CN111016908B - Vehicle driving position determining method and device, storage medium and electronic equipment - Google Patents

Abstract

The present disclosure relates to a method, a device, a storage medium and an electronic device for determining the driving position of a vehicle, so as to solve the problem of inaccurate positioning of a vehicle positioning device in the related art. The method includes: determining the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, the target position information including the position to be reached by the vehicle at the second target sampling time, and the second target sampling time is: Taking the next sampling time of the first target sampling time as the starting point of several consecutive sampling moments; according to the first historical trajectory information and target position information, determine whether the vehicle is in a steering state; if it is determined that the vehicle is in a steering state, use the target position In the information, the position that the vehicle will reach at each second target sampling time replaces the position collected by the vehicle's positioning device at each second target sampling time, so as to determine the driving position of the vehicle at each second target sampling time.

Description

Vehicle driving position determination method, device, storage medium and electronic device

technical field

The present disclosure relates to the field of vehicles, and in particular, to a method, a device, a storage medium, and an electronic device for determining a driving position of a vehicle.

Background technique

At present, in the application system of the Internet of Vehicles, the real-time display of the vehicle position in the monitoring center is the most commonly used function. This function usually uploads the driving position of the vehicle to the monitoring center by adding a positioning device to the OBD interface of the vehicle. However, limited by the cost and performance of the added positioning device, the accuracy of the obtained driving position is low in most cases. Since the driving position collected by the positioning device is calculated from the previous driving position, when the vehicle turns, the inertial drift of the positioning device will cause positioning deviation, resulting in positioning errors. For example, the driving position collected by the positioning device after the vehicle is turned The location is still on the road before the turn. In addition, the driving position located by the positioning device usually needs to be displayed. The positioning deviation will cause a sudden jump when displaying the driving position of the vehicle, and the error is very obvious. Moreover, if the number of vehicles is large, the display position jump will greatly affect the user experience.

SUMMARY OF THE INVENTION

The purpose of the present disclosure is to provide a method, device, storage medium and electronic device for determining the driving position of a vehicle, so as to improve the accuracy of vehicle positioning.

In order to achieve the above object, according to a first aspect of the present disclosure, there is provided a method for determining a driving position of a vehicle, the method comprising:

Determine the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, wherein the first historical trajectory information includes the history of the vehicle traveling to the first target sampling time Driving position, the target position information includes the position that the vehicle will reach at the second target sampling time, and the second target sampling time is the time starting point with the sampling time next to the first target sampling time Several consecutive sampling moments;

determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information;

If it is determined that the vehicle is in the steering state, the position that the vehicle will reach at each second target sampling time in the target position information is used to replace the position collected by the vehicle's positioning device at each second target sampling time , to determine the driving position of the vehicle at each of the second target sampling moments.

Optionally, the determining the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time includes:

determining a target location determination model matching the first historical trajectory information from the stored multiple location determination models;

The first historical trajectory information is input into the target position determination model to obtain the target position information output by the target position determination model.

Optionally, each of the position determination models corresponds to the probability of occurrence of the model in the historical driving of the vehicle;

The determining a target location determination model that matches the first historical trajectory information from the stored multiple location determination models includes:

according to the first historical trajectory information, respectively determining the mean square error of each of the position determination models;

For each of the position determination models, the ratio of the mean square error of the position determination model to the probability of occurrence of the position determination model in the historical driving of the vehicle is determined as the model error of the position determination model;

A position determination model with the smallest model error is determined as the target position determination model.

Optionally, the multiple location determination models are determined in the following manner:

Obtaining stored second historical track information corresponding to the vehicle, each of the second historical track information includes a historical driving position corresponding to the vehicle in a historical driving process;

Clustering the second historical track information to obtain a clustering result, where the clustering result includes a track category and the second historical track information under each track category;

Taking each of the trajectory categories as a target trajectory category, and using the target trajectory information to train the long-short-term memory network model to obtain a position determination model corresponding to the target trajectory category, where the target trajectory information is the target The second historical track information under the track category.

Optionally, the second target sampling moment is a preset number of sampling moments that take the next sampling moment of the first target sampling moment as a time starting point;

The long-short-term memory network model is trained using the target trajectory information to obtain a position determination model corresponding to the target trajectory category, including:

Taking a part of the target trajectory information as input data, and using the input data as output data with a preset number of historical driving positions after the target trajectory information, the long-short-term memory network model is trained to obtain the same value as the target trajectory information. The position determination model corresponding to the target trajectory category.

Optionally, the determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information includes:

determining the first traveling direction of the vehicle at the first target sampling time according to the first historical trajectory information;

Determine the second driving direction of the vehicle according to the driving position of the vehicle at the first target sampling time and the position to be reached at the next sampling time corresponding to the first target sampling time in the target position information;

If the included angle between the first travel direction and the second travel direction is greater than a preset angle threshold, determine that the vehicle is in a steering state;

If the included angle between the first traveling direction and the second traveling direction is less than or equal to the preset angle threshold, it is determined that the vehicle is not in a steering state.

Optionally, if it is determined that the vehicle is in the steering state, the method further includes:

Taking the latest sampling time in the second target sampling time as the new first target sampling time, and repeating the process of determining the target of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time location information, and determining whether the vehicle is in a steering state according to the first historical trajectory information and the target location information, and using the target location information if it is determined that the vehicle is in the steering state The position that the vehicle will reach at each second target sampling time replaces the position collected by the vehicle's positioning device at each second target sampling time, so as to determine the driving of the vehicle at each second target sampling time location steps.

Optionally, the method further includes:

If it is determined that the vehicle is not in the steering state, according to the position information collected by the positioning device of the vehicle, determine the driving position of the vehicle at the next sampling time of the first target sampling time; and

Taking the next sampling time of the first target sampling time as the new first target sampling time, and repeating the process of determining the target position of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time information, and determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information, and if it is determined that the vehicle is in the steering state, using the target position information The position that the vehicle will reach at each second target sampling time replaces the position collected by the positioning device of the vehicle at each second target sampling time, so as to determine the driving position of the vehicle at each second target sampling time A step of.

Optionally, the method further includes:

determining a display position corresponding to the target driving position according to the target driving position of the vehicle, where the target driving position is one of the driving positions determined during the driving of the vehicle;

generating target display information according to the sampling time corresponding to the target driving position and the display position corresponding to the target driving position;

The target display information is displayed.

According to a second aspect of the present disclosure, there is provided an apparatus for determining a driving position of a vehicle, the apparatus comprising:

A first determination module, configured to determine the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, wherein the first historical trajectory information includes the vehicle traveling to the first target location. A historical travel position passed by a target sampling time, the target position information includes the position that the vehicle will reach at the second target sampling time, and the second target sampling time is a lower value of the first target sampling time A sampling moment is a number of consecutive sampling moments at the beginning of time;

a second determining module, configured to determine whether the vehicle is in a steering state according to the first historical trajectory information and the target position information;

The processing module is configured to, if it is determined that the vehicle is in the steering state, use the position that the vehicle will reach at each second target sampling time in the target position information to replace the positioning device of the vehicle at each second target sampling time The position collected at each moment is used to determine the driving position of the vehicle at each of the second target sampling moments.

Optionally, the first determining module includes:

a first determination submodule, configured to determine a target location determination model matching the first historical trajectory information from the stored multiple location determination models;

The second determination sub-module is configured to input the first historical trajectory information into the target position determination model to obtain the target position information output by the target position determination model.

Optionally, each of the position determination models corresponds to the probability of occurrence of the model in the historical driving of the vehicle;

The first determination submodule includes:

a third determination sub-module, configured to determine the mean square error of each of the position determination models according to the first historical trajectory information;

The fourth determination sub-module is configured to determine, for each of the position determination models, the ratio of the mean square error of the position determination model to the probability of occurrence of the position determination model in the historical driving of the vehicle as the value of the position determination model. model error;

The fifth determination sub-module is used for determining the position determination model with the smallest model error as the target position determination model.

Optionally, the device is configured to determine a plurality of position determination models through the following modules:

an acquisition module, configured to acquire the stored second historical trajectory information corresponding to the vehicle, each of the second historical trajectory information includes a historical driving position corresponding to the vehicle in a historical driving process;

a clustering module, configured to perform clustering on the second historical trajectory information to obtain a clustering result, where the clustering result includes a trajectory category and the second historical trajectory information under each trajectory category;

A training module is used to respectively use each of the trajectory categories as a target trajectory category, and use the target trajectory information to train the long-short-term memory network model to obtain a position determination model corresponding to the target trajectory category, wherein the target trajectory The information is the second historical track information under the target track category.

Optionally, the second target sampling moment is a preset number of sampling moments that take the next sampling moment of the first target sampling moment as a time starting point;

The training module is used to train a long-short-term memory network model with a part of the target trajectory information as input data and a preset number of historical driving positions after the target trajectory information of the input data as output data , to obtain the position determination model corresponding to the target trajectory category.

Optionally, the second determining module includes:

a sixth determination sub-module, configured to determine the first driving direction of the vehicle at the first target sampling time according to the first historical trajectory information;

A seventh determination sub-module, configured to determine the vehicle according to the driving position of the vehicle at the first target sampling moment and the position to be reached at the next sampling moment corresponding to the first target sampling moment in the target position information the second direction of travel of the vehicle;

a first judgment submodule, configured to determine that the vehicle is in a steering state if the included angle between the first travel direction and the second travel direction is greater than a preset angle threshold;

A second judging submodule, configured to determine that the vehicle is not in a steering state if the included angle between the first traveling direction and the second traveling direction is less than or equal to the preset angle threshold.

Optionally, if it is determined that the vehicle is in the steering state, the device is further configured to use the latest sampling time in the second target sampling time as a new first target sampling time, and return to the first target sampling time. The determining module, the second determining module and the processing module are configured to repeatedly execute the determining of the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, and the The first historical trajectory information and the target position information are used to determine whether the vehicle is in a steering state, and if it is determined that the vehicle is in the steering state, use the target position information to determine whether the vehicle is in each second The position to be reached at the target sampling time replaces the position collected by the positioning device of the vehicle at each second target sampling time, so as to determine the driving position of the vehicle at each second target sampling time.

Optionally, the device further includes:

The third determining module is configured to determine, according to the position information collected by the positioning device of the vehicle, the position of the vehicle at the next sampling time of the first target sampling time, if it is determined that the vehicle is not in the steering state the driving position; and

The device is further configured to use the next sampling time of the first target sampling time as a new first target sampling time, and the second determining module repeatedly executes the first history according to the vehicle corresponding to the first target sampling time. trajectory information, determining target position information of the vehicle, and determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information, and determining if the vehicle is in the Steering state, the position that the vehicle will reach at each second target sampling time in the target position information is used to replace the position collected by the vehicle's positioning device at each second target sampling time, so as to determine the position of the vehicle at each second target sampling time. The driving position of the second target sampling time.

Optionally, the device further includes:

a fourth determination module, configured to determine a display position corresponding to the target driving position according to the target driving position of the vehicle, where the target driving position is one of the driving positions determined during the driving of the vehicle;

an information generation module, configured to generate target display information according to the sampling time corresponding to the target driving position and the display position corresponding to the target driving position;

The display module is used for displaying the target display information.

According to a third aspect of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

According to a fourth aspect of the present disclosure, there is provided an electronic device, comprising:

a memory on which a computer program is stored;

A processor for executing the computer program in the memory to implement the steps of the method in the first aspect of the present disclosure.

Through the above technical solution, the target position information of the vehicle is determined according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, and whether the vehicle is in a steering state is determined according to the first historical trajectory information and the target position information, and, if It is determined that the vehicle is in a steering state, and the position that the vehicle will reach at each second target sampling time in the target position information is used to replace the position collected by the vehicle's positioning device at each second target sampling time, so as to determine the vehicle at each second target sampling time. driving position. In this way, during the driving process of the vehicle, the position that the vehicle may reach in the future is predicted based on the obtained driving position of the vehicle, and when it is recognized that the vehicle is in a steering state, the predicted position is used to replace the actual collected position of the vehicle positioning device. position to determine the driving position of the vehicle, thereby solving the problem of inaccurate positioning of the positioning device when the vehicle is in a steering state, improving the accuracy of the driving position determination, which is beneficial to the subsequent position display based on the driving position and avoids the vehicle display position. jumping situation.

Other features and advantages of the present disclosure will be described in detail in the detailed description that follows.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present disclosure, and constitute a part of the specification, and together with the following detailed description, are used to explain the present disclosure, but not to limit the present disclosure. In the attached image:

Fig. 1 is the display schematic diagram before and after using the road network matching method to correct the driving position;

FIG. 2 is a flowchart of a method for determining a driving position of a vehicle provided according to an embodiment of the present disclosure;

3 is an exemplary flowchart of the steps of determining the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time in the method for determining the driving position of the vehicle provided according to the present disclosure;

4 is a block diagram of an apparatus for determining a driving position of a vehicle provided according to an embodiment of the present disclosure;

5 is a block diagram of an electronic device according to an exemplary embodiment;

Fig. 6 is a block diagram of an electronic device according to an exemplary embodiment.

Detailed ways

The specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure, but not to limit the present disclosure.

At present, in the application system of the Internet of Vehicles, the real-time display of the vehicle position in the monitoring center is the most commonly used function. This function usually uploads the driving position of the vehicle to the monitoring center by adding a positioning device to the OBD interface of the vehicle. However, limited by the cost and performance of the installed positioning device, the accuracy of the driving position obtained in most cases is low. Therefore, most of the driving positions are scattered along the driving road, and further road network matching is required to determine the driving position. Corrected to the corresponding road to ensure the reasonableness of the displayed vehicle position.

In the related art, the correction method of road network matching adopts the comparison between the driving position and each road to calculate the vertical distance and the direction angle, and takes the road with the shortest vertical distance and the smallest direction angle comparison deviation as the matching road, and compares the driving position to the matching road. The vertical point is used as the corrected vehicle display position. However, the above method cannot correct the positioning deviation caused by the inertial drift of the characteristics of the positioning device when the vehicle is turning. After the correction, the displayed position of the vehicle after turning is still on the unsteered road. Therefore, the displayed position of the vehicle will suddenly jump during this process. On the road after turning, the display position will be wrong, especially when the number of monitored vehicles is large, the display position of each vehicle will jump, which will greatly reduce the user experience.

Figure 1 shows the driving position before correction (solid circle) and the display position after correction (hollow circle). The performance of jumping is very obvious (see the corresponding positions of serial numbers 8 and 9).

Therefore, the present disclosure proposes a method, device, storage medium and electronic device for determining the driving position of a vehicle, so as to solve the problem of inaccurate positioning of a vehicle positioning device in the related art.

FIG. 2 is a flowchart of a method for determining a driving position of a vehicle according to an embodiment of the present disclosure. As shown in Figure 2, the method may include the following steps.

In step 21, the target position information of the vehicle is determined according to the first historical trajectory information of the vehicle corresponding to the first target sampling time.

During the driving process of the vehicle, the positioning device on the vehicle is in a working state, and data collection is performed periodically according to a certain time interval (for example, 1s). In this process, the moment of data collection is the sampling moment, and the sampling moment described in this solution can be understood with reference to the above method.

The first historical trajectory information may include a historical driving position where the vehicle travels to the first target sampling time. Here, the first target sampling time can be understood as "the current sampling time", and the first historical trajectory information can be understood as a position sequence. The time and the historical driving position passed by each previous sampling time. For example, during the driving process of the vehicle, the sampling times are sequentially t ₁ to t _n (n is a positive integer greater than 1). If the first target sampling time is the sampling time t ₁₀ (there are sampling times t ₁ to t ₉ before) , then the first historical track information includes the historical travel positions passed by the vehicle from t ₁ to t ₁₀ respectively. It should be noted that the historical travel position used in the present disclosure is essentially the travel position (that is, the travel position corresponding to the sampling time in history), which is here for the convenience of distinction.

The target position information may include a position to be reached by the vehicle at a second target sampling moment, wherein the second target sampling moment is a number of consecutive sampling moments starting from a sampling moment next to the first target sampling moment. The number of the second target sampling time can be set manually, for example, set to a preset number (greater than or equal to 1), that is, the second target sampling time is a preset that takes the next sampling time of the first target sampling time as the starting point of time number of sampling moments. For example, during the driving process of the vehicle, the sampling times are t ₁ to t _n (n is a positive integer greater than 1). If the first target sampling time is the sampling time t ₁₀ , the second target sampling time may be t ₁₁ . (the preset number is 1), or, the second target sampling times may be t ₁₁ , t ₁₂ and t ₁₃ (the preset number is 3).

According to the first historical trajectory information of the vehicle, the target position information of the vehicle is determined, that is, according to the historical driving position of the vehicle so far, the possible position of the vehicle at the next sampling time or at the next sampling time is estimated. Wherein, when using the first historical trajectory information to determine the target position information of the vehicle, all the first historical trajectory information may be used, or only the part of the first historical trajectory information that is close to the first target sampling time may be used to reduce The amount of data processing can be set according to specific needs in practical applications. For example, if the first historical track information includes the historical driving positions of the vehicle from t ₁ to t ₁₀ respectively, then when determining the target position information, the 10 historical records of the vehicle from t ₁ to t ₁₀ can be used. Driving position (full use), or, 5 historical driving positions (partial use) passed by the host vehicle from t ₆ to t ₁₀ may be used.

In step 22, it is determined whether the vehicle is in a steering state according to the first historical trajectory information and the target position information.

The current driving trend of the vehicle can be known according to the first historical track information that the vehicle has traveled, and the subsequent driving trend of the vehicle can be known according to the current driving position of the vehicle (that is, the driving position of the vehicle at the first target sampling time) and the target position information. The driving trend can be compared to determine whether the vehicle is in a steering state. If the two driving trends are relatively close, it means that the driving trends of the vehicle are not much different before and after, so it can be determined that the vehicle is not in the steering state; in turn.

In step 23, if it is determined that the vehicle is in a steering state, the position that the vehicle will reach at each second target sampling time in the target position information is used to replace the position collected by the vehicle's positioning device at each second target sampling time to determine the vehicle's position at each second target sampling time. The driving position of each second target sampling time.

As mentioned above, if the vehicle is in the steering state, the data collected by the vehicle's positioning device will have a large error. Therefore, the target position information determined in step 11 can be used for the vehicle to arrive at each second target sampling time. The position corresponds to the position collected by the positioning device that replaces the vehicle at each second target sampling time, so as to determine the driving position of the vehicle at each second target sampling time. The corresponding substitution refers to replacing the position collected by the positioning device at the second target sampling time ta with _a position in the target position information corresponding to the second target sampling time _ta .

Through the above technical solution, the target position information of the vehicle is determined according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, and whether the vehicle is in a steering state is determined according to the first historical trajectory information and the target position information, and, if It is determined that the vehicle is in a steering state, and the position that the vehicle will reach at each second target sampling time in the target position information is used to replace the position collected by the vehicle's positioning device at each second target sampling time, so as to determine the vehicle at each second target sampling time. driving position. In this way, during the driving process of the vehicle, the position that the vehicle may reach in the future is predicted based on the obtained driving position of the vehicle, and when it is recognized that the vehicle is in a steering state, the predicted position is used to replace the actual collected position of the vehicle positioning device. position to determine the driving position of the vehicle, thus, it can solve the problem of inaccurate positioning of the positioning device when the vehicle is in the steering state, improve the accuracy of the determination of the driving position, which is beneficial to the subsequent position display based on the driving position, and avoids the appearance of the vehicle display position jumping situation.

In order to make those skilled in the art better understand the technical solutions provided by the embodiments of the present invention, the corresponding steps and related concepts above are described in detail below.

In the initial situation, the vehicle has just started to travel, and the vehicle does not have enough historical driving positions at this time. Therefore, even if the target position information is determined, there is still a problem of inaccurate results. Therefore, in a possible situation, the position collected by the vehicle positioning device can be directly used as the driving position of the vehicle at several sampling moments after the vehicle starts driving, wherein the sampling moments after the vehicle starts driving The number can be set according to the experience value. Afterwards, after there are enough historical driving positions, the method provided by the present disclosure can be executed.

The following describes in detail the determination of the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time in step 21 .

In a possible implementation manner, step 21 may include the following steps, as shown in FIG. 3 .

In step 31, a target location determination model matching the first historical trajectory information is determined from the stored multiple location determination models.

The multiple location determination models are obtained in advance and stored in corresponding storage locations (for example, a database storing data of the vehicle), and can be directly obtained from the corresponding storage locations in actual use.

The generation method of the location determination model will be described in detail below. In a possible implementation, multiple location determination models may be determined in the following manner:

acquiring the stored second historical track information corresponding to the vehicle;

Clustering the second historical trajectory information to obtain a clustering result;

Each trajectory category is regarded as the target trajectory category, and the long-short-term memory network model is trained by using the target trajectory information to obtain the position determination model corresponding to the target trajectory category.

The second historical trajectory information is obtained based on the historical driving process of the vehicle, and may be stored in a corresponding storage location (eg, a database storing vehicle data). Each second historical trajectory information includes a historical driving position corresponding to the vehicle in a historical driving process, and each second historical trajectory information can be regarded as composed of historical driving positions in a historical driving process in a chronological order. sequence.

After acquiring the stored second historical trajectory information corresponding to the vehicle, a plurality of second historical trajectory information may be clustered to obtain a clustering result. The clustering result includes track categories and second historical track information under each track category. For example, when the second historical trajectory information is clustered, it may be based on a K-Means clustering algorithm (K-Means clustering algorithm, K-means clustering algorithm), and the distance metric between the second historical trajectory information uses DTW (Dynamic Time Warping, Dynamic Time Warping) distance, where DTW is used to measure the similarity between sequences of different lengths. In addition, the methods of calculating the DTW distance between data and using the K-Means clustering algorithm to cluster the data to obtain the clustering result belong to the prior art, and are not described herein again.

After the clustering result is obtained, the model can be trained separately for each track category obtained after the clustering, and the second historical track information under the corresponding track category is used during training. For example, each trajectory category can be regarded as the target trajectory category, and the long-short-term memory network model can be trained by using the target trajectory information to obtain the position determination model corresponding to the target trajectory category. The target trajectory information is the second historical trajectory information under the target trajectory category. Therefore, each trajectory category in the clustering result is sequentially regarded as a target trajectory category, and model training is performed for the target trajectory category, so that a position determination model corresponding to each trajectory category can be correspondingly trained.

In a possible embodiment, using the target trajectory information to train the long-short-term memory network model to obtain a position determination model corresponding to the target trajectory category, the following steps may be included:

Taking part of the target trajectory information as input data, and using the input data with a preset number of historical driving positions after the target trajectory information as output data, train the long-short-term memory network model to obtain a position determination model corresponding to the target trajectory category .

From the above, the second target sampling time may be a preset number, therefore, model training can be performed based on this, and a preset number of historical driving positions can be used as output data. When training the model, a part of the target trajectory information is used as input data, and a preset number of historical driving positions after the target trajectory information of the input data are used as output data, and the long-short-term memory network model is trained to obtain the target trajectory category. The corresponding position determination model. For example, if the preset number is 2, and the second historical track information is [D1, D2, D3, D4, D5, D6, D7, D8, D9, D10], then [D1, D2, D3, D4, D5] as input data and [D6, D7] as output data for model training, or, you can use [D4, D5, D6, D7, D8] as input data and [D9, D10] as output For the selection methods of other training data, reference can be made here, and details are not repeated here. And, the way of training the long-short-term memory network model is in the prior art, and will not be described in detail here.

The above method is used to cluster the second historical trajectory information of the vehicle's historical driving, and according to the clustering results, a position determination model is generated for each trajectory category, which has strong pertinence and is helpful for determining the model in the use position. more accurate results are obtained.

The following describes the step 31 of determining a target position determination model matching the first historical trajectory information from a plurality of stored position determination models. In a possible implementation, step 31 may include the following steps:

According to the first historical trajectory information, the mean square error of each position determination model is determined respectively;

For each position determination model, the ratio of the mean square error of the position determination model to the occurrence probability of the position determination model in the historical driving of the vehicle is determined as the model error of the position determination model;

The position determination model with the smallest model error is determined as the target position determination model.

The mean square error can reflect the degree of difference between the estimated value and the true value, which can be the expected value of the square of the difference between the estimated value of the parameter and the true value of the parameter. Therefore, according to the first historical trajectory information, for each position determination model, the mean square error of the position determination model can be determined, and its formula can be as follows:

为位置确定模型针对输入的第一历史轨迹信息得到的输出结果(估计值)，θ为第一历史轨迹信息中对应于输出结果的实际数据(真实值)。在确定位置确定模型的均方误差时，可以选取第一历史轨迹信息中的不同部分分别作为输入数据，以得到位置确定模型的多种输出结果，针对每种输出结果分别计算均方误差，并将其中的最小者作为该位置确定模型的均方误差。由此，可以确定出每一位置确定模型的均方误差。in,

is the output result (estimated value) obtained by the position determination model for the inputted first historical trajectory information, and θ is the actual data (true value) corresponding to the output result in the first historical trajectory information. When determining the mean square error of the position determination model, different parts of the first historical trajectory information can be selected as input data to obtain various output results of the position determination model, and the mean square errors are calculated for each output result respectively, and Take the smallest of them as the mean squared error of the location determination model. From this, the mean squared error of each position determination model can be determined.

After determining the mean square error of each determination model, for each location determination model, the ratio of the mean square error of the location determination model to the occurrence probability of the location determination model in the historical driving of the vehicle is determined as the location determination model model error. In this way, the model error obtained in combination with the occurrence probability can reduce the influence of the difference in the amount of data on the effect of the position determination model, so that the determined model error is more accurate.

Wherein, each stored location determination model corresponds to the occurrence probability of the model in the historical driving of the vehicle. Referring to the determination method of the position determination model received above, firstly, the second historical trajectory information of the vehicle is clustered to obtain the trajectory category and the second historical trajectory information under each trajectory category. After that, a position determination model is determined for each trajectory category. Therefore, the occurrence probability of the position determination model in the historical driving of the vehicle can be that the second historical trajectory information under the trajectory category to which the position determination model belongs accounts for the second history of the vehicle. The ratio of trajectory information. For example, all the second historical trajectory information of the vehicle is divided into 3 categories (A, B, C), where the number of category A is 450, the number of category B is 100, and the number of category C is 50, then the location of category A is determined. The appearance probability of the model in the historical driving of the vehicle is 450/(450+100+50)=0.75, and the same is true for others.

The magnitude of the model error can reflect the matching degree between the position determination model and the first historical trajectory information, and the smallest model error indicates that the position determination model can best match the historical driving position of the current driving process of the vehicle. Therefore, after obtaining the model error corresponding to each position determination model, the position determination model with the smallest model error is used as the target position determination model for subsequent data processing, so that the data processing results can be closer to the current driving conditions of the vehicle. Processing results are more accurate.

In step 32, the first historical trajectory information is input into the target position determination model to obtain target position information output by the target position determination model.

In the above manner, the target position determination model with the highest matching degree with the first historical trajectory information is determined by using a plurality of stored position determination models, and the target position information is determined by the target position determination model, so as to realize the position that the vehicle may reach in the future. The prediction is closer to the current driving condition of the vehicle and has a higher degree of accuracy.

In a possible implementation, in step 12, determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information may include the following steps:

determining the first traveling direction of the vehicle at the first target sampling time according to the first historical trajectory information;

Determine the second driving direction of the vehicle according to the driving position of the vehicle at the first target sampling time and the position to be reached at the next sampling time corresponding to the first target sampling time in the target position information;

If the included angle between the first traveling direction and the second traveling direction is greater than the preset angle threshold, it is determined that the vehicle is in a steering state;

If the included angle between the first traveling direction and the second traveling direction is less than or equal to the preset angle threshold, it is determined that the vehicle is not in a steering state.

Wherein, the first traveling direction of the vehicle at the first target sampling time can be determined according to the first historical track information. For example, the driving position of the vehicle at the last sampling time of the first target sampling time may be directed to the direction of the driving position of the vehicle at the first target sampling time as the first driving direction. For another example, the first driving direction can also be determined according to the driving position of the vehicle at the first target sampling moment and several sampling moments before the first target sampling moment. The driving position at the next sampling time (time t+1) points to the driving change direction of the driving position of the vehicle at the next sampling time (time t+1), and multiple driving change directions can be obtained. A plurality of directions get a direction in the middle position as the first direction of travel). And, according to the traveling position of the vehicle at the first target sampling time and the position to be reached at the next sampling time corresponding to the first target sampling time in the target position information, the second traveling direction of the vehicle can be determined. For example, a direction in which the vehicle's traveling position at the first target sampling moment points to the vehicle's traveling position at the next sampling moment of the first target sampling moment may be used as the second traveling direction.

The first driving direction can reflect the current driving trend of the vehicle, and the second driving direction can reflect the future driving trend of the vehicle. Therefore, the angle between the first driving direction and the second driving direction can reflect the degree of change in the driving trend of the vehicle. The greater the angle between them, the greater the change in the driving trend of the vehicle, and the more likely it is in a steering state. Therefore, if the angle between the first travel direction and the second travel direction is greater than the preset angle threshold, it is determined that the vehicle is in a steering state; if the angle between the first travel direction and the second travel direction is less than or equal to the preset angle Threshold to determine that the vehicle is not in a steering state. Wherein, the preset angle threshold may be set according to an empirical value. For example, the preset angle threshold may be within an angle interval of [15°, 20°].

In a possible implementation, if it is determined through step 22 that the vehicle is not in a steering state, on the basis of each step shown in FIG. 2 , the method provided by the present disclosure may further include the following steps:

According to the position information collected by the positioning device of the vehicle, determine the driving position of the vehicle at the next sampling time of the first target sampling time; and,

The next sampling time of the first target sampling time is taken as the new first target sampling time, and step 21 , step 22 and step 23 are repeatedly executed.

When the vehicle is not in the steering state, the positioning device of the vehicle will not be affected by the steering, and there will be no large error. Therefore, the position information of the vehicle at the first target sampling time can be determined directly according to the position information collected by the positioning device of the vehicle. The driving position at the next sampling time. For example, the position collected by the vehicle positioning device at the next sampling time of the first target sampling time may be directly used as the driving position of the vehicle at the next sampling time of the first target sampling time. In addition, since it is still unknown whether the vehicle is turning or not, it is still necessary to continue to judge. Therefore, the next sampling time of the first target sampling time is taken as the new first target sampling time, and steps 21 to 23 are repeatedly executed, that is, continue to be based on The existing driving position determines whether the vehicle is in a steering state and performs corresponding processing, and this process can continue until the vehicle stops running.

In a possible implementation manner, if it is determined through step 22 that the vehicle is in a steering state, on the basis of each step shown in FIG. 2 , the method provided by the present disclosure may further include the following steps:

The latest sampling time in the second target sampling time is taken as the new first target sampling time, and step 21 , step 22 and step 23 are repeatedly executed.

After it is determined that the vehicle is in a steering state, the target position information determined at the first target sampling time will be used to replace the corresponding data collected by the positioning device, and when the latest sampling time in the second target sampling time is reached, the vehicle's subsequent The state is unknown again. Therefore, the latest sampling time in the second target sampling time is taken as the new first target sampling time, and steps 21 to 23 are repeated, that is, continue to determine whether the vehicle is in Steering state, and processing accordingly, this process can continue until the vehicle stops running.

In a possible implementation, the method provided by the present disclosure may further include the following steps:

According to the target driving position of the vehicle, determine the display position corresponding to the target driving position, and the target driving position is one of the driving positions determined during the driving of the vehicle;

Generate target display information according to the sampling time corresponding to the target driving position and the display position corresponding to the target driving position;

Display target display information.

Based on the content given above, the driving position at each sampling time during the driving process of the vehicle can be determined. According to the target driving position of the vehicle, the display position corresponding to the target driving position can be determined, wherein the target driving position is one of the driving positions determined during the driving of the vehicle, and each driving position of the vehicle can be regarded as the target driving position respectively Location. The display position corresponding to the target driving position can refer to the correction method for road network matching given in the previous section, that is, by calculating the vertical distance from the driving position to the nearby road and comparing the direction angle, select the shortest vertical distance and the smallest direction angle deviation. The road of , is used as the matching road, and the vertical point from the driving position to the matching road is used as the display position of the driving position.

Therefore, the target display information can be generated according to the sampling time corresponding to the target driving position and the display position corresponding to the target driving position. The target display information may be used to indicate the sampling time corresponding to the target driving position and the display position corresponding to the target driving position.

After the target display information is generated, the target display information can be displayed on the corresponding visualization page. For example, in the visualization interface, the vehicle icon is displayed at the sampling time and display position corresponding to the target display information.

In the above manner, display is performed based on the determined driving position of the vehicle to visually display the driving trajectory of the vehicle, and since the determined driving position of the vehicle is accurate, there will be no position jump during display, which improves user experience .

FIG. 4 is a block diagram of an apparatus for determining a driving position of a vehicle according to an embodiment of the present disclosure. As shown in FIG. 4, the apparatus 40 may include:

The first determination module 41 is configured to determine the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, wherein the first historical trajectory information includes the vehicle traveling to the The historical driving position passed by the first target sampling time, the target position information includes the position that the vehicle will reach at the second target sampling time, and the second target sampling time is based on the first target sampling time. The next sampling moment is several consecutive sampling moments of the time starting point;

A second determination module 42, configured to determine whether the vehicle is in a steering state according to the first historical trajectory information and the target position information;

The processing module 43 is configured to, if it is determined that the vehicle is in the steering state, use the position that the vehicle will reach at each second target sampling time in the target position information to replace the positioning device of the vehicle at each second target. The position collected at the sampling time is used to determine the driving position of the vehicle at each of the second target sampling time.

Optionally, the first determining module 41 includes:

a first determination submodule, configured to determine a target location determination model matching the first historical trajectory information from the stored multiple location determination models;

The second determination sub-module is configured to input the first historical trajectory information into the target position determination model to obtain the target position information output by the target position determination model.

Optionally, each of the position determination models corresponds to the probability of occurrence of the model in the historical driving of the vehicle;

The first determination submodule includes:

a third determination sub-module, configured to determine the mean square error of each of the position determination models according to the first historical trajectory information;

The fourth determination sub-module is configured to determine, for each of the position determination models, the ratio of the mean square error of the position determination model to the probability of occurrence of the position determination model in the historical driving of the vehicle as the value of the position determination model. model error;

The fifth determination sub-module is used for determining the position determination model with the smallest model error as the target position determination model.

Optionally, the device 40 is configured to determine a plurality of position determination models through the following modules:

an acquisition module, configured to acquire the stored second historical trajectory information corresponding to the vehicle, each of the second historical trajectory information includes a historical driving position corresponding to the vehicle in a historical driving process;

a clustering module, configured to perform clustering on the second historical trajectory information to obtain a clustering result, where the clustering result includes a trajectory category and the second historical trajectory information under each trajectory category;

A training module is used to respectively use each of the trajectory categories as a target trajectory category, and use the target trajectory information to train the long-short-term memory network model to obtain a position determination model corresponding to the target trajectory category, wherein the target trajectory The information is the second historical track information under the target track category.

Optionally, the second target sampling moment is a preset number of sampling moments that take the next sampling moment of the first target sampling moment as a time starting point;

The training module is used to train a long-short-term memory network model with a part of the target trajectory information as input data and a preset number of historical driving positions after the target trajectory information of the input data as output data , to obtain the position determination model corresponding to the target trajectory category.

Optionally, the second determining module 42 includes:

a sixth determination sub-module, configured to determine the first driving direction of the vehicle at the first target sampling time according to the first historical trajectory information;

A seventh determination sub-module, configured to determine the vehicle according to the driving position of the vehicle at the first target sampling moment and the position to be reached at the next sampling moment corresponding to the first target sampling moment in the target position information the second direction of travel of the vehicle;

a first judgment submodule, configured to determine that the vehicle is in a steering state if the included angle between the first travel direction and the second travel direction is greater than a preset angle threshold;

A second judging submodule, configured to determine that the vehicle is not in a steering state if the included angle between the first traveling direction and the second traveling direction is less than or equal to the preset angle threshold.

Optionally, if it is determined that the vehicle is in the steering state, the device 40 is further configured to use the latest sampling time in the second target sampling time as a new first target sampling time, and return to the first target sampling time. a determining module 41, the second determining module 42 and the processing module 43, to repeatedly execute the determining of the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, and The determining whether the vehicle is in a steering state according to the first historical track information and the target position information, and if it is determined that the vehicle is in the steering state, using the vehicle in the target position information The position to be reached at each second target sampling time replaces the position collected by the positioning device of the vehicle at each second target sampling time, so as to determine the driving position of the vehicle at each second target sampling time.

Optionally, the device 40 further includes:

The third determining module is configured to determine, according to the position information collected by the positioning device of the vehicle, the position of the vehicle at the next sampling time of the first target sampling time, if it is determined that the vehicle is not in the steering state the driving position; and

The device 40 is further configured to take the next sampling time of the first target sampling time as a new first target sampling time, and return to the first determining module 41, the second determining module 42 and the processing Module 43, to repeat the execution of the first historical trajectory information of the vehicle corresponding to the first target sampling time, to determine the target position information of the vehicle, and to repeat the execution according to the first historical trajectory information and the target position information. , determine whether the vehicle is in the steering state, and if it is determined that the vehicle is in the steering state, use the position that the vehicle will reach at each second target sampling time in the target position information to replace the position of the vehicle The location collected by the positioning device at each second target sampling time is used to determine the driving position of the vehicle at each second target sampling time.

Optionally, the device 40 further includes:

a fourth determination module, configured to determine a display position corresponding to the target driving position according to the target driving position of the vehicle, where the target driving position is one of the driving positions determined during the driving of the vehicle;

an information generation module, configured to generate target display information according to the sampling time corresponding to the target driving position and the display position corresponding to the target driving position;

The display module is used for displaying the target display information.

Regarding the apparatus in the above-mentioned embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.

Fig. 5 is a block diagram of an electronic device according to an exemplary embodiment. As shown in FIG. 5 , the electronic device 700 may include: a processor 701 and a memory 702 . The electronic device 700 may also include one or more of a multimedia component 703 , an input/output (I/O) interface 704 , and a communication component 705 .

Wherein, the processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned method for determining the driving position of the vehicle. The memory 702 is used to store various types of data to support operations on the electronic device 700, such data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data, Such as contact data, messages sent and received, pictures, audio, video, and so on. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, SRAM for short), electrically erasable programmable read-only memory ( Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), read-only Memory (Read-Only Memory, ROM for short), magnetic memory, flash memory, magnetic disk or optical disk. Multimedia components 703 may include screen and audio components. Wherein the screen can be, for example, a touch screen, and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in memory 702 or transmitted through communication component 705 . The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, a button, and the like. These buttons can be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of them , which is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module and so on.

In an exemplary embodiment, the electronic device 700 may be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing (Digital Signal) Processing Device (DSPD for short), Programmable Logic Device (PLD for short), Field Programmable Gate Array (FPGA for short), controller, microcontroller, microprocessor or other electronic components , which is used to execute the above-mentioned method for determining the driving position of the vehicle.

In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for determining the driving position of a vehicle are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned method for determining the driving position of the vehicle.

Fig. 6 is a block diagram of an electronic device according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. 6 , the electronic device 1900 includes a processor 1922 , which may be one or more in number, and a memory 1932 for storing computer programs executable by the processor 1922 . A computer program stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 1922 may be configured to execute the computer program to perform the above-described vehicle driving position determination method.

In addition, the electronic device 1900 may also include a power supply assembly 1926, which may be configured to perform power management of the electronic device 1900, and a communication component 1950, which may be configured to enable communication of the electronic device 1900, eg, wired or wireless communication. Additionally, the electronic device 1900 may also include an input/output (I/O) interface 1958 . Electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, and the like.

In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for determining the driving position of a vehicle are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 1932 including program instructions, and the above-mentioned program instructions can be executed by the processor 1922 of the electronic device 1900 to complete the above-mentioned method for determining the driving position of the vehicle.

In another exemplary embodiment, there is also provided a computer program product comprising a computer program executable by a programmable apparatus, the computer program having, when executed by the programmable apparatus, for performing the above The code part of the vehicle driving position determination method.

The preferred embodiments of the present disclosure have been described above in detail with reference to the accompanying drawings. However, the present disclosure is not limited to the specific details of the above-mentioned embodiments. Various simple modifications can be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure. These simple modifications all fall within the protection scope of the present disclosure.

In addition, it should be noted that each specific technical feature described in the above-mentioned specific implementation manner may be combined in any suitable manner under the circumstance that there is no contradiction. In order to avoid unnecessary repetition, various possible combinations are not described in the present disclosure.

In addition, the various embodiments of the present disclosure can also be arbitrarily combined, as long as they do not violate the spirit of the present disclosure, they should also be regarded as the contents disclosed in the present disclosure.

Claims (12)

1. A method for determining the driving position of a vehicle, wherein the method comprises: Determine the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, wherein the first historical trajectory information includes the history of the vehicle traveling to the first target sampling time Driving position, the target position information includes the position that the vehicle will reach at the second target sampling time, and the second target sampling time is the time starting point with the sampling time next to the first target sampling time A preset number of consecutive sampling moments; determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information; If it is determined that the vehicle is in the steering state, the position that the vehicle will reach at each second target sampling time in the target position information is used to replace the position collected by the vehicle's positioning device at each second target sampling time , to determine the driving position of the vehicle at each of the second target sampling moments. 2 . The method according to claim 1 , wherein the determining the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time comprises: 3 . determining a target location determination model matching the first historical trajectory information from the stored multiple location determination models; The first historical trajectory information is input into the target position determination model to obtain the target position information output by the target position determination model. 3. The method according to claim 2, wherein each of the position determination models corresponds to the occurrence probability of the model in the historical driving of the vehicle; The determining a target location determination model that matches the first historical trajectory information from the stored multiple location determination models includes: according to the first historical trajectory information, respectively determining the mean square error of each of the position determination models; For each of the position determination models, the ratio of the mean square error of the position determination model to the probability of occurrence of the position determination model in the historical driving of the vehicle is determined as the model error of the position determination model; A position determination model with the smallest model error is determined as the target position determination model. 4. The method according to claim 2, wherein the plurality of position determination models are determined in the following manner: Obtaining stored second historical track information corresponding to the vehicle, each of the second historical track information includes a historical driving position corresponding to the vehicle in a historical driving process; Clustering the second historical track information to obtain a clustering result, where the clustering result includes a track category and the second historical track information under each track category; Taking each of the trajectory categories as a target trajectory category, and using the target trajectory information to train the long-short-term memory network model to obtain a position determination model corresponding to the target trajectory category, where the target trajectory information is the target The second historical track information under the track category. 5. The method according to claim 4, wherein the training of the long-short-term memory network model using the target trajectory information to obtain a position determination model corresponding to the target trajectory category, comprising: Taking a part of the target trajectory information as input data, and using the input data as output data with a preset number of historical driving positions after the target trajectory information, the long-short-term memory network model is trained to obtain the same value as the target trajectory information. The position determination model corresponding to the target trajectory category. 6. The method according to claim 1, wherein the determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information comprises: determining the first traveling direction of the vehicle at the first target sampling time according to the first historical trajectory information; Determine the second driving direction of the vehicle according to the driving position of the vehicle at the first target sampling time and the position to be reached at the next sampling time corresponding to the first target sampling time in the target position information; If the included angle between the first travel direction and the second travel direction is greater than a preset angle threshold, determine that the vehicle is in a steering state; If the included angle between the first traveling direction and the second traveling direction is less than or equal to the preset angle threshold, it is determined that the vehicle is not in a steering state. 7. The method according to claim 1, wherein, if it is determined that the vehicle is in the steering state, the method further comprises: Taking the latest sampling time in the second target sampling time as the new first target sampling time, and repeating the process of determining the target of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time location information, and determining whether the vehicle is in a steering state according to the first historical trajectory information and the target location information, and using the target location information if it is determined that the vehicle is in the steering state The position that the vehicle will reach at each second target sampling time replaces the position collected by the vehicle's positioning device at each second target sampling time, so as to determine the driving of the vehicle at each second target sampling time location steps. 8. The method of claim 1, wherein the method further comprises: If it is determined that the vehicle is not in the steering state, according to the position information collected by the positioning device of the vehicle, determine the driving position of the vehicle at the next sampling time of the first target sampling time; and Taking the next sampling time of the first target sampling time as the new first target sampling time, and repeating the process of determining the target position of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time information, and determining whether the vehicle is in a steering state according to the first historical trajectory information and the target position information, and if it is determined that the vehicle is in the steering state, using the target position information The position that the vehicle will reach at each second target sampling time replaces the position collected by the positioning device of the vehicle at each second target sampling time, so as to determine the driving position of the vehicle at each second target sampling time A step of. 9. The method according to any one of claims 1-8, wherein the method further comprises: determining a display position corresponding to the target driving position according to the target driving position of the vehicle, where the target driving position is one of the driving positions determined during the driving of the vehicle; generating target display information according to the sampling time corresponding to the target driving position and the display position corresponding to the target driving position; The target display information is displayed. 10. A device for determining a driving position of a vehicle, wherein the device comprises: A first determination module, configured to determine the target position information of the vehicle according to the first historical trajectory information of the vehicle corresponding to the first target sampling time, wherein the first historical trajectory information includes the vehicle traveling to the first target location. A historical travel position passed by a target sampling time, the target position information includes the position that the vehicle will reach at the second target sampling time, and the second target sampling time is a lower value of the first target sampling time A sampling moment is a preset number of consecutive sampling moments of the time starting point; a second determining module, configured to determine whether the vehicle is in a steering state according to the first historical trajectory information and the target position information; The processing module is configured to, if it is determined that the vehicle is in the steering state, use the position that the vehicle will reach at each second target sampling time in the target position information to replace the positioning device of the vehicle at each second target sampling time The position collected at each moment is used to determine the driving position of the vehicle at each of the second target sampling moments. 11. A computer-readable storage medium on which a computer program is stored, characterized in that, when the program is executed by a processor, the steps of the method according to any one of claims 1-9 are implemented. 12. An electronic device, characterized in that, comprising: a memory on which a computer program is stored; A processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-9.

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