Vehicle driving track rendering generation system and method, medium and equipment
Through the vehicle driving trajectory rendering and generation system, sensors and GPS data are used for filtering and deep learning processing, high-precision trajectory data are generated and rendered on the underlying map, solving the problem of single data and insufficient correlation in traditional systems, and improving the navigation function Test efficiency and accuracy.
Patent Information
- Application Number
- CN202411154315.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-22
- Publication Date
- 2024-12-27
AI Technical Summary
The data of the traditional vehicle driving trajectory rendering generation system is single and the correlation between the modules is not strong, so it fails to effectively generate high-precision driving trajectory data, which affects the testing efficiency and accuracy of the navigation function.
Design a vehicle driving trajectory rendering and generation system, including on-board data acquisition unit, data processing unit, data storage unit and trajectory rendering unit, data acquisition through acceleration sensor, gyroscope and GPS locator, and data calibration is performed using filtering and denoising algorithms , The machine learning module generates high-precision trajectory data based on deep learning algorithms and renders it on the underlying map through the graphics library software.
It realizes real-time capture of vehicle driving data, generates high-precision driving trajectory data, improves the testing efficiency and accuracy of navigation functions, and can render key information such as vehicle position, speed and direction in real time.
Smart Images
Figure CN119205983A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a vehicle driving trajectory rendering generation system, method, medium and equipment. Background Art
[0002] With the rapid development of intelligent transportation systems, the accuracy and real-time performance of navigation functions have become the focus of users. The data of the traditional vehicle driving trajectory rendering generation system is single, and the data correlation between the modules of the system is not strong. There is no complete and efficient trajectory generation system for generating vehicle driving trajectories. Therefore, in order to solve this problem, it is necessary to design a vehicle driving trajectory rendering generation system that automatically generates high-precision driving trajectory data, which is of great significance to improve the test efficiency and accuracy of the navigation function. Summary of the invention
[0003] The purpose of the present invention is to overcome the shortcomings of the above-mentioned background technology and to provide a vehicle driving trajectory rendering generation system, method, medium and device, which can process, analyze and render the collected vehicle driving data through a data processing unit and a trajectory rendering unit to form high-precision driving trajectory data.
[0004] In a first aspect, a vehicle driving trajectory rendering generation system is provided, comprising:
[0005] On-vehicle data collection unit, used to collect vehicle driving data;
[0006] A data processing unit, which is in communication with the vehicle-mounted data acquisition unit and is used to generate driving trajectory data according to the vehicle driving data;
[0007] A data storage unit is connected to the data processing unit for storing the driving trajectory data; and
[0008] The trajectory rendering unit is communicatively connected with the data storage unit and is used for rendering the driving trajectory data to obtain trajectory rendering data.
[0009] In some embodiments, the vehicle-mounted data acquisition unit includes:
[0010] An acceleration sensor, used to detect vehicle acceleration data;
[0011] Gyroscope, used to detect vehicle driving posture;
[0012] GPS locator, used to collect vehicle location information and driving speed data.
[0013] In some embodiments, the data processing unit includes a data preprocessing module communicatively connected to the vehicle-mounted data acquisition unit, and a machine learning module communicatively connected to the data preprocessing module;
[0014] The data preprocessing module is used to filter, denoise and calibrate the vehicle driving data based on a filtering algorithm and a denoising algorithm;
[0015] The machine learning module is used to optimize the trajectory data of the pre-processed vehicle driving data based on a deep learning algorithm and generate driving trajectory data.
[0016] In some embodiments, the data storage unit includes a data encryption module communicatively connected to the data processing unit, a cloud storage module communicatively connected to the data encryption module, and a local storage module;
[0017] The data encryption module is used to encrypt the driving trajectory data based on an encryption algorithm;
[0018] The cloud storage module is used to store the encrypted driving trajectory data;
[0019] The local storage module is used to store the encrypted driving trajectory data.
[0020] In some embodiments, the trajectory rendering unit is further used to set the driving trajectory data on an underlying map, and use graphics library software to draw a trajectory of the driving trajectory data on the underlying map.
[0021] In a second aspect, a vehicle driving trajectory rendering generation method is provided, comprising the following steps:
[0022] Control the vehicle-mounted data acquisition unit to collect vehicle driving data;
[0023] Controlling the data processing unit to generate driving trajectory data according to the vehicle driving data;
[0024] Control the data storage unit to store the driving trajectory data;
[0025] The trajectory rendering unit is controlled to render the driving trajectory data to obtain trajectory rendering data.
[0026] In some embodiments, the step of generating driving trajectory data according to the vehicle driving data specifically includes the following steps:
[0027] Performing filtering, denoising and data calibration on the vehicle driving data based on a filtering algorithm and a denoising algorithm;
[0028] The pre-processed vehicle driving data is optimized based on the deep learning algorithm, and the driving trajectory data is generated.
[0029] In some embodiments, the step of rendering the driving trajectory data to obtain trajectory rendering data specifically includes the following steps:
[0030] The driving trajectory data is set on the underlying map, and the driving trajectory data is plotted on the underlying map using graphics library software.
[0031] In a third aspect, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the vehicle driving trajectory rendering generation method as described above is implemented.
[0032] In a fourth aspect, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor implements the vehicle driving trajectory rendering generation method as described above when executing the computer program.
[0033] Compared with the prior art, the present invention can capture vehicle driving data in real time, namely key information such as the position, speed, direction, etc. of the vehicle during driving, and process, analyze and render the collected vehicle driving data through a data processing unit and a trajectory rendering unit to form high-precision driving trajectory data. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Figure 1 It is a structural schematic diagram of a vehicle driving trajectory rendering and generating system of the present invention. DETAILED DESCRIPTION
[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0036] See also Figure 1As shown, an embodiment of the present invention provides a vehicle driving trajectory rendering generation system, comprising: an on-board data acquisition unit, a data processing unit, a data storage unit and a trajectory rendering unit. The on-board data acquisition unit is used to collect vehicle driving data; the data processing unit is connected in communication with the on-board data acquisition unit, and is used to generate driving trajectory data according to the vehicle driving data; the data storage unit is connected in communication with the data processing unit, and is used to store the driving trajectory data; the trajectory rendering unit is connected in communication with the data storage unit, and is used to render the driving trajectory data to obtain trajectory rendering data.
[0037] Specifically, in this embodiment, the present invention can capture vehicle driving data in real time, that is, key information such as the position, speed, direction, etc. of the vehicle during driving, and process, analyze and render the collected vehicle driving data through a data processing unit and a trajectory rendering unit to form high-precision driving trajectory data.
[0038] Optionally, the vehicle-mounted data acquisition unit includes:
[0039] An acceleration sensor, used to detect vehicle acceleration data;
[0040] Gyroscope, used to detect vehicle driving posture;
[0041] GPS locator, used to collect vehicle location information and driving speed data.
[0042] Specifically, in this embodiment,
[0043] GPS locator: A high-precision, multi-band GPS receiver is used, which can receive signals from multiple satellites and calculate the vehicle's current latitude, longitude, altitude and other location information by calculating the distance difference between the satellite signal and the receiver. At the same time, the GPS locator should have good signal tracking capabilities and be able to maintain stable positioning accuracy in areas with weak signals such as urban canyons and tunnels.
[0044] Gyroscope: A gyroscope is a sensor that can measure angular velocity. It senses the rotation of the vehicle around each axis and provides the system with real-time data on the vehicle's attitude (i.e. pitch angle, roll angle, and yaw angle). Use a high-precision, low-noise gyroscope to ensure the accuracy of the measurement results.
[0045] Accelerometer: The accelerometer is used to measure the acceleration of the vehicle in all directions, including the acceleration generated by forward, backward, left and right turns, etc. The accelerometer can indirectly calculate the speed change of the vehicle and calibrate the speed in combination with GPS data.
[0046] After the system is powered on, each sensor is initialized first, including configuring parameters such as working mode, sampling frequency, accuracy level, etc. At the same time, a communication connection with the GPS locator is established to ensure that satellite signals can be received in real time.
[0047] Synchronous sampling: In order to ensure the synchronization and consistency of data, the data acquisition module needs to control the GPS locator, gyroscope and acceleration sensor to collect data at the same time point. This is usually achieved through a microcontroller (MCU) or digital signal processor (DSP), which can accurately control the sampling timing of each sensor.
[0048] Data reading: Read the raw data from each sensor according to the set sampling frequency. For the GPS locator, mainly read the longitude and latitude, speed, timestamp and other information; for the gyroscope and acceleration sensor, read the angular velocity and acceleration values measured by each.
[0049] Optionally, the data processing unit includes a data preprocessing module communicatively connected to the vehicle-mounted data acquisition unit, and a machine learning module communicatively connected to the data preprocessing module;
[0050] The data preprocessing module is used to filter, denoise and calibrate the vehicle driving data based on a filtering algorithm and a denoising algorithm;
[0051] The machine learning module is used to optimize the trajectory data of the pre-processed vehicle driving data based on a deep learning algorithm and generate driving trajectory data.
[0052] Specifically, in this embodiment,
[0053] Filtering technology: Apply digital filtering technology (such as low-pass filter, Kalman filter, etc.) to smooth the noise in GPS and sensor data. Low-pass filter can effectively remove high-frequency noise, while Kalman filter can combine system model and observation data to make optimal estimation of state, further improving data accuracy and stability.
[0054] Denoising algorithm: The denoising algorithm in signal processing technology (such as wavelet transform, median filtering, etc.) is used to process the data of the gyroscope and acceleration sensor to eliminate the noise caused by mechanical vibration, electromagnetic interference, etc.
[0055] GPS and sensor data fusion: Combining the absolute position information provided by GPS and the relative motion information provided by the gyroscope and accelerometer, the vehicle's position, speed, direction and other parameters are calibrated through data fusion algorithms (such as extended Kalman filter, unscented Kalman filter, etc.) to improve data accuracy and reliability.
[0056] The acceleration sensor data is then used to calculate the vehicle's speed through integration operations, and compared and calibrated with the speed data provided by the GPS to reduce errors.
[0057] In the Machine Learning module,
[0058] Anomaly detection: Apply machine learning models (such as isolation forests, support vector machines, etc.) to perform anomaly detection on the collected data, identify and eliminate data points that obviously deviate from normal driving conditions, such as false data when GPS signals are lost, erroneous data when sensors fail, etc.
[0059] Data completion: For missing data due to sensor failure or signal loss, machine learning models (such as time series prediction models, interpolation algorithms, etc.) are used to complete the data to ensure data integrity and continuity.
[0060] Trajectory optimization: Use deep learning technology (such as recurrent neural network RNN, long short-term memory network LSTM, etc.) to learn trajectory data, identify and optimize unreasonable parts of the trajectory (such as sharp turns, sudden stops, etc.), so that the generated driving trajectory is smoother and in line with actual driving habits.
[0061] Feature extraction: Deep learning models are used to automatically extract key features from trajectory data (such as road type, traffic conditions, driving behavior, etc.), providing strong support for subsequent analysis and evaluation.
[0062] Finally, the data after preprocessing and AI algorithm processing are integrated to construct complete driving trajectory data. These data include key information such as vehicle location, speed, direction, acceleration, timestamp, etc.
[0063] Optionally, the data storage unit includes a data encryption module communicatively connected to the data processing unit, a cloud storage module communicatively connected to the data encryption module, and a local storage module;
[0064] The data encryption module is used to encrypt the driving trajectory data based on an encryption algorithm;
[0065] The cloud storage module is used to store the encrypted driving trajectory data;
[0066] The local storage module is used to store the encrypted driving trajectory data.
[0067] Specifically, in this embodiment,
[0068] 1. Data encryption: Before transferring data from the processing module to the storage location, the data is encrypted to ensure the security of the data during transmission. Encryption algorithms such as AES and RSA are used to encrypt the data to prevent unauthorized access.
[0069] Protocol selection: Choose a suitable network communication protocol (such as HTTPS, MQTT, etc.) for data transmission. HTTPS provides an encrypted communication channel, which is suitable for transmitting sensitive data; while MQTT is a lightweight message transmission protocol suitable for communication between IoT devices. If the system involves a large amount of real-time data transmission, MQTT may be a more suitable choice.
[0070] 2. Cloud Storage
[0071] Select a cloud service provider: Select a suitable cloud service provider (such as AWS, Azure, Alibaba Cloud, etc.) according to your needs, and create corresponding cloud service instances (such as cloud database, object storage, etc.).
[0072] Data upload: Upload the processed data to the cloud storage service through the API interface or SDK. During the upload process, you can configure the data storage format, partition strategy, index strategy, etc. to optimize the data storage efficiency and query performance.
[0073] Data backup and recovery: Back up data stored in the cloud regularly to prevent data loss or damage. At the same time, develop a data recovery plan so that data can be quickly restored when needed.
[0074] 3. Local server storage
[0075] Hardware configuration: Select or build a local server with sufficient storage space and computing power to ensure that it can meet the needs of data storage and query.
[0076] Database design: Design the database architecture according to the characteristics of the data and query requirements, including table structure, indexes, stored procedures, etc. Use a relational database (such as MySQL, PostgreSQL) or a non-relational database (such as MongoDB, Redis) to store data, depending on the structure of the data and the query mode.
[0077] Data storage: Store the processed data in the database through batch import or real-time writing. At the same time, regularly perform database maintenance tasks such as index reconstruction and defragmentation to maintain database performance and stability.
[0078] 4. Data security and privacy protection
[0079] Access control: strictly control access to data to ensure that only authorized users or applications can access stored data. Ways to implement access control include identity authentication, permission management, etc.
[0080] Data encryption: During the storage process, sensitive data can be encrypted and stored to improve data security. Encryption technologies such as transparent data encryption (TDE) and column-level encryption are used to encrypt data to ensure that data is not accessed by unauthorized persons during storage.
[0081] Audit and monitoring: Audit and monitor the data storage and access process, and record data operation logs and access records to facilitate tracing and investigation when security incidents occur.
[0082] Optionally, the trajectory rendering unit is further used to set the driving trajectory data on an underlying map, and use graphics library software to draw a trajectory of the driving trajectory data on the underlying map.
[0083] Specifically, in this embodiment,
[0084] 1. User interface design
[0085] Track selection interface: Design an intuitive and easy-to-use interface that allows users to easily select the track data they want to replay. The interface should display a list or map view of track data, and users can filter by time range, vehicle number, route name, etc.
[0086] Control buttons: Control buttons such as play, pause, stop, fast forward, and fast rewind are provided on the playback interface so that users can control the playback process of the track as needed.
[0087] Parameter settings: Allow users to set playback speed, viewing angle, whether to display vehicle information (such as speed, direction) and other parameters to improve playback flexibility and user experience.
[0088] 2. Data reading and analysis
[0089] Data interface: connects with the data storage module and provides an interface for reading trajectory data. According to the user's selection on the interface, the corresponding trajectory data is retrieved from the database.
[0090] Data parsing: Parse the read trajectory data and extract key information, such as location coordinates (latitude and longitude), timestamp, speed, direction, etc. This information will be used for subsequent simulation and rendering.
[0091] 3. Trajectory simulation and rendering
[0092] Map integration: Select a suitable map service (such as Google Maps, OpenStreetMap, etc.) as the underlying map and overlay the trajectory data on the map for display.
[0093] Trajectory drawing: Draw the trajectory on the map using a graphics library (such as WebGL, OpenGL, Canvas, etc.). According to the location coordinates and timestamp information in the trajectory data, calculate the trajectory points of the trajectory and connect them to form a smooth curve.
[0094] Dynamic simulation: simulate the vehicle's driving process based on the speed and timestamp information in the trajectory data. Smooth playback of the trajectory is achieved by dynamically changing the display position and time interval of the trajectory points.
[0095] Visual Enhancement: To improve the visual quality of the replay, you can add additional visual elements such as vehicle icons, speed indicators, direction arrows, etc. These elements will be dynamically updated as the track is replayed.
[0096] 4. Interaction and feedback
[0097] Real-time feedback: During playback, the current time point, speed, location and other information are displayed in real time so that users can understand the driving status of the vehicle.
[0098] User interaction: Allow users to interact with the playback interface by clicking, dragging, etc. For example, users can change the playback position by dragging the timeline, or click a point on the map to view detailed information about the point.
[0099] Error handling: If an error or abnormal situation occurs during the process of reading data, parsing data, or rendering trajectories, the error information should be fed back to the user in a timely manner, and corresponding solutions or suggestions should be provided.
[0100] The embodiment of the present invention also provides a method for rendering and generating a vehicle driving trajectory, comprising the following steps:
[0101] Control the vehicle-mounted data acquisition unit to collect vehicle driving data;
[0102] Controlling the data processing unit to generate driving trajectory data according to the vehicle driving data;
[0103] Control the data storage unit to store the driving trajectory data;
[0104] The trajectory rendering unit is controlled to render the driving trajectory data to obtain trajectory rendering data.
[0105] The step of generating driving trajectory data according to the vehicle driving data specifically comprises the following steps:
[0106] Performing filtering, denoising and data calibration on the vehicle driving data based on a filtering algorithm and a denoising algorithm;
[0107] The pre-processed vehicle driving data is optimized based on the deep learning algorithm, and the driving trajectory data is generated.
[0108] The step of rendering the driving trajectory data to obtain trajectory rendering data specifically includes the following steps:
[0109] The driving trajectory data is set on the underlying map, and the driving trajectory data is plotted on the underlying map using graphics library software.
[0110] Therefore, the present invention can capture vehicle driving data in real time, that is, key information such as the position, speed, direction, etc. of the vehicle during driving, and process, analyze and render the collected vehicle driving data through a data processing unit and a trajectory rendering unit to form high-precision driving trajectory data.
[0111] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the method steps of the above method are implemented.
[0112] The present invention implements all or part of the process in the above method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.
[0113] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, all or part of the method steps in the above method are implemented.
[0114] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of a computer device, and uses various interfaces and lines to connect various parts of the entire computer device.
[0115] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, video data, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0116] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, servers or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.
[0117] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), servers, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0118] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0120] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.
Claims
1. A vehicle driving trajectory rendering generation system, characterized in that: include: On-vehicle data collection unit, used to collect vehicle driving data; A data processing unit, which is in communication with the vehicle-mounted data acquisition unit and is used to generate driving trajectory data according to the vehicle driving data; A data storage unit, connected to the data processing unit for storing the driving trajectory data; as well as, The trajectory rendering unit is communicatively connected with the data storage unit and is used for rendering the driving trajectory data to obtain trajectory rendering data.
2. The vehicle driving trajectory rendering generation system according to claim 1, characterized in that: The vehicle-mounted data acquisition unit comprises: An acceleration sensor, used to detect vehicle acceleration data; Gyroscope, used to detect vehicle driving posture; GPS locator, used to collect vehicle location information and driving speed data.
3. The vehicle driving trajectory rendering generation system according to claim 1, characterized in that: The data processing unit includes a data preprocessing module communicatively connected to the vehicle-mounted data acquisition unit, and a machine learning module communicatively connected to the data preprocessing module; The data preprocessing module is used to filter, denoise and calibrate the vehicle driving data based on a filtering algorithm and a denoising algorithm; The machine learning module is used to optimize the trajectory data of the pre-processed vehicle driving data based on a deep learning algorithm and generate driving trajectory data.
4. The vehicle driving trajectory rendering generation system according to claim 1, characterized in that: The data storage unit includes a data encryption module communicatively connected to the data processing unit, a cloud storage module communicatively connected to the data encryption module, and a local storage module; The data encryption module is used to encrypt the driving trajectory data based on an encryption algorithm; The cloud storage module is used to store the encrypted driving trajectory data; The local storage module is used to store the encrypted driving trajectory data.
5. The vehicle driving trajectory rendering generation system according to claim 1, characterized in that: The trajectory rendering unit is also used to set the driving trajectory data on the underlying map and use graphics library software to draw the trajectory of the driving trajectory data on the underlying map.
6. A vehicle driving trajectory rendering generation method, characterized in that: The following steps are involved: Control the vehicle-mounted data acquisition unit to collect vehicle driving data; Controlling the data processing unit to generate driving trajectory data according to the vehicle driving data; Control the data storage unit to store the driving trajectory data; The trajectory rendering unit is controlled to render the driving trajectory data to obtain trajectory rendering data.
7. The vehicle driving trajectory rendering generation method according to claim 6, characterized in that: The step of generating driving trajectory data according to the vehicle driving data specifically comprises the following steps: Performing filtering, denoising and data calibration on the vehicle driving data based on a filtering algorithm and a denoising algorithm; The pre-processed vehicle driving data is optimized based on the deep learning algorithm, and the driving trajectory data is generated.
8. The vehicle driving trajectory rendering generation method according to claim 6, characterized in that: The step of rendering the driving trajectory data to obtain trajectory rendering data specifically includes the following steps: The driving trajectory data is set on the underlying map, and the driving trajectory data is plotted on the underlying map using graphics library software.
9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle driving trajectory rendering generation method according to any one of claims 6 to 8 is implemented.
10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor runs the computer program, the vehicle driving trajectory rendering generation method according to any one of claims 6 to 8 is implemented.