Interaction method of wearable multi-mode tactile feedback system for rider

Abstract

The invention discloses an interaction method of a wearable multi-mode tactile feedback system for a rider, and the method comprises the steps: obtaining the data of a rear moving object through a laser radar, and constructing a danger value approaching function according to the data of the moving object; analyzing real-time traffic data and weather data to obtain a traffic complexity value, adjusting the weight of an approach danger value function through the traffic complexity value, and calculating to obtain an approach danger value; performing danger level classification by approaching the danger value, and obtaining a first multi-mode tactile feedback mode according to the danger level; and acquiring human body physiological data through the human body physiological sensor, and adjusting the first multi-mode tactile feedback mode through the human body physiological data to obtain a second multi-mode tactile feedback mode.

Description

Technical Field

[0001] The present invention relates to the technical field of multimodal tactile feedback, and in particular to an interaction method of a wearable multimodal tactile feedback system for a rider. Background Art

[0002] In the modern urban traffic environment, cyclists, as vulnerable traffic participants, face many safety challenges on the road. Studies have shown that cyclists' environmental perception ability directly affects their riding safety.

[0003] Traditional means of environmental perception mainly rely on visual information, such as road signs, traffic lights, and rearview mirrors. These methods have the problems of untimely information acquisition and limited field of vision. When cyclists move quickly, it is difficult to obtain changes in the surrounding environment in real time. Therefore, when faced with emergencies, cyclists often cannot respond in time, which leads to safety hazards. In addition, in a complex traffic environment, cyclists are often faced with too much information. The overload of visual information may lead to cognitive burden and increase the risk of slow response. Riders need to quickly identify and respond to various information in a rapidly changing environment, which places higher demands on their visual and cognitive abilities.

[0004] Existing tactile feedback systems, such as sensory helmets, have improved riders’ environmental perception to a certain extent, but most systems are still limited to a single feedback mode, which only uses simple vibrations to remind riders of the direction or distance of surrounding vehicles, and cannot accurately remind riders of the speed or movement trajectory of approaching vehicles. In addition, in complex traffic environments, the feedback frequency will increase due to too many surrounding vehicles, but too frequent or strong feedback may make it difficult for riders to distinguish key tactile information, causing information interference and making it impossible to judge the actual traffic status.

[0005] In view of the above problems in the prior art, the purpose of the present invention is to design an interactive method for a wearable multimodal tactile feedback system for cyclists. Summary of the invention

[0006] In view of this, an object of the present invention is to propose an interaction method of a wearable multimodal tactile feedback system for cyclists, which can solve the above-mentioned problems.

[0007] The present invention provides an interaction method of a wearable multimodal tactile feedback system for a rider, based on a wearable multimodal tactile feedback system for a rider, comprising: A wearable feedback vest is mounted on the rider's upper body to generate tactile feedback signals on the rider's upper body; A laser radar is arranged on the rear side of the wearable feedback vest and connected to the controller to obtain data of moving objects behind the rider; A human physiological sensor is disposed on a wearable feedback vest and connected to a controller for collecting human physiological data; A tactile feedback element group is distributed on the back side of the wearable feedback vest and connected to the controller for providing multi-modal tactile feedback to the rider; The method comprises: Acquiring rear-side moving object data through the laser radar, and constructing a proximity risk value function according to the moving object data; The traffic complexity value is obtained by analyzing real-time traffic data and weather data, and the weight of the proximity danger value function is adjusted according to the traffic complexity value to calculate the proximity danger value; Classifying the danger level by approaching the danger value, and obtaining a first multimodal tactile feedback mode according to the danger level; Human physiological data is acquired through the human physiological sensor, and the first multimodal tactile feedback mode is adjusted according to the human physiological data to obtain a second multimodal tactile feedback mode.

[0008] Further, the acquiring rear moving object data by the laser radar and constructing a proximity risk value function according to the moving object data includes: The relative distance of the moving object behind is obtained by the laser radar ,speed , acceleration ,volume ; Relative distance of the object moving behind ,speed , acceleration ,volume Constructing a function close to the danger value , the calculation formula is as follows: , in, The weight of The weight of The weight of The weight of .

[0009] Furthermore, the traffic complexity value obtained by analyzing real-time traffic data and weather data includes: Obtain vehicle density data, road congestion index data, and average vehicle speed data; Obtain visibility data, rainfall data, and wind speed data; The real-time traffic data and weather data are input into the pre-trained traffic complexity prediction model to obtain the traffic complexity value C.

[0010] Furthermore, the traffic complexity prediction model is trained through the following steps: Collect historical real-time traffic data and weather data, mark traffic complexity target values ​​based on the historical real-time traffic data and weather data, and construct training samples; The traffic complexity prediction model is constructed through the XGBoost model, and the initial training samples are fitted through the XGBoost model decision tree to initialize the traffic complexity prediction value; Based on the loss function, the gradient of each sample is calculated, and a new decision tree is constructed using the gradient and the traffic complexity target value; The traffic complexity prediction value of the new decision tree is weightedly summed with the traffic complexity prediction value of the previous round, and the traffic complexity prediction model is iteratively updated.

[0011] Further, adjusting the weight of the approach risk value function by the traffic complexity value includes: When the traffic complexity value C is greater than 0.5, increase Weight , Weight , the calculation formula is as follows: , in, for The initial weight of for The initial weight of When the traffic complexity value C is less than 0.5, increase Weight ,Increase Weight , the calculation formula is as follows: , in, for The initial weight of for The initial weight of right Weight , Weight , Weight , Weight Perform normalization adjustment to obtain the adjusted plan Weight , Weight , Weight , Weight .

[0012] Further, the tactile feedback element group includes: a vibration motor group, a piezoelectric element group, and a thermoelectric element group; The vibration motor groups are respectively arranged on the upper back part of the wearable feedback vest, and are respectively used for tactile feedback of the direction of the nearest rear moving object; The piezoelectric element groups are respectively arranged at the middle of the back of the wearable feedback vest, and are respectively used for tactile feedback of the distance of the closest rear moving object; The thermoelectric element groups are respectively arranged at the waist of the wearable feedback vest, and are respectively used for speed tactile feedback of the closest rear moving object.

[0013] Further, the classifying the danger level by approaching the danger value and acquiring the first multimodal tactile feedback mode according to the danger level includes: Classify the proximity danger value exceeding the first threshold value as a high danger level, perform relative distance feedback of the closest rear moving object through the piezoelectric element group, perform speed feedback of the closest rear moving object through the thermoelectric element group, and perform direction feedback of the closest rear moving object through the vibration motor group; Classify the proximity danger value lower than the first threshold and higher than the second threshold as a medium danger level, and perform relative distance feedback of the closest rear moving object through the piezoelectric element group, and perform direction feedback of the closest rear moving object through the vibration motor group; The proximity danger value equal to or lower than the second threshold is classified as a low danger level, and the direction feedback of the closest rear moving object is performed through the vibration motor group.

[0014] Further, the human physiological sensor includes: a heart rate sensor and a breathing rate sensor; The heart rate sensor is arranged at the front chest of the wearable feedback vest to collect the heart rate data of the rider; The breathing rate sensor is arranged at the front chest of the wearable feedback vest and is used to collect the breathing rate data of the rider.

[0015] Further, the acquiring of human physiological data through the human physiological sensor, and adjusting the first multimodal tactile feedback mode according to the human physiological data to obtain the second multimodal tactile feedback mode include: Analyze the current physiological state of the human body through the heart rate data and the respiratory rate data, wherein the current physiological state of the human body includes: a tense state, a fatigue state, and a relaxed state; If the current physiological state of the human body is a state of tension, the direction feedback of the nearest rear moving object is provided through the vibration motor group at all danger levels; If the current physiological state of the human body is fatigue, the relative distance feedback of the closest rear moving object is performed through the piezoelectric element group at all risk levels, and the direction feedback of the closest rear moving object is performed through the vibration motor group; If the current physiological state of the human body is a relaxed state, the first multimodal tactile feedback mode is retained.

[0016] Further, analyzing the current physiological state of the human body through the heart rate data and the respiratory rate data includes: If the heart rate is >120 per minute and the breathing rate is >20 per minute, the current physiological state of the human body is tense; If the heart rate is less than 60 per minute and the breathing rate is less than 12 per minute, the current physiological state of the human body is fatigue; If 60≤heartbeats per minute≤120 and 12≤respirations per minute≤20, the current physiological state of the human body is a relaxed state.

[0017] Beneficial effects of the present invention: The first is to use lidar to detect data on moving objects (such as cars, bicycles or other pedestrians) behind the cyclist, and establish a proximity hazard value function through speed, acceleration, relative distance, and volume to quantify the danger of the moving objects, allowing the system to accurately perceive the source of danger.

[0018] The second is to introduce traffic complexity (such as traffic congestion and weather conditions) to further improve the scenario adaptability of the hazard perception system. Through weighted analysis (combining real-time traffic data and weather data), the scenario adaptability of hazard value assessment is improved, that is, more attention is paid to potential hazards in highly complex scenarios.

[0019] Third, by classifying the danger level, we design the first tactile feedback mode, so that users can perceive the threat level of the object behind them in real time (such as direction, distance, speed and other information). In combination with multi-modal tactile elements (vibration, piezoelectric, thermoelectric and other signals), we can achieve fine adjustment between the types of tactile feedback.

[0020] Fourth, the user's real-time physiological data (heart rate, breathing rate, etc.) is collected through human physiological sensors to determine the user's current physiological state (tension, fatigue, relaxation). The first tactile feedback mode is adjusted. When in a state of tension or fatigue, the tactile feedback mode is adjusted to simplify secondary signals or enhance key signals, further improving the user's perception efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 1 is a position structure diagram of the tactile feedback element group of the first embodiment.

[0023] Figure 2 It is a flow chart of the method of embodiment 2. DETAILED DESCRIPTION

[0024] To facilitate understanding by those skilled in the art, the structure of the present invention is now further described in detail with reference to the embodiments and the accompanying drawings. It should be understood that the steps mentioned in the present embodiment, unless otherwise specified in their order, can be adjusted in order according to actual needs, and can even be executed simultaneously or partially simultaneously.

[0025] Embodiment 1 An embodiment of the present invention provides a wearable multimodal tactile feedback system for a rider, comprising: A wearable feedback vest is mounted on the rider's upper body to generate tactile feedback signals on the rider's upper body; A laser radar is arranged on the rear side of the wearable feedback vest and connected to the controller to obtain data of moving objects behind the rider; A human physiological sensor is disposed on a wearable feedback vest and connected to a controller for collecting human physiological data; Further, the human physiological sensor includes: a heart rate sensor and a breathing rate sensor; The heart rate sensor is arranged at the front chest of the wearable feedback vest to collect the heart rate data of the rider; The breathing rate sensor is arranged at the front chest of the wearable feedback vest and is used to collect the breathing rate data of the rider.

[0026] In this embodiment, the heart rate sensor can specifically use a photoelectric volumetric pulse wave sensor, an LED light source (generally green light or infrared light) emits light to the skin, part of the light is absorbed by the skin, and part is reflected or passed through the blood, a photodetector receives the reflected light, measures its intensity, and thus obtains the pulse waveform, and analyzes the number of heartbeats per minute through the frequency of the pulse signal. The respiratory rate sensor can specifically use a piezoelectric sensor, which expands and contracts periodically with inhalation and exhalation, triggering electrical signals of different pressures, and the respiratory cycle and respiratory rate per minute are obtained through signal analysis.

[0027] Traditional tactile feedback devices often do not take into account the physiological state of the rider or user (such as changes in heart rate and breathing rate), and cannot accurately adapt to the needs of users in dynamic environments. By placing heart rate sensors and breathing rate sensors on the chest of the vest, and taking advantage of their proximity to the core areas of the human body (heart and chest), accurate physiological data can be collected in real time to fully monitor the user's physiological dynamics (such as tension, fatigue or physiological stress). The chest position fits the heart and chest, and the heart rate and breathing rate signals have little interference and high accuracy. By collecting the user's heart rate dynamics and breathing rhythm, the user's physiological state (such as energy level, physical stress, health status, etc.) can be fully evaluated for subsequent tactile feedback adjustments.

[0028] A tactile feedback element group is distributed on the back side of the wearable feedback vest and connected to the controller for providing multi-modal tactile feedback to the rider; Further, if Figure 1 As shown, the tactile feedback element group includes: a vibration motor group 2, a piezoelectric element group 3, and a thermoelectric element group 4; The vibration motor groups 2 are respectively arranged on the upper back part of the wearable feedback vest 1, and are respectively used for tactile feedback of the direction of the nearest rear moving object; The piezoelectric element groups 3 are respectively arranged at the middle of the back of the wearable feedback vest 1, and are respectively used for tactile feedback of the distance of the closest rear moving object; The thermoelectric element groups 4 are respectively arranged at the waist of the wearable feedback vest 1, and are respectively used for speed tactile feedback of the closest rear moving object.

[0029] In this embodiment, by reasonably dividing the functional division of piezoelectric + thermoelectric + vibration, it is possible to solve the redundant conflict problem of signal transmission and reduce the learning cost of tactile feedback. Each feedback method (vibration prompt direction, thermoelectric prompt speed, piezoelectric prompt distance) always adheres to a certain dimension of information, reducing the cognitive burden of the user. The prompt content of each tactile feedback is fixed to avoid cognitive errors of the user. The sense of presence is relatively stable and not easy to cause obvious interference, which is suitable for transmitting gradually changing relative distances. The physical pressure sense of piezoelectricity is very suitable for simulating the sense of urgency of approaching objects. Speed ​​essentially reflects the speed of movement of objects and the sense of threatening speed. The first reaction of users to perceive urgency is often related to changes in temperature (such as coldness). Changes in cold and warm temperatures can easily trigger strong emotional awakening and highlight the speed attribute of the object approaching. The vibration signal has a clear position and is particularly suitable for transmitting directional spatial information, so that users can immediately locate the source of the threat. By binding specific information (distance, speed, direction) to a fixed component combination, while maximizing the high sensitivity of different neurons in the user's skin to pressure, temperature and vibration, an efficient feedback solution is formed.

[0030] Embodiment 2 like Figure 1 As shown, an embodiment of the present invention provides an interaction method of a wearable multimodal tactile feedback system for a rider, comprising: S1 acquires rear-side moving object data through the laser radar, and constructs a proximity risk value function according to the moving object data; S101 obtains the relative distance of the rear moving object through the laser radar ,speed , acceleration ,volume ; S102 Relative distance of the moving object behind ,speed , acceleration ,volume Constructing a function close to the danger value , the calculation formula is as follows: , in, The weight of The weight of The weight of The weight of .

[0031] In this step, the speed directly reflects the speed of the object's movement. The greater the speed, the shorter the time the object takes to approach the cyclist, and the higher the danger. High-speed threatening objects may pose a greater risk of collision to cyclists, so they need to be given priority consideration in the calculation of the danger value.

[0032] Acceleration represents the dynamic trend of speed change and is an important physical quantity that describes the motion properties of an object. If the acceleration is positive, it means that the object is approaching the rider and accelerating, and the danger is significantly increasing. If the acceleration is negative (decelerating), the threat of the object may gradually decrease. Acceleration enables the system to predict the potential danger of threatening objects in the future, rather than just static analysis of the current speed.

[0033] Distance is the core variable for assessing proximity danger and has a direct impact on the danger value; the smaller the distance, the greater the danger, because it takes less time for an object to invade the rider's safe space; through distance information, the system can determine whether the threatening object has invaded the "safe distance" range, thereby triggering timely warning measures.

[0034] The size of an object determines the degree of possible physical harm; large objects (such as cars or trucks) pose a significant threat to cyclists even at a long distance. Small objects (such as pedestrians or bicycles) pose less of a threat to cyclists, but other variables must still be considered to determine the danger; in complex traffic environments, introducing volume factors can distinguish different types of threatening objects and improve the level of detail and accuracy of the assessment.

[0035] Speed, acceleration, distance and volume are directly related to danger, which conforms to the objective physical law of potential conflict between moving objects and cyclists. Therefore, these four parameters are selected to construct the approach danger value function. . Lidar technology obtains spatial information of surrounding objects by sending pulsed laser beams and receiving reflected signals. Data of multiple moving objects behind the rider can be directly obtained through Lidar. Lidar transmits laser beams to target objects and receives laser signals reflected by target objects, calculates the time difference from laser emission to return (i.e., propagation time t), and calculates the relative distance according to the propagation speed of laser (usually the speed of light c). The position change of the target is tracked using multi-frame data of Lidar, and the relative speed is calculated in combination with the time interval. Acceleration is calculated by the rate of change of the object's speed, so it can be derived based on continuous multi-frame Lidar speed data. Lidar reconstructs the three-dimensional contour of the target object through multi-point cloud data (Point Cloud), thereby estimating its volume. Lidar scans the environment to generate a point cloud model of the object. These point clouds contain the three-dimensional coordinates of different points on the surface of the object. According to the point cloud grouping, a bounding box generation algorithm is applied to separate the target object and calculate its three-dimensional envelope box (length, width, height), thereby estimating the volume. The above calculation methods are all prior art and will not be described in detail in the present invention.

[0036] S2 obtains the traffic complexity value by analyzing the real-time traffic data and weather data, adjusts the weight of the approaching danger value function by the traffic complexity value, and calculates the approaching danger value; S201 obtains vehicle density data, road congestion index data, and average vehicle speed data; S202 obtains visibility data, rainfall data, and wind speed data; S203 inputs the real-time traffic data and weather data into a pre-trained traffic complexity prediction model to obtain a traffic complexity value C.

[0037] Furthermore, the traffic complexity prediction model is trained through the following steps: Collect historical real-time traffic data and weather data, mark traffic complexity target values ​​based on the historical real-time traffic data and weather data, and construct training samples; The traffic complexity prediction model is constructed through the XGBoost model, and the initial training samples are fitted through the XGBoost model decision tree to initialize the traffic complexity prediction value; Based on the loss function, the gradient of each sample is calculated, and a new decision tree is constructed using the gradient and the traffic complexity target value; The traffic complexity prediction value of the new decision tree is weightedly summed with the traffic complexity prediction value of the previous round, and the traffic complexity prediction model is iteratively updated.

[0038] In this step, traffic complexity dynamically reflects the risk level in different scenarios. By adjusting the weights, the approach-to-hazard value function can more accurately adapt to complex traffic environments. For example, in bad weather or high congestion, it pays more attention to the impact of certain key factors. By building a traffic complexity model through multi-dimensional characteristics (vehicle density, congestion index, vehicle speed, weather indicators, etc.), the combined impact of traffic and environmental factors can be comprehensively considered.

[0039] Vehicle density data, road congestion index data, average vehicle speed data, etc. can be directly accessed to the map platform to obtain, and existing maps add open APIs to obtain these real-time traffic data. Visibility data, rainfall data, wind speed data, etc. can be directly accessed to the weather forecast platform to obtain local weather conditions. These access technologies are existing technologies and will not be elaborated in this invention.

[0040] The XGBoost (Extreme Gradient Boosting) model can handle mixed data (numerical and categorical variables) and can build an accurate prediction model based on the mixed input of real-time traffic data and weather data. Traffic data and weather conditions often have complex nonlinear correlations (for example, high density + low visibility = high traffic complexity, but this is not a simple linear relationship). Therefore, using the XGBoost model to build a traffic complexity prediction model can accurately predict the current traffic complexity.

[0041] Real-time dynamic updating of traffic complexity can effectively improve the sensitivity and scene adaptability of the danger value function, especially in a rapidly changing traffic environment. Therefore, the weight of the proximity danger value function is adjusted by the traffic complexity value, and then the proximity danger value is calculated. At this time, the proximity danger value obtained is the most accurate. Specifically: S204 When the traffic complexity value C is greater than 0.5, increase Weight , Weight , the calculation formula is as follows: , in, for The initial weight of for The initial weight of S205 When the traffic complexity value C is less than 0.5, increase Weight ,Increase Weight , the calculation formula is as follows: , in, for The initial weight of for The initial weight of S206 pair Weight , Weight , Weight , Weight Perform normalization adjustment to obtain the adjusted plan Weight , Weight , Weight , Weight .

[0042] In this step, the higher the traffic complexity value (i.e., C is close to 1), the more attention should be paid to the impact of speed and acceleration on danger (i.e., increasing and The weight of the speed and acceleration is the weight of the speed and acceleration. In a high traffic complexity environment (such as high congestion, low visibility, bad weather, etc.), traffic flow becomes more unpredictable, and sudden acceleration, braking or speed change of vehicles may pose a greater safety hazard to cyclists. Speed ​​and acceleration are dynamic variables that directly reflect the drastic changes in the state of motion of objects, so they need to be paid more attention in complex environments. When traffic complexity is high, such as heavy rain or high vehicle density, it is more difficult for cyclists to process visual and auditory information, and the judgment of distance may not be accurate enough. At this time, it is more necessary to focus on fast-approaching dynamic threats (such as vehicles approaching at high speed or suddenly accelerating). Speed ​​and acceleration are the indicators that can most quickly reflect these threats. In a high-complexity scenario, focusing on relative distance may not be enough to quickly recognize threats. For example: a vehicle approaching at a long distance but at high speed is more dangerous than a vehicle approaching at a close distance but at a low speed. Similarly, although large objects are highly dangerous, in a traffic environment dominated by dynamic threats, the threats of speed and acceleration are more direct. In a high-complexity environment, dynamic threats dominate.

[0043] The lower the traffic complexity value (i.e., C is close to 0), the more attention should be paid to the impact of relative distance and volume on danger (i.e., increasing and In low-complexity environments (e.g., low congestion, good weather conditions), roads are more orderly, objects usually follow expected trajectories, and dynamic variables (speed and acceleration) fluctuate less. At this time, relative distance and volume become the static features of primary concern. Large objects (such as trucks) that are close at hand may pose a threat to cyclists even if they are stationary at low speed. In low-complexity scenarios, cyclists have more time to judge distance and path, so they can pay more attention to static variables (such as which objects are closer and more dangerous). At this time, speed and acceleration are no longer the primary source of threat. Volume is a direct quantitative indicator of the threat of an object to the environment. Large objects (such as trucks and buses) are still much more dangerous than small vehicles (such as bicycles) even at low speeds. Therefore, increasing the weight of the volume of close objects in low-complexity situations can more reasonably reflect static threats. In a more orderly environment, changes in object speed and acceleration have less impact on the risk of cyclists. When dynamic threats are reduced, paying too much attention to speed and acceleration can easily lead to false alarms or information redundancy. At this time, the weight of speed and acceleration is reduced, and more attention is paid to static variables. In low-complexity environments, static threats dominate.

[0044] S3 classifies the danger level by approaching the danger value, and obtains a first multimodal tactile feedback mode according to the danger level; S301 classifies the proximity danger value exceeding the first threshold value as a high danger level, performs relative distance feedback of the closest rear moving object through the piezoelectric element group, performs speed feedback of the closest rear moving object through the thermoelectric element group, and performs direction feedback of the closest rear moving object through the vibration motor group; S302 classifies the proximity danger value lower than the first threshold and higher than the second threshold as a medium danger level, performs relative distance feedback of the closest rear moving object through the piezoelectric element group, and performs direction feedback of the closest rear moving object through the vibration motor group; S303 classifies the proximity danger value equal to or lower than the second threshold value as a low danger level, and performs direction feedback of the closest rear moving object through the vibration motor group.

[0045] In this step, the constantly changing distance, speed and direction of the moving objects behind, such as cars, bicycles or pedestrians, are the information that the rider needs to pay most attention to, but there are often delays or blind spots in obtaining this data through line of sight or external sensing devices. Distance perception: The piezoelectric element provides real-time feedback on the relative distance to help the rider perceive the proximity of the object. The closer the relative distance between the rider and the moving object behind, the stronger the pressure feedback given by the piezoelectric element. Speed ​​perception: Thermoelectric elements can indicate the speed of the objects behind through changes in temperature, especially dangerous objects approaching at high speed. If the speed of the moving object behind becomes faster and faster, the temperature of the thermoelectric element becomes higher and higher. Direction perception: The vibration motor provides azimuth feedback, allowing the rider to quickly locate the direction of the threat source.

[0046] Compared with providing feedback for multiple approaching objects at the same time, this solution only provides feedback for the "closest" rear object. Dynamic priority adjustment avoids information confusion and user comprehension difficulties caused by multiple signal feedback. In situations with different levels of danger, the distance, speed or direction information is used as the main information (the feedback focus is adjusted according to the danger level), allowing users to focus on the most important information and improve judgment efficiency.

[0047] At different danger levels, users have different needs for feedback signals. At high danger levels, comprehensive and strong feedback (such as distance, speed, and direction) is required to quickly judge threats and respond. Medium danger level: Partial information about the risk (such as distance and direction) is required to provide sufficient warnings without interfering with operations. Low danger level: Since the surrounding traffic environment is not complicated, only directional prompts are provided to avoid distracting users from the riding path. This design solves the problem of how to dynamically adjust the complexity and intensity of feedback signals.

[0048] S4 acquires human physiological data through the human physiological sensor, and adjusts the first multimodal tactile feedback mode according to the human physiological data to obtain a second multimodal tactile feedback mode.

[0049] S401 analyzes the current physiological state of the human body through the heart rate data and the respiratory rate data, wherein the current physiological state of the human body includes: a tense state, a fatigue state, and a relaxed state; S4011 If the heart rate per minute is >120 and the breathing rate per minute is >20, the current physiological state of the human body is a state of tension; S4012 If the heart rate is less than 60 and the breathing rate is less than 12, the current physiological state of the human body is fatigue; S4013 If 60≤heartbeats per minute≤120 and 12≤respirations per minute≤20, the current physiological state of the human body is a relaxed state.

[0050] S402: if the current physiological state of the human body is a tense state, feedback of the direction of the nearest rear moving object is provided through the vibration motor group at all danger levels; S403: If the current physiological state of the human body is fatigue, then at all risk levels, the relative distance feedback of the closest rear moving object is performed through the piezoelectric element group, and the direction feedback of the closest rear moving object is performed through the vibration motor group; S404: If the current physiological state of the human body is a relaxed state, retaining the first multi-modal tactile feedback mode.

[0051] In this step, in dangerous environments or information-intensive scenarios, users are prone to being unable to quickly judge key information due to the high complexity or diversity of tactile signals, resulting in excessive perception burden or delayed response. When the physiological state is tense (increasing user sensitivity), the vibration motor group is used to provide directional feedback first, focusing the tactile information on the most critical prompt direction to avoid additional signals (such as distance or speed) from interfering with the user. In the tense state, only the direction of the obstacle is fed back, and the user can make decisions and take action quickly. In the fatigue state, the user's sensitivity to tactile signals decreases, and it is easy to miss important information. When the physiological state is fatigue, the system will provide two signals: directional feedback (vibration motor group) and distance feedback (piezoelectric element group). The dual-modal signal enhances the perception effect, while highlighting key information (such as obstacle direction and distance) to make up for the problem of insufficient attention. The feedback mode is dynamically adapted through physiological data, which can help users quickly deal with environmental changes.

[0052] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0056] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0057] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0058] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0059] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0060] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Claims

1. An interactive method for a wearable multimodal tactile feedback system for a rider, characterized in that: Based on a wearable multimodal tactile feedback system for cyclists, comprising: A wearable feedback vest is mounted on the rider's upper body to generate tactile feedback signals on the rider's upper body; A laser radar is arranged on the rear side of the wearable feedback vest and connected to the controller to obtain data of moving objects behind the rider; A human physiological sensor is disposed on a wearable feedback vest and connected to a controller for collecting human physiological data; A tactile feedback element group is distributed on the back side of the wearable feedback vest and connected to the controller for providing multi-modal tactile feedback to the rider; The method comprises: Acquiring rear-side moving object data through the laser radar, and constructing a proximity risk value function according to the moving object data; The traffic complexity value is obtained by analyzing real-time traffic data and weather data, and the weight of the proximity danger value function is adjusted according to the traffic complexity value to calculate the proximity danger value; Classifying the danger level by approaching the danger value, and obtaining a first multimodal tactile feedback mode according to the danger level; Human physiological data is acquired through the human physiological sensor, and the first multimodal tactile feedback mode is adjusted according to the human physiological data to obtain a second multimodal tactile feedback mode.

2. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 1, characterized in that: The step of acquiring rear moving object data by the laser radar and constructing a proximity risk value function according to the moving object data comprises: The relative distance of the moving object behind is obtained by the laser radar ,speed , acceleration ,volume ; Relative distance of the object moving behind ,speed , acceleration ,volume Constructing a function close to the danger value , the calculation formula is as follows: ， in, The weight of The weight of The weight of The weight of .

3. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 2, characterized in that: The traffic complexity value obtained by analyzing real-time traffic data and weather data includes: Obtain vehicle density data, road congestion index data, and average vehicle speed data; Obtain visibility data, rainfall data, and wind speed data; The real-time traffic data and weather data are input into the pre-trained traffic complexity prediction model to obtain the traffic complexity value C.

4. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 3, characterized in that: The traffic complexity prediction model is trained through the following steps: Collect historical real-time traffic data and weather data, mark traffic complexity target values ​​based on the historical real-time traffic data and weather data, and construct training samples; The traffic complexity prediction model is constructed through the XGBoost model, and the initial training samples are fitted through the XGBoost model decision tree to initialize the traffic complexity prediction value; Based on the loss function, the gradient of each sample is calculated, and a new decision tree is constructed using the gradient and the traffic complexity target value; The traffic complexity prediction value of the new decision tree is weightedly summed with the traffic complexity prediction value of the previous round, and the traffic complexity prediction model is iteratively updated.

5. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 3, characterized in that: The method of adjusting the weight of the approach danger value function by the traffic complexity value includes: When the traffic complexity value C is greater than 0.5, increase Weight , Weight , the calculation formula is as follows: ， in, for The initial weight of for The initial weight of When the traffic complexity value C is less than 0.5, increase Weight ,Increase Weight , the calculation formula is as follows: ， in, for The initial weight of for The initial weight of right Weight , Weight , Weight , Weight Perform normalization adjustment to obtain the adjusted Weight , Weight , Weight , Weight .

6. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 1, characterized in that: The tactile feedback element group includes: a vibration motor group, a piezoelectric element group, and a thermoelectric element group; The vibration motor groups are respectively arranged on the upper back part of the wearable feedback vest, and are respectively used for tactile feedback of the direction of the nearest rear moving object; The piezoelectric element groups are respectively arranged at the middle of the back of the wearable feedback vest, and are respectively used for tactile feedback of the distance of the closest rear moving object; The thermoelectric element groups are respectively arranged at the waist of the wearable feedback vest, and are respectively used for speed tactile feedback of the closest rear moving object.

7. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 6, characterized in that: The step of classifying the danger level by approaching the danger value and acquiring the first multi-modal tactile feedback mode according to the danger level includes: Classify the proximity danger value exceeding the first threshold value as a high danger level, perform relative distance feedback of the closest rear moving object through the piezoelectric element group, perform speed feedback of the closest rear moving object through the thermoelectric element group, and perform direction feedback of the closest rear moving object through the vibration motor group; Classify the proximity danger value lower than the first threshold and higher than the second threshold as a medium danger level, and perform relative distance feedback of the closest rear moving object through the piezoelectric element group, and perform direction feedback of the closest rear moving object through the vibration motor group; The proximity danger value equal to or lower than the second threshold is classified as a low danger level, and the direction feedback of the closest rear moving object is performed through the vibration motor group.

8. The wearable multimodal tactile feedback system interaction method for a rider according to claim 1, characterized in that: The human physiological sensors include: a heart rate sensor and a breathing frequency sensor; The heart rate sensor is arranged at the front chest of the wearable feedback vest to collect the heart rate data of the rider; The breathing rate sensor is arranged at the front chest of the wearable feedback vest and is used to collect the breathing rate data of the rider.

9. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 8, characterized in that: The step of acquiring human physiological data through the human physiological sensor, adjusting the first multimodal tactile feedback mode according to the human physiological data, and obtaining the second multimodal tactile feedback mode comprises: Analyze the current physiological state of the human body through the heart rate data and the respiratory rate data, wherein the current physiological state of the human body includes: a tense state, a fatigue state, and a relaxed state; If the current physiological state of the human body is a state of tension, the direction feedback of the nearest rear moving object is provided through the vibration motor group at all danger levels; If the current physiological state of the human body is fatigue, the relative distance feedback of the closest rear moving object is performed through the piezoelectric element group at all risk levels, and the direction feedback of the closest rear moving object is performed through the vibration motor group; If the current physiological state of the human body is a relaxed state, the first multimodal tactile feedback mode is retained.

10. The interactive method of a wearable multimodal tactile feedback system for a rider according to claim 9, characterized in that: Analyzing the current physiological state of the human body through heart rate data and respiratory rate data includes: If the heart rate is >120 per minute and the breathing rate is >20 per minute, the current physiological state of the human body is tense; If the heart rate is less than 60 per minute and the breathing rate is less than 12 per minute, the current physiological state of the human body is fatigue; If 60≤heartbeats per minute≤120 and 12≤respirations per minute≤20, the current physiological state of the human body is a relaxed state.

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