Street riding safety evaluation method based on VR visual perception and physiological signals

Abstract

The invention discloses a street riding safety evaluation method based on VR visual perception and physiological signals, and relates to the technical field of large model construction. The objective of the invention is to solve the technical problem that the safety perception of a rider in a dynamic riding process is difficult to comprehensively reflect due to the lack of the combination of subjective and objective feelings of the rider in the evaluation of the street riding safety feeling in the prior art. According to the technical scheme provided by the invention, the street riding safety evaluation method based on VR visual perception and physiological signals comprises the following steps: acquiring a video of a riding road section; a step of preprocessing the video of the riding road section; a step of displaying the preprocessed video of the riding road section to a user through VR equipment; collecting physiological signals of a user; and evaluating the safety of the riding road section according to the physiological signal of the user. The method is suitable for street riding safety evaluation work.

Description

Technical Field

[0001] The invention relates to the technical field of large model construction, and specifically to street riding safety evaluation based on VR visual perception and physiological signals. Background Art

[0002] As a healthy, environmentally friendly and economical way of travel, cycling has received more and more attention in urban transportation in recent years. In order to improve the safety and attractiveness of the cycling environment, academia and industry are actively exploring various evaluation methods for street cycling environments. Existing cycling environment evaluation methods can be roughly divided into the following categories:

[0003] 1. Evaluation methods based on trajectory data: This type of method evaluates the friendliness of urban streets to cyclists by analyzing the trajectory data of cyclists and combining it with road network information. For example, some studies use GPS data to identify preferences for cycling routes, thereby inferring which street conditions help improve cycling safety and comfort. However, trajectory data can only reflect the macro-path choices of cyclists and cannot provide an in-depth understanding of their specific feelings during cycling.

[0004] 2. Evaluation methods based on street view big data: Some studies use street view data to evaluate the urban street environment, extracting the physical characteristics of the street, such as greening rate, traffic volume, etc. through image recognition technology, and then scoring the safety and comfort of the cycling environment. For example, street view data such as Google Street View are widely used to analyze street characteristics. However, these data are usually static and lack the ability to perceive the real-time process of cycling, and cannot fully reflect the feelings of cyclists in a dynamic environment.

[0005] 3. Methods based on questionnaires and subjective evaluation: In order to evaluate the subjective sense of safety of cyclists, some studies have adopted the method of questionnaire surveys to collect cyclists' subjective feelings about different street environments. This method can directly reflect the psychological feelings of cyclists, but its disadvantage is that it lacks objective physiological data support, and the results are easily affected by the emotions and memories of the participants.

[0006] 4. Experimental research based on physiological signals: In recent years, some studies have begun to try to evaluate the psychological state of cyclists in different environments through physiological signals, such as using physiological indicators such as heart rate and skin electricity to reflect the stress level and sense of security of cyclists. However, such studies are often limited to laboratory environments, lack the reproducibility of real cycling environments, and fail to combine with the dynamic scenes of the cycling process.

[0007] Although the existing technology has made some progress in the evaluation of street riding environment, it still has the following shortcomings:

[0008] 1. Lack of real-time perception data: Most existing technologies rely on static images or big data, which makes it difficult to truly restore the temporal and spatial dynamics of the riding process, and cannot fully capture the rider's immediate perception of the environment and emotional changes during the actual riding process.

[0009] 2. Ignoring subjective sense of safety: Many existing methods only evaluate from the objective environmental conditions, and lack the means to quantify the subjective perception of cyclists during the riding process. The subjective sense of safety of cyclists plays a key role in influencing their travel decisions and route selection.

[0010] 3. Limitations of data types: Trajectory data and street view big data can only provide limited descriptions of the environment. It is difficult to reflect the psychological and physiological state of cyclists during riding, and it is impossible to directly and effectively evaluate the sense of safety.

[0011] 4. Lack of dynamic and immersive environmental experience: Existing methods are difficult to provide a realistic riding environment experience, especially the perception of dynamic changes in the environment, and cannot fully evaluate the psychological reactions of cyclists in complex street environments.

[0012] In summary, the existing technology lacks the combination of subjective and objective feelings of riders in the evaluation of street cycling safety, and it is difficult to fully reflect the rider's safety perception during dynamic riding. Therefore, there is an urgent need for a new evaluation method that can combine real-time perception, subjective evaluation and physiological signals to comprehensively enhance the understanding and improvement of street cycling safety. Summary of the invention

[0013] To solve the problems existing in the prior art, most of the prior art relies on static images or big data, which makes it difficult to truly restore the spatiotemporal dynamics of the riding process, and cannot fully capture the rider's immediate perception of the environment and emotional changes during the actual riding process. Many existing methods only evaluate from the objective environmental conditions, and lack the means to quantify the rider's subjective perception during the riding process. The subjective sense of security of the rider plays a key role in influencing their travel decisions and route selection. Trajectory data and street view big data can only provide limited environmental descriptions, which makes it difficult to reflect the rider's psychological and physiological state during the riding process, and cannot directly and effectively evaluate the sense of security. In addition, the existing methods are difficult to provide a real riding environment experience, especially the perception of dynamic changes in the environment, and cannot fully evaluate the rider's psychological reactions in a complex street environment. The technical solutions provided by the present invention are:

[0014] A street riding safety evaluation method based on VR visual perception and physiological signals, the method comprising:

[0015] Steps to collect video of the cycling section;

[0016] A step of preprocessing the video of the cycling section;

[0017] The step of displaying the pre-processed video of the riding section to the user through a VR device;

[0018] Steps for collecting user physiological signals;

[0019] The step of evaluating the safety of the cycling section according to the user physiological signal.

[0020] Furthermore, a preferred embodiment is provided, wherein the video includes dynamic features of representative streets in the city, wherein the streets include busy commercial areas, residential areas and traffic arteries.

[0021] Furthermore, a preferred embodiment is provided, wherein the preprocessing includes video editing, stabilization and image quality enhancement to generate a VR video suitable for a virtual reality experience.

[0022] Furthermore, a preferred embodiment is provided, wherein the physiological signal includes skin conductance and heart rate data.

[0023] Based on the same inventive concept, the present invention also provides a street riding safety evaluation device based on VR visual perception and physiological signals, the device comprising:

[0024] A module for collecting videos of cycling sections;

[0025] A module for preprocessing the video of the cycling section;

[0026] A module for displaying the pre-processed video of the riding section to the user through a VR device;

[0027] A module for collecting user physiological signals;

[0028] A module for evaluating the safety of the cycling section according to the user's physiological signal.

[0029] Based on the same inventive concept, the present invention also provides a method for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, comprising:

[0030] According to the user's physiological signals and in combination with the environmental element characteristics of the video of the riding section, a hierarchical analysis method is used to establish a quantitative relationship between street environmental characteristics and riding safety, and a comprehensive model for evaluating the safety of riding on urban streets is constructed.

[0031] Based on the same inventive concept, the present invention also provides a device for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, comprising:

[0032] According to the user's physiological signals and in combination with the environmental element characteristics of the video of the riding section, a hierarchical analysis method is used to establish a quantitative relationship between street environmental characteristics and riding safety, and a module for constructing a comprehensive model for evaluating the safety of riding on urban streets is constructed.

[0033] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program, and when the computer reads the computer program, the computer executes the described method.

[0034] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method described.

[0035] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0036] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0037] The present invention provides a method for constructing a street cycling safety evaluation model based on VR visual perception and physiological signals. Through the steps of typical road section selection and video recording, the present invention can ensure that the data in the evaluation process is representative and covers different types of street environments. This method effectively avoids the problem of single data source and insufficient representativeness in existing research, making the evaluation results more universal and applicable.

[0038] The present invention provides a method for constructing a street cycling safety evaluation model based on VR visual perception and physiological signals. Through video processing and VR video generation, the present invention successfully achieves dynamic restoration of the street cycling environment and provides an immersive experience for the subjects through VR equipment. Compared with static street view data, this immersive experience can more realistically reflect the psychological state of cyclists in different street environments, thereby more comprehensively evaluating the sense of cycling safety.

[0039] The present invention provides a method for constructing a street cycling safety evaluation model based on VR visual perception and physiological signals. During the VR experiment and physiological signal collection process, the present invention can capture the immediate physiological reactions of participants during cycling by real-time recording of physiological signals such as skin electricity and heart rate. Compared with the traditional questionnaire-based method, this method introduces objective physiological data, which can effectively overcome the influence of emotional fluctuations and memory bias in subjective evaluation, and improve the objectivity and accuracy of the evaluation.

[0040] The present invention provides a method for constructing a street riding safety evaluation model based on VR visual perception and physiological signals. By combining subjective safety evaluation with data verification, the present invention adopts a method of mutual verification of multiple data sources to ensure the accuracy and validity of the data. By screening physiological signal data and removing noise data, this method effectively improves the reliability of the results, making the final safety evaluation results more scientific and authoritative.

[0041] The present invention provides a method for constructing a street cycling safety evaluation model based on VR visual perception and physiological signals. Through hierarchical analysis and model construction, the present invention establishes a quantitative relationship between environmental factors and cycling safety, and forms a comprehensive evaluation model. Compared with existing methods, this model based on weighted analysis can more scientifically and systematically evaluate the impact of different environmental factors on cycling safety, and provide more powerful data support and theoretical basis for the design and optimization of urban streets.

[0042] The present invention provides a method for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, which is suitable for application in street riding safety evaluation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flowchart of a method for building a street cycling safety evaluation model based on VR visual perception and physiological signals. DETAILED DESCRIPTION

[0044] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0045] Embodiment 1: This embodiment provides a street riding safety evaluation method based on VR visual perception and physiological signals, the method comprising:

[0046] Steps to collect video of the cycling section;

[0047] A step of preprocessing the video of the cycling section;

[0048] The step of displaying the pre-processed video of the riding section to the user through a VR device;

[0049] Steps for collecting user physiological signals;

[0050] The step of evaluating the safety of the cycling section according to the user physiological signal.

[0051] Implementation method 2: This implementation method is a further limitation of the street riding safety evaluation method based on VR visual perception and physiological signals provided in implementation method 1. The video includes dynamic features of representative streets in the city, and the streets include bustling commercial areas, residential areas and traffic arteries.

[0052] Implementation method three: This implementation method further limits the street riding safety evaluation method based on VR visual perception and physiological signals provided in implementation method one. The preprocessing includes video editing, stabilization and image quality enhancement to generate VR videos suitable for virtual reality experience.

[0053] Implementation method 4: This implementation method further limits the street riding safety evaluation method based on VR visual perception and physiological signals provided in implementation method 1, wherein the physiological signals include skin electricity and heart rate data.

[0054] Embodiment 5: This embodiment provides a street riding safety evaluation device based on VR visual perception and physiological signals, the device comprising:

[0055] A module for collecting videos of cycling sections;

[0056] A module for preprocessing the video of the cycling section;

[0057] A module for displaying the pre-processed video of the riding section to the user through a VR device;

[0058] A module for collecting user physiological signals;

[0059] A module for evaluating the safety of the cycling section according to the user's physiological signal.

[0060] Embodiment 6: This embodiment provides a method for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, including:

[0061] According to the user physiological signals provided in the first embodiment, combined with the environmental element characteristics of the video of the riding section, the analytic hierarchy process is used to establish the quantitative relationship between street environmental characteristics and riding safety, and to construct a comprehensive model for evaluating the safety of riding on urban streets.

[0062] Specifically, this implementation combines VR technology with physiological signal collection to achieve a real-time and comprehensive evaluation of the rider's subjective sense of safety. Specifically, the following steps are included:

[0063] 1. Typical road section selection and video recording:

[0064] First, select different types of representative road sections in the city, including busy commercial areas, residential areas, and traffic arteries. The selection of these road sections should take into account diversity and representativeness to ensure that the evaluation results are generally applicable. Under suitable weather conditions, use a sports camera to record the riding process from a first-person perspective to ensure that the recorded video can reflect the typical characteristics of the street, such as road conditions, traffic flow, and green conditions. When recording the video, maintain a stable riding speed and ensure that there are no interference factors during the recording process.

[0065] 2. Video processing and VR video generation:

[0066] The recorded cycling videos are preprocessed, including video editing, stabilization, and image quality enhancement, to ensure that the final video is suitable for VR experience. Afterwards, the processed cycling videos are converted into VR video format, so that participants can experience the street cycling environment in an immersive way through VR devices. The generation of VR videos needs to ensure the smoothness and high resolution of the videos to enhance the immersion of the experimental participants. This method is different from the static street view data in the prior art, and can truly reproduce the dynamic changes of the environment during cycling and simulate the visual effects of real cycling.

[0067] 3. VR Experiment and Physiological Signal Collection

[0068] Recruit experimental participants to conduct VR cycling experiments. Before the experiment begins, participants are equipped with physiological signal collection devices such as skin electricity and heart rate, and are equipped with VR headsets to adapt to the equipment for a period of time. During the experiment, participants will simulate cycling, watch recorded cycling videos through VR devices, and collect their physiological signals, such as skin electricity response (GSR) and heart rate, in real time through sensors. These physiological signals reflect the physiological and emotional responses of participants in different street environments, such as stress levels and tension. The entire experimental process should be conducted in a controlled experimental environment to reduce the impact of external interference on the experimental results. The purpose of this step is to capture the participants' immediate physiological responses to different street environments through physiological signals, which is an important supplement to the existing static evaluation methods.

[0069] 4. Subjective sense of security evaluation and data verification:

[0070] After the VR experiment, participants will watch the VR video again and fill out a standardized sense of safety questionnaire. The questionnaire includes a subjective sense of safety score for the street environment and a detailed perceptual description of a specific scene. By comparing the questionnaire results with the physiological signal data, noise data irrelevant to sense of safety is screened and removed to ensure the accuracy and validity of the data. The combination of subjective evaluation and physiological signals can ensure that the final evaluation results include both objective physiological response data and the subjective feelings of the participants, thereby improving the comprehensiveness and reliability of the results.

[0071] 5. Hierarchical analysis and model building:

[0072] Combining the results of environmental factor evaluation and physiological signal analysis, the analytic hierarchy process (AHP) was used to establish the quantitative relationship between street environmental factors and cycling safety. Specifically, the street environment was first objectively scored according to its characteristics (such as road width, traffic flow, green coverage, etc.), and then the physiological signals and subjective sense of safety scores of the experimental participants were combined to determine the degree of influence of each environmental factor on cycling safety. Through weighted analysis, a comprehensive urban street cycling safety evaluation model was constructed. This model can intuitively reflect the impact of different environmental factors on cycling safety, provide a scientific basis for the design and improvement of urban streets, and help urban planners make more humane design decisions.

[0073] This embodiment has significant differences compared with the prior art:

[0074] 1. Dynamic environment restoration and immersive experience: Different from the static street view data in the prior art, the present invention realizes the dynamic restoration of street cycling scenes through VR equipment, so that the subjects can obtain a real immersive experience, which fully reflects the time and space continuity during the cycling process.

[0075] 2. Combination of physiological signal collection and subjective perception: Existing street cycling environment evaluation methods often lack consideration of the physiological reactions of cyclists. This invention combines objective physiological data with subjective perception evaluation through real-time collection of physiological signals such as skin electricity and heart rate, and comprehensively evaluates the sense of cycling safety. The introduction of physiological signals solves the problem of lack of real-time perception data in existing methods.

[0076] 3. Hierarchical analysis and comprehensive evaluation model: The present invention adopts the hierarchical analysis method (AHP) and weighted method to construct an evaluation model for urban street cycling safety. Different from the existing qualitative evaluation method based on static indicators, the present invention provides a quantitative comprehensive evaluation model that can more scientifically reflect the impact of environmental factors on cycling safety.

[0077] Practical application effect

[0078] By introducing VR technology and measuring physiological signals, the sense of safety of cyclists is comprehensively analyzed from the perspectives of subjective perception and objective data. This method can not only identify street environments that need improvement, but also provide references for urban planning departments to help them formulate more humane street design plans, improve the cycling friendliness of the city, and promote the development of green travel modes.

[0079] Embodiment 7: This embodiment provides a device for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, including:

[0080] According to the user physiological signals provided in the first embodiment and in combination with the environmental element characteristics of the video of the cycling section, a hierarchical analysis method is used to establish a quantitative relationship between street environmental characteristics and cycling safety, and a module for constructing a comprehensive model for evaluating the safety of cycling on urban streets.

[0081] Embodiment 8: This embodiment provides a computer storage medium for storing a computer program. When the computer reads the computer program, the computer executes the method provided in embodiment 1.

[0082] Embodiment 9: This embodiment provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method provided in embodiment 1.

[0083] Embodiment 10: This embodiment provides a computer program product, which is a computer program. When the computer program is executed, the method provided in embodiment 1 is implemented.

[0084] Implementation Method XI: Combination Figure 1 This embodiment is described in detail by using specific examples to further describe the technical solution provided above. Specifically:

[0085] Step-by-step arrangement of technical solutions

[0086] Video Collection:

[0087] Choose a representative cycling route in the city and record a video of the cycling process.

[0088] Detailed description: First, select different types of city streets, such as busy commercial areas, residential areas, and traffic arteries, to ensure that the street features are diverse and representative. Use a sports camera to record street videos from a first-person perspective under typical weather conditions to ensure that sufficient dynamic features are captured, including traffic flow, road conditions, greenery, and other information. Maintain a stable riding speed during recording to avoid interference that affects video quality.

[0089] Video preprocessing:

[0090] The collected cycling videos are preprocessed to make them suitable for virtual reality (VR) experience.

[0091] Detailed description: Preprocessing includes video editing, stabilization and image quality enhancement. Video editing is used to remove redundant parts to make the video content more compact and highlight the key points; stabilization is used to reduce jitter in the video to make it smoother to watch; image quality enhancement includes color adjustment and clarity optimization to improve the visual effect of the video, so that users can get a more realistic street experience when watching through VR devices.

[0092] VR Experience:

[0093] Use VR devices to show users pre-processed videos to achieve an immersive experience of the cycling section.

[0094] Detailed description: The pre-processed video is imported into the VR device, and the user gets an immersive experience by wearing a head-mounted display and headphones. The experience simulates the real riding environment, and the user can intuitively feel the dynamic changes of the street, thus getting a nearly real street riding experience.

[0095] Physiological signal collection:

[0096] While users are experiencing VR, their physiological signal data is collected.

[0097] Detailed description: During the user experience, physiological signal collection devices, such as skin conductivity sensors and heart rate sensors, are used to record the user's physiological reactions in real time. These physiological signals reflect the user's psychological reactions to different street environments, such as stress levels and tension. The collected data serves as an important basis for subsequent riding safety evaluation.

[0098] Safety evaluation:

[0099] Based on the user's physiological signals, the safety of the cycling section is quantitatively evaluated.

[0100] Detailed description: Combining the user's physiological signal data and the user's subjective rating of the street environment, the analytic hierarchy process (AHP) is used to establish a relationship model between street environment characteristics and cycling safety. The model analyzes the impact of different street characteristics on user safety through weighted analysis, and ultimately generates a comprehensive street safety evaluation, providing a scientific basis for urban planning and street design.

[0101] Improved specific implementation method

[0102] According to the above step descriptions, the specific operations of each step can be further enriched to ensure the completeness of the technical solution.

[0103] Select road section and video capture:

[0104] Select busy commercial areas, residential areas, traffic arteries and other sections in the city, and use a sports camera to record cycling videos. It is recommended to record during peak and off-peak traffic hours to reflect the diversity of the street environment.

[0105] Video Editing and Stabilization:

[0106] Professional video processing software is used to edit the captured video and remove irrelevant parts. Stabilization technology is then used to reduce jitter in the video to ensure smooth viewing. Finally, the image quality is enhanced to ensure that the final VR video is clear and high-resolution.

[0107] VR device experience:

[0108] After wearing the VR headset, users can experience the urban cycling environment in all aspects. The sounds of the street environment are played through headphones to provide a more immersive experience. During the experiment, it is necessary to ensure that the device is comfortable to wear and give each user enough time to adapt to the device to avoid data deviation caused by device discomfort.

[0109] Real-time physiological signal acquisition:

[0110] The user's skin conductance changes are measured by a galvanic skin sensor, and the user's heart rate fluctuations are measured by a heart rate monitor. The collected signals are recorded synchronously with the video content so that the physiological signals can be matched with specific street features during subsequent data analysis.

[0111] Hierarchy Analysis and Evaluation Model:

[0112] The analytic hierarchy process (AHP) combines physiological signal data and user subjective ratings to evaluate the impact of different street characteristics on cycling safety. For example, wide roads may bring a stronger sense of safety, while sections with heavy traffic may increase users' tension. The comprehensive model generated by weight analysis provides detailed data support for urban planners. Specific embodiments

[0114] Example 1: Cycling Safety Evaluation in Busy Commercial Areas

[0115] Videos were recorded and preprocessed in the busy commercial areas of the city. Users wore VR devices to watch street videos, and the skin electricity and heart rate were measured by physiological signal acquisition equipment. The riding safety in the busy commercial areas was evaluated by combining subjective sense of safety scores and hierarchical analysis method.

[0116] Example 2: Safety evaluation of cycling environment in residential areas

[0117] Select residential areas in the city to record videos during the morning and evening rush hours, generate VR videos after preprocessing, and display them to users through VR devices. Collect users' physiological signals such as skin electricity and heart rate, and combine them with users' subjective sense of safety scores to evaluate the cycling safety in residential areas using the hierarchical analysis method.

[0118] Example 3: Safety analysis of riding on traffic arterial roads

[0119] Videos of traffic arteries were recorded, including high and low traffic flow conditions, and VR videos were generated after video preprocessing. Users were experienced through VR devices, and their physiological signals, including skin charge and heart rate data, were collected. Combined with subjective questionnaire scores, a comprehensive analysis of cycling safety on traffic arteries was conducted using the analytic hierarchy process.

[0120] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable modification and improvement of the present invention, combination of implementation modes and equivalent substitution within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A street riding safety evaluation method based on VR visual perception and physiological signals, characterized in that: The method comprises: Steps to collect video of the cycling section; A step of preprocessing the video of the cycling section; The step of displaying the pre-processed video of the riding section to the user through a VR device; Steps for collecting user physiological signals; The step of evaluating the safety of the cycling section according to the user physiological signal.

2. The street riding safety evaluation method based on VR visual perception and physiological signals according to claim 1 is characterized in that: The video includes dynamic features of representative streets in the city, including busy commercial areas, residential areas and traffic arteries.

3. The street riding safety evaluation method based on VR visual perception and physiological signals according to claim 1 is characterized in that: The preprocessing includes video editing, stabilization and image quality enhancement to generate VR videos suitable for virtual reality experience.

4. The street riding safety evaluation method based on VR visual perception and physiological signals according to claim 1 is characterized in that: The physiological signals include skin electrical activity and heart rate data.

5. A street riding safety evaluation device based on VR visual perception and physiological signals, characterized in that: The device comprises: A module for collecting videos of cycling sections; A module for preprocessing the video of the cycling section; A module for displaying the pre-processed video of the riding section to the user through a VR device; A module for collecting user physiological signals; A module for evaluating the safety of the cycling section according to the user's physiological signal.

6. A method for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, characterized in that: include: According to the user physiological signal of claim 1, combined with the environmental element characteristics of the video of the riding section, a hierarchical analysis method is used to establish a quantitative relationship between street environmental characteristics and riding safety, and a comprehensive model for evaluating the safety of riding on urban streets is constructed.

7. A device for constructing a street riding safety evaluation model based on VR visual perception and physiological signals, characterized in that: include: According to the user physiological signal of claim 1, combined with the environmental factor characteristics of the video of the riding section, a hierarchical analysis method is used to establish a quantitative relationship between street environmental characteristics and riding safety, and a module for constructing a comprehensive model for evaluating the safety of riding on urban streets is constructed.

8. A computer storage medium for storing a computing program, characterized in that: When the computer reads the computer program, the computer executes the method of claim 1.

9. A computer, comprising a processor and a storage medium, characterized in that: When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method of claim 1 is implemented.

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