specifically including the following steps:

[0082] Step 2.1: Use the yolov5 target detection algorithm to detect other vehicles and traffic participants in adjacent lanes. Define the traffic density based on the number of vehicles traveling in the same direction as the vehicle captured by the front camera. The situation where the number of vehicles traveling in the same direction is 3 to 4 is defined as medium traffic density, and the situation where the number of vehicles traveling in the same direction is greater than 4 is defined as high traffic density.

[0083] Step 2.2: In a traffic environment, drivers are often influenced by individuals who may be at higher risk on the road, thus affecting their driving behavior. To this end, we merge pedestrians and cyclists identified by the object detection algorithm into a unified category, named vulnerable traffic participants, as input features for the model.

[0084] Step 3: Feature fusion and elimination of redundant variables, including the following steps:

[0085] Step 3.1: The vehicle dynamics parameters extracted from the CAN-BUS system, including speed, acceleration, relative distance, accelerator pedal opening, headway, etc., are integrated with the traffic environment information obtained by the target detection algorithm and normalized.

[0086] Step 3.2: Use the message integrity check MIC, classification and regression tree CART, random forest RF, and gradient boosting ensemble learning XGBoost algorithms to calculate the correlation between candidate input features and the vehicle speed. The average value of each candidate variable is calculated by adding the correlation results of the four different algorithms and dividing by the total number of algorithms. All variables are calculated and ranked in turn. According to the ranking, the 7 variables with the highest importance are selected as the input of the model.

[0087] Step 4: Establish an Informer-FDR vehicle speed prediction model to predict vehicle speed. The Informer-FDR vehicle speed prediction model includes: an Informer time series prediction model, an expanded causal convolution module, and a residual connection module.

[0088] The Informer time series prediction model is an improvement on the Transformer model. It uses a multi-head probabilistic sparse self-attention mechanism to give greater weight to important features, reducing the time complexity of the network model. At the same time, it uses distillation operations between self-attention layers to reduce the input length of the next layer of the model, and uses generative reasoning decoding to improve the prediction speed and solve the long-term dependency problem of time series data.

[0089] The Informer-FDR model of the embodiment of the present invention is an improvement on the Informer model. The conventional convolutional layer in the distillation layer between the attention blocks in the original encoder is replaced by the dilated causal convolution, and the average pooling layer is introduced to improve the model performance. The activation function of the distillation layer is set to the rectified linear unit PRELU with parameters to speed up the convergence of the model. In addition, a residual connection module is added to enable information to be transmitted across the depth of the layer. This design not only improves the stability of training, but also alleviates the problem of deep network degradation. The overall network structure is as follows Figure 2 shown.

[0090] Step 4.1: Build the Informer deep learning model.

[0091] The Informer time series prediction model uses a multi-head probabilistic sparse self-attention mechanism to give greater weight to important features, reducing the time complexity of the network model. At the same time, it uses distillation operations between self-attention layers to reduce the input length of the next layer of the model, and uses generative reasoning decoding to improve the prediction speed and solve the long-term dependency problem of time series data.

[0092] In the rolling forecast setting with a fixed window size, the input at time point t is:

[0093]

[0094] Where L is the length of the sample sequence, D is the feature dimension, and in X, a single data sample is:

[0095]

[0096] In the formula, It represents the data of the input of the i-th model at the j-th time step. The vehicle speed is usually used as the output indicator, and the label Y is recorded as:

[0097]

[0098] In the formula, y i represents the output of the i-th model at each time step.

[0099] The encoder in Informer is composed of a stack of multi-head sparse probabilistic self-attention mechanisms and distillation operations. The traditional self-attention mechanism requires the cost of quadratic dot product calculations, which is the main reason for its low prediction training speed. The sparse probabilistic self-attention mechanism selects a few dot product pairs with higher scores and ignores other dot product pairs with lower scores, thereby reducing time complexity and memory usage. The specific operation is as follows:

[0100]

[0101] in, is a sparse matrix containing sparsity measurements; Q and K generated during encoder training represent query vectors and key vectors, respectively. In addition, V represents the value vector and d represents the input dimension.

[0102] The sparse probabilistic self-attention mechanism is defined as:

[0103]

[0104] Where, L K 1 / L K =q(k j |q i );Define the i queries of sparsity measurement as q i ;q i and k j from Q and K respectively. In practice, the input lengths of the query vector and the key vector are usually equal in the self-attention computation, making the total time complexity and space complexity of the sparse probabilistic ProbSparse self-attention both o(LlnL). The sparse probabilistic ProbSparse self-attention selects the results with higher scores in the sparsity measure and calculates the corresponding scores.

[0105] Informer adds a convolutional layer and a max pooling layer between each pair of self-attention layers to reduce the input length. After the previous self-attention layer, a convolutional layer with a kernel size of 3 and a stride of 1 is used to enhance the perception of contextual information features. Subsequently, a max pooling layer with a kernel size of 3 and a stride of 2 is used to prioritize the main features, thereby generating smaller but more focused feature maps for the subsequent self-attention layer:

[0106]

[0107] In the formula, the sparse probabilistic self-attention is contained in [·] AB In the figure, 1-D convolution filtering is implemented by Conv(·) with ELU(·) as the activation function. The encoder combines sparse probabilistic self-attention and distillation operations to generate feature maps.

[0108] The time series input to the decoder It is divided into two parts, namely the known sequence before the predicted point and forecast series that require masking future data The formula is as follows:

[0109]

[0110] Where, L token is the length of the start marker sequence; L y is the length of the predicted sequence; dmodel is the model dimension.

[0111] In the vehicle speed prediction problem, the time series information represents the running status of the vehicle at every moment, and the order of the data is crucial. Since the Informer model does not have an iterative operation like the RNN model, it is necessary to position encode each input information of the sequence. The specific process is shown in the formula:

[0112]

[0113] Where: pos represents the feature vector position, d model Represents the dimension of the feature vector. Given the importance of time information for time series prediction, the time information in the time series is encoded, and the location information and time information are integrated into the input model.

[0114] In order to solve several major shortcomings of the Informer model when applied to vehicle speed prediction in the following vehicle scenario: the traditional convolutional layer can only review historical information of linear size; stacking the self-attention block with the standard convolutional layer may lead to repeated and meaningless calculations and cause future information leakage. The embodiment of the present invention solves this problem by introducing dilated causal convolution.

[0115] Step 4.2: Replace the maximum pooling layer of the distillation layer in the decoder of the Informer model with an average pooling layer, and change the conventional convolution layer to a dilated causal convolution. The structure of the dilated causal convolution is as follows: Figure 3 shown.

[0116] Dilated causal convolution consists of two parts: causal convolution and dilated convolution. Causal convolution is different from traditional convolution. Causal convolution is subject to time constraints. It convolves the output of time t only with the time t and earlier elements of the previous layer to ensure causality in time series prediction, so that the output information of the network is only affected by past input information. The dilated convolution operation is as follows:

[0117]

[0118] Where K is the convolution kernel size, s is the element of X, and F i is the weight of the convolution kernel, t is the time step, and td·i represents the past direction.

[0119] On this basis, the parameterized rectified linear unit PRELU is introduced as the activation function of the distillation layer. Compared with the exponential linear unit ELU, the parameterized rectified linear unit PRELU pushes the output mean to zero and does not significantly increase the computational complexity or overfitting risk, thereby promoting faster model convergence. The improved distillation operation is shown in the formula:

[0120]

[0121] Where, the sparse probability ProbSparse self-attention is contained in [·] AB In the figure, the dilated causal convolution is implemented by DCConv(·), the activation function is PReLU(·), and the final output is generated by AvgPool(·).

[0122] Step 4.3: Add a residual connection module, which consists of three sparse probabilistic self-attention layers and two distillation layers. In order to ensure that the feature maps output by these two layers have the same shape when added together, the embodiment of the present invention uses 1*1 convolution to merge the output of the first sparse probabilistic self-attention layer in the main encoder with the output of the third sparse probabilistic self-attention layer, ensuring the effective transmission of feature information.

[0123] In order to verify the speed prediction effect of the method of the present invention in the vehicle following scenario, the vehicle following dataset constructed by the embodiment of the present invention is used for training to predict the vehicle speed after 2 seconds. The absolute error between the actual value and the predicted value of each frame is calculated. The test results and the error are shown in Figure 2. Figure 4 As shown in the figure, it can be seen that the error predicted by the method of the embodiment of the present invention is always kept at a low level, only a few significant errors appear, and the fit with the actual vehicle speed is high.

[0124] Furthermore, the method is compared with other mainstream time series prediction methods to verify the superiority of the present invention. Figure 5 The comparison between the method of the present invention and other vehicle speed prediction methods in a specific vehicle following segment is given. As can be seen from the figure, the prediction performance of GRU is poor, while Autoformer, Transformer and LSTM can capture the trend of data fluctuations and some extreme values, but there is a certain degree of deviation in the prediction. Compared with other models, the Informer-FDR model has the best prediction effect, can more accurately capture the fluctuation trend of the data sequence, and has a higher degree of fit with the actual data. In particular, the prediction deviation of the Informer-FDR model at the extreme point is small, indicating that it can more accurately capture the random situation in traffic conditions.

[0125] Another embodiment of the present invention further provides a vehicle speed prediction system in a dynamic following vehicle scenario taking into account a traffic environment, comprising:

[0126] A vehicle following case data extraction module is used to extract vehicle following case data by setting standards and thresholds for vehicle following processes in a natural driving data set;

[0127] The feature fusion and normalization processing module is used to obtain the traffic environment features related to the car following case data using the target detection algorithm, extract the vehicle dynamics parameters in the car following case data, fuse the vehicle dynamics parameters with the traffic environment features, and obtain the fusion features after normalization processing;

[0128] A redundant variable removal module is used to remove redundant variables in fusion features by using a combined feature selection method;

[0129] The vehicle speed prediction model training and prediction output module is used to divide the fused features after removing redundant variables into data sets, train the pre-established Informer-FDR vehicle speed prediction model, and obtain the vehicle speed prediction results using the trained Informer-FDR vehicle speed prediction model.

[0130] Another embodiment of the present invention further proposes an electronic device, comprising: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the vehicle speed prediction method in the dynamic following scenario considering the traffic environment.

[0131] Another embodiment of the present invention further proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle speed prediction method in the dynamic following scenario considering the traffic environment is implemented.

[0132] Exemplarily, the instructions stored in the memory may be divided into one or more modules / units, which are stored in a computer-readable storage medium and executed by the processor to complete the vehicle speed prediction method in a dynamic following scenario considering a traffic environment according to the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the server.

[0133] The electronic device may be a computing device such as a smart phone, a notebook, a PDA, and a cloud server. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the electronic device may also include more or fewer components, or a combination of certain components, or different components, for example, the electronic device may also include an input / output device, a network access device, a bus, etc.

[0134] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0135] The memory may be an internal storage unit of the server, such as a hard disk or memory of the server. The memory may also be an external storage device of the server, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the server. Furthermore, the memory may include both an internal storage unit of the server and an external storage device. The memory is used to store the computer-readable instructions and other programs and data required by the server. The memory may also be used to temporarily store data that has been output or is to be output.

[0136] It should be noted that the information interaction, execution process and other contents between the above-mentioned module units are based on the same concept as the method embodiment. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0137] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0138] 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 present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0139] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] The embodiments described above 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, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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, and should all be included in the protection scope of the present application.

Claims

1. A vehicle speed prediction method considering a dynamic following vehicle scenario in a traffic environment, characterized in that: The following steps are involved: By setting the standards and thresholds of the car-following process in the natural driving dataset, the car-following case data is extracted; Use the target detection algorithm to obtain the traffic environment characteristics related to the car following case data, extract the vehicle dynamics parameters in the car following case data, fuse the vehicle dynamics parameters with the traffic environment characteristics, and obtain the fusion features after normalization; Combined feature selection method is used to remove redundant variables in fusion features; The fused features after removing redundant variables are divided into data sets, the pre-established Informer-FDR vehicle speed prediction model is trained, and the trained Informer-FDR vehicle speed prediction model is used to obtain the vehicle speed prediction result.

2. The vehicle speed prediction method in a dynamic following scenario considering traffic environment according to claim 1 is characterized in that: In the step of extracting the car-following case data by setting the standards and thresholds of the car-following process in the natural driving data set, the standards and thresholds of the car-following process are as follows, and the car-following case data is selected when the following conditions are met: The relative speed of the front and rear vehicles is less than 2.5m / s; The lateral offset between the front and rear vehicles is less than 2.5m; The following time is greater than 10 seconds; The extracted car-following case data are divided into two road types: urban roads and highways.

3. The vehicle speed prediction method in a dynamic following scenario considering traffic environment according to claim 1 is characterized in that: The step of using the target detection algorithm to obtain traffic environment characteristics related to the vehicle following case data includes: The yolov5 target detection algorithm is used to detect other vehicles and traffic participants in adjacent lanes. The traffic density is defined according to the number of vehicles traveling in the same direction as the vehicle captured by the camera. The situation where the number of vehicles traveling in the same direction is 1-2 is defined as low traffic density, the situation where the number of vehicles traveling in the same direction is 3-4 is defined as medium traffic density, and the situation where the number of vehicles traveling in the same direction is greater than 4 is defined as high traffic density. The pedestrians and cyclists identified by the target detection algorithm are merged into a unified category, defined as vulnerable traffic participants, which are used as input features of the Informer-FDR vehicle speed prediction model.

4. The vehicle speed prediction method in a dynamic following vehicle scenario considering traffic environment according to claim 1 is characterized in that: When extracting the vehicle dynamics parameters in the vehicle following case data, the vehicle's own driving information is read through the CAN bus, and the vehicle's own driving information includes speed, acceleration, relative distance, accelerator pedal opening, and headway.

5. The vehicle speed prediction method in a dynamic following scenario considering traffic environment according to claim 1 is characterized in that: The combined feature selection method for removing redundant variables in the fusion feature includes: Use message integrity check MIC, classification and regression tree CART, random forest RF and gradient boosting ensemble learning XGBoost algorithm to calculate the correlation between candidate input features and vehicle speed; The average value of each candidate variable is calculated by adding the relevant results of the four different algorithms and dividing it by the total number of algorithms. All variables are calculated and ranked in turn. Based on the ranking, the first few variables with the highest importance are selected as the input of the model.

6. The vehicle speed prediction method in a dynamic following vehicle scenario considering traffic environment according to claim 1 is characterized in that: The Informer-FDR vehicle speed prediction model establishes the Informer deep learning model in the following way: In the rolling forecast setting with a fixed window size, the input at time point t is: Where L is the length of the sample sequence, D is the feature dimension, and in X, a single data sample is: In the formula, t i j Represents the data of the input of the i-th model at the j-th time step; Using the vehicle speed as the output indicator: In the formula, y i represents the output of the i-th model at each time step; The encoder of the Informer deep learning model is composed of a multi-head sparse probabilistic self-attention mechanism and a stack of distillation operations: in, is a sparse matrix containing sparsity measurements; Q and K generated during encoder training represent query vectors and key vectors, respectively, V represents the value vector, and d represents the input dimension; the sparse probabilistic self-attention mechanism is defined as: Where, L K 1 / L K =q(k j |q i );Define the i queries of sparsity measurement as q i ;q i and k j from Q and K respectively; The Informer deep learning model adds a convolutional layer and a max pooling layer between each pair of self-attention layers to reduce the input length; after the previous self-attention layer, a convolutional layer with a kernel size of 3 and a stride of 1 is used to enhance the perception of contextual information features; then, a max pooling layer with a kernel size of 3 and a stride of 2 is used to prioritize the main features, thereby generating smaller but more focused feature maps for the subsequent self-attention layer: In the formula, the sparse probabilistic self-attention is contained in [·] AB In the figure, 1-D convolution filtering is implemented by Conv(·) with ELU(·) as the activation function. The encoder combines sparse probabilistic self-attention and distillation operations to generate feature maps. Time series input to the decoder of the Informer deep learning model It is divided into two parts, namely the known sequence before the predicted point and forecast series that require masking future data Where, L token is the length of the start marker sequence; L y is the length of the predicted sequence; d model is the model dimension; The position of each input information in the sequence is encoded as follows: In the formula, pos represents the feature vector position, d model Represents the dimension of the feature vector. Given the importance of time information for time series prediction, the time information in the time series is encoded, and the location information and time information are integrated into the input model.

7. The vehicle speed prediction method in a dynamic following vehicle scenario taking into account the traffic environment according to claim 6 is characterized in that: The Informer-FDR vehicle speed prediction model replaces the maximum pooling layer of the distillation layer in the decoder of the Informer deep learning model with an average pooling layer, and changes the convolution layer to a dilated causal convolution; the dilated causal convolution consists of two parts: causal convolution and dilated convolution; the causal convolution is subject to time constraints, and the output of time t is convolved only with the time t and earlier elements of the previous layer to ensure causality in time series prediction, so that the output information of the network is only affected by past input information; The dilated convolution operation is as follows: Where K is the convolution kernel size, s is the element of X, and F i is the weight of the convolution kernel, t is the time step, and td·i represents the past direction.

8. The vehicle speed prediction method in a dynamic following scenario considering traffic environment according to claim 7 is characterized in that: The Informer-FDR vehicle speed prediction model introduces PRELU as the activation function of the distillation layer. PRELU pushes the output mean to zero. The improved distillation operation is as follows: In the formula, the sparse probabilistic self-attention is contained in [·] AB In the figure, the dilated causal convolution is implemented by DCConv(·), the activation function is PReLU(·), and the final output is generated by AvgPool(·).

9. The vehicle speed prediction method in a dynamic following vehicle scenario taking into account the traffic environment according to claim 8 is characterized in that: The Informer-FDR vehicle speed prediction model adds a residual connection module, which consists of three sparse probabilistic self-attention layers and two distillation layers. In order to ensure that the feature maps output by these two layers have the same shape when added together, a 1*1 convolution is used to merge the output of the first sparse probabilistic self-attention layer in the main encoder with the output of the third sparse probabilistic self-attention layer to ensure the effective transmission of feature information.

10. A vehicle speed prediction system considering a dynamic following vehicle scenario in a traffic environment, characterized in that: include: A vehicle following case data extraction module is used to extract vehicle following case data by setting standards and thresholds for vehicle following processes in a natural driving data set; The feature fusion and normalization processing module is used to obtain the traffic environment features related to the car following case data using the target detection algorithm, extract the vehicle dynamics parameters in the car following case data, fuse the vehicle dynamics parameters with the traffic environment features, and obtain the fusion features after normalization processing; A redundant variable removal module is used to remove redundant variables in fusion features by using a combined feature selection method; The vehicle speed prediction model training and prediction output module is used to divide the fused features after removing redundant variables into data sets, train the pre-established Informer-FDR vehicle speed prediction model, and obtain the vehicle speed prediction results using the trained Informer-FDR vehicle speed prediction model.

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