Vehicle speed estimation method based on transition probability matrix and rule constraint optimization

Abstract

The application discloses a vehicle speed estimation method based on transition probability matrix and rule constraint optimization, and comprises the following steps: acquiring GPS data in the process of vehicle driving, and calculating corresponding speed data and acceleration data; performing data preprocessing on the GPS data, the speed data and the acceleration data to obtain a vehicle driving data set, and constructing speed transition probability matrices under multiple traffic scenes according to the vehicle driving data set; dividing a current journey of a target vehicle into multiple sub-journeys according to the traffic scenes, and determining speed transition probability matrices corresponding to the sub-journeys; predicting the speed of the target vehicle at a next moment based on the speed transition probability matrices and preset constraint rules according to a starting speed of the target vehicle, and obtaining a speed estimation sequence of each sub-journey; and performing speed smoothing, correction and sequence splicing on the speed estimation sequence to obtain vehicle speed time series data. The application improves the accuracy of vehicle speed estimation and can be applied to the technical field of intelligent transportation.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a vehicle speed estimation method based on transition probability matrix and rule constraint optimization. Background Technology

[0002] Currently, the transportation sector is focusing on synergistic pathways for pollution and carbon reduction, constructing and improving a refined carbon emission statistical accounting system aimed at achieving carbon peaking and carbon neutrality. As a core tool for quantifying vehicle pollutant and carbon emissions, emission inventories are shifting from regional road network-level macro- and meso-level statistics to single-vehicle-level micro-analysis, with their level of refinement depending on the characterization capabilities of individual vehicle driving data. Under strict regulations on the collection, storage, and use of personal travel data, existing traffic detection technologies struggle to obtain complete travel trajectories for all individual vehicles across the entire region. Path planning algorithms only output static travel path schemes and cannot obtain vehicle motion information. Therefore, vehicle speed estimation methods are needed to predict vehicle speeds along travel paths to obtain complete vehicle travel trajectories.

[0003] Existing vehicle speed estimation methods can be categorized into mathematical interpolation, mathematical modeling, and time-series prediction model-based methods. Mathematical interpolation is unsuitable for scenarios with high data sparsity and cannot effectively handle speed estimation tasks in complex traffic environments. Mathematical modeling is computationally expensive, prone to getting trapped in local optima, and is often used in closed highway scenarios. Time-series prediction models have high requirements for data volume and computational resources, suffer from uninterpretability and error accumulation issues, and struggle to solve long-distance speed estimation problems.

[0004] Therefore, there is an urgent need to propose a vehicle speed estimation method that can accurately reconstruct the instantaneous motion state of an individual vehicle. Summary of the Invention

[0005] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0006] Therefore, one objective of this invention is to provide a vehicle speed estimation method based on transition probability matrix and rule constraint optimization. This method improves the accuracy of vehicle speed estimation and is applicable to vehicle speed estimation for different distances in various traffic scenarios.

[0007] Another objective of this invention is to provide a vehicle speed estimation device based on transition probability matrix and rule constraint optimization.

[0008] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include: On one hand, embodiments of the present invention provide a vehicle speed estimation method based on transition probability matrix and rule constraint optimization, comprising the following steps: The GPS data during the vehicle's operation is acquired, and the corresponding speed and acceleration data are calculated based on the GPS data. The GPS data, the speed data, and the acceleration data are preprocessed to obtain a vehicle driving dataset, and speed transition probability matrices for multiple traffic scenarios are constructed based on the vehicle driving dataset. The current journey of the target vehicle is divided into multiple sub-journeys according to the traffic scenario, and the speed transition probability matrix corresponding to each sub-journey is determined. Based on the initial speed of the target vehicle, the speed of the target vehicle at the next moment is predicted based on the speed transition probability matrix and preset constraint rules, thereby obtaining the speed estimation sequence of each sub-journey; The speed estimation sequence is smoothed, corrected, and spliced ​​to obtain the vehicle speed time series data corresponding to the current journey of the target vehicle.

[0009] Furthermore, in one embodiment of the present invention, the GPS data includes vehicle ID, vehicle latitude and longitude coordinates, and the time of data collection, and the speed data and the acceleration data are calculated by the following formula:

[0010]

[0011]

[0012] in, express Time's up The distance the vehicle traveled at any given time. Represents the Earth's radius. express The vehicle's latitude and longitude coordinates at that moment. express The vehicle's latitude and longitude coordinates at that moment. express Vehicle speed at any given moment express Vehicle acceleration at any moment express Time's up The time interval between moments.

[0013] Furthermore, in one embodiment of the present invention, the step of preprocessing the GPS data, the speed data, and the acceleration data to obtain a vehicle driving dataset specifically includes: Anomaly detection is performed on the GPS data, the speed data, and the acceleration data. GPS data with abnormal latitude and longitude is removed, and speed data and acceleration data with abnormal acceleration are corrected. The vehicle motion status is identified based on the speed data, and the GPS data, speed data, and acceleration data are divided into multiple travel records based on the identification results. Obtain an OSM city road network map, match the GPS data with the nearest road segment based on the nearest neighbor matching method, and remove the GPS data, speed data, and acceleration data where the distance between the vehicle's latitude and longitude coordinates and the matched road segment is greater than a preset threshold; The GPS data, speed data, and acceleration data corresponding to the travel records are summarized based on the vehicle ID to obtain the vehicle driving dataset.

[0014] Furthermore, in one embodiment of the present invention, traffic scenarios are divided according to travel time, travel area and road type. Travel time includes peak period, off-peak period and off-peak period. Travel area includes main urban area and non-main urban area. Road type includes urban expressway, urban expressway ramp, highway, highway ramp, arterial road, arterial road ramp, secondary arterial road, secondary arterial road ramp, branch road and residential road.

[0015] Furthermore, in one embodiment of the present invention, the step of constructing speed transition probability matrices for multiple traffic scenarios based on the vehicle driving dataset specifically includes: The vehicle speed range is determined based on the vehicle driving dataset, and the vehicle speed range is discretized into multiple speed intervals according to a preset speed interval. Travel records are obtained from the vehicle driving dataset, and the travel time, travel area and road type are determined based on the GPS data corresponding to the travel records, thereby determining the traffic scenario type corresponding to the travel records; The vehicle driving dataset is divided into multiple travel record sets for different traffic scenarios based on the traffic scenario type. Based on the travel record set, determine multiple speed pairs for adjacent moments in the corresponding traffic scenario, and calculate the speed transition probability of the vehicle speed changing from the first speed interval to the second speed interval at adjacent moments based on the speed pairs. The speed transition probability matrix for the corresponding traffic scenario is determined based on the speed transition probability.

[0016] Furthermore, in one embodiment of the present invention, the step of dividing the current journey of the target vehicle into multiple sub-journeys according to the traffic scenario and determining the speed transition probability matrix corresponding to each sub-journey specifically includes: Obtain GPS navigation information corresponding to the current journey of the target vehicle, determine multiple traffic scenarios through which the target vehicle passes based on the GPS navigation information, and divide the current journey into multiple sub-journeys based on the multiple traffic scenarios through which the target vehicle passes. The corresponding speed transition probability matrix is ​​determined based on the traffic scenario corresponding to each sub-trip.

[0017] Furthermore, in one embodiment of the present invention, the step of predicting the speed of the target vehicle at the next moment based on the initial speed of the target vehicle, the speed transition probability matrix, and preset constraint rules, to obtain the speed estimation sequence of each of the sub-trips, specifically includes: Determine the travel distance for each sub-trip and initialize the cumulative estimated distance; Based on the starting speed, the speed of the target vehicle at the next moment is predicted based on the speed transition probability matrix corresponding to the current sub-trip and the preset high-speed preference constraint rule, low-speed jump constraint rule, and tail deceleration constraint rule, and the cumulative estimated distance is updated until the cumulative estimated distance is equal to the travel distance of the current sub-trip, thus obtaining the speed estimation sequence of the current sub-trip; The cumulative estimated distance is reset, and the end speed of the previous sub-trip is used as the new starting speed. Based on the speed transition probability matrix corresponding to the next sub-trip and the preset high-speed preference constraint rule, low-speed jump constraint rule, and tail deceleration constraint rule, the speed estimation sequence of the next sub-trip is predicted. The high-speed preference constraint rule is as follows: if the road type of the current sub-trip is a highway / urban expressway, or the travel time of the current sub-trip is during off-peak hours, or the travel area of ​​the current sub-trip is outside the main urban area, the probability weight of the high-speed interval in the corresponding speed transition probability matrix is ​​increased. The low-speed detachment constraint rule is as follows: if the vehicle speed is in a low-speed state for a preset duration, the corresponding detachment speed is determined according to the road type of the current sub-trip, and the vehicle speed is corrected to the detachment speed. The tail deceleration constraint rule is as follows: if the current sub-trip is the last sub-trip of the current trip, and the cumulative estimated distance reaches a preset proportion of the travel distance of the current sub-trip, the current speed of the target vehicle is uniformly decelerated according to a preset acceleration until the vehicle speed drops to 0.

[0018] Furthermore, in one embodiment of the present invention, the step of smoothing, correcting, and concatenating the speed estimation sequence to obtain the vehicle speed time-series data corresponding to the current journey of the target vehicle specifically includes: The speed estimation sequence of each sub-journey is smoothed using exponential smoothing. The speed estimation sequence is scaled and corrected based on the distance error of each sub-journey; The speed estimation sequence is spliced ​​together according to the order of each sub-journey to obtain the vehicle speed time series data.

[0019] On the other hand, embodiments of the present invention provide a vehicle speed estimation device based on transition probability matrix and rule constraint optimization, comprising: The data acquisition module is used to acquire GPS data during vehicle operation and calculate the corresponding speed and acceleration data based on the GPS data. The speed transition probability matrix construction module is used to preprocess the GPS data, the speed data, and the acceleration data to obtain a vehicle driving dataset, and to construct speed transition probability matrices for multiple traffic scenarios based on the vehicle driving dataset. The sub-trip segmentation module is used to divide the current trip of the target vehicle into multiple sub-trips according to the traffic scenario, and determine the speed transition probability matrix corresponding to each sub-trip; The speed estimation module is used to predict the speed of the target vehicle at the next moment based on the initial speed of the target vehicle, the speed transition probability matrix, and preset constraint rules, so as to obtain the speed estimation sequence of each sub-journey. The estimation sequence processing module is used to smooth, correct, and concatenate the speed estimation sequence to obtain the vehicle speed time series data corresponding to the current journey of the target vehicle.

[0020] On the other hand, embodiments of the present invention provide an electronic device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above-described vehicle speed estimation method based on transition probability matrix and rule constraint optimization.

[0021] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the above-described vehicle speed estimation method based on transition probability matrix and rule constraint optimization.

[0022] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described vehicle speed estimation method based on transition probability matrix and rule constraint optimization.

[0023] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention: This invention acquires GPS data during vehicle travel, calculates corresponding speed and acceleration data based on the GPS data, preprocesses the GPS, speed, and acceleration data to obtain a vehicle travel dataset, and constructs speed transition probability matrices for multiple traffic scenarios based on the vehicle travel dataset. The current journey of the target vehicle is divided into multiple sub-journeys according to the traffic scenarios, and the speed transition probability matrix corresponding to each sub-journey is determined. Based on the target vehicle's initial speed, the speed of the target vehicle at the next moment is predicted based on the speed transition probability matrix and preset constraint rules, resulting in speed estimation sequences for each sub-journey. Speed ​​estimation sequences are then smoothed, corrected, and concatenated to obtain the vehicle speed time-series data corresponding to the target vehicle's current journey. This invention extracts speed change probability features under different traffic scenarios to form corresponding speed transition probability matrices, and performs vehicle speed estimation at various moments based on these speed transition probability matrices, improving the accuracy of vehicle speed estimation. Through rule constraint optimization and speed smoothing correction, it overcomes problems such as long-distance error accumulation, speed fluctuations, and low-speed state stagnation in speed estimation, exhibiting strong robustness across different travel distances and applicable to vehicle speed estimation for different distances in various traffic scenarios. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating the steps of a vehicle speed estimation method based on transition probability matrix and rule constraint optimization provided in an embodiment of the present invention; Figure 2 A schematic diagram illustrating the principle of the vehicle speed estimation method provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the spatial distribution of vehicle GPS data provided in an embodiment of the present invention; Figure 4A schematic diagram of the speed transition probability matrix in the main urban area provided in an embodiment of the present invention; Figure 5 A schematic diagram of the non-main urban area speed transfer probability matrix provided in an embodiment of the present invention; Figure 6 This is a schematic diagram comparing the speed estimation sequence of the sub-stroke with the actual value provided in an embodiment of the present invention; Figure 7 A comparative diagram showing the velocity estimation results of different velocity estimation methods provided in the embodiments of the present invention; Figure 8 A schematic diagram showing the performance comparison of different speed estimation methods provided in embodiments of the present invention over different travel distances; Figure 9 A structural block diagram of a vehicle speed estimation device based on transition probability matrix and rule constraint optimization provided in an embodiment of the present invention; Figure 10 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0028] The vehicle speed estimation method based on transition probability matrix and rule constraint optimization provided in this invention can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vehicle speed estimation method based on transition probability matrix and rule constraint optimization, but is not limited to the above forms.

[0029] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0030] It should be noted that in various specific embodiments of the present invention, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of the present invention require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of the embodiments of the present invention acquired.

[0031] Reference Figure 1This invention provides a vehicle speed estimation method based on transition probability matrix and rule constraint optimization, specifically including the following steps: S101. Acquire GPS data during vehicle operation, and calculate the corresponding speed and acceleration data based on the GPS data; S102. Perform data preprocessing on GPS data, speed data, and acceleration data to obtain vehicle driving dataset, and construct speed transition probability matrices for multiple traffic scenarios based on the vehicle driving dataset. S103. Divide the current journey of the target vehicle into multiple sub-journeys according to the traffic scenario, and determine the speed transition probability matrix corresponding to each sub-journey; S104. Based on the initial speed of the target vehicle, predict the speed of the target vehicle at the next moment according to the speed transition probability matrix and the preset constraint rules, and obtain the speed estimation sequence of each sub-journey. S105. Perform speed smoothing, correction, and sequence splicing on the speed estimation sequence to obtain the vehicle speed time series data corresponding to the current journey of the target vehicle.

[0032] Specifically, this embodiment of the invention extracts the speed change probability features under different traffic scenarios to form a corresponding speed transition probability matrix. Based on this speed transition probability matrix, vehicle speed is estimated at each time point, which improves the accuracy of vehicle speed estimation. Through rule constraint optimization and speed smoothing correction, it overcomes problems such as long-distance error accumulation, speed fluctuation, and low-speed state stagnation in speed estimation. It has strong robustness under different travel distances and is suitable for vehicle speed estimation of different distances under various traffic scenarios.

[0033] like Figure 2 The diagram shown is a schematic representation of the vehicle speed estimation method provided in this embodiment of the invention, illustrating the vehicle's speed from its starting point (speed). ) to the destination (speed) The velocity estimation process is described below in conjunction with... Figure 2 The vehicle speed estimation method of this invention will be fully described in this embodiment.

[0034] As an optional implementation, the GPS data includes the vehicle ID, vehicle latitude and longitude coordinates, and the time of data collection. Speed ​​and acceleration data are calculated using the following formula:

[0035]

[0036]

[0037] in, express Time's up The distance the vehicle traveled at any given time. Represents the Earth's radius. express The vehicle's latitude and longitude coordinates at that moment. express The vehicle's latitude and longitude coordinates at that moment. express Vehicle speed at any given moment express Vehicle acceleration at any moment express Time's up The time interval between moments.

[0038] Specifically, high-resolution GPS data is acquired during vehicle operation, with data fields including vehicle number, collection date, collection time, longitude, and latitude. Latitude and longitude coordinates recorded from two adjacent data points and Calculate the vehicle's travel distance using the Haversine formula. The calculation formula is as follows:

[0039] In the formula, r is the Earth's radius, with a default value of 6378 km; Calculate the speed corresponding to each data record and acceleration The calculation formula is as follows:

[0040]

[0041] In the formula, T is the fixed time interval for GPS data acquisition.

[0042] As an optional implementation, GPS data, speed data, and acceleration data are preprocessed to obtain a vehicle driving dataset, which specifically includes: S1021. Perform outlier detection on GPS data, speed data, and acceleration data, remove GPS data with abnormal latitude and longitude, and correct speed data with abnormal speed and acceleration data with abnormal acceleration. S1022. Identify the vehicle's motion state based on the speed data, and divide the GPS data, speed data, and acceleration data into multiple travel records based on the identification results; S1023. Obtain the OSM city road network map, match GPS data with the nearest road segment based on the nearest neighbor matching method, and remove GPS data, speed data and acceleration data where the distance between the vehicle's latitude and longitude coordinates and the matched road segment is greater than a preset threshold. S1024. Summarize the GPS data, speed data, and acceleration data corresponding to the travel records based on the vehicle ID to obtain the vehicle driving dataset.

[0043] Specifically, GPS data, velocity data, and acceleration data undergo data preprocessing, including outlier handling, motion state identification, and map matching; for latitude and longitude anomalies, if the longitude or latitude field has a value of -99, the record is removed; for velocity and acceleration anomalies, if... and ,when At that time, ;if or ,but If a vehicle's speed is below 5 km / h for 5 consecutive minutes, the vehicle's movement status is identified as a stopped state to distinguish between two consecutive trips. A trip number field is added to the data record. An OSM city road network map is obtained. Since the nearest neighbor matching method is fast in large-scale map matching scenarios but easily affected by noise, the nearest neighbor matching method is used to match GPS data with the nearest road segment, and data records where the distance between the GPS point and the matched road segment is greater than 50m are removed. Based on the vehicle number, the preprocessed data results are divided into training and testing sets.

[0044] Since vehicle-mounted sensor GPS signals may be affected by factors such as building obstruction, weather changes, and multipath effects, resulting in position drift or data loss, it is necessary to process outliers in vehicle GPS data to avoid interference from erroneous data in subsequent speed estimation. This includes mapping GPS points to the road network through map matching to correct positioning deviations, and identifying vehicle parking status to exclude vehicle parking data that is irrelevant to speed estimation.

[0045] As an optional implementation method, traffic scenarios are further divided according to travel time, travel area, and road type. Travel time includes peak hours, off-peak hours, and off-peak hours; travel area includes main urban area and non-main urban area; and road type includes urban expressway, urban expressway ramp, highway, highway ramp, arterial road, arterial road ramp, secondary arterial road, secondary arterial road ramp, branch road, and residential road.

[0046] Specifically, under the combined effects of road design, traffic control, user behavior, and environmental factors, the characteristics of vehicle speed changes vary significantly across different traffic scenarios. A Markov chain is a stochastic process with Markov properties, its core feature being memorylessness, meaning the probability of transitioning from the current state to the next state depends only on the current state and is independent of previous states.

[0047] Speed ​​change can be viewed as a stochastic process with Markov properties, where the speed of a vehicle at the next moment depends only on its speed at the current moment. Therefore, the vehicle speed transition probability matrix can be used to characterize the vehicle speed change features in different traffic scenarios.

[0048] According to the embodiments of the present invention, traffic scenarios are divided into 60 types based on travel time, travel area and road type. Travel time includes peak period, off-peak period and flat period. Travel area includes main urban area and non-main urban area. Road type includes 10 types, namely urban expressway, urban expressway ramp, highway, highway ramp, main road, main road ramp, secondary road, secondary road ramp, branch road and residential road.

[0049] As an optional implementation, a speed transition probability matrix for multiple traffic scenarios is constructed based on the vehicle driving dataset, specifically including: S1025. Determine the range of vehicle speed values ​​based on the vehicle driving dataset, and discretize the range of vehicle speed values ​​into multiple speed intervals according to the preset speed interval intervals. S1026. Obtain travel records from the vehicle driving data set, and determine the travel time, travel area and road type based on the GPS data corresponding to the travel records, thereby determining the traffic scenario type corresponding to the travel records. S1027. Divide the vehicle driving dataset into multiple travel record sets for different traffic scenarios based on the type of traffic scenario. S1028. Determine the speed tuples of multiple adjacent moments in the corresponding traffic scenario based on the travel record set, and calculate the speed transition probability of the vehicle speed changing from the first speed interval to the second speed interval at adjacent moments based on the speed tuples. S1029. Determine the speed transition probability matrix for the corresponding traffic scenario based on the speed transition probability.

[0050] Specifically, based on the characteristics of the training set data, the speed range is set to [0, V], and the speed interval is set to 1 km / h, discretizing the speed range into V speed intervals. Data records for each trip are extracted from the training set based on the trip number field. To ensure the speed continuity of adjacent records, the time difference between adjacent data records is calculated, and data records with a time difference exceeding the sampling interval T are filtered out. The trip time period is determined based on the GPS data collection time, the trip area is determined based on latitude and longitude, and the road type is determined based on the road segment matched by the GPS location. The training set data is then classified according to traffic scenarios. Under each traffic scenario, when the current speed belongs to... When the interval is reached, the velocity at the next moment belongs to... The probability of an interval can be calculated using the following formula:

[0051] In the formula, For vehicles from speed range Shift to speed range The probability, Indicates the vehicle's speed range Shift to speed range The number of samples; The speed transition probability matrix for each traffic scenario is generated as shown in the following formula:

[0052] In the formula, P is the velocity transition probability matrix.

[0053] As a further optional implementation, the current journey of the target vehicle is divided into multiple sub-journeys according to the traffic scenario, and the speed transition probability matrix corresponding to each sub-journey is determined, specifically including: S1031. Obtain the GPS navigation information corresponding to the current trip of the target vehicle, determine multiple traffic scenarios through which the target vehicle passes based on the GPS navigation information, and divide the current trip into multiple sub-trips based on the multiple traffic scenarios through which the target vehicle passes. S1032. Determine the corresponding speed transition probability matrix based on the traffic scenario corresponding to each sub-trip.

[0054] As a further optional implementation, based on the initial speed of the target vehicle, the speed of the target vehicle at the next moment is predicted according to the speed transition probability matrix and preset constraint rules, resulting in a speed estimation sequence for each sub-journey, which specifically includes: S1041. Determine the travel distance for each sub-trip and initialize the cumulative estimated distance; S1042. Based on the starting speed, predict the speed of the target vehicle at the next moment according to the speed transition probability matrix corresponding to the current sub-trip and the preset high-speed preference constraint rule, low-speed jump constraint rule, and tail deceleration constraint rule, and update the cumulative estimated distance until the cumulative estimated distance is equal to the travel distance of the current sub-trip, and obtain the speed estimation sequence of the current sub-trip. S1043. Reset the cumulative estimated distance, take the end speed of the previous sub-trip as the new starting speed, and predict the speed estimation sequence of the next sub-trip based on the speed transition probability matrix corresponding to the next sub-trip and the preset high-speed preference constraint rule, low-speed jump constraint rule, and tail deceleration constraint rule. The high-speed preference constraint rule is as follows: if the road type of the current sub-trip is a highway / urban expressway, or the travel time of the current sub-trip is during off-peak hours, or the travel area of ​​the current sub-trip is outside the main urban area, the probability weight of the high-speed interval in the corresponding speed transition probability matrix is ​​increased. The low-speed detachment constraint rule is as follows: if the vehicle speed is in a low-speed state for a preset time, the corresponding detachment speed is determined according to the road type of the current sub-trip, and the vehicle speed is corrected to the detachment speed; The tail deceleration constraint rule is as follows: if the current sub-trip is the last sub-trip of the current trip, and the cumulative estimated distance reaches a preset proportion of the travel distance of the current sub-trip, the current speed of the target vehicle is uniformly decelerated according to the preset acceleration until the vehicle speed drops to 0.

[0055] Specifically, a single vehicle trip is divided into several sub-trips according to traffic scenario classification, and the travel distance corresponding to each sub-trip is calculated based on the latitude and longitude records of the beginning and end data. Initialize cumulative estimated distance When estimating the speed for each sub-journey, a high-speed preference mechanism, a low-speed detachment mechanism, and a tail deceleration mechanism are added as constraint rules.

[0056] High-speed preference mechanism: If the current traffic scenario is a highway or urban expressway, or the travel time is during off-peak hours, or the travel area is outside the main urban area, then the probability weight of the high-speed interval (>60km / h) in the corresponding speed transition probability matrix will be increased. Low-speed disengagement mechanism: If the vehicle speed remains at a low speed for one minute continuously, a disengagement speed is set; the low-speed limit for highways and urban expressways is 40 km / h, and the disengagement speed is... The speed limit for other road types is 10 km / h, and the speed limit for hopping off is... ; Tail deceleration mechanism: Estimates the velocity sequence for the last sub-stroke and accumulates the estimated distance. At that time, the vehicle was traveling at -1.5 m / s2 The vehicle decelerates uniformly to 0 km / h, and its speed at the next moment... .

[0057] Since vehicles travel at high speeds for a large proportion of their journeys on highways, urban expressways, during off-peak hours, or in non-urban areas, this study strengthens the high-speed preference of vehicles in these traffic scenarios by increasing the probability weight of high-speed intervals in the speed transition probability matrix, thereby improving the fit of the speed estimation sequence to the actual driving conditions. Furthermore, by implementing a low-speed jump mechanism, the study avoids vehicles remaining in low-speed intervals for extended periods, reducing the error in the speed estimation sequence. Finally, by implementing a tail-end deceleration mechanism, the study ensures that the speed estimation sequence drops to 0 km / h at the end of the journey, improving the accuracy of the speed estimation sequence.

[0058] Based on the constraints described above, the speed at the next moment is estimated according to the current speed and the speed transition probability matrix of the current traffic scenario, and the cumulative estimated distance is updated synchronously until... , The update formula is as follows:

[0059] The ending speed of the previous sub-trip is used as the starting speed of the next sub-trip, and the speed of the next sub-trip is estimated accordingly. This process is repeated until the speed estimation sequence for each sub-trip is obtained. .

[0060] As a further optional implementation, the speed estimation sequence is smoothed, corrected, and spliced ​​to obtain the vehicle speed time-series data corresponding to the current journey of the target vehicle, specifically including: S1051. The speed estimation sequence of each sub-journey is smoothed by exponential smoothing. S1052. Scaling and correcting the speed estimation sequence based on the distance error of each sub-journey; S1053. The speed estimation sequence is spliced ​​according to the order of each sub-journey to obtain the vehicle speed time series data.

[0061] Specifically, to improve sequence stability, exponential smoothing is used to smooth the velocity estimation sequences of each sub-journey, as shown in the following formula:

[0062] In the formula, These are the preset weighting coefficients. express The smoothing value at time, express The original estimate at time [time] express The smoothing value at any given time; To improve the accuracy of the velocity estimation sequence, the velocity estimation sequence is scaled according to the distance error of each sub-journey, as shown in the following formula:

[0063]

[0064] In the formula, This is the distance error coefficient; For the speed estimation sequence of the i-th sub-journey, Estimate the sequence length for the speed of sub-strokes; By connecting the speed estimation sequences of all sub-trips, the time-series data of vehicle speed corresponding to a single trip is finally obtained.

[0065] The method steps of the embodiments of the present invention have been described above. The vehicle speed estimation method of the present invention will be further described below with reference to a specific embodiment.

[0066] The data source for this embodiment of the invention is GPS data of 20 taxis traveling in a certain city of a certain province from June 24 to August 25, 2024. The sampling interval is 2 seconds, and a total of 38,440,201 original GPS data records are obtained. The data fields include vehicle number "DEVICE_ID", collection date "DATE", collection time "TIME", longitude "LONGITUDE", and latitude "LATITUDE".

[0067] Figure 3 An example of the spatial distribution map of vehicle GPS data in August in an embodiment of the present invention is shown, which shows that the example vehicle mainly travels in the main urban area of ​​the city.

[0068] First, the vehicle's travel distance, speed, and acceleration are calculated based on GPS data. Then, the GPS data, speed data, and acceleration data are preprocessed, including outlier handling, motion state recognition, and map matching.

[0069] In this embodiment of the invention, GPS data from 19 vehicles is used as the training set, with a total of 36,043,513 original data entries and a valid data ratio of 88.00%; GPS data from the remaining vehicle is used as the test set, with a total of 2,396,688 original data entries and a valid data ratio of 90.58%.

[0070] Calculate the speed transition probability matrix for different traffic scenarios. In this embodiment of the invention, based on the characteristics of the training set data, the speed value range is [0, 150]. Extract data records of each vehicle trip from the training set, calculate the time difference between adjacent data records, and filter data records whose time difference exceeds the sampling interval of 2 seconds. When calculating the speed transition probability matrix, the weighting coefficient α is set to 0.5.

[0071] like Figure 4 The diagram shown is a schematic representation of the speed transition probability matrix (after logarithmic transformation) in the main urban area according to an embodiment of the present invention, representing (a) off-peak period, (b) peak period, and (c) off-peak period, respectively. Figure 5 The diagram shows a non-main urban area speed transfer probability matrix (after logarithmic transformation) in an embodiment of the present invention, which also represents (a) off-peak period, (b) peak period and (c) off-peak period. The darker the color, the greater the transfer probability.

[0072] According to the classification of traffic scenarios, a single trip of a vehicle in the training set is divided into several sub-trips, and the travel distance corresponding to each sub-trip is calculated based on the latitude and longitude recorded at the beginning and end of the sub-trip. Under the constraints of the high-speed preference mechanism, the low-speed jump mechanism, and the tail deceleration mechanism, the speed of each sub-trip is estimated.

[0073] The vehicle speed estimation sequence from step S104 is smoothed using exponential smoothing, with a weighting coefficient α of 0.5. The speed estimation sequence is then scaled based on the distance error of each sub-trip. By concatenating the speed estimation sequences of all sub-trips, the speed estimation sequence for a single vehicle trip can be obtained.

[0074] like Figure 6 The diagram shown is a comparison between the sub-stroke speed estimation sequence and the true value in an embodiment of the present invention. It can be seen that in this embodiment of the present invention, after exponential smoothing and distance error adjustment, the noise of the speed estimation sequence is reduced and more stable, and the distance error is smaller.

[0075] To evaluate the performance of the vehicle speed estimation method, this embodiment of the invention selects speed absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) as evaluation indicators, and the calculation formulas are as follows:

[0076]

[0077]

[0078] In the formula, To estimate the speed, This represents the actual speed. Based on the speed probability transition matrix constructed according to embodiments of the present invention, speed estimation is performed on the test set according to the aforementioned speed estimation steps. The performance of the method of the present invention on the test set is observed by comparing it with other vehicle speed estimation methods.

[0079] like Figure 7The following are examples of velocity estimation sequences provided by different velocity estimation methods in the embodiments of the present invention: (a) Proposed, (b) SA&SO, (c) MCMC&SO, and (d) MCMC. It can be seen that the velocity estimation sequence of the present invention has good velocity continuity and smoothness of velocity change.

[0080] The performance evaluation results of different velocity estimation methods are shown in Table 1 below.

[0081] Table 1

[0082] The results in Table 1 show that the MAE, MSE, and MAPE indices of this invention are superior to other baseline methods, indicating that this invention can improve the accuracy of velocity estimation.

[0083] Furthermore, the total distance error and its percentage error are calculated to compare the performance of each method over different travel distances. The calculation formula is as follows:

[0084]

[0085] In the formula, D is the estimated total travel distance, which is the sum of the sub-trip distances; D is the actual total travel distance.

[0086] like Figure 8 The diagram shows a performance comparison of different speed estimation methods at different travel distances in the embodiments of the present invention, including (a) a comparison of MAPE and (b) a comparison of average distance error. It can be seen that even as the travel distance increases, the MAPE, total distance error and percentage error of the present invention are low and remain relatively stable. This indicates that the present invention can overcome the problem of error accumulation, has good robustness at different travel distances, and is suitable for long-distance speed estimation scenarios.

[0087] It can be recognized that the embodiments of the present invention extract the speed change probability features under different traffic scenarios to form corresponding speed transition probability matrices. Based on these speed transition probability matrices, vehicle speed estimation at various times is performed, improving the accuracy of vehicle speed estimation. Through rule-based constraint optimization and speed smoothing correction, problems such as long-distance error accumulation, speed fluctuations, and low-speed stagnation in speed estimation are overcome. The invention exhibits strong robustness across different travel distances and is applicable to vehicle speed estimation for various distances in multiple traffic scenarios. Compared with existing technologies, the present invention performs well in different traffic scenarios and under different travel distances.

[0088] Reference Figure 9This invention provides a vehicle speed estimation device based on transition probability matrix and rule constraint optimization, comprising: The data acquisition module is used to acquire GPS data during vehicle operation and calculate the corresponding speed and acceleration data based on the GPS data. The speed transition probability matrix construction module is used to preprocess GPS data, speed data, and acceleration data to obtain vehicle driving datasets, and to construct speed transition probability matrices for multiple traffic scenarios based on the vehicle driving datasets. The sub-trip segmentation module is used to divide the current trip of the target vehicle into multiple sub-trips according to the traffic scenario, and determine the speed transition probability matrix corresponding to each sub-trip; The speed estimation module is used to predict the speed of the target vehicle at the next moment based on the initial speed of the target vehicle, the speed transition probability matrix, and the preset constraint rules, so as to obtain the speed estimation sequence of each sub-journey. The estimation sequence processing module is used to smooth, correct, and concatenate the speed estimation sequence to obtain the vehicle speed time series data corresponding to the current journey of the target vehicle.

[0089] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0090] Reference Figure 10 This invention provides an electronic device, comprising: At least one processor; At least one memory for storing at least one program; When the above-mentioned at least one program is executed by the above-mentioned at least one processor, the above-mentioned at least one processor implements the above-mentioned vehicle speed estimation method based on transition probability matrix and rule constraint optimization.

[0091] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0092] This invention also provides a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the aforementioned vehicle speed estimation method based on transition probability matrix and rule constraint optimization.

[0093] This invention provides a computer-readable storage medium that can execute a vehicle speed estimation method based on transition probability matrix and rule constraint optimization provided in the method embodiments of this invention. It can execute any combination of implementation steps of the method embodiments and has the corresponding functions and beneficial effects of the method.

[0094] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned vehicle speed estimation method based on transition probability matrix and rule constraint optimization.

[0095] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0096] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0097] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0098] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0099] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0100] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0101] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0103] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0104] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0105] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0106] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0107] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A vehicle speed estimation method based on transition probability matrix and rule constraint optimization, characterized in that, Includes the following steps: The GPS data during the vehicle's operation is acquired, and the corresponding speed and acceleration data are calculated based on the GPS data. The GPS data, the speed data, and the acceleration data are preprocessed to obtain a vehicle driving dataset, and speed transition probability matrices for multiple traffic scenarios are constructed based on the vehicle driving dataset. The current journey of the target vehicle is divided into multiple sub-journeys according to the traffic scenario, and the speed transition probability matrix corresponding to each sub-journey is determined. Based on the initial speed of the target vehicle, the speed of the target vehicle at the next moment is predicted based on the speed transition probability matrix and preset constraint rules, thereby obtaining the speed estimation sequence of each sub-journey; The speed estimation sequence is smoothed, corrected, and spliced ​​to obtain the vehicle speed time series data corresponding to the current journey of the target vehicle; The step of constructing speed transition probability matrices for multiple traffic scenarios based on the vehicle driving dataset specifically includes: The vehicle speed range is determined based on the vehicle driving dataset, and the vehicle speed range is discretized into multiple speed intervals according to a preset speed interval. Travel records are obtained from the vehicle driving dataset, and the travel time, travel area and road type are determined based on the GPS data corresponding to the travel records, thereby determining the traffic scenario type corresponding to the travel records; The vehicle driving dataset is divided into multiple travel record sets for different traffic scenarios based on the traffic scenario type. Based on the travel record set, determine multiple speed pairs for adjacent moments in the corresponding traffic scenario, and calculate the speed transition probability of the vehicle speed changing from the first speed interval to the second speed interval at adjacent moments based on the speed pairs. Determine the speed transition probability matrix for the corresponding traffic scenario based on the speed transition probability; The step of predicting the speed of the target vehicle at the next moment based on the initial speed of the target vehicle, the speed transition probability matrix, and preset constraint rules, to obtain the speed estimation sequence for each sub-journey, specifically includes: Determine the travel distance for each sub-trip and initialize the cumulative estimated distance; Based on the starting speed, the speed of the target vehicle at the next moment is predicted based on the speed transition probability matrix corresponding to the current sub-trip and the preset high-speed preference constraint rule, low-speed jump constraint rule, and tail deceleration constraint rule, and the cumulative estimated distance is updated until the cumulative estimated distance is equal to the travel distance of the current sub-trip, thus obtaining the speed estimation sequence of the current sub-trip; The cumulative estimated distance is reset, and the end speed of the previous sub-trip is used as the new starting speed. Based on the speed transition probability matrix corresponding to the next sub-trip and the preset high-speed preference constraint rule, low-speed jump constraint rule, and tail deceleration constraint rule, the speed estimation sequence of the next sub-trip is predicted. The high-speed preference constraint rule is as follows: if the road type of the current sub-trip is a highway / urban expressway, or the travel time of the current sub-trip is during off-peak hours, or the travel area of ​​the current sub-trip is outside the main urban area, the probability weight of the high-speed interval in the corresponding speed transition probability matrix is ​​increased. The low-speed detachment constraint rule is as follows: if the vehicle speed is in a low-speed state for a preset duration, the corresponding detachment speed is determined according to the road type of the current sub-trip, and the vehicle speed is corrected to the detachment speed. The tail deceleration constraint rule is as follows: if the current sub-trip is the last sub-trip of the current trip, and the cumulative estimated distance reaches a preset proportion of the travel distance of the current sub-trip, the current speed of the target vehicle is uniformly decelerated according to a preset acceleration until the vehicle speed drops to 0.

2. The vehicle speed estimation method based on transition probability matrix and rule constraint optimization according to claim 1, characterized in that, The GPS data includes the vehicle ID, vehicle latitude and longitude coordinates, and the time of data collection. The speed data and the acceleration data are calculated using the following formula: in, express Time's up The distance the vehicle traveled at any given time. Represents the Earth's radius. express The vehicle's latitude and longitude coordinates at that moment. express The vehicle's latitude and longitude coordinates at that moment. express Vehicle speed at any given moment express Vehicle acceleration at any moment express Time's up The time interval between moments.

3. The vehicle speed estimation method based on transition probability matrix and rule constraint optimization according to claim 1, characterized in that, The process of preprocessing the GPS data, the speed data, and the acceleration data to obtain a vehicle driving dataset specifically includes: Anomaly detection is performed on the GPS data, the speed data, and the acceleration data. GPS data with abnormal latitude and longitude is removed, and speed data and acceleration data with abnormal acceleration are corrected. The vehicle motion status is identified based on the speed data, and the GPS data, speed data, and acceleration data are divided into multiple travel records based on the identification results. Obtain an OSM city road network map, match the GPS data with the nearest road segment based on the nearest neighbor matching method, and remove the GPS data, speed data, and acceleration data where the distance between the vehicle's latitude and longitude coordinates and the matched road segment is greater than a preset threshold; The GPS data, speed data, and acceleration data corresponding to the travel records are summarized based on the vehicle ID to obtain the vehicle driving dataset.

4. The vehicle speed estimation method based on transition probability matrix and rule constraint optimization according to claim 1, characterized in that: Traffic scenarios are categorized based on travel time, travel area, and road type. Travel time includes peak hours, off-peak hours, and non-peak hours. Travel area includes the main urban area and non-main urban area. Road type includes urban expressways, urban expressway ramps, highways, highway ramps, arterial roads, arterial road ramps, secondary arterial roads, secondary arterial road ramps, branch roads, and residential roads.

5. The vehicle speed estimation method based on transition probability matrix and rule constraint optimization according to claim 1, characterized in that, The step of dividing the current journey of the target vehicle into multiple sub-journeys based on the traffic scenario and determining the speed transition probability matrix corresponding to each sub-journey specifically includes: Obtain GPS navigation information corresponding to the current journey of the target vehicle, determine multiple traffic scenarios through which the target vehicle passes based on the GPS navigation information, and divide the current journey into multiple sub-journeys based on the multiple traffic scenarios through which the target vehicle passes. The corresponding speed transition probability matrix is ​​determined based on the traffic scenario corresponding to each sub-trip.

6. A vehicle speed estimation method based on transition probability matrix and rule constraint optimization according to any one of claims 1 to 5, characterized in that, The process of smoothing, correcting, and concatenating the speed estimation sequence to obtain the vehicle speed time-series data corresponding to the current journey of the target vehicle specifically includes: The speed estimation sequence of each sub-journey is smoothed using exponential smoothing. The speed estimation sequence is scaled and corrected based on the distance error of each sub-journey; The speed estimation sequence is spliced ​​together according to the order of each sub-journey to obtain the vehicle speed time series data.

7. A vehicle speed estimation device based on transition probability matrix and rule constraint optimization, characterized in that, A vehicle speed estimation method based on transition probability matrix and rule constraint optimization as described in any one of claims 1 to 6 includes: The data acquisition module is used to acquire GPS data during vehicle operation and calculate the corresponding speed and acceleration data based on the GPS data. The speed transition probability matrix construction module is used to preprocess the GPS data, the speed data, and the acceleration data to obtain a vehicle driving dataset, and to construct speed transition probability matrices for multiple traffic scenarios based on the vehicle driving dataset. The sub-trip segmentation module is used to divide the current trip of the target vehicle into multiple sub-trips according to the traffic scenario, and determine the speed transition probability matrix corresponding to each sub-trip; The speed estimation module is used to predict the speed of the target vehicle at the next moment based on the initial speed of the target vehicle, the speed transition probability matrix, and preset constraint rules, so as to obtain the speed estimation sequence of each sub-journey. The estimation sequence processing module is used to smooth, correct, and concatenate the speed estimation sequence to obtain the vehicle speed time series data corresponding to the current journey of the target vehicle.

8. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a vehicle speed estimation method based on transition probability matrix and rule constraint optimization as described in any one of claims 1 to 6.

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