A high-definition double-recording driving navigation operation optimization method and system

Abstract

The present application relates to the technical field of navigation optimization, in particular to a high-definition double-recording driving navigation operation optimization method and system, which can enhance the practicability and reliability of the navigation system, and provide more comprehensive, accurate and timely navigation service for drivers; the method comprises the following steps: obtaining the historical driving experience information of the vehicle driver, and setting the navigation optimization frequency based on the historical driving experience information; based on the navigation optimization frequency, collecting the real-time lane video stream information of the lane where the vehicle is located and the lane in the same direction through the vehicle-mounted high-definition double-recording camera; for each lane, the real-time lane video stream information is subjected to decision feature extraction to obtain the lane real-time condition portrait; the lane real-time condition portrait is input into the pre-trained lane condition evaluation model to obtain the lane real-time condition easy-to-travel index; the distance between the lane and the lane where the vehicle is located is calculated to obtain the lane span.

Description

Technical Field

[0001] This invention relates to the technical field of navigation optimization, and in particular to a high-definition dual-recording vehicle navigation operation optimization method and system. Background Technology

[0002] In modern transportation systems, with the rapid increase in the number of vehicles and the increasing complexity of road environments, drivers face numerous challenges while driving, such as unfamiliar routes, traffic congestion, and unexpected accidents. Therefore, driving navigation systems have become an indispensable part of modern driving.

[0003] Existing driving navigation methods primarily rely on map data and GPS positioning technology to provide drivers with route planning and navigation services. However, while this navigation method, based on static data and simple location information, can provide basic route planning and navigation functions, it cannot reflect the current traffic conditions in real time. Especially on multi-lane roads, drivers often need to determine the optimal lane themselves, which can easily lead to misjudgments. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a high-definition dual-recording driving navigation operation optimization method and system that enhances the practicality and reliability of navigation systems and provides drivers with more comprehensive, accurate, and timely navigation services.

[0005] In a first aspect, the present invention provides a method for optimizing the operation of high-definition dual-recording vehicle navigation, the method comprising:

[0006] Obtain the driver's historical driving experience information and set the navigation optimization frequency based on the historical driving experience information;

[0007] Based on the navigation optimization frequency, real-time lane video stream information of the lane where the vehicle is located and its lanes in the same direction is collected through an in-vehicle high-definition dual-recording camera;

[0008] For each lane, decision features are extracted from the real-time lane video stream information to obtain a real-time lane condition profile;

[0009] The real-time lane condition profile is input into a pre-trained lane condition evaluation model to obtain the lane real-time condition ease-of-drive index.

[0010] Calculate the distance between the lane and the lane where the vehicle is located to obtain the lane span, which represents the number of lane changes required for the vehicle to change lanes from its current lane to the new lane;

[0011] By combining lane span, a comprehensive analysis of the lane's real-time mobility index is conducted to obtain the corresponding lane's driving navigation characteristic index.

[0012] The lane with the highest driving navigation feature index among multiple lanes is selected as the vehicle's optimal driving lane in real time and displayed to the driver.

[0013] Furthermore, the real-time lane condition profile includes quantitative feature values ​​of road surface condition, quantitative feature values ​​of accident condition, vehicle density, non-motorized vehicle density, and pedestrian density.

[0014] Furthermore, the mathematical calculation formula for the lane condition evaluation model is as follows:

[0015]

[0016] Where E represents the lane availability index, w i C represents the weight coefficient of the i-th feature. i Let represent the quantized feature value of the i-th feature, and n represent the total number of features.

[0017] Furthermore, the method for constructing the lane condition evaluation model includes:

[0018] Collect lane video stream data;

[0019] The collected data is cleaned, denoised, and labeled.

[0020] Deep learning models are selected as the basic architecture for the lane condition evaluation model. These deep learning models include decision trees, random forests, neural networks, convolutional neural networks, and recurrent neural networks.

[0021] The preprocessed dataset is divided into a training set and a test set, and the selected model is trained using the training set data.

[0022] The trained model is evaluated using test set data to verify its generalization ability and prediction accuracy; based on the evaluation results, the model is adjusted and optimized.

[0023] The trained lane condition evaluation model is deployed into the vehicle navigation system.

[0024] Furthermore, the formula for calculating the driving navigation feature index is as follows:

[0025] I i =α·E i -β·D i

[0026] Among them, E i The D index indicates the real-time ease of driving of a lane. i Indicates lane span, I i This represents the driving navigation feature index, where α represents the weight of the lane mobility index and β represents the weight of the lane span; α·E iβ·D represents the positive contribution of the lane mobility index to the characteristic index. i This indicates the negative impact of lane span on the characteristic index.

[0027] Furthermore, the method for constructing the real-time lane condition profile includes:

[0028] The received real-time lane video stream is preprocessed, including removing image noise and enhancing contrast;

[0029] Image recognition is performed on the preprocessed lane video stream data to identify lane boundary lines and segment the lanes from the background;

[0030] The detected lanes are numbered and marked;

[0031] Analyze images of the lane area and extract feature values ​​that reflect road surface quality;

[0032] Detect whether there are signs of an accident in the lane and quantify the extent of its impact;

[0033] Count the number and distribution of vehicles in the lanes, and calculate the vehicle density;

[0034] Identify non-motorized vehicles and pedestrians near the lane and calculate their density;

[0035] The extracted decision features are combined to construct a real-time lane condition profile.

[0036] Furthermore, the method for obtaining the lane span includes:

[0037] The vehicle uses an in-vehicle GPS positioning system combined with a high-definition dual-recording camera to obtain the lane markings of the vehicle's current lane and the target lane.

[0038] Obtain the standard lane width, calculate the number of lanes between the vehicle's current lane and the target lane, and multiply by the standard lane width to get the accurate lane change distance;

[0039] Based on the lane change distance, the number of lane changes required for a vehicle to change from the current lane to the target lane is evaluated to obtain the lane span.

[0040] On the other hand, this application also provides a high-definition dual-recording driving navigation operation optimization system, the system comprising:

[0041] The driving experience analysis module acquires the driver's historical driving experience information and sets the navigation optimization frequency based on the historical driving experience information;

[0042] The real-time data acquisition module, based on the navigation optimization frequency, collects real-time lane video stream information of the lane where the vehicle is located and its lanes in the same direction through an in-vehicle high-definition dual-recording camera;

[0043] The decision feature extraction module extracts decision features from the real-time lane video stream information for each lane to obtain a real-time lane condition profile. The real-time lane condition profile includes quantitative feature values ​​of road surface conditions, quantitative feature values ​​of accident conditions, vehicle density, non-motorized vehicle density, and pedestrian density.

[0044] The lane condition evaluation module inputs the real-time lane condition profile into a pre-trained lane condition evaluation model to obtain the lane real-time condition ease-of-travel index.

[0045] The lane span calculation module calculates the distance between the lane and the lane where the vehicle is located to obtain the lane span, which represents the number of lane changes required for the vehicle to change lanes from its current lane to the new lane.

[0046] The comprehensive analysis module, in conjunction with lane span, performs a comprehensive analysis of the lane's real-time mobility index to obtain the corresponding lane's driving navigation characteristic index.

[0047] The optimal lane recommendation module extracts the lane with the highest driving navigation feature index from multiple lanes as the vehicle's real-time optimal driving lane and displays it to the driver.

[0048] Thirdly, this application provides an electronic device including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any of the methods described above.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows: the method collects lane video stream information in real time through an in-vehicle high-definition dual-recording camera, which can dynamically reflect the current road conditions and traffic situation; it can adjust the navigation strategy in a timely manner and provide drivers with the latest road condition information, thereby effectively responding to dynamic changes such as sudden accidents and road congestion;

[0051] By extracting decision features and constructing lane condition profiles from real-time lane video stream information, the system can intelligently assess the traffic conditions of each lane. Combined with a pre-trained lane condition evaluation model, the system can calculate the lane's real-time mobility index, providing drivers with a scientific basis for decision-making.

[0052] When recommending the optimal lane, this method not only considers the lane's traffic availability index, but also calculates the lane span, which is the number of lane changes required for a vehicle to change lanes from the current lane to the target lane; this helps reduce unnecessary lane changing operations, improve driving efficiency, and reduce safety hazards caused by frequent lane changes.

[0053] By acquiring historical driving experience information of vehicle drivers and setting navigation optimization frequency accordingly, this method can make personalized adjustments based on different drivers' driving habits and preferences, making the navigation system closer to the actual needs of drivers and improving the satisfaction of the navigation experience.

[0054] By reflecting traffic conditions in real time and recommending the best lanes, drivers can anticipate and avoid potential dangers in advance. This not only reduces drivers' psychological stress and fatigue, but also significantly improves driving safety and reduces the occurrence of traffic accidents.

[0055] Compared to traditional navigation methods based on static data and simple location information, this method significantly enhances the practicality and reliability of the navigation system by introducing high-definition dual-recording cameras and intelligent decision-making algorithms. It can provide drivers with more comprehensive, accurate, and timely navigation services, meeting the urgent needs of modern transportation systems for efficient, safe, and convenient navigation. Attached Figure Description

[0056] Figure 1 This is a flowchart of the present invention;

[0057] Figure 2 This is a flowchart illustrating the construction method of the lane condition evaluation model;

[0058] Figure 3 This is a structural diagram of a high-definition dual-recording vehicle navigation operation optimization system. Detailed Implementation

[0059] As will be apparent to those skilled in the art from the description of this application, this application can be implemented as a method, apparatus, electronic device, and computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, this application can also be implemented as a computer program product contained in one or more computer-readable storage media, which includes computer program code.

[0060] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, optical disc read-only memory, optical storage devices, magnetic storage devices, or any combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0061] The acquisition, storage, use, and processing of data in this application all comply with relevant national laws and regulations.

[0062] This application describes the provided methods, apparatus, and electronic devices using flowcharts and / or block diagrams.

[0063] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0064] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0065] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0066] This application will now be described with reference to the accompanying drawings.

[0067] Example 1: As Figures 1 to 2 As shown, the high-definition dual-recording vehicle navigation operation optimization method of the present invention specifically includes the following steps:

[0068] S1. Obtain the driver's historical driving experience information and set the navigation optimization frequency based on the historical driving experience information;

[0069] Methods for obtaining information on a vehicle driver's historical driving experience include:

[0070] By utilizing the background functions of the in-vehicle system or navigation software, the driver's driving data can be automatically collected and recorded without the driver's active operation.

[0071] The user interface guides drivers to fill out questionnaires or set preferences, and allows them to manually enter information related to their driving experience.

[0072] The collected raw data is cleaned to remove noisy data, outliers, etc., to ensure the accuracy and reliability of the data;

[0073] Extract features that reflect a driver's driving experience from the cleaned data;

[0074] Based on the extracted features, machine learning methods are used to evaluate the driver's driving experience and form quantitative historical driving experience information;

[0075] The historical driving experience information includes:

[0076] Years of driving experience: Records the length of time a driver has held a driver's license, serving as one of the basic indicators for assessing driving experience;

[0077] Mileage: The total number of miles driven reflects the driver's total driving experience. Drivers who drive long distances are more familiar with various road conditions and driving skills.

[0078] Frequent driving routes: Recording the routes that drivers frequently travel helps to understand the driver's familiarity with a specific area and their understanding of the traffic conditions in that area;

[0079] Driving behavior data: Speed ​​control reflects a driver's speed control ability; lane changing habits reflect a driver's lane selection preferences and judgment; braking and acceleration assess a driver's reaction speed and driving smoothness; following distance reflects a driver's safety awareness and driving skills;

[0080] Accident records: These include the number of traffic accidents, their types, and the determination of liability. This information reflects the driver's safe driving ability and accident prevention awareness.

[0081] Traffic violation records: Records of traffic violations such as speeding, running red lights, and illegal lane changes are important evidence for assessing a driver's driving etiquette and safety awareness;

[0082] Driving preferences: Driving preferences derived from user settings or behavioral analysis, including whether they prefer expressways, whether they are willing to take detours to avoid congestion, and their preferences for lane selection.

[0083] In this step, by acquiring and analyzing the driver's historical driving experience information, the navigation optimization frequency can be customized to provide more personalized navigation services for drivers with different levels of experience. Based on a comprehensive assessment of driving behavior data, accident records, and traffic violation records, potential risky behaviors of the driver can be identified, and timely safe driving suggestions or warnings can be provided, thereby effectively improving driving safety. By analyzing frequently traveled routes and driving preferences, the navigation system can more accurately predict the driver's travel needs, providing more precise route planning and real-time traffic information to help drivers avoid congested areas and improve travel efficiency. Using machine learning methods to quantitatively evaluate driving experience not only improves... The accuracy and objectivity of the assessment provide a solid foundation for subsequent data analysis and decision support. By reducing unnecessary user input and active operations, and by providing an intuitive and easy-to-use user interface to guide users in completing questionnaires or setting preferences, this step significantly improves the convenience and satisfaction of the user experience. Long-term tracking and analysis of driving behavior data helps drivers to self-assess and improve their driving habits, thereby gradually forming good driving habits and safety awareness. This step, by comprehensively evaluating the driver's historical driving experience information and setting personalized navigation optimization strategies accordingly, not only improves driving safety and efficiency but also enhances the user experience and promotes the improvement of driving habits, demonstrating significant beneficial effects.

[0084] S2. Based on navigation optimization frequency, real-time lane video stream information of the lane where the vehicle is located and its lanes in the same direction is collected through an in-vehicle high-definition dual-recording camera;

[0085] Methods for collecting real-time lane video stream information of the vehicle's lane and its lanes in the same direction include:

[0086] The navigation optimization frequency determines the rate at which the camera captures video frames;

[0087] The video stream captured by the camera is transmitted in real time to the onboard computing unit for processing via the in-vehicle network; in order to reduce bandwidth consumption, compression algorithms can be used to compress the video data;

[0088] The acquired raw video stream is preprocessed to improve the accuracy of subsequent analysis;

[0089] Image segmentation techniques were used to identify different lane boundaries and to perform preliminary labeling of different objects within each lane.

[0090] Considering processing power limitations, not all collected data needs to be permanently saved; a buffer can be set up to retain only important data from the most recent period.

[0091] For some key events, relevant video clips are selectively saved for later analysis or as evidence.

[0092] During the processing, personal identification information should be blurred to protect the privacy of other road users.

[0093] In this step, by adjusting the rate at which the camera captures video frames based on navigation optimization frequency, high-quality real-time lane video stream information can be obtained when needed, providing drivers with timely and accurate navigation suggestions. The video stream is transmitted in real-time to the onboard computing unit via the in-vehicle network for processing, and compression algorithms reduce bandwidth usage, improving data transmission and processing efficiency. This not only reduces the demand on onboard system hardware resources but also reduces latency during data transmission, ensuring system response speed. Preprocessing the acquired raw video stream significantly improves the accuracy of subsequent image analysis and lane recognition, helping to more accurately extract a real-time lane condition profile and providing more reliable data support for navigation optimization. Using image segmentation technology to identify lane boundaries and perform preliminary labeling of different objects within the lane is a key step in realizing a real-time lane condition profile. This allows for the automatic differentiation of lanes, vehicles, pedestrians, and non-motorized vehicles. Traffic elements such as vehicles provide a rich information foundation for subsequent decision analysis; managing data by setting up buffers to retain only important data from the most recent period and optimizing data storage within processing capacity constraints effectively reduces waste of system resources; at the same time, selectively saving video clips of key events preserves important evidence while avoiding redundant storage of irrelevant data; obfuscating personal identification information during processing demonstrates an emphasis on privacy protection; this helps enhance user trust in the system and ensures that the privacy rights of other road users are not infringed while providing navigation services; step S2 efficiently and accurately collects real-time lane video stream information of the vehicle's lane and its lanes in the same direction, providing a solid data foundation for subsequent navigation optimization; furthermore, this step also focuses on data processing efficiency, privacy protection, and data management optimization, further improving the overall performance of the system and user experience.

[0094] S3. For each lane, decision features are extracted from the real-time lane video stream information to obtain a real-time lane condition profile; the real-time lane condition profile includes road surface condition quantitative feature values, accident condition quantitative feature values, vehicle density, non-motorized vehicle density, and pedestrian density;

[0095] The method for constructing the real-time lane condition profile includes:

[0096] The received real-time lane video stream is preprocessed, including removing image noise and enhancing contrast, to improve image quality and provide clear visual input for subsequent processing.

[0097] Image processing techniques are used to identify lane boundary lines and segment the lanes from the background;

[0098] The detected lanes are numbered and marked so that each lane can be analyzed independently in the future;

[0099] By analyzing images of the lane area, feature values ​​reflecting road surface quality are extracted;

[0100] Image recognition technology is used to detect signs of accidents within a lane and to quantify their impact.

[0101] Vehicle density is calculated by statistically analyzing the number and distribution of vehicles within the lanes;

[0102] The object detection algorithm is used to identify non-motorized vehicles and pedestrians near the lane and to calculate their density.

[0103] The extracted decision features are combined to construct a real-time lane condition profile; this profile is a multi-dimensional data set that contains comprehensive information about the lane's condition at a specific moment.

[0104] In this step, by preprocessing and extracting decision features from the real-time lane video stream, the current real-time condition of the lane can be reflected instantly. This includes accurate assessments of road surface quality, accident conditions, vehicle density, non-motorized vehicle density, and pedestrian density, providing drivers with timely and accurate navigation information. The real-time lane condition profile is a multi-dimensional dataset that not only covers traditional traffic flow information but also incorporates key information such as road surface quality, accident conditions, and the density of non-motorized vehicles and pedestrians. This comprehensive information integration enables the navigation system to more comprehensively assess lane traffic conditions, thereby making more scientific and reasonable navigation suggestions. The intelligent feature extraction and profile construction in step S3 provide strong data support for subsequent navigation optimization. Through the analysis of the real-time lane condition profile, the system can intelligently identify the optimal driving lane and provide drivers with real-time navigation guidance. This enhances driving convenience and significantly improves road safety and efficiency. Because this step reflects the overall lane conditions in real time and provides drivers with accurate navigation suggestions based on these conditions, it significantly improves the user experience. Drivers no longer need to worry about unfamiliar routes or traffic congestion; they can simply follow the navigation system's guidance. Besides providing convenience to individual drivers, the real-time lane condition profile also provides important reference data for traffic management departments. Through the analysis and mining of this data, traffic management departments can more accurately grasp the changing trends of traffic conditions, thereby formulating more scientific and reasonable traffic management strategies. The beneficial effects of the S3 step are not only reflected in improving the real-time performance and accuracy of the navigation system, but also in providing users with intelligent decision support, improving user experience, and promoting traffic management.

[0105] S4. Input the real-time lane condition profile into the pre-trained lane condition evaluation model to obtain the lane real-time condition ease-of-travel index;

[0106] The method for constructing the lane condition evaluation model includes:

[0107] By using vehicle-mounted high-definition dual-recording cameras or other traffic monitoring equipment, a large amount of lane video stream data is collected; at the same time, combined with multi-source information such as traffic management department records, GPS positioning data, and vehicle sensor data, a comprehensive dataset is formed.

[0108] The collected data is cleaned, denoised, and labeled.

[0109] Based on the characteristics of the problem and the data, a deep learning model is selected as the basic architecture of the lane condition evaluation model. The deep learning model includes decision tree, random forest, neural network, convolutional neural network and recurrent neural network.

[0110] The preprocessed dataset is divided into a training set and a test set. The selected model is trained using the training set data. During the training process, the model's performance is optimized by adjusting its parameters and structure, enabling it to accurately predict the real-time accessibility index of lanes.

[0111] The trained model is evaluated using test set data to verify its generalization ability and prediction accuracy; based on the evaluation results, the model is adjusted and optimized.

[0112] The trained lane condition evaluation model is deployed into the vehicle navigation system to achieve real-time lane condition evaluation.

[0113] The mathematical calculation formula for the lane condition evaluation model is as follows:

[0114]

[0115] Where E represents the lane availability index, w i C represents the weight coefficient of the i-th feature. i Let represent the quantized feature value of the i-th feature, and n represent the total number of features.

[0116] In this step, by inputting real-time lane condition profiles into a pre-trained lane condition evaluation model, the lane mobility index can be calculated in real time, providing drivers with immediate road condition information and significantly improving the accuracy and efficiency of driving decisions. A comprehensive dataset is constructed using multi-source information, including in-vehicle high-definition dual-recording cameras, traffic management department records, GPS positioning data, and vehicle sensor data. This allows the evaluation model to more comprehensively consider various factors affecting lane conditions, thereby improving the accuracy and reliability of the evaluation. A deep learning model is used as the basic architecture of the lane condition evaluation model, realizing the intelligent and automated evaluation of lane conditions. The deep learning model can automatically learn and extract features from the data, reducing manual intervention and subjective judgment. The model's ability to adapt to changing lane conditions at different times and locations enhances the objectivity and consistency of evaluations. Through training, parameter tuning, performance optimization, and test set evaluation, the model's generalization ability and prediction accuracy are ensured. Its practicality and stability are improved by handling lane condition changes at different times and locations. Real-time and accurate lane condition evaluation information helps drivers plan routes in advance and avoid congestion and dangerous sections, thereby improving driving safety and comfort. Simultaneously, for traffic management departments, this information helps optimize traffic flow management and alleviate traffic congestion. Continuous optimization and improvement of the model can drive the entire intelligent transportation system towards greater intelligence and efficiency, providing strong technical support for urban traffic management.

[0117] S5. Calculate the distance between the lane and the lane where the vehicle is located to obtain the lane span, where the lane span represents the number of lane changes required for the vehicle to change lanes from its current lane to the new lane.

[0118] The method for obtaining the lane span includes:

[0119] The vehicle uses an in-vehicle GPS positioning system combined with a high-definition dual-recording camera to obtain the lane markings of the vehicle's current lane and the target lane.

[0120] Obtain the standard lane width, which is usually 3.5 meters or 3.75 meters. Calculate the number of lanes between the vehicle's current lane and the target lane, and multiply this by the standard lane width to get the accurate lane change distance.

[0121] Based on lane change distance, the number of lane changes required for a vehicle to change from the current lane to the target lane is evaluated to obtain the lane span;

[0122] The output lane span, a quantitative indicator, will serve as an important basis for subsequent steps to comprehensively analyze the real-time lane condition ease of driving index and determine the optimal driving lane.

[0123] In this step, by combining the vehicle's GPS positioning system with a high-definition dual-recording camera, the system can accurately and in real-time acquire the lane markings of the vehicle's current lane and the target lane. This ensures rapid positioning and lane span calculation even in complex and ever-changing road environments. Lane span, as a quantitative indicator, not only considers the straight-line distance from the current lane to the target lane, but more importantly, it directly reflects the actual difficulty of lane changes by calculating the number of lane changes required. This quantitative approach allows drivers to more intuitively understand the complexity and risks of lane changes when receiving navigation suggestions, thus enabling them to make more reasonable driving decisions. By comprehensively considering multiple factors such as lane span and the real-time lane mobility index, the navigation system can more accurately recommend the optimal driving lane. This helps reduce blind lane changes and misjudgments by drivers during driving, improving driving safety and lane awareness. This improves road traffic efficiency; simultaneously, reducing unnecessary lane changes helps lower fuel consumption and emissions, promoting green travel; the calculation and application of lane span makes the navigation system more intelligent and user-friendly; by providing drivers with clear and intuitive lane change suggestions, it reduces driver anxiety and unease in complex traffic environments, enhancing user experience and satisfaction; the lane span calculation method in this step has high adaptability and can be applied to different types of roads and traffic environments; whether in congested urban areas or on highways, it can accurately calculate lane span by adjusting standard lane widths and considering factors such as traffic rules, providing effective navigation support for drivers; step S5, by accurately calculating lane span in real time, provides an important basis for subsequent navigation decisions, significantly improving the intelligence and practicality of the driving navigation system.

[0124] S6. Combine lane span with a comprehensive analysis of the lane's real-time mobility index to obtain the corresponding lane's driving navigation characteristic index.

[0125] The lane real-time condition ease of driving index is calculated through a pre-trained model. This index reflects the overall driving conditions of the lane, including the lane's accident rate, vehicle density, road surface conditions, etc. Each index is a quantified value used to represent the current traffic efficiency and safety of the lane.

[0126] Lane span refers to the minimum number of lane changes required for a vehicle to move from its current lane to a target lane; this is an important metric because frequent or multiple lane changes can increase the risk of accidents and affect driving comfort.

[0127] Based on actual application scenarios and demand analysis, different weights are assigned to lane span and real-time traffic ease index; the real-time traffic ease index directly reflects the current traffic conditions of the lane; while lane span serves as an auxiliary factor, taking into account the ease of lane changing and potential risks.

[0128] The formula for calculating the driving navigation feature index is:

[0129] I i =α·E i -β·D i Among them, E i The D index indicates the real-time ease of driving of a lane. i Indicates lane span, I i This represents the driving navigation feature index, where α represents the weight of the lane mobility index and β represents the weight of the lane span; α·E i β·D represents the positive contribution of the lane mobility index to the characteristic index. i This indicates the negative impact of lane span on the characteristic index.

[0130] In this step, by introducing two key parameters—the lane availability index and lane span—and combining their respective weights for comprehensive analysis, navigation decisions no longer rely solely on static map data and simple location information. Instead, they can dynamically assess the traffic efficiency and safety of each lane in real time. This intelligent decision-making method is closer to actual driving scenarios and helps provide drivers with more accurate navigation suggestions. The lane availability index directly reflects the traffic conditions of the lane, including factors such as accident rate, vehicle density, and road surface conditions, while lane span considers the ease of lane changing and potential risks. By comprehensively considering these two factors, drivers can avoid choosing lanes that appear clear but are actually accident-prone or require frequent lane changes, thereby reducing the probability of accidents and improving driving safety. Frequent or multiple lane changes not only increase the risk of accidents but also affect driver comfort. Introducing lane span as a negative influencing factor makes navigation decisions more inclined to select lanes requiring fewer lane changes; this helps reduce unnecessary driver operations and improves driving smoothness and comfort. Traditional navigation methods often only provide basic route planning and navigation functions, failing to reflect real-time traffic conditions. This step, by comprehensively analyzing the real-time lane mobility index and lane span, enables the navigation system to more accurately assess the actual traffic capacity of each lane, providing drivers with more practical and reliable navigation suggestions. This helps improve the overall practicality and market competitiveness of the navigation system. Step S6, by comprehensively analyzing the real-time lane mobility index and lane span, calculates the driving navigation characteristic index, providing drivers with more intelligent, safe, comfortable, and practical navigation services. This not only improves the performance of the navigation system but also makes a positive contribution to the intelligent development of modern transportation systems.

[0131] S7. From multiple lanes, extract the lane with the highest driving navigation feature index as the vehicle's real-time optimal driving lane and display it to the driver;

[0132] The driving navigation feature indices are aggregated to form a dataset containing the driving navigation feature indices of all lanes traveling in the same direction;

[0133] The driving navigation feature indices in the dataset are compared; by comparison, the lane with the largest feature index is identified; after comprehensively considering the real-time traffic conditions and the ease of lane changing, this lane is considered the optimal driving choice under the current circumstances.

[0134] After determining the lane with the largest characteristic index, a feasibility verification is conducted to ensure the rationality of the selected lane;

[0135] If the selected lane is found to have significant negative factors during the verification process, the system will re-evaluate other lanes and select the second-best lane as the recommendation.

[0136] Once the optimal driving lane is determined in real time, recommended information is displayed to the driver via in-vehicle display screen and voice prompts;

[0137] The information displayed includes the optimal lane number, directional guidance, lane change suggestions, and precautions.

[0138] In this step, by calculating the driving navigation characteristic index of each lane in real time and selecting the lane with the highest characteristic index as the recommendation, it is possible to ensure that the information provided by the navigation system is highly matched with the current traffic conditions. This improvement in real-time performance and accuracy helps drivers make more informed decisions in complex traffic environments, reducing driving risks caused by misjudgments or information delays. Selecting the optimal driving lane in real time not only considers the lane's traffic efficiency but also the ease of lane changing, thereby reducing unnecessary lane changes and potential driving conflicts. This helps improve driving smoothness and comfort, optimizing the driving experience. Feasibility verification is conducted before determining the optimal lane to ensure that the selected lane does not have significant negative factors, further enhancing driving safety. At the same time, by providing clear directional guidance, lane-changing suggestions, and precautions, it helps drivers make more informed decisions. The system enables safer lane changing and driving processes. By introducing advanced technologies such as real-time lane video stream information, decision feature extraction, and lane condition evaluation models, it achieves intelligent analysis and judgment of traffic conditions. This intelligent navigation method not only improves the performance of the navigation system but also lays the foundation for the development of future intelligent transportation systems. When a large number of drivers adopt this navigation suggestion based on real-time information, it helps to promote the even distribution of traffic flow, reduce excessive congestion in certain lanes, and thus improve the overall efficiency of the transportation system. Step S7 extracts the lane with the highest driving navigation feature index as the real-time optimal driving lane and displays relevant information to the driver. This not only improves the real-time performance and accuracy of navigation, optimizes the driving experience, and enhances driving safety, but also improves the intelligence level of the navigation system and is expected to have a positive impact on traffic flow optimization.

[0139] Example 2: Figure 3 As shown, the high-definition dual-recording vehicle navigation operation optimization system of the present invention specifically includes the following modules;

[0140] The driving experience analysis module acquires the driver's historical driving experience information and sets the navigation optimization frequency based on the historical driving experience information;

[0141] The real-time data acquisition module, based on the navigation optimization frequency, collects real-time lane video stream information of the lane where the vehicle is located and its lanes in the same direction through an in-vehicle high-definition dual-recording camera;

[0142] The decision feature extraction module extracts decision features from the real-time lane video stream information for each lane to obtain a real-time lane condition profile. The real-time lane condition profile includes quantitative feature values ​​of road surface conditions, quantitative feature values ​​of accident conditions, vehicle density, non-motorized vehicle density, and pedestrian density.

[0143] The lane condition evaluation module inputs the real-time lane condition profile into a pre-trained lane condition evaluation model to obtain the lane real-time condition ease-of-travel index.

[0144] The lane span calculation module calculates the distance between the lane and the lane where the vehicle is located to obtain the lane span, which represents the number of lane changes required for the vehicle to change lanes from its current lane to the new lane.

[0145] The comprehensive analysis module, in conjunction with lane span, performs a comprehensive analysis of the lane's real-time mobility index to obtain the corresponding lane's driving navigation characteristic index.

[0146] The optimal lane recommendation module extracts the lane with the highest driving navigation feature index from multiple lanes as the vehicle's real-time optimal driving lane and displays it to the driver.

[0147] This system, through a real-time data acquisition module combined with an in-vehicle high-definition dual-recording camera, can acquire real-time video stream information of the vehicle's lane and its lanes in the same direction; enabling the navigation system to dynamically reflect the current traffic conditions, thereby providing more accurate and timely navigation information.

[0148] The driving experience analysis module sets the navigation optimization frequency based on the driver's historical driving experience, enabling the system to intelligently adjust according to the habits and preferences of different drivers, thereby improving the system's adaptability and user satisfaction.

[0149] The system transforms complex real-time video information into a quantified real-time lane condition profile through a decision feature extraction module and a lane condition evaluation module, and uses a lane condition evaluation model to calculate a mobility index. This comprehensive decision-making capability enables the system to more accurately assess the traffic conditions of each lane and provide drivers with more reasonable lane selection suggestions.

[0150] The lane span calculation module takes into account the distance between lanes and the number of lane changes. When recommending the optimal lane, it not only considers the traffic conditions of the lane, but also takes into account the convenience and safety of lane changes, which helps to reduce unnecessary lane change operations and improve driving efficiency.

[0151] By reflecting traffic conditions in real time and recommending the best lane, the system can help drivers anticipate and avoid potential dangers, thereby reducing driving risks and improving driving safety. The system displays the best driving lane in real time, enabling drivers to make decisions more easily and confidently while driving, thus enhancing driving comfort and convenience.

[0152] In summary, this high-definition dual-recording driving navigation optimization system significantly enhances the practicality and reliability of the navigation system by introducing high-definition dual-recording cameras and intelligent decision-making algorithms. It can provide drivers with more comprehensive, accurate, and timely navigation services, meeting the urgent needs of modern transportation systems for efficient, safe, and convenient navigation.

[0153] The various variations and specific embodiments of the high-definition dual-recording driving navigation operation optimization method in the aforementioned Embodiment 1 are also applicable to the high-definition dual-recording driving navigation operation optimization system of this embodiment. Through the foregoing detailed description of the high-definition dual-recording driving navigation operation optimization method, those skilled in the art can clearly understand the implementation method of the high-definition dual-recording driving navigation operation optimization system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0154] In addition, this application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via the bus. When the computer program is executed by the processor, it implements the various processes of the above-described method embodiment for controlling output data and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0155] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A high-definition dual-recording driving navigation operation optimization method, characterized in that, The method comprises: acquiring historical driving experience information of a vehicle driver, and setting a navigation optimization frequency based on the historical driving experience information; based on the navigation optimization frequency, collecting real-time lane video stream information of a lane where the vehicle is located and a lane in the same direction thereof through a vehicle-mounted high-definition double-recording camera; for each lane, performing decision feature extraction on the real-time lane video stream information to obtain a lane real-time condition portrait; inputting the lane real-time condition portrait into a pre-trained lane condition evaluation model to obtain a lane real-time condition ease index; calculating a distance between the lane and the lane where the vehicle is located to obtain a lane span, the lane span representing a number of lane changes required for the vehicle to change from the lane where the vehicle is located to the lane; comprehensively analyzing the lane real-time condition ease index in combination with the lane span to obtain a driving navigation feature index of the lane; from a plurality of lanes, extracting a lane with the largest driving navigation feature index as a real-time optimal driving lane of the vehicle, and displaying the lane to the driver; the lane real-time condition portrait comprises road surface condition quantitative feature values, accident condition quantitative feature values, vehicle density, non-motor vehicle density and pedestrian density; A mathematical calculation formula of the lane condition evaluation model is: ; wherein E represents a lane real-time condition easy-to-go index, wi represents a weight coefficient of an i th feature, Ci represents a quantitative feature value of the i th feature, and n represents a total number of features. a calculation formula of the driving navigation feature index is: Ii = a Ei - b Di; wherein, Ei represents the lane real-time condition ease index, Di represents the lane span, Ii represents the driving navigation feature index, a represents a weight of the lane ease index, b represents a weight of the lane span; a Ei represents a positive contribution of the lane ease index to the feature index, b Di represents a negative influence of the lane span on the feature index; an acquisition method of the lane span comprises: acquiring an identifier of a current lane where the vehicle is located and an identifier of a target lane through a vehicle-mounted GPS positioning system in combination with a high-definition double-recording camera; acquiring a standard lane width, calculating a number of lanes separated between the current lane of the vehicle and the target lane, and multiplying the number of lanes by the standard lane width to obtain an accurate lane change distance; based on the lane change distance, evaluating a number of lane changes required for the vehicle to change from the current lane to the target lane to obtain the lane span.

2. The method of claim 1, wherein, a construction method of the lane condition evaluation model comprises: collecting lane video stream data; performing cleaning, denoising and labeling work on the collected data; selecting a deep learning model as a basic architecture of the lane condition evaluation model, the deep learning model comprising a decision tree, a random forest, a neural network, a convolutional neural network and a recurrent neural network; dividing a preprocessed data set into a training set and a test set, and training the selected model using the training set data; evaluating the trained model using the test set data to verify its generalization ability and prediction accuracy; adjusting and optimizing the model according to the evaluation result; deploying the trained lane condition evaluation model to a vehicle-mounted navigation system.

3. The method of claim 1, wherein, a construction method of the lane real-time condition portrait comprises: preprocessing the received real-time lane video stream, including removing image noise and enhancing contrast; performing image recognition on the preprocessed lane video stream data to identify lane boundary lines and segment the lane from the background; numbering and marking the detected lane; analyzing images of the lane area to extract feature values reflecting road surface quality; Detecting whether there is an accident in the lane and quantifying the impact; Counting the number and distribution of vehicles in the lane and calculating the vehicle density; Identifying non-motor vehicles and pedestrians near the lane and calculating their density; Combining the extracted decision features to construct a real-time lane condition portrait.

4. A high-definition dual-recording car navigation operation optimization system, characterized in that, The system is applied to the high-definition dual-recording driving navigation operation optimization method of any one of claims 1-3, and the system comprises: A driving experience analysis module that obtains historical driving experience information of the vehicle driver and sets a navigation optimization frequency based on the historical driving experience information; A real-time data acquisition module that collects real-time lane video stream information of the lane where the vehicle is located and its same-direction lane based on the navigation optimization frequency through the vehicle-mounted high-definition dual-recording camera; A decision feature extraction module that extracts decision features from the real-time lane video stream information for each lane to obtain a real-time lane condition portrait; the real-time lane condition portrait includes road condition quantification feature values, accident condition quantification feature values, vehicle density, non-motor vehicle density, and pedestrian density; A lane condition evaluation module that inputs the real-time lane condition portrait into a pre-trained lane condition evaluation model to obtain a real-time lane condition ease index; A lane span calculation module that calculates the distance between the lane and the lane where the vehicle is located to obtain a lane span, which represents the number of lane changes required for the vehicle to change lanes from the lane where it is located to the lane; A comprehensive analysis module that combines the lane span to comprehensively analyze the real-time lane condition ease index and obtain a driving navigation feature index corresponding to the lane; An optimal lane recommendation module that extracts the lane with the largest driving navigation feature index from multiple lanes as the real-time optimal driving lane for the vehicle and displays it to the driver.

5. A high-definition double-recording driving navigation operation optimization electronic device, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected through the bus, characterized in that, The computer program is executed by the processor to realize the steps in the method of any one of claims 1-3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps in the method of any one of claims 1-3.

Images