Driving information processing method and device based on smart helmet

Abstract

The present invention relates to the field of driving data processing, and in particular to a driving information processing method and device based on a smart helmet, the method comprising the following steps: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet; performing histogram equalization processing on the real-time driving process monitoring video, and performing global time delay interpolation smoothing processing to obtain a time delay smoothed monitoring video; performing deep road semantic feature segmentation on the time delay smoothed monitoring video, and performing scene spatial structure modeling to construct a dynamic twin scene model; performing current scene driving trajectory prediction on the user driving posture data, and performing safe passage path planning to generate a scene safe passage path; marking all dynamic vehicles in the scene based on the dynamic twin scene model. The present invention realizes efficient driving information processing and improves driving safety for drivers.

Description

Technical Field

[0001] The present invention relates to the field of driving data processing, and in particular to a driving information processing method and device based on a smart helmet. Background Art

[0002] With the rapid development of intelligent transportation technology, driving safety has become a focus of social concern. Traditional driving assistance systems mostly rely on on-board sensors and monitoring equipment, and have defects such as limited information collection range and untimely fault detection, making it difficult to fully perceive the complex and changing driving environment. In recent years, driving information processing methods based on wearable devices have attracted widespread attention. As an intelligent wearable device, driving smart helmets can integrate multiple high-definition cameras, IMU sensors, etc., to collect various indicators of the driver's physiological state, driving behavior and surrounding environment in real time, providing strong guarantees for improving driving safety.

[0003] How to effectively process and analyze the massive amount of driving information obtained from smart helmets and extract potential safety hazards and warning information from it has become a key issue that needs to be solved urgently. Traditional information processing methods often rely on manual analysis and experience judgment, which are inefficient and difficult to cope with the ever-changing driving environment and cannot improve the driving safety of drivers. Therefore, it is urgent to establish a driving information processing method based on smart helmets, using artificial intelligence technologies such as computer vision and pattern recognition to achieve real-time perception, dynamic analysis and risk prediction of the driving environment, thereby providing drivers with timely and accurate safety decision support. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a driving information processing method and device based on a smart helmet to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for processing driving information based on a smart helmet, comprising the following steps:

[0006] Step S1: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet; performing histogram equalization processing on the real-time driving process monitoring video and performing global delay interpolation smoothing processing to obtain a delay-smoothed monitoring video;

[0007] Step S2: Perform deep road semantic feature segmentation on the time-delay smoothed monitoring video, perform scene spatial structure modeling, and construct a dynamic twin scene model;

[0008] Step S3: Predict the current driving trajectory of the scene based on the user's driving posture data, and perform safe passage path planning to generate a scene safe passage path;

[0009] Step S4: Mark the dynamic vehicles in all scenes based on the dynamic twin scene model; predict the dynamic movement trajectory of the dynamic vehicles in all scenes, thereby generating multi-period corrected driving prediction trajectories;

[0010] Step S5: Calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, perform safety risk analysis, and mark the vehicle as a high-risk vehicle;

[0011] Step S6: Make risk avoidance decisions for high-risk vehicles based on the multi-period corrected driving prediction trajectory and generate risk decision results; project the risk decision results onto the smart helmet to complete the driving information processing operation.

[0012] The present invention enhances the contrast and brightness of video images through histogram equalization processing, improves image quality, and makes monitoring videos clearer. Global time delay interpolation smoothing processing reduces jitter and discontinuity in the video, improves user viewing experience, and ensures video fluency. Deep road semantic feature segmentation and scene spatial structure modeling accurately understand road conditions and scene structures, provide accurate scene information for subsequent safe passage path planning, construct a dynamic twin scene model to capture and reflect the dynamic changes of road scenes in real time, improve the driving system's ability to understand and predict scenes, driving trajectory prediction and safe passage path planning predict the user's driving trajectory in advance and plan a safe path, improve driving safety and efficiency, generate a scene safe passage path to avoid potential dangers, and provide users with safe driving. Guidance, dynamic movement trajectory prediction and multi-period correction driving prediction trajectory accurately predict the movement trajectory of vehicles in the scene, improve the system's understanding and prediction ability of vehicle behavior, generate correction driving prediction trajectory to optimize driving path, improve driving efficiency and safety, collision probability calculation and safety risk analysis to identify high-risk vehicles, discover potential collision risks in advance, reduce the occurrence of traffic accidents, mark high-risk vehicles to warn drivers, improve drivers' awareness of potential dangers, enhance safety awareness, risk avoidance decisions and risk decision results are projected onto smart helmets to provide real-time risk prompts and decision support, help drivers make correct decisions, complete driving information processing operations to optimize the driver's driving experience, improve driving safety and comfort, and realize intelligent driving assistance functions.

[0013] Preferably, step S1 includes the following steps:

[0014] Step S11: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet;

[0015] Step S12: performing brightness deviation detection on the real-time driving process monitoring video to identify the brightness deviation area;

[0016] Step S13: performing histogram equalization processing based on the brightness deviation area to generate a brightness enhanced monitoring video;

[0017] Step S14: performing driving scene edge analysis on the brightness enhanced monitoring video to extract driving scene edge data;

[0018] Step S15: Optimizing edge details of the brightness-enhanced monitoring video according to the edge data of the travel scene to construct an edge-optimized monitoring video;

[0019] Step S16: performing global delay interpolation smoothing processing on the edge optimized monitoring video to obtain a delay smoothed monitoring video.

[0020] The present invention provides basic data for subsequent driving information processing by acquiring driving monitoring videos and user driving posture data in real time. User driving posture data can be used to analyze user behavior and help improve driving behavior and safety. Brightness deviation detection identifies brightness anomalies in the video and improves image quality. Identifying brightness deviation areas can provide target areas for subsequent image enhancement and optimization. Histogram equalization processing improves the contrast and brightness balance of the image and enhances the visual effect of the monitoring video. Generating brightness enhanced monitoring videos can improve video quality and make details more clearly visible. Driving scene edge analysis identifies important edge information in the driving scene and improves the accuracy of image understanding and analysis. Extracting travel scene edge data can provide important clues for subsequent image processing and optimization. Edge detail optimization can highlight important edge information in the video and improve the clarity and visual effect of the image. Constructing edge optimized monitoring videos can make images more recognizable and enjoyable. Global delay interpolation smoothing processing reduces jitter and discontinuity in the video and improves the viewing experience of the video. Obtaining delay smoothed monitoring videos can make the video smoother and reduce the discomfort of users when watching.

[0021] Preferably, the specific steps of step S16 are:

[0022] Perform time-series window segmentation on edge-optimized monitoring videos and extract time-series images of each frame;

[0023] Calculating the timestamp of each frame of the time-series image;

[0024] Calculate the time interval between the upper and lower frames according to the timestamp to obtain the inter-frame interval duration of all image frames;

[0025] Fit the interval duration distribution of all image frames to obtain the global interval distribution map of the video;

[0026] Perform interval discrete unevenness identification on the global interval distribution map of the video and extract the points with uneven interval duration;

[0027] Positioning the time-series image frames based on the unevenly spaced time points, and extracting all the unevenly spaced image frames;

[0028] Calculating the inter-frame delay of all non-uniformly spaced image frames to obtain the delay parameters of the non-uniformly spaced frames;

[0029] Performing average inter-frame interval statistics on the inter-frame interval durations of all image frames to generate a mean inter-frame interval value;

[0030] Performing delay compensation calculation on the delay parameters of the non-uniformly spaced frames according to the mean inter-frame interval value, thereby obtaining the delay compensation parameters of the non-uniformly spaced frames;

[0031] Performing time-series serialization processing on each frame of the time-series image according to the timestamp to obtain a time-series image frame sequence;

[0032] The delay compensation parameters of non-uniform interval frames are used to perform delay interpolation and smoothing processing on the time sequence image frame sequence, thereby obtaining a delay smoothed monitoring video.

[0033] The present invention accurately manages and analyzes the time of the video by extracting the time sequence image of each frame and calculating the timestamp. Calculating the interval length between frames and performing distribution fitting can reveal the time relationship between frames in the video and understand the time distribution law between video frames. Identifying and extracting points with uneven intervals can help discover time anomalies in the video, further optimize the time series of the video, calculate the delay parameters of the unevenly spaced frames to understand the changes in the time intervals between video frames, and provide a basis for subsequent delay compensation. Generating the mean inter-frame interval value and calculating the delay compensation parameters can count and adjust the time interval of the video, thereby improving the time smoothness and continuity of the video. The time series serialization processing and delay interpolation smoothing can effectively handle the delay of unevenly spaced frames, eliminate the screen freeze and discontinuity caused by uneven intervals during video playback, and improve the smoothness of the video.

[0034] Preferably, the specific steps of step S2 are:

[0035] Step S21: performing scene object visual recognition on the time-delay smoothed monitoring video and marking objects in the driving scene, wherein the objects in the driving scene include static road signs, road obstacles, and dynamic vehicles;

[0036] Step S22: performing deep road semantic feature segmentation on the time-delay smoothed monitoring video to generate driving scene road semantic features;

[0037] Step S23: performing scene spatial structure analysis on static road signs and road obstacles to generate scene static spatial structure data;

[0038] Step S24: performing scene spatial structure modeling on the driving scene road semantic features and the scene static spatial structure data to construct a three-dimensional driving scene model;

[0039] Step S25: Perform dynamic movement evolution simulation on the three-dimensional driving scene model according to the dynamic vehicle to construct a dynamic twin scene model.

[0040] The present invention improves driving safety by identifying static road signs, road obstacles and dynamic vehicles, allowing drivers to have a real-time understanding of the driving environment. Marking these objects can enhance the readability of video content, making information clearer and more concise. It generates road semantic features of driving scenes to understand key information such as road conditions and vehicle positions, thereby improving the ability to recognize and analyze driving scenes. It performs spatial structural analysis on static road signs and road obstacles to identify potential safety hazards and optimize driving path planning, thereby improving the driver's perception of the driving environment. Constructing a three-dimensional driving scene model can provide smart helmets with more realistic and accurate environmental simulation, enhance the global cognition and decision-making capabilities of the driving monitoring system, and simulate the real-time changes of vehicles in driving scenes through dynamic mobile evolution simulation, helping smart helmets better predict and respond to traffic conditions, and improving the driver's sense of security and driving efficiency.

[0041] Preferably, the specific steps of step S3 are:

[0042] Step S31: Calculate the driving space volume based on the user's driving posture data to generate the user's standard driving volume;

[0043] Step S32: Analyze the user's driving trajectory on the time-delay smoothed monitoring video to extract the user's time-series driving trajectory;

[0044] Step S33: Predicting the current scene driving trajectory of the user's time series driving trajectory based on the user's standard driving volume to generate a predicted driving trajectory within the scene;

[0045] Step S34: Calculate the obstacle area volume of the dynamic twin scene model to generate obstacle area volume parameters;

[0046] Step S35: Based on the volume parameters of the obstacle area, a safe passage path is planned for the predicted driving trajectory in the scene to generate a safe passage path for the scene.

[0047] The present invention can accurately evaluate the size and space requirements of the user's vehicle through driving space volume calculation, providing a basis for subsequent driving trajectory analysis and path planning. The generated user standard driving volume understands the size and shape of the vehicle, providing more accurate information for the smart helmet to support driving decisions. Through driving trajectory analysis, the user's trajectory changes during driving can be captured, including the vehicle's moving path and speed changes. The extracted time-series driving trajectory data provides important information for subsequent driving trajectory prediction and safe path planning, helping the smart helmet to better understand the user's driving behavior. The driving trajectory prediction can predict the user's future driving behavior based on the user's standard driving volume and historical driving trajectory. The smart helmet can identify potential driving conflicts and dangerous situations in advance based on the future driving path and behavior, and generate the predicted driving trajectory in the scene, thereby improving driving safety. The obstacle area volume calculation can help identify the size and spatial range of obstacles in the scene, and provide the key parameters required for safe path planning. The generated obstacle area volume parameters provide a reference for obstacle avoidance and safe passage for driving path planning. The safe passage path planning combines the user's driving trajectory prediction and obstacle area volume parameters to ensure that the smart helmet provides a safe driving path for the user. The generated scene safe passage path can avoid potential collisions and dangerous situations, and improve driving safety and stability.

[0048] Preferably, the specific steps of step S35 are:

[0049] Perform obstacle location on the dynamic twin scene model and mark the obstacle coordinates;

[0050] Calculate the encounter time of the predicted driving trajectory in the scene according to the obstacle position coordinates to obtain the obstacle encounter time of the predicted trajectory;

[0051] Expand the safe space volume of the obstacle area volume parameters to generate the obstacle safe space bounding volume;

[0052] Based on the obstacle encounter time point of the predicted trajectory, the potential collision area of ​​the obstacle safety space bounding volume is analyzed to generate the user's potential collision area;

[0053] Make normal passage inferences on the user's potential collision area and generate user passage inference results;

[0054] When the user's traffic prediction result is that the vehicle can pass normally, the predicted driving trajectory in the scene is used as the safe passage path of the scene;

[0055] When the user's passage is speculated to be impossible to pass normally, a safe passage path is planned for the user's potential collision area to generate a scene-safe passage path.

[0056] By accurately locating and marking the coordinates of obstacle locations, the present invention provides accurate basic information for subsequent path planning and collision avoidance. Real-time marking of obstacle locations allows the smart helmet to perceive the surrounding environment in a timely manner, improving driving safety. By calculating the time point at which the predicted trajectory will encounter the obstacle, potential collision risks can be assessed and avoidance measures can be taken in advance. Once the encounter time point is obtained, the smart helmet can plan a driving strategy to avoid collisions with obstacles. This expands the safe space volume around the obstacle to create a more relaxed driving environment and reduce potential collision risks. Generating a safe space envelope provides a more comprehensive collision avoidance solution for the smart helmet, ensuring driving safety. Potential collision area analysis can determine the collision risk faced by the user and formulate corresponding countermeasures. Generating a potential collision area allows the smart helmet to provide early warning and avoid potential collision hazards, ensuring driving safety. Normal traffic prediction can assess the user's driving situation in the potential collision area to determine whether a safe passage path exists. The generated traffic prediction results allow the smart helmet to make real-time decisions to ensure the user's safe passage. Safe passage path planning can adjust the driving route according to actual conditions, ensuring safe driving in complex environments. Planning a safe passage path based on the traffic prediction results allows the smart helmet to adjust its driving strategy in real time to minimize collision risks.

[0057] Preferably, the specific steps of step S4 are:

[0058] Step S41: Perform dynamic vehicle optical flow recognition on the dynamic twin scene model and mark all dynamic vehicles in the scene;

[0059] Step S42: Calculate the optical flow displacement of all dynamic vehicles in the scene one by one to generate the vehicle moving speed;

[0060] Step S43: Analyze the moving directions of all dynamic vehicles in the scene and generate the moving directions of the vehicles;

[0061] Step S44: performing dynamic trajectory prediction on the vehicle moving speed and direction to generate predicted driving trajectories of all vehicles in the scene;

[0062] Step S45: performing road constraint correction on the predicted driving trajectories of all vehicles in the scene based on the static spatial structure data of the scene to generate corrected predicted driving trajectories;

[0063] Step S46: Perform multi-period collaborative analysis on the corrected driving prediction trajectory to generate a multi-period corrected driving prediction trajectory.

[0064] The present invention can accurately detect and mark moving vehicles in the scene through dynamic vehicle optical flow recognition, providing basic data for subsequent vehicle movement speed and direction analysis. The marked dynamic vehicle smart helmet can better perceive the surrounding traffic conditions and improve the driver's perception of the surrounding environment. The optical flow displacement calculation one by one can accurately measure the movement speed of each vehicle, providing important data for subsequent vehicle behavior analysis and prediction. The generated vehicle movement speed information smart helmet monitors traffic flow in real time and provides corresponding driving suggestions. The vehicle movement direction analysis can reveal the driving direction of each vehicle, helping the smart helmet to better understand the relative motion relationship between vehicles. The generated vehicle movement direction information smart helmet predicts traffic conflicts and takes corresponding avoidance measures. The dynamic movement trajectory prediction is combined with the vehicle movement direction information. The vehicle speed and direction can be accurately predicted, and the future driving path of the vehicle can be helped by the smart helmet to make more accurate driving predictions. The generated driving prediction trajectory smart helmet plans a safe path and provides timely driving suggestions to improve the driver's driving safety. Road constraint correction can adjust the driving prediction trajectory according to the road structure information to ensure that the generated trajectory complies with the actual road rules and conditions. The corrected driving prediction trajectory smart helmet can better adapt to the road environment, improve driving safety and traffic efficiency. Multi-period collaborative analysis can comprehensively consider the driving prediction trajectory in different time periods to provide more comprehensive and robust driving path planning. The generated multi-period corrected driving prediction trajectory smart helmet can better adapt to changes in different traffic conditions and time periods, and provide more reliable driving suggestions.

[0065] Preferably, the specific steps of step S5 are:

[0066] Step S51: matching the multi-period corrected driving prediction trajectory with the meeting time based on the scenario safe passage path, and marking the meeting section time point;

[0067] Step S52: Cut the dynamic twin scene model into time window regions according to the time point of the encounter section and extract the intersection region model;

[0068] Step S53: Calculating the collision probability of the intersection area model to generate driving collision probability data;

[0069] Step S54: performing safety risk analysis on the vehicle collision probability data to generate a safety risk value for each vehicle;

[0070] Step S55: risk identification is performed on the safety risk value of each vehicle based on a preset driving safety risk threshold. When the preset driving safety risk threshold is greater than the safety risk value of the vehicle, it is marked as a high-risk vehicle.

[0071] The present invention can identify potential vehicle encounters through time matching, helping smart helmets predict the behavior of other vehicles while driving. By marking the time points of encounter sections, smart helmets can better understand the relative positions and relationships between vehicles, improving driving safety. Time window region segmentation can divide the scene into different time periods and spatial regions, facilitating refined processing of vehicle encounters. Extracting the intersection region model allows smart helmets to more accurately capture the areas where vehicles collide, providing a basis for subsequent collision probability calculations. Collision probability calculations quantify the probability of collision between vehicles and provide an objective assessment of driving safety. The generated driving collision probability data allows smart helmets to predict potential collision risks and take necessary safety measures in advance. Safety risk analysis comprehensively considers the vehicle collision probability and other factors to generate a corresponding safety risk value for each vehicle. By generating a safety risk value, smart helmets can more accurately assess the driving safety status of each vehicle and take targeted safety measures. Risk identification quickly marks potential high-risk vehicles by comparing the vehicle's safety risk value with a preset threshold, allowing helmet users to take timely action. High-risk vehicle marking alerts drivers to dangerous situations, reducing the probability of potential accidents and enhancing driving safety.

[0072] Preferably, the specific steps of step S6 are:

[0073] Step S61: highlighting the risky vehicle trajectories of high-risk vehicles based on the multi-period corrected driving prediction trajectories, and extracting the risky vehicle driving prediction trajectories;

[0074] Step S62: making a risk avoidance decision based on the predicted driving trajectory of the risky vehicle and generating a risk decision result;

[0075] Step S63: Project the risk decision result to the smart helmet to complete the driving information processing operation.

[0076] By analyzing the driving prediction trajectories corrected over multiple time periods, the present invention can identify high-risk vehicles and highlight their trajectories, thereby increasing awareness of potential dangers. By extracting the driving prediction trajectories of risky vehicles, the smart helmet monitors and records the driving paths of these vehicles in real time, providing a basis for subsequent risk avoidance decisions. Risk avoidance decisions made based on the driving prediction trajectories of risky vehicles can quickly respond to potential dangers and take necessary actions to avoid accidents. The generated risk decision results include avoidance strategies and action recommendations, which improve the driver's adaptability and driving safety. The risk decision results are projected onto the smart helmet in real time, allowing the driver to intuitively understand the potential risks and avoidance measures taken, thereby improving the efficiency of dealing with dangerous situations. Completing the driving information processing operation means that the smart helmet has successfully integrated and presented the risk decision results to the user, providing the user with comprehensive driving safety protection and assisted driving functions.

[0077] In this specification, a driving information processing device based on a smart helmet is provided, which is used to execute the driving information processing method based on the smart helmet as described above, including:

[0078] The video enhancement module is used to obtain real-time driving process monitoring video and user driving posture data based on the smart helmet; the real-time driving process monitoring video is processed by histogram equalization and global delay interpolation smoothing to obtain delay-smoothed monitoring video;

[0079] The spatial modeling module is used to perform deep road semantic feature segmentation on time-delayed smoothed monitoring videos, model the scene spatial structure, and construct a dynamic twin scene model;

[0080] The safe path planning module is used to predict the driving trajectory of the current scene based on the user's driving posture data, and to plan a safe passage path to generate a safe passage path for the scene;

[0081] The driving trajectory prediction module is used to mark dynamic vehicles in all scenarios based on the dynamic twin scene model; perform dynamic movement trajectory prediction on dynamic vehicles in all scenarios, thereby generating multi-period corrected driving prediction trajectories;

[0082] The safety risk analysis module is used to calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, and conduct safety risk analysis to mark high-risk vehicles;

[0083] The risk avoidance decision module makes risk avoidance decisions for high-risk vehicles based on multi-period corrected driving prediction trajectories and generates risk decision results. The risk decision results are projected onto the smart helmet to complete the driving information processing operation.

[0084] The present invention enhances the contrast of the video, makes the details in the video clearer, improves the quality and visibility of the monitoring video, reduces the jitter and discontinuity in the video through smoothing, improves the video fluency and coherence, and makes the monitoring video easier to analyze and understand. By segmenting the road scene in the video and extracting semantic features, the road environment is understood and different objects are identified, providing a basis for subsequent modeling and path planning. Establishing a spatial structure model of the scene can help the helmet system better understand the surrounding environment and improve the accuracy and safety of driving decisions. By predicting the driving trajectory of the user in the current scene, potential risks and dangerous situations can be identified in advance, providing a basis for safe passage path planning, and generating a safe passage path avoidance system for the scene. Avoid collisions and optimize driving paths, improve driving safety and efficiency, mark dynamic vehicles in the scene and predict their movement trajectories. The smart helmet system better understands the behavior of surrounding vehicles and reduces driving risks. Generating a revised driving prediction trajectory can help the system more accurately predict vehicle position and movement trajectory, improve driving safety. By calculating collision probability and conducting risk analysis, high-risk vehicles can be identified and corresponding measures can be taken to improve driving safety. Based on the driving prediction trajectory and risk analysis results, the system can generate corresponding risk avoidance decisions to help drivers avoid potential dangerous situations. The risk decision results are projected onto the smart helmet, allowing drivers to understand the risk situation in real time and improve the timeliness and accuracy of driving decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 This is a schematic flow chart of the steps of a method for processing driving information based on a smart helmet according to the present invention;

[0086] Figure 2 Detailed implementation flow chart of step S1;

[0087] Figure 3 Detailed implementation flow chart of step S2;

[0088] Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0089] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0090] This application provides a method and device for processing driving information based on a smart helmet. The execution entities of the method and device include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that are equipped with the system, which can be regarded as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0091] See also Figures 1 to 4 The present invention provides a method for processing driving information based on a smart helmet, and the method for processing driving information based on a smart helmet comprises the following steps:

[0092] Step S1: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet; performing histogram equalization processing on the real-time driving process monitoring video and performing global delay interpolation smoothing processing to obtain a delay-smoothed monitoring video;

[0093] Step S2: Perform deep road semantic feature segmentation on the time-delay smoothed monitoring video, perform scene spatial structure modeling, and construct a dynamic twin scene model;

[0094] Step S3: Predict the current driving trajectory of the scene based on the user's driving posture data, and perform safe passage path planning to generate a scene safe passage path;

[0095] Step S4: Mark the dynamic vehicles in all scenes based on the dynamic twin scene model; predict the dynamic movement trajectory of the dynamic vehicles in all scenes, thereby generating multi-period corrected driving prediction trajectories;

[0096] Step S5: Calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, perform safety risk analysis, and mark the vehicle as a high-risk vehicle;

[0097] Step S6: Make risk avoidance decisions for high-risk vehicles based on the multi-period corrected driving prediction trajectory and generate risk decision results; project the risk decision results onto the smart helmet to complete the driving information processing operation.

[0098] The present invention enhances the contrast and brightness of video images through histogram equalization processing, improves image quality, and makes monitoring videos clearer. Global time delay interpolation smoothing processing reduces jitter and discontinuity in the video, improves user viewing experience, and ensures video fluency. Deep road semantic feature segmentation and scene spatial structure modeling accurately understand road conditions and scene structures, provide accurate scene information for subsequent safe passage path planning, construct a dynamic twin scene model to capture and reflect the dynamic changes of road scenes in real time, improve the driving system's ability to understand and predict scenes, driving trajectory prediction and safe passage path planning predict the user's driving trajectory in advance and plan a safe path, improve driving safety and efficiency, generate a scene safe passage path to avoid potential dangers, and provide users with safe driving. Guidance, dynamic movement trajectory prediction and multi-period correction driving prediction trajectory accurately predict the movement trajectory of vehicles in the scene, improve the system's understanding and prediction ability of vehicle behavior, generate correction driving prediction trajectory to optimize driving path, improve driving efficiency and safety, collision probability calculation and safety risk analysis to identify high-risk vehicles, discover potential collision risks in advance, reduce the occurrence of traffic accidents, mark high-risk vehicles to warn drivers, improve drivers' awareness of potential dangers, enhance safety awareness, risk avoidance decisions and risk decision results are projected onto smart helmets to provide real-time risk prompts and decision support, help drivers make correct decisions, complete driving information processing operations to optimize the driver's driving experience, improve driving safety and comfort, and realize intelligent driving assistance functions.

[0099] In the embodiment of the present invention, see Figure 1 , is a flowchart of the steps of a method for processing driving information based on a smart helmet according to the present invention. In this example, the steps of the method include:

[0100] Step S1: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet; performing histogram equalization processing on the real-time driving process monitoring video and performing global delay interpolation smoothing processing to obtain a delay-smoothed monitoring video;

[0101] In this embodiment, the sensors of the smart helmet (such as cameras, accelerometers, gyroscopes, etc.) are ensured to work properly, and a connection is established with the computing device through a suitable interface (such as Bluetooth or Wi-Fi). The real-time driving process monitoring video is obtained from the camera of the smart helmet to ensure that the video data is smooth and clear. At the same time, the user's driving posture data, including the vehicle's speed, acceleration, steering angle, etc., are collected. These data should be recorded synchronously with the video stream for subsequent analysis. Each frame of the image is extracted from the real-time monitoring video, and a suitable histogram equalization algorithm (such as global equalization or adaptive histogram equalization (CLAHE)) is selected. ) to enhance the contrast of the image, apply the histogram equalization algorithm to each frame of the image, calculate the equalization value of each pixel to improve the brightness and contrast of the image, use image processing libraries such as OpenCV, call related functions to implement equalization processing, ensure that each extracted frame of the image is synchronized with the timestamp of the user's driving posture data, select a suitable interpolation algorithm (such as linear interpolation, spline interpolation, or resampling) to deal with the time delay problem, for each time period, interpolate according to the timestamp difference, smooth the delay in the video stream, and generate new frames through the interpolation algorithm to ensure smooth video playback without obvious lag.

[0102] Step S2: Perform deep road semantic feature segmentation on the time-delay smoothed monitoring video, perform scene spatial structure modeling, and construct a dynamic twin scene model;

[0103] In this embodiment, a suitable deep learning semantic segmentation model (such as U-Net, DeepLab, SegNet, etc.) is selected to ensure that the model can effectively handle traffic scenes. If a pre-trained model is used, ensure that an appropriate dataset (such as Cityscapes or KITTI) is loaded for fine-tuning. If a custom model is used, it is necessary to prepare training data containing annotations, and pre-process each frame in the time-delayed smoothed monitoring video, including normalization, resizing, etc., to meet the model input requirements. The pre-processed image of each frame is input, and semantic segmentation is performed using a deep learning model to generate a semantic feature map for each frame. The output feature map is converted into a label map containing different categories (such as roads, vehicles, pedestrians, traffic signs, etc.). Based on the generated semantic feature map, static objects in the scene (such as roads, buildings, etc.) are identified. Buildings, traffic signs, etc.), extract their geometric features (such as position, shape, and size). For dynamic objects in the scene (such as moving vehicles), record their motion paths and relative positions instead of static features. Select suitable 3D modeling software or libraries (such as Blender, Open3D, Unity, etc.) to build the scene model. According to the extracted spatial features, reconstruct the 3D structure of the static scene, define the geometric shape and spatial position of each object, define the motion trajectory for dynamic objects, and add corresponding animation effects to the model to ensure the accuracy of the model's dynamic performance. Optimize the details of the constructed 3D scene model, including adding textures, lighting effects, and physical properties to enhance the realism of the model. Save the final dynamic twin scene model in an appropriate file format (such as FBX, OBJ) for subsequent use and display.

[0104] Step S3: Predict the current driving trajectory of the scene based on the user's driving posture data, and perform safe passage path planning to generate a scene safe passage path;

[0105] In this embodiment, the user's real-time driving posture data is collected, including speed, acceleration, steering angle, etc. These data should be combined with the environmental data in the dynamic twin scene model. The static and dynamic features of the current environment (such as road structure, obstacle location, traffic signs, etc.) are extracted from the dynamic twin scene model. It is suitable for dynamic system state estimation and can handle noise and uncertainty. Recurrent neural networks (RNN) or long short-term memory networks (LSTM) are suitable for processing time series data and can capture complex driving behavior patterns. The user's driving posture data is input into the selected prediction model together with the scene features. The predicted driving trajectory in the current scene is generated through model calculation and output as a series of spatial coordinate points (such as (x, y, t)). Determine the constraints for safe passage, including: considering the volume and turning radius of the user's vehicle. Extract the location, shape and safe space of obstacles from the dynamic twin scene model. Consider factors such as driving direction, speed limit and traffic signals. Select a suitable path planning algorithm, for example: suitable for finding the optimal path in a known environment. Suitable for finding the shortest path in a weighted graph. Suitable for path planning in complex and dynamic environments. Path Planning Execution: The system takes the predicted trajectory, scene characteristics, and safe passage constraints as input. It uses the selected algorithm to calculate a safe passage path, ensuring that the path avoids obstacles and complies with traffic regulations. It then smooths the generated path to reduce sharp turns, acceleration, and deceleration, improving driving comfort. The optimized path is then checked to ensure that it meets safety standards and eliminates potential collision risks.

[0106] Step S4: Mark the dynamic vehicles in all scenes based on the dynamic twin scene model; predict the dynamic movement trajectory of the dynamic vehicles in all scenes, thereby generating multi-period corrected driving prediction trajectories;

[0107] In this embodiment, the current dynamic twin scene model is loaded to ensure that it contains real-time traffic information and scene features, a real-time video stream or image sequence containing vehicles is obtained from the monitoring system, and a suitable target detection algorithm (such as YOLO, SSD, Faster R-CNN, etc.) is selected to be able to identify dynamic vehicles in real time. Each frame in the real-time video stream is processed, and the selected target detection algorithm is applied to identify and mark dynamic vehicles. The position, speed, category, timestamp and other information of each detected dynamic vehicle are recorded and stored in a data structure for subsequent use. The dynamic vehicle labeling data extracted from the first step includes the position, speed and time information of each vehicle. Environmental features (such as road structure, traffic signals, etc.) are extracted from the dynamic twin scene model. Dynamic trajectory prediction model selection: Select a suitable dynamic trajectory prediction model, such as: suitable for dynamic system state estimation, able to process vehicle motion noise, suitable for processing time series data, able to capture the vehicle's driving mode, and predict according to the vehicle's physical characteristics (such as speed, acceleration). The current state of the dynamic vehicle (position, Speed, direction) as input, combined with environmental data, use the selected prediction model to generate the future movement trajectory of each vehicle, and output the coordinates of a series of time points (such as (x, y, t)). The predicted trajectory of the dynamic vehicle is divided into time periods (such as short-term prediction, medium-term prediction and long-term prediction) to ensure that the trajectory of each time period can be analyzed. Constraint definition: Road constraints: Considering the road structure and traffic rules, the predicted trajectory of the vehicle is constrained and corrected. The relative position of the predicted trajectory with other dynamic objects (such as other vehicles, pedestrians, etc.) is checked to ensure that there is no collision risk. The predicted trajectory of each time period is optimized using a path optimization algorithm (such as A* or Dijkstra) to ensure that it is within the road range and avoids obstacles. The corrected trajectory is smoothed to reduce sharp turns and unnecessary acceleration and deceleration, thereby improving driving comfort.

[0108] Step S5: Calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, perform safety risk analysis, and mark the vehicle as a high-risk vehicle;

[0109] In this embodiment, the corrected driving prediction trajectories of all vehicles extracted from the previous steps contain the movement information of each vehicle in different time periods, and the safe passage path data in the scene is loaded to ensure that road boundaries, lane lines and other traffic facilities are included. Collision model selection: Select a suitable collision probability calculation model, such as: based on the geometric relationship between vehicle position and shape, calculate the collision probability of relative position, consider the dynamic model of vehicle speed and direction, predict future collision risks, randomly generate multiple scenes, analyze the probability of collision, perform intersection detection on the corrected driving prediction trajectories of each pair of vehicles, and determine whether a collision will occur in the future time period. According to the intersection situation and factors such as speed and distance, the collision probability of each pair of vehicles is calculated and output as a probability value. The calculated collision probability is stored in a data structure for subsequent analysis and selection of an appropriate A comprehensive safety risk assessment model is used, such as calculating the safety risk value based on the collision probability and other factors (such as vehicle type and driving behavior), classifying risks based on historical data and rules, calculating the safety risk value of each vehicle by comprehensively considering factors such as collision probability, vehicle speed, distance, driving environment, etc., setting a safety risk threshold based on historical data, usually based on industry standards or empirical data, comparing the safety risk value of each vehicle, and marking it as a high-risk vehicle if the value is higher than the threshold. The information of all high-risk vehicles, including their location, speed, risk value, etc., is saved for subsequent processing and alarms, and the results of collision probability and high-risk vehicles are visualized, using a graphical interface to display the vehicle location, predicted trajectory and risk level, and generate a risk analysis report containing detailed information on high-risk vehicles, collision probability data and recommended safety measures.

[0110] Step S6: Make risk avoidance decisions for high-risk vehicles based on the multi-period corrected driving prediction trajectory and generate risk decision results; project the risk decision results onto the smart helmet to complete the driving information processing operation.

[0111] In this embodiment, the corrected driving prediction trajectories of all vehicles are extracted, including information on high-risk vehicles, and all vehicles marked as high-risk and their related data (such as location, speed, and driving direction) are collected. For each high-risk vehicle, the selected model is used to analyze the intersection of its current trajectory and its own trajectory, and a corresponding avoidance strategy is generated, such as: lane change: change lanes from the current lane when it is safe, slow down: slow down when approaching a high-risk vehicle, stop: stop and wait when necessary to avoid conflict, and store the detailed information of each decision (such as the selected avoidance strategy, execution conditions, etc.) for subsequent use, ensure that the communication interface (such as Bluetooth, Wi-Fi) between the smart helmet and the vehicle works normally and can receive data, and convert the risk decision results into smart The risk decision results are formatted in a format recognizable by the helmet (such as JSON or a specific protocol format), including the decision type, execution time and related conditions. The formatted risk decision results are sent to the smart helmet through the communication interface, and the risk decision results are displayed on the display screen of the smart helmet to ensure that the driver can quickly understand and respond. Visual signals (such as icons, text prompts) can be used for display. If feasible, audio prompts can be added to enhance the driver's perception of risks. A feedback mechanism is implemented in the smart helmet to collect the driver's response to the risk decision results (such as confirmation, ignore, execution, etc.), and the driver's feedback and decision results are stored in the system for subsequent analysis and optimization. Based on the collected feedback data, the risk avoidance decision model is regularly optimized to improve the accuracy and real-time performance of the decision.

[0112] In this embodiment, refer to Figure 2 , is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0113] Step S11: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet;

[0114] Step S12: performing brightness deviation detection on the real-time driving process monitoring video to identify the brightness deviation area;

[0115] Step S13: performing histogram equalization processing based on the brightness deviation area to generate a brightness enhanced monitoring video;

[0116] Step S14: performing driving scene edge analysis on the brightness enhanced monitoring video to extract driving scene edge data;

[0117] Step S15: Optimizing edge details of the brightness-enhanced monitoring video according to the edge data of the travel scene to construct an edge-optimized monitoring video;

[0118] Step S16: performing global delay interpolation smoothing processing on the edge optimized monitoring video to obtain a delay smoothed monitoring video.

[0119] In this embodiment, a camera on a smart helmet is used to record a video of the driving process in real time, ensuring that the camera's viewing angle can capture the road ahead and the surrounding environment. The built-in sensors of the helmet (such as an accelerometer and a gyroscope) are used to collect the user's driving posture data, including the tilt angle and movement state of the head. The video stream and the posture data are synchronized in time so that the video frame can be matched with the user's driving posture in subsequent analysis. A brightness deviation detection is performed on each frame of video, and areas with brightness lower than or higher than the normal range are identified and marked as brightness deviation areas. The position information of the identified brightness deviation areas is recorded to provide a basis for subsequent processing. A suitable histogram equalization algorithm, such as CLAHE (adaptive histogram equalization), is selected to avoid over-enhancement. Perform histogram equalization on the brightness deviation areas to improve the contrast and brightness of these areas, generate a brightness enhanced monitoring video, select a suitable edge detection algorithm, such as Canny edge detection or Sobel operator, perform edge analysis on each frame in the brightness enhanced monitoring video, extract edge data of the driving scene, select an edge detail optimization algorithm, such as high-pass filtering or sharpening processing, optimize the edge details of the brightness enhanced monitoring video based on the extracted edge data, increase edge clarity, output the processed edge optimized monitoring video, ensure that the enhanced edge features are more obvious, perform time-delay interpolation smoothing on the continuous frames in the edge optimized monitoring video, reduce jitter and blur caused by motion, generate a time-delay smoothed monitoring video, and ensure that the video is smooth and the picture is clear.

[0120] In this embodiment, the specific steps of step S16 are:

[0121] Perform time-series window segmentation on edge-optimized monitoring videos and extract time-series images of each frame;

[0122] Calculating the timestamp of each frame of the time-series image;

[0123] Calculate the time interval between the upper and lower frames according to the timestamp to obtain the inter-frame interval duration of all image frames;

[0124] Fit the interval duration distribution of all image frames to obtain the global interval distribution map of the video;

[0125] Perform interval discrete unevenness identification on the global interval distribution map of the video and extract the points with uneven interval duration;

[0126] Positioning the time-series image frames based on the unevenly spaced time points, and extracting all the unevenly spaced image frames;

[0127] Calculating the inter-frame delay of all non-uniformly spaced image frames to obtain the delay parameters of the non-uniformly spaced frames;

[0128] Performing average inter-frame interval statistics on the inter-frame interval durations of all image frames to generate a mean inter-frame interval value;

[0129] Performing delay compensation calculation on the delay parameters of the non-uniformly spaced frames according to the mean inter-frame interval value, thereby obtaining the delay compensation parameters of the non-uniformly spaced frames;

[0130] Performing time-series serialization processing on each frame of the time-series image according to the timestamp to obtain a time-series image frame sequence;

[0131] The delay compensation parameters of non-uniform interval frames are used to perform delay interpolation and smoothing processing on the time sequence image frame sequence, thereby obtaining a delay smoothed monitoring video.

[0132] In this embodiment, edge optimization monitoring video is loaded from the file system, and a video processing library (such as OpenCV) is used to segment the video according to a set time window (such as per second or per frame). A loop can be used to traverse each frame in the video, and the frames in each window are extracted according to the time interval. The frames in each window are saved in a list or array for subsequent processing. The frame rate (FPS) is obtained through the metadata of the video file. For each frame image, a timestamp is calculated, where the frame index increases from 0. The timestamp of each frame generated is stored in an array and associated with each frame image. For each frame image, the time interval with the previous frame is calculated. All calculated frame interval durations are saved in an array to form a time interval data sequence. Statistical tools (such as NumPy and SciPy) are used to analyze the frame interval data, calculate the frequency distribution of each time interval, select a suitable distribution model (such as normal distribution or gamma distribution) for fitting, and the fit method in scipy.stats can be used for fitting. Matplotlib or other visualization libraries are used to generate a global interval distribution graph of the video to show the distribution of each time interval. The standard deviation of the time interval is calculated, and a reasonable threshold is defined to identify which Some time intervals are uneven, and all time interval points that exceed the threshold are recorded as uneven duration points, and their indexes and corresponding durations are stored. According to the indexes of the uneven duration points extracted in step 5, the corresponding unevenly spaced image frames are extracted from the original image sequence, and the extracted unevenly spaced image frames are saved in a new data structure for subsequent processing. Inter-frame delay calculation is performed on all extracted unevenly spaced image frames, and the calculated delay parameters of the unevenly spaced frames are saved in an array. The frame interval durations of all image frames are averaged, and the generated average inter-frame interval value is recorded. According to the average inter-frame interval Interval, perform delay compensation calculation on the delay parameters of non-uniformly spaced frames, serialize the time-series images of each frame according to the calculated timestamp, ensure that the images are arranged in chronological order, form a new time-series image frame sequence, and facilitate subsequent delay interpolation and smoothing processing. Select a suitable interpolation algorithm (such as linear interpolation, spline interpolation) to perform delay smoothing processing on the time-series image frame sequence, and perform smooth interpolation on the time-series image frame sequence according to the calculated delay compensation parameters to generate a delay smoothing monitoring video. Save the final generated delay smoothing monitoring video as a video file (such as MP4 or AVI) for subsequent viewing and use.

[0133] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0134] Step S21: performing scene object visual recognition on the time-delay smoothed monitoring video and marking objects in the driving scene, wherein the objects in the driving scene include static road signs, road obstacles, and dynamic vehicles;

[0135] Step S22: performing deep road semantic feature segmentation on the time-delay smoothed monitoring video to generate driving scene road semantic features;

[0136] Step S23: performing scene spatial structure analysis on static road signs and road obstacles to generate scene static spatial structure data;

[0137] Step S24: performing scene spatial structure modeling on the driving scene road semantic features and the scene static spatial structure data to construct a three-dimensional driving scene model;

[0138] Step S25: Perform dynamic movement evolution simulation on the three-dimensional driving scene model according to the dynamic vehicle to construct a dynamic twin scene model.

[0139] In this embodiment, a suitable object detection algorithm (such as YOLO, Faster R-CNN) is selected to identify objects in the video, each frame of the video is processed, static road signs, road obstacles and dynamic vehicles are identified, and bounding boxes and labels are generated for each identified object. The recognition results (including object location and type) are stored in a data structure for subsequent use. A suitable deep learning model (such as U-Net, DeepLab) is selected for road semantic segmentation, each frame of the image is preprocessed (such as normalization and resizing) to adapt to the model input, the processed frame is input into the semantic segmentation model, a road semantic feature map of each frame is generated, and the generated driving scene road semantic feature map is saved, including segmentation information of categories such as roads, pedestrians, and vehicles. Based on the identified static road signs and road obstacles, their spatial features (such as position, size and shape) are extracted, and geometric modeling methods (such as point clouds or three-dimensional meshes) are used to establish static spatial structure data of the scene to represent the layout of objects in three-dimensional space. The static spatial structure data of the scene is saved for subsequent modeling. A three-dimensional modeling software or library (such as Blender, Open3D) is used to construct a three-dimensional driving scene model. According to the generated road semantic features and the static spatial structure data generated in step S23, a complete three-dimensional driving scene model is constructed. The model is optimized in detail to ensure the realism and accuracy of the model, including texture mapping and lighting effects. The constructed three-dimensional driving scene model is saved in an appropriate format (such as OBJ, FBX) for subsequent use. A suitable dynamic simulation framework (such as Unity, Gazebo) is selected to implement the dynamic twin scene model. According to the identified dynamic vehicle, its motion behavior (such as speed, acceleration and path) is defined, and the movement of the dynamic vehicle is realized in the three-dimensional driving scene model. Dynamic evolution simulation is performed to simulate the vehicle motion in the real driving scene. The generated dynamic twin scene model is saved for subsequent analysis and visualization.

[0140] In this embodiment, refer to Figure 4 , is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0141] Step S31: Calculate the driving space volume based on the user's driving posture data to generate the user's standard driving volume;

[0142] Step S32: Analyze the user's driving trajectory on the time-delay smoothed monitoring video to extract the user's time-series driving trajectory;

[0143] Step S33: Predicting the current scene driving trajectory of the user's time series driving trajectory based on the user's standard driving volume to generate a predicted driving trajectory within the scene;

[0144] Step S34: Calculate the obstacle area volume of the dynamic twin scene model to generate obstacle area volume parameters;

[0145] Step S35: Based on the volume parameters of the obstacle area, a safe passage path is planned for the predicted driving trajectory in the scene to generate a safe passage path for the scene.

[0146] In this embodiment, the user's driving posture data is collected, including the size, position and driving status of the vehicle (such as speed, steering angle), and the user's standard driving volume is calculated according to the vehicle's geometric shape (such as a cuboid or a polygon). The calculated user's standard driving volume is recorded and saved as a data structure, and the user's driving trajectory is extracted from the time-delay smoothed monitoring video. The object detection result identified in the previous step is used to extract the user's time-series driving trajectory by analyzing the timestamp and vehicle position in the video and record it as a sequence of coordinate points (such as (x, y, t)). The extracted user's time-series driving trajectory is stored in an array for subsequent use, and the user's time-series driving trajectory is predicted using a machine learning or deep learning model (such as an LSTM or Kalman filter). Based on the user's standard driving volume and the extracted driving trajectory data, the prediction model is trained so that it can take into account The vehicle's motion characteristics, input the current user driving trajectory data, generate the future driving prediction trajectory in the scene, record it as a sequence of coordinate points, and store the generated driving prediction trajectory for subsequent path planning. According to the object detection results in the dynamic twin scene model, identify the obstacles in the scene, and calculate the volume of each identified obstacle. Usually, a simple geometric volume formula (such as a cuboid, cylinder, etc.) is used, and the total obstacle area volume is calculated. Select a suitable path planning algorithm (such as A* algorithm, Dijkstra algorithm or RRT) to plan the user's safe passage path. The user's standard driving volume and obstacle area volume parameters are input into the path planning algorithm to ensure that the vehicle's space requirements and obstacle positions are considered during the planning process. Based on the predicted driving trajectory and obstacle area, a safe passage path is generated and recorded as a sequence of coordinate points.

[0147] In this embodiment, the specific steps of step S35 are:

[0148] Perform obstacle location on the dynamic twin scene model and mark the obstacle coordinates;

[0149] Calculate the encounter time of the predicted driving trajectory in the scene according to the obstacle position coordinates to obtain the obstacle encounter time of the predicted trajectory;

[0150] Expand the safe space volume of the obstacle area volume parameters to generate the obstacle safe space bounding volume;

[0151] Based on the obstacle encounter time point of the predicted trajectory, the potential collision area of ​​the obstacle safety space bounding volume is analyzed to generate the user's potential collision area;

[0152] Make normal passage inferences on the user's potential collision area and generate user passage inference results;

[0153] When the user's traffic prediction result is that the vehicle can pass normally, the predicted driving trajectory in the scene is used as the safe passage path of the scene;

[0154] When the user's passage is speculated to be impossible to pass normally, a safe passage path is planned for the user's potential collision area to generate a scene-safe passage path.

[0155] In this embodiment, the identified obstacle data, including the type and location of the obstacle, is extracted from the dynamic twin scene model.

[0156] Generate position coordinates (such as (x, y, z)) for each identified obstacle, record its precise position in three-dimensional space, store the obstacle's position information in a data structure for subsequent use, analyze the user's predicted driving trajectory frame by frame, calculate the relative position of each frame and the obstacle's position coordinates, and use the following formula to calculate the encounter time point with each obstacle: encounter time point = current time + (obstacle distance / user speed), save all calculated obstacle encounter time points, set the expansion parameters of the obstacle safety space (such as buffer size) according to safety standards and vehicle size, expand the position coordinates of each obstacle, and generate a safe space bounding volume. Geometric bodies (such as spheres or cubes) can be used to represent the safe area. Based on the obstacle encounter time point of the predicted trajectory, analyze the collision area that occurs within the encounter time, analyze the safe space bounding volume of each obstacle, and determine The intersection with the predicted trajectory is recorded as a potential collision area, and the generated user potential collision area is saved for subsequent inference results. According to the size of the potential collision area and the user's driving volume, it is judged whether the user can pass normally, and the user's passage inference result is generated, marked as "passable normally" or "unpassable normally". The passage inference result is recorded for subsequent path selection. If the user's passage inference result is "passable normally", the driving prediction trajectory in the scene is directly used as a safe passage path. If the result is "unpassable normally", it is necessary to plan a safe passage path for the user's potential collision area, and use a suitable path planning algorithm (such as A* or Dijkstra) to generate a new safe passage path. The constraints of the potential collision area and the position of the obstacles are used as input parameters to ensure that the planned path avoids the collision area, and generate a new scene safe passage path, which is recorded as a sequence of coordinate points.

[0157] In this embodiment, step S4 includes the following steps:

[0158] Step S41: Perform dynamic vehicle optical flow recognition on the dynamic twin scene model and mark all dynamic vehicles in the scene;

[0159] Step S42: Calculate the optical flow displacement of all dynamic vehicles in the scene one by one to generate the vehicle moving speed;

[0160] Step S43: Analyze the moving directions of all dynamic vehicles in the scene and generate the moving directions of the vehicles;

[0161] Step S44: performing dynamic trajectory prediction on the vehicle moving speed and direction to generate predicted driving trajectories of all vehicles in the scene;

[0162] Step S45: performing road constraint correction on the predicted driving trajectories of all vehicles in the scene based on the static spatial structure data of the scene to generate corrected predicted driving trajectories;

[0163] Step S46: Perform multi-period collaborative analysis on the corrected driving prediction trajectory to generate a multi-period corrected driving prediction trajectory.

[0164] In this embodiment, a real-time video stream or image sequence is extracted from a dynamic twin scene model, and an optical flow algorithm (such as the Lucas-Kanade method or the Horn-Schunck method) is used to process the video frames to identify dynamic vehicles in the scene. The identified dynamic vehicles are marked, and the position coordinates and optical flow vectors of each vehicle are generated. The marking results are stored in a data structure, including the position information and optical flow data of each vehicle, for use in subsequent steps. By analyzing the position changes of dynamic vehicles in consecutive frames, the optical flow displacement of each vehicle is calculated, using the formula: displacement = current frame position - previous frame position. The moving speed of the vehicle is calculated based on the optical flow displacement and the time interval (frame rate): speed = displacement / time interval. The moving speed of each vehicle is stored in an array, and the moving direction of the vehicle is analyzed based on the optical flow vector. The inverse tangent function is usually used to calculate the direction angle: direction = tan-1(Δx / Δy). The calculated direction angle is standardized to a value between 0 and 360 degrees for subsequent use. The moving directions of all dynamic vehicles are recorded in a data structure, associated with the identification of each vehicle, and a suitable trajectory prediction algorithm (such as The method uses linear regression, Kalman filtering or LSTM) to predict the movement trajectory of vehicles, takes the movement speed and direction of each vehicle as input parameters, combines historical trajectory data for model training, uses the model to predict the future position of each vehicle, generates driving prediction trajectories for all vehicles, and stores the predicted driving trajectories as a sequence of coordinate points for use in subsequent steps. Road information, such as road boundaries, lane lines and traffic signs, is extracted from the static spatial structure data of the scene. A path constraint algorithm (such as graph-based path planning or constrained optimization) is used to correct the driving prediction trajectory of each vehicle to ensure that the trajectory conforms to the road constraints. The corrected trajectory is recorded as a new sequence of coordinate points to ensure that it is within the road range. The corrected driving prediction trajectory is stored and divided into time periods (such as short period, medium period and long period) to ensure that the trajectory data of each time period can be analyzed. A multi-time period analysis algorithm (such as time series analysis or ensemble learning) is used to analyze the corrected driving prediction trajectory of each time period, identify the change trend, and comprehensively analyze the results to generate multi-time period corrected driving prediction trajectories to ensure that the predicted trajectories of each time period are correlated with each other.

[0165] In this embodiment, step S5 includes the following steps:

[0166] Step S51: matching the multi-period corrected driving prediction trajectory with the meeting time based on the scenario safe passage path, and marking the meeting section time point;

[0167] Step S52: Cut the dynamic twin scene model into time window regions according to the time point of the encounter section and extract the intersection region model;

[0168] Step S53: Calculating the collision probability of the intersection area model to generate driving collision probability data;

[0169] Step S54: performing safety risk analysis on the vehicle collision probability data to generate a safety risk value for each vehicle;

[0170] Step S55: risk identification is performed on the safety risk value of each vehicle based on a preset driving safety risk threshold. When the preset driving safety risk threshold is greater than the safety risk value of the vehicle, it is marked as a high-risk vehicle.

[0171] In this embodiment, the safe passage path data based on the dynamic twin scene is obtained, the corrected driving prediction trajectory of multiple time periods is loaded from the monitoring system, the driving prediction trajectory is time-matched with the safe passage path, and the time point of the encounter section is found. The following method can be used: the vehicle position and the path are interpolated according to the timestamp to determine the encounter time point, the intersection point of the path is detected using a geometric method, and the time point of the encounter section is recorded. The dynamic twin scene model is loaded from the system, and the time window area is defined according to the marked encounter section time point. The area should include the spatial range where the meeting occurs. The dynamic twin scene model is cut using a computational geometry algorithm to extract the intersection area model. Boolean operations (such as intersection) can be used to obtain the intersection part, and the extracted intersection area model is stored for subsequent collision probability calculation. According to the extracted intersection area model, Analyze the positions of vehicles and obstacles in the intersection area, select a suitable collision probability calculation model (such as Monte Carlo simulation or probability map), calculate the collision probability for each pair of intersecting vehicles and obstacles, consider vehicle speed, distance and driving direction, select a suitable risk assessment model (such as decision tree or weighted scoring system) to analyze the driving collision probability data, and calculate the safety risk value for each vehicle based on the collision probability and other relevant factors (such as vehicle type and driving environment). Safety risk value = P (collision) × risk coefficient. Set a preset driving safety risk threshold, usually based on historical data and safety standards, to compare the safety risk value of each vehicle to determine whether it exceeds the preset threshold. If the safety risk value is greater than the threshold, it will be marked as a high-risk vehicle, and the information of the high-risk vehicle will be saved for subsequent processing and alarm.

[0172] In this embodiment, step S6 includes the following steps:

[0173] Step S61: highlighting the risky vehicle trajectories of high-risk vehicles based on the multi-period corrected driving prediction trajectories, and extracting the risky vehicle driving prediction trajectories;

[0174] Step S62: making a risk avoidance decision based on the predicted driving trajectory of the risky vehicle and generating a risk decision result;

[0175] Step S63: Project the risk decision result to the smart helmet to complete the driving information processing operation.

[0176] In this embodiment, the information of vehicles marked as high-risk is extracted from the previous analysis, including their position, speed and predicted trajectory, and the driving prediction trajectory data corrected in multiple periods is loaded to ensure that the driving information of all relevant vehicles is included. The driving prediction trajectory of high-risk vehicles is screened out, and the trajectory of high-risk vehicles is highlighted using a graphics library (such as Matplotlib or OpenGL). This can be achieved by changing the color, line width or adding marks. The highlighted driving prediction trajectory of the risky vehicle is saved, and an appropriate risk avoidance decision model (such as a decision tree, reinforcement learning or rule engine) is selected. The driving prediction trajectory of the risky vehicle, environmental information (such as road conditions, obstacle locations) and vehicle dynamic characteristics (such as speed and acceleration) are used as input parameters. According to safety standards and driving rules, avoidance decision rules are defined. For example, if the predicted trajectory of the risky vehicle intersects with its own driving path, it is necessary to change lanes or slow down. The selected model is used for calculation to generate risk avoidance decision results, such as "change lanes to the left", "slow down" or "stop and wait". Ensure that the communication interface between the smart helmet and the vehicle (such as Bluetooth or Wi-Fi) is working properly and can receive external data. The risk decision results are converted into a format recognizable by the smart helmet (such as JSON or a specific protocol format). The converted risk decision results are sent to the smart helmet through the communication interface and displayed on the display screen of the smart helmet to ensure that the driver can quickly understand and respond. Visual signals (such as icons, text prompts) can be used for display. A feedback mechanism is implemented in the smart helmet to collect the driver's response to the decision results (such as confirmation, ignore) for subsequent system optimization.

[0177] In this embodiment, a driving information processing device based on a smart helmet is provided, which is used to execute the above-mentioned driving information processing method based on a smart helmet, including:

[0178] The video enhancement module is used to obtain real-time driving process monitoring video and user driving posture data based on the smart helmet; the real-time driving process monitoring video is processed by histogram equalization and global delay interpolation smoothing to obtain delay-smoothed monitoring video;

[0179] The spatial modeling module is used to perform deep road semantic feature segmentation on time-delayed smoothed monitoring videos, model the scene spatial structure, and construct a dynamic twin scene model;

[0180] The safe path planning module is used to predict the driving trajectory of the current scene based on the user's driving posture data, and to plan a safe passage path to generate a safe passage path for the scene;

[0181] The driving trajectory prediction module is used to mark dynamic vehicles in all scenarios based on the dynamic twin scene model; perform dynamic movement trajectory prediction on dynamic vehicles in all scenarios, thereby generating multi-period corrected driving prediction trajectories;

[0182] The safety risk analysis module is used to calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, and conduct safety risk analysis to mark high-risk vehicles;

[0183] The risk avoidance decision module makes risk avoidance decisions for high-risk vehicles based on multi-period corrected driving prediction trajectories and generates risk decision results. The risk decision results are projected onto the smart helmet to complete the driving information processing operation.

[0184] The present invention enhances the contrast of the video, makes the details in the video clearer, improves the quality and visibility of the monitoring video, reduces the jitter and discontinuity in the video through smoothing, improves the video fluency and coherence, and makes the monitoring video easier to analyze and understand. By segmenting the road scene in the video and extracting semantic features, the road environment is understood and different objects are identified, providing a basis for subsequent modeling and path planning. Establishing a spatial structure model of the scene can help the helmet system better understand the surrounding environment and improve the accuracy and safety of driving decisions. By predicting the driving trajectory of the user in the current scene, potential risks and dangerous situations can be identified in advance, providing a basis for safe passage path planning, and generating a safe passage path avoidance system for the scene. Avoid collisions and optimize driving paths, improve driving safety and efficiency, mark dynamic vehicles in the scene and predict their movement trajectories. The smart helmet system better understands the behavior of surrounding vehicles and reduces driving risks. Generating a revised driving prediction trajectory can help the system more accurately predict vehicle position and movement trajectory, improve driving safety. By calculating collision probability and conducting risk analysis, high-risk vehicles can be identified and corresponding measures can be taken to improve driving safety. Based on the driving prediction trajectory and risk analysis results, the system can generate corresponding risk avoidance decisions to help drivers avoid potential dangerous situations. The risk decision results are projected onto the smart helmet, allowing drivers to understand the risk situation in real time and improve the timeliness and accuracy of driving decisions.

[0185] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0186] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing driving information based on a smart helmet, characterized in that: The following steps are involved: Step S1: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet; performing histogram equalization processing on the real-time driving process monitoring video and performing global delay interpolation smoothing processing to obtain a delay-smoothed monitoring video; Step S2: Perform deep road semantic feature segmentation on the time-delay smoothed monitoring video, perform scene spatial structure modeling, and construct a dynamic twin scene model; Step S3: Predict the current driving trajectory of the scene based on the user's driving posture data, and perform safe passage path planning to generate a scene safe passage path; Step S4: Mark all dynamic vehicles in the scene based on the dynamic twin scene model; Predict the dynamic movement trajectory of dynamic vehicles in all scenarios, thereby generating multi-period corrected driving prediction trajectories; Step S5: Calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, perform safety risk analysis, and mark the vehicle as a high-risk vehicle; Step S6: Making risk avoidance decisions for high-risk vehicles based on the multi-period corrected driving prediction trajectory, generating risk decision results; projecting the risk decision results onto the smart helmet to complete the driving information processing operation; Among them, the specific steps of step S1 are: Step S11: obtaining real-time driving process monitoring video and user driving posture data based on the smart helmet; Step S12: performing brightness deviation detection on the real-time driving process monitoring video to identify the brightness deviation area; Step S13: performing histogram equalization processing based on the brightness deviation area to generate a brightness enhanced monitoring video; Step S14: performing driving scene edge analysis on the brightness enhanced monitoring video to extract driving scene edge data; Step S15: Optimizing edge details of the brightness-enhanced monitoring video according to the edge data of the travel scene to construct an edge-optimized monitoring video; Step S16: performing global delay interpolation smoothing processing on the edge optimized monitoring video to obtain a delay smoothed monitoring video; Among them, the specific steps of step S16 are: Perform time-series window segmentation on edge-optimized monitoring videos and extract time-series images of each frame; Calculating the timestamp of each frame of the time-series image; Calculate the time interval between the upper and lower frames according to the timestamp to obtain the inter-frame interval duration of all image frames; Fit the interval duration distribution of all image frames to obtain the global interval distribution map of the video; Perform interval discrete unevenness identification on the global interval distribution map of the video and extract the points with uneven interval duration; Positioning the time-series image frames based on the unevenly spaced time points, and extracting all the unevenly spaced image frames; Calculating the inter-frame delay of all non-uniformly spaced image frames to obtain the delay parameters of the non-uniformly spaced frames; Performing average inter-frame interval statistics on the inter-frame interval durations of all image frames to generate a mean inter-frame interval value; Performing delay compensation calculation on the delay parameters of the non-uniformly spaced frames according to the mean inter-frame interval value, thereby obtaining the delay compensation parameters of the non-uniformly spaced frames; Performing time-series serialization processing on each frame of the time-series image according to the timestamp to obtain a time-series image frame sequence; The delay compensation parameters of non-uniform interval frames are used to perform delay interpolation and smoothing processing on the time sequence image frame sequence, thereby obtaining a delay smoothed monitoring video.

2. The method for processing driving information based on a smart helmet according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing scene object visual recognition on the time-delay smoothed monitoring video and marking objects in the driving scene, wherein the objects in the driving scene include static road signs, road obstacles, and dynamic vehicles; Step S22: performing deep road semantic feature segmentation on the time-delay smoothed monitoring video to generate driving scene road semantic features; Step S23: performing scene spatial structure analysis on static road signs and road obstacles to generate scene static spatial structure data; Step S24: performing scene spatial structure modeling on the driving scene road semantic features and the scene static spatial structure data to construct a three-dimensional driving scene model; Step S25: Perform dynamic movement evolution simulation on the three-dimensional driving scene model according to the dynamic vehicle to construct a dynamic twin scene model.

3. The method for processing driving information based on a smart helmet according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Calculate the driving space volume based on the user's driving posture data to generate the user's standard driving volume; Step S32: Analyze the user's driving trajectory on the time-delay smoothed monitoring video to extract the user's time-series driving trajectory; Step S33: Predicting the current scene driving trajectory of the user's time series driving trajectory based on the user's standard driving volume to generate a predicted driving trajectory within the scene; Step S34: Calculate the obstacle area volume of the dynamic twin scene model to generate obstacle area volume parameters; Step S35: Based on the volume parameters of the obstacle area, a safe passage path is planned for the predicted driving trajectory in the scene to generate a safe passage path for the scene.

4. The method for processing driving information based on a smart helmet according to claim 3, characterized in that: The specific steps of step S35 are: Perform obstacle location on the dynamic twin scene model and mark the obstacle coordinates; Calculate the encounter time of the predicted driving trajectory in the scene according to the obstacle position coordinates to obtain the obstacle encounter time of the predicted trajectory; Expand the safe space volume of the obstacle area volume parameters to generate the obstacle safe space bounding volume; Based on the obstacle encounter time point of the predicted trajectory, the potential collision area of ​​the obstacle safety space bounding volume is analyzed to generate the user's potential collision area; Make normal passage inferences on the user's potential collision area and generate user passage inference results; When the user's traffic prediction result is that the vehicle can pass normally, the predicted driving trajectory in the scene is used as the safe passage path of the scene; When the user's passage is speculated to be impossible to pass normally, a safe passage path is planned for the user's potential collision area to generate a scene-safe passage path.

5. The method for processing driving information based on a smart helmet according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Perform dynamic vehicle optical flow recognition on the dynamic twin scene model and mark all dynamic vehicles in the scene; Step S42: Calculate the optical flow displacement of all dynamic vehicles in the scene one by one to generate the vehicle moving speed; Step S43: Analyze the moving directions of all dynamic vehicles in the scene and generate the moving directions of the vehicles; Step S44: performing dynamic trajectory prediction on the vehicle moving speed and direction to generate predicted driving trajectories of all vehicles in the scene; Step S45: performing road constraint correction on the predicted driving trajectories of all vehicles in the scene based on the static spatial structure data of the scene to generate corrected predicted driving trajectories; Step S46: Perform multi-period collaborative analysis on the corrected driving prediction trajectory to generate a multi-period corrected driving prediction trajectory.

6. The method for processing driving information based on a smart helmet according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: matching the multi-period corrected driving prediction trajectory with the meeting time based on the scenario safe passage path, and marking the meeting section time point; Step S52: Cut the dynamic twin scene model into time window regions according to the time point of the encounter section and extract the intersection region model; Step S53: Calculating the collision probability of the intersection area model to generate driving collision probability data; Step S54: performing safety risk analysis on the vehicle collision probability data to generate a safety risk value for each vehicle; Step S55: risk identification is performed on the safety risk value of each vehicle based on a preset driving safety risk threshold. When the preset driving safety risk threshold is greater than the safety risk value of the vehicle, it is marked as a high-risk vehicle.

7. The method for processing driving information based on a smart helmet according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: highlighting the risky vehicle trajectories of high-risk vehicles based on the multi-period corrected driving prediction trajectories, and extracting the risky vehicle driving prediction trajectories; Step S62: making a risk avoidance decision based on the predicted driving trajectory of the risky vehicle and generating a risk decision result; Step S63: Project the risk decision result to the smart helmet to complete the driving information processing operation.

8. A driving information processing device based on a smart helmet, characterized in that: Used to execute the driving information processing method based on the smart helmet as claimed in claim 1, comprising: The video enhancement module is used to obtain real-time driving process monitoring video and user driving posture data based on the smart helmet; the real-time driving process monitoring video is processed by histogram equalization and global delay interpolation smoothing to obtain delay-smoothed monitoring video; The spatial modeling module is used to perform deep road semantic feature segmentation on time-delayed smoothed monitoring videos, model the scene spatial structure, and construct a dynamic twin scene model; The safe path planning module is used to predict the driving trajectory of the current scene based on the user's driving posture data, and to plan a safe passage path to generate a safe passage path for the scene; The driving trajectory prediction module is used to mark dynamic vehicles in all scenarios based on the dynamic twin scene model; perform dynamic movement trajectory prediction on dynamic vehicles in all scenarios, thereby generating multi-period corrected driving prediction trajectories; The safety risk analysis module is used to calculate the collision probability of the multi-period corrected driving prediction trajectory based on the scenario safe passage path, and conduct safety risk analysis to mark high-risk vehicles; The risk avoidance decision module makes risk avoidance decisions for high-risk vehicles based on multi-period corrected driving prediction trajectories and generates risk decision results. The risk decision results are projected onto the smart helmet to complete the driving information processing task. The video enhancement module is used to obtain real-time driving process monitoring video and user driving posture data based on the smart helmet; perform histogram equalization processing on the real-time driving process monitoring video and perform global delay interpolation smoothing processing to obtain a delay smoothed monitoring video, specifically for; The smart helmet is used to obtain real-time driving process monitoring videos and user driving posture data; brightness deviation detection is performed on the real-time driving process monitoring videos to identify brightness deviation areas; histogram equalization is performed based on the brightness deviation areas to generate brightness-enhanced monitoring videos; driving scene edge analysis is performed on the brightness-enhanced monitoring videos to extract travel scene edge data; edge detail optimization is performed on the brightness-enhanced monitoring videos based on the travel scene edge data to construct edge-optimized monitoring videos; global delay interpolation and smoothing processing is performed on the edge-optimized monitoring videos to obtain delay-smoothed monitoring videos; The specific steps of performing global delay interpolation smoothing processing on the edge optimization monitoring video to obtain the delay smoothed monitoring video are: The edge optimization monitoring video is segmented into time windows to extract the time sequence image of each frame; the timestamp of the time sequence image of each frame is calculated; the time interval of the upper and lower frames is calculated according to the timestamp to obtain the inter-frame interval duration of all image frames; the inter-frame interval duration of all image frames is fitted with the interval duration distribution to obtain the video global interval distribution map; the global interval distribution map of the video is identified for interval discrete unevenness to extract the points with uneven interval duration; the time sequence image frames are located based on the points with uneven interval duration to extract all the image frames with uneven interval duration; all the non-uniform interval maps are fitted with the interval duration distribution of all image frames. The method comprises the following steps: performing inter-frame delay calculation on the image frames to obtain the delay parameters of the non-uniformly spaced frames; performing average inter-frame interval statistics on the inter-frame interval durations of all image frames to generate a mean inter-frame interval value; performing delay compensation calculation on the delay parameters of the non-uniformly spaced frames according to the mean inter-frame interval value to obtain the delay compensation parameters of the non-uniformly spaced frames; performing time-series serialization processing on the time-series images of each frame according to the timestamp to obtain a time-series image frame sequence; performing time-delay interpolation smoothing processing on the time-series image frame sequence using the delay compensation parameters of the non-uniformly spaced frames to obtain a delay-smoothed monitoring video.

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