Vehicle-mounted child interactive education method and device and vehicle
By assessing vehicle stability in real time and dynamically adjusting the interaction method, the problem of dizziness and vision damage caused by unstable driving in in-vehicle education systems has been solved, achieving a safe and continuous educational experience for children and enhancing the fun and adaptability of education.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DEEPAL AUTOMOBILE TECH CO LTD
- Filing Date
- 2026-04-30
- Publication Date
- 2026-07-14
AI Technical Summary
Existing in-vehicle children's education systems cannot dynamically adjust the interaction method according to the vehicle's movement, which can easily cause dizziness and vision damage to children when they are bumpy or making sharp turns, and the educational experience is also poor.
By acquiring vehicle motion parameters and road condition types, the vehicle's stability is assessed in real time, and interactive educational content is dynamically switched, including visual, verbal, and tactile interactions. This ensures that visual interaction is blocked when the vehicle is unstable, and only verbal and tactile interactions are retained. When the vehicle is stable, multiple interaction methods are enabled. The educational content is optimized by combining a dynamic knowledge mapping library and a child behavior recognition model.
It effectively avoids dizziness and vision damage in children, improves driving safety and passenger comfort, while maintaining the continuity and fun of education, adapting to different driving scenarios and individual child needs, and improving the effectiveness of in-vehicle education.
Smart Images
Figure CN122390929A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle electronics technology, specifically to an in-vehicle interactive education method, device, and vehicle for children. Background Technology
[0002] With the increasing prevalence of private cars, children are spending significantly more time traveling by car, and these fragmented in-car travel times are gradually becoming an important scenario for children's extracurricular learning and fun education. Most existing in-car children's education systems only play fixed audio and animated video educational content, without dynamically adjusting the interactive format based on the actual driving conditions of the vehicle.
[0003] When the vehicle is turning, accelerating, decelerating, or driving on bumpy roads, the body shakes noticeably. Children in the back seat who watch the in-vehicle visual display for extended periods are highly susceptible to dizziness and vision damage. Traditional solutions cannot adaptively disable visual interaction based on road conditions and rely solely on voice playback, resulting in a poor educational and interactive experience. Summary of the Invention
[0004] In view of the shortcomings of the prior art, the purpose of this application is to provide an in-vehicle interactive education method, device and vehicle for children, which can assess the stability of the vehicle body in real time according to the vehicle motion parameters and road conditions. When the vehicle is bumpy and unstable, the visual interaction is automatically turned off, and only the voice and tactile interaction are retained, which can effectively avoid motion sickness and vision damage in children and ensure driving safety.
[0005] In a first aspect, embodiments of this application provide an in-vehicle interactive education method for children, comprising:
[0006] Obtain vehicle motion parameters and road condition type, and confirm vehicle stability based on the vehicle motion parameters and road condition type;
[0007] When the vehicle stability is greater than or equal to a preset stability threshold and the duration exceeds a preset time threshold, a first type of interactive educational content is generated. The interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction, and tactile interaction. Otherwise, a second type of interactive educational content is generated. The interaction method of the second type of interactive educational content includes language interaction and / or tactile interaction, but does not include visual interaction.
[0008] In the above technical solution, by acquiring vehicle motion parameters and road condition types, the vehicle stability can be accurately confirmed, and unstable driving states such as vehicle bumps, sharp turns, and rapid acceleration and deceleration can be identified in real time. When the vehicle stability does not meet the preset conditions, the preset conditions include the vehicle stability being greater than the preset stability threshold and the duration exceeding the preset time threshold. Visual interaction is automatically blocked, and only voice interaction and / or tactile interaction are retained. This effectively avoids dizziness, motion sickness and other discomfort caused by children watching visual images when the vehicle is shaking. At the same time, it reduces the driving safety hazards caused by children being distracted by focusing on visual content, thus taking into account both driving safety and children's eye health and riding comfort.
[0009] Furthermore, based on the vehicle's stability, two types of interactive educational content are dynamically switched. When the vehicle is stable (i.e., the vehicle's stability is greater than the preset stability threshold and the duration exceeds the preset time threshold), at least one interactive method—visual, verbal, or tactile—is enabled to enrich the presentation of educational content, enhance the fun and interactivity of children's learning, and improve the in-vehicle education experience. When the vehicle is unstable (i.e., the vehicle's stability is less than the preset stability threshold, or the vehicle's stability is greater than or equal to the preset stability threshold but the duration does not exceed the preset time threshold), the educational process is not interrupted; instead, it switches to a non-visual interactive method. This avoids the safety risks associated with visual interaction and ensures the continuity of children's education during fragmented in-vehicle travel time, preventing interruptions in the educational process due to changes in road conditions.
[0010] This application can dynamically adapt to various complex in-vehicle driving scenarios, ensuring driving safety and children's eye health while maintaining uninterrupted in-vehicle children's educational process. It achieves safe, intelligent, and continuous interactive early childhood education in fragmented mobile scenarios, taking into account driving safety, children's physical and mental health, and the effectiveness of in-vehicle education.
[0011] In one embodiment, determining vehicle stability based on the vehicle motion parameters and road condition type includes:
[0012] The vehicle motion parameters are normalized to obtain normalized parameter values;
[0013] The normalized parameter values are subjected to nonlinear feature mapping to obtain the mapping result;
[0014] The vehicle stability is obtained by multiplying the mapping result by the road condition correction factor; the road condition correction factor is determined according to the road condition type.
[0015] In the above technical solution, by normalizing vehicle motion parameters, the dimensional differences of motion parameters for different types of vehicles can be eliminated, avoiding evaluation bias caused by inconsistent parameter units. This ensures that all types of motion parameters can participate uniformly in stability calculation, improving the standardization of calculation results. Based on this, nonlinear feature mapping processing of the normalized parameter values can accurately capture the nonlinear correlation between vehicle motion parameters and vehicle stability. Compared to linear mapping, this better reflects the complex changes in the vehicle's motion state during actual driving, effectively improving the accuracy of the mapping results in representing the vehicle's true stability state and avoiding misjudgments of stability due to the limitations of linear mapping. Simultaneously, introducing a road condition correction coefficient determined based on road condition type and multiplying it by the mapping result to obtain the final vehicle stability score enables accurate correction of stability assessment by road condition factors. Different road condition types have varying degrees of influence on vehicle stability. The road condition correction coefficient quantifies the influence weight of different road conditions, making the vehicle stability assessment results more consistent with actual driving scenarios, avoiding bias caused by relying solely on vehicle motion parameter assessments, and further improving the accuracy and adaptability of stability judgment.
[0016] In one embodiment, the vehicle motion parameters include longitudinal acceleration, lateral acceleration, and angular velocity. These parameters comprehensively capture the vehicle's longitudinal acceleration / deceleration, lateral turning, and body rotation during driving, fully reflecting the vehicle's actual driving posture and providing comprehensive and effective basic data support for stability assessment.
[0017] In one embodiment, before acquiring vehicle motion parameters and road condition type, the method further includes:
[0018] Real-time acquisition of driving status assessment results;
[0019] When the driving status assessment result indicates that the current vehicle driving status is abnormal, child status monitoring is performed, and the steps to confirm vehicle stability based on the vehicle motion parameters and road condition type and subsequent steps are suspended.
[0020] When the driving status assessment result indicates that the current vehicle driving status is stable, continue to execute the steps of obtaining vehicle motion parameters and road condition type, and the steps of confirming vehicle stability based on the vehicle motion parameters and road condition type.
[0021] In the above technical solution, by acquiring driving status assessment results in real time, abnormal driving conditions of the vehicle can be identified in advance, such as sudden braking, sharp turns, high-speed bumps, vehicle malfunctions, etc. In case of abnormality, the stability confirmation and subsequent interactive education steps can be stopped in time, so as to avoid performing interactive education operations in dangerous scenarios where the vehicle is extremely unstable, which would exacerbate safety hazards. At the same time, the child status monitoring is performed simultaneously, which can keep track of the child's riding status in real time when the vehicle is abnormally moving, such as whether he / she is crying, sitting abnormally, or doing dangerous things, so as to take protective measures in time and further ensure the safety of children riding in the car.
[0022] In one embodiment, generating the first type of interactive educational content or the second type of interactive educational content includes: filtering and matching interactive educational content based on a pre-built dynamic knowledge mapping library, wherein the interactive educational content includes the first type of interactive educational content or the second type of interactive educational content; the dynamic knowledge mapping library includes an age group dimension, a road condition type dimension, and a knowledge type dimension;
[0023] Interactive educational content is filtered and matched based on a pre-built dynamic knowledge mapping base, including:
[0024] Real-time identification of the vehicle's current road condition type and matching of the corresponding road condition type dimension parameters;
[0025] Obtain children's age information and match the corresponding age group dimension parameters;
[0026] Based on the vehicle stability assessment results and the preset educational objectives, the corresponding knowledge type dimension parameters are determined;
[0027] By combining the age group dimension parameter, the road condition dimension parameter, and the knowledge type dimension parameter, appropriate interactive educational content is retrieved from the dynamic knowledge mapping library.
[0028] In the above technical solution, the constructed dynamic knowledge mapping library covers three core dimensions: age group, road condition type, and knowledge type. It can take into account both individual differences of children and the characteristics of in-vehicle scenarios: by matching the child's age dimension parameter, educational content that matches the child's cognitive level and interests can be pushed, avoiding poor learning effects caused by content that is too difficult or too easy; by matching the road condition dimension parameter, the educational content can be adapted to the current driving scenario; by combining the vehicle stability judgment result with the preset educational goals to determine the knowledge type dimension parameter, it can ensure that the educational content not only fits the interactive form of the current driving state, but also meets the preset enlightenment and learning goals, greatly improving the pertinence and actual learning effect of in-vehicle education.
[0029] In one embodiment, after generating the first type of interactive educational content or the second type of interactive educational content, the method further includes:
[0030] Collect children's behavioral data;
[0031] The pre-trained child behavior recognition model analyzes the child behavior data in real time to obtain the child behavior type, which includes focused behavior type and distracted behavior type.
[0032] When a child's behavior is identified as a focused behavior type, continue with the current interactive educational content;
[0033] When a child's behavior is identified as distracted, a pause command for the educational content is triggered, a prompt voice is generated, and the child's behavior is monitored in real time. When the child's behavior is detected as returning to focused behavior, the interactive educational content is resumed according to the interruption point.
[0034] In the above technical solution, by collecting children's behavioral data and using a pre-trained children's behavior recognition model for real-time analysis, it is possible to accurately distinguish between children's focused behavior types and distracted behavior types. This allows for real-time monitoring of children's learning status during in-vehicle education, avoiding the drawbacks of traditional in-vehicle education such as "blindly playing and ignoring feedback." Differentiated control strategies are adopted for different behavior types to ensure that children can continue to learn efficiently when focused and to pause in time when distracted, avoiding ineffective playback and significantly improving the actual effect and time utilization of in-vehicle fragmented education.
[0035] In one embodiment, after generating the first type of interactive educational content or the second type of interactive educational content, the method further includes: collecting and summarizing children's behavior data and learning progress data within a preset time period, performing fusion analysis on the children's behavior data and the learning progress data, and generating learning tasks adapted to children's fragmented time during mobile travel.
[0036] In the aforementioned technical solution, by collecting and aggregating children's behavioral and learning progress data within a preset timeframe, a comprehensive understanding of children's learning status, interests, and knowledge acquisition during multiple in-vehicle trips can be achieved. Through fusion analysis, the limitations of single-trip education are overcome, generating learning tasks tailored to fragmented time, connecting scattered in-vehicle learning moments, avoiding disorganized learning content, and ensuring efficient use of fragmented in-vehicle time, thus improving the systematic and coherent nature of children's in-vehicle learning. Furthermore, based on children's behavioral data, their focus duration, points of interest, and easily distracted scenarios can be identified; based on learning progress data, their knowledge weaknesses and learning pace can be clearly identified. The combination of these two factors creates a comprehensive learning profile of the child. The resulting learning tasks are not only adapted to the duration of fragmented in-vehicle time but also tailored to children's individual learning needs, avoiding problems such as tasks being too difficult, too easy, or unsuitable in duration, further enhancing the personalization and effectiveness of in-vehicle education.
[0037] Secondly, embodiments of this application provide an in-vehicle interactive educational device for children, comprising:
[0038] The data acquisition module is configured to acquire vehicle motion parameters and road condition types;
[0039] The stability confirmation module is configured to confirm the vehicle stability based on the vehicle motion parameters and road condition type;
[0040] The content generation module is configured to generate a first type of interactive educational content when the vehicle stability is greater than or equal to a preset stability threshold and the duration exceeds a preset time threshold. The interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction, and tactile interaction. Otherwise, it generates a second type of interactive educational content. The interaction method of the second type of interactive educational content includes language interaction and / or tactile interaction, but does not include visual interaction.
[0041] In one embodiment, the stability confirmation module includes:
[0042] The normalization unit is configured to normalize the vehicle motion parameters to obtain normalized parameter values;
[0043] The mapping unit is configured to perform nonlinear feature mapping on the normalized parameter values to obtain the mapping result;
[0044] The correction unit is configured to multiply the mapping result by a road condition correction coefficient to obtain the vehicle stability; wherein the road condition correction coefficient is determined based on the road condition type.
[0045] Thirdly, embodiments of this application provide a vehicle that includes the aforementioned in-vehicle interactive educational device for children. Attached Figure Description
[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application will be described below.
[0047] Figure 1 This is a flowchart illustrating the in-vehicle interactive education method for children disclosed in an embodiment of this application;
[0048] Figure 2 This is a schematic diagram of one embodiment of the in-vehicle interactive educational device for children disclosed in this application.
[0049] Figure 3 This is a schematic diagram of another embodiment of the in-vehicle interactive educational device for children disclosed in this application.
[0050] Figure 4 This is a schematic diagram of the vehicle structure disclosed in an embodiment of this application.
[0051] Explanation of reference numerals in the attached figures:
[0052] 10-Data acquisition module, 20-Stability confirmation module, 30-Content generation module, 31-Normalization unit, 32-Mapping unit, 33-Correction unit, 100-Vehicle. Detailed Implementation
[0053] With the increasing prevalence of private cars, children are spending significantly more time traveling by car, and these fragmented in-car travel times are gradually becoming an important scenario for children's extracurricular learning and fun education. Most existing in-car children's education systems only play fixed audio and animated video educational content, without dynamically adjusting the interactive format based on the actual driving conditions of the vehicle.
[0054] When the vehicle is turning, accelerating, decelerating, or traveling on bumpy roads, the body sways significantly. Prolonged viewing of the in-vehicle visual interface by children in the back seat can easily cause dizziness and vision damage. Traditional solutions cannot adaptively disable visual interaction based on road conditions, relying solely on voice playback, resulting in a poor educational and interactive experience.
[0055] The embodiments of this application are described below with reference to the accompanying drawings.
[0056] In one embodiment, see Figure 1 As shown in the embodiment of this application, a vehicle-mounted interactive education method for children is disclosed, which includes:
[0057] S1: Obtain vehicle motion parameters and road condition type, and confirm vehicle stability based on the vehicle motion parameters and road condition type; when the vehicle stability is greater than or equal to a preset stability threshold and the duration exceeds a preset time threshold, proceed to step S2; otherwise, proceed to step S3.
[0058] S2, Generate a first type of interactive educational content, wherein the interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction and tactile interaction;
[0059] S3, Generate a second type of interactive educational content. The interaction methods of the second type of interactive educational content include language interaction and / or tactile interaction, but do not include visual interaction.
[0060] This embodiment accurately confirms vehicle stability by acquiring vehicle motion parameters and road condition types, and can identify unstable driving states such as vehicle bumps, sharp turns, and rapid acceleration and deceleration in real time. When the vehicle stability does not meet preset conditions, including vehicle stability exceeding a preset stability threshold and the duration exceeding a preset time threshold, visual interaction is automatically blocked, and only voice interaction and / or tactile interaction are retained. This effectively avoids dizziness, motion sickness, and other discomfort caused by children watching visual images when the vehicle is shaking, while reducing driving safety hazards caused by children being distracted by visual content, thus balancing driving safety with children's eye health and riding comfort.
[0061] Furthermore, based on the vehicle's stability, two types of interactive educational content are dynamically switched. When the vehicle is stable (i.e., the vehicle's stability is greater than the preset stability threshold and the duration exceeds the preset time threshold), at least one interactive method—visual, verbal, or tactile—is enabled to enrich the presentation of educational content, enhance the fun and interactivity of children's learning, and improve the in-vehicle education experience. When the vehicle is unstable (i.e., the vehicle's stability is less than the preset stability threshold, or the vehicle's stability is greater than or equal to the preset stability threshold but the duration does not exceed the preset time threshold), the educational process is not interrupted; instead, it switches to a non-visual interactive method. This avoids the safety risks associated with visual interaction and ensures the continuity of children's education during fragmented in-vehicle travel time, preventing interruptions in the educational process due to changes in road conditions.
[0062] This application can dynamically adapt to various complex in-vehicle driving scenarios, ensuring driving safety and children's eye health while maintaining uninterrupted in-vehicle children's educational process. It achieves safe, intelligent, and continuous interactive early childhood education in fragmented mobile scenarios, taking into account driving safety, children's physical and mental health, and the effectiveness of in-vehicle education.
[0063] In one optional implementation, determining vehicle stability based on the vehicle motion parameters and road condition type includes:
[0064] The vehicle motion parameters are normalized to obtain normalized parameter values. Normalization eliminates dimensional differences in motion parameters between different types of vehicles, avoids evaluation biases caused by inconsistent parameter units, ensures that all types of motion parameters can be uniformly used in stability calculations, and improves the standardization of calculation results.
[0065] Nonlinear feature mapping is applied to the normalized parameter values to obtain the mapping results. Nonlinear feature mapping can accurately capture the nonlinear correlation between vehicle motion parameters and vehicle stability. Compared with linear mapping, it can better reflect the complex changes in the motion state of the vehicle during actual driving, effectively improving the accuracy of the mapping results in representing the true stability state of the vehicle and avoiding misjudgment of stability due to the limitations of linear mapping.
[0066] The vehicle stability is obtained by multiplying the mapping result by the road condition correction factor.
[0067] In one optional implementation, the vehicle motion parameters include longitudinal acceleration, lateral acceleration, and angular velocity. These parameters comprehensively capture the vehicle's longitudinal acceleration / deceleration, lateral turning, and body rotation during driving, fully reflecting the vehicle's actual driving posture and providing comprehensive and effective basic data support for stability assessment.
[0068] When vehicle motion parameters include longitudinal acceleration, lateral acceleration, and angular velocity, confirming vehicle stability specifically includes:
[0069] The vehicle motion parameters are normalized to obtain normalized parameter values. The normalization expression is as follows:
[0070] .in, For normalized parameter values, For the longitudinal acceleration of the vehicle, As the first weighting coefficient, For the lateral acceleration of the vehicle, This is the second weighting coefficient. Let ω be the vehicle's angular velocity. This is the third weighting coefficient. This is a preset normalization baseline value.
[0071] For example, The value is 15.
[0072] The units for vehicle longitudinal acceleration and vehicle lateral acceleration are... The unit of vehicle angular velocity is rad / s. Weighted normalization can eliminate the dimensional differences between different parameters.
[0073] A nonlinear feature mapping is performed on the normalized parameter values to obtain the mapping result. Specifically, the expression for the nonlinear feature mapping is: .in, This is the mapping result.
[0074] The mapping result is multiplied by the road condition correction factor to obtain the vehicle stability. Specifically, the expression for vehicle stability is:
[0075] Where S represents vehicle stability and K represents road condition correction factor.
[0076] For example, the road condition correction factor is determined based on the road condition type, specifically including:
[0077] The intelligent driving VLA (Vision-Language-Action Model) large-scale model identifies the current road condition type of the vehicle in real time; then, based on the identified road condition type, it matches the corresponding road condition correction coefficient K value from a preset correction coefficient table. Specifically, K is set to 1.2 for urban roads, 1.0 for highways, and 1.1 for scenic roads.
[0078] In a specific example, the preset stability threshold is 0.85, and the preset time threshold is 3 seconds. When the vehicle is traveling on urban roads, the following data is collected: vehicle longitudinal acceleration. lateral acceleration of the vehicle Vehicle angular velocity First weighting coefficient Second weighting coefficient and the third weighting coefficient The values of are all 1, and the road condition correction factor K is 1.2. Substituting these values into the expression for vehicle stability, the vehicle stability is calculated as follows: At this point, the vehicle stability is greater than or equal to the preset stability threshold of 0.85. The vehicle motion parameters and road condition information are continuously collected. If the vehicle stability is greater than or equal to the preset stability threshold and the duration exceeds the preset time threshold, the first type of interactive educational content is generated. The interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction and tactile interaction.
[0079] In one alternative implementation, before acquiring vehicle motion parameters and road condition type, the method further includes:
[0080] Real-time acquisition of driving status assessment results;
[0081] When the driving status assessment result indicates that the current vehicle driving status is abnormal, child status monitoring is performed, and the steps to confirm vehicle stability based on the vehicle motion parameters and road condition type and subsequent steps are suspended.
[0082] When the driving status assessment result indicates that the current vehicle driving status is stable, continue to execute the steps of obtaining vehicle motion parameters and road condition type, and the steps of confirming vehicle stability based on the vehicle motion parameters and road condition type.
[0083] This implementation method can identify abnormal vehicle conditions in advance by acquiring real-time driving status assessment results, such as sudden braking, sharp turns, high-speed bumps, and vehicle malfunctions. In case of abnormalities, it can promptly pause the stability confirmation and subsequent interactive education steps, avoiding the exacerbation of safety hazards by continuing interactive education operations in dangerous scenarios where the vehicle is in extremely unstable driving conditions. At the same time, it can simultaneously monitor the child's status, such as whether the child is crying, has an abnormal sitting posture, or is making any dangerous movements, so as to take timely protective measures and further ensure the safety of children riding in the vehicle.
[0084] For example, the driving status assessment result is calculated in real time through vehicle sensor data and algorithm judgment, that is, vehicle motion data is collected, such as acceleration, angular velocity, vehicle speed, steering angle, vehicle posture, etc., and driving status characteristic values are determined by combining road conditions and driving behavior. The driving status characteristic values are compared with preset safety thresholds. If the safety threshold is exceeded, it indicates that the current vehicle driving status is abnormal; otherwise, it indicates that the current vehicle driving status is stable.
[0085] In one optional implementation, generating the first type of interactive educational content or the second type of interactive educational content includes: filtering and matching interactive educational content based on a pre-built dynamic knowledge mapping library, wherein the interactive educational content includes the first type of interactive educational content or the second type of interactive educational content; the dynamic knowledge mapping library includes an age group dimension, a road condition type dimension, and a knowledge type dimension;
[0086] Interactive educational content is filtered and matched based on a pre-built dynamic knowledge mapping base, including:
[0087] Real-time identification of the vehicle's current road condition type and matching of the corresponding road condition type dimension parameters;
[0088] Obtain children's age information and match the corresponding age group dimension parameters;
[0089] Based on the vehicle stability assessment results and the preset educational objectives, the corresponding knowledge type dimension parameters are determined;
[0090] By combining the age group dimension parameter, the road condition dimension parameter, and the knowledge type dimension parameter, appropriate interactive educational content is retrieved from the dynamic knowledge mapping library.
[0091] In this implementation, the constructed dynamic knowledge mapping library covers three core dimensions: age group, road condition type, and knowledge type. It can take into account both individual differences among children and the characteristics of in-vehicle scenarios: by matching the child's age dimension parameter, educational content that matches the child's cognitive level and interests can be pushed, avoiding poor learning effects caused by content that is too difficult or too easy; by matching the road condition dimension parameter, the educational content can be adapted to the current driving scenario; by combining the vehicle stability judgment result with the preset educational goals to determine the knowledge type dimension parameter, it can ensure that the educational content not only fits the interactive form of the current driving state, but also meets the preset enlightenment and learning goals, greatly improving the pertinence and actual learning effect of in-vehicle education.
[0092] For example, the age group dimension includes the lower age group of 3-6 years old and the upper age group of 7-12 years old.
[0093] The road condition type dimension includes urban roads, expressways, and scenic roads.
[0094] The knowledge types mentioned include: popular science knowledge, language expression, and safety common sense.
[0095] The educational content is strongly correlated with the road conditions identified by the intelligent driving big data model, and is displayed through AR overlay on the in-vehicle terminal. Specifically, this includes:
[0096] a. Traffic rules: Younger children (3-6 years old) learn traffic light color recognition, zebra crossing identification, lane differentiation, and basic rules for riding in vehicles; Older children (7-12 years old) learn the working principle of traffic lights, the meaning of road signs, basic traffic regulations, and safe travel norms, etc.
[0097] b. Vehicle cognition: Younger children learn about vehicles, car exterior parts, the functions of car lights, and the role of seat belts / child seats; older children learn about the basic structure of cars, the core functions and principles of intelligent driving, and the role of vehicle safety devices, etc.
[0098] c. English learning for beginners: Younger children learn English vocabulary related to transportation / roads and basic spoken English for daily travel; older children learn English phrases for traffic scenarios, automotive-related professional terminology, and English dialogues for travel scenarios.
[0099] d. Popular Science Knowledge: Combining elements such as roads, bridges, green buildings, and natural landscapes recognized by intelligent driving systems, corresponding physics knowledge, geographical knowledge, and popular science knowledge about animals and plants are pushed according to age groups.
[0100] e. Safety Knowledge: Focusing on children's safety in cars, this section provides information on car safety taboos, emergency avoidance methods, road safety precautions, and prevention of contact with strangers, categorized by age group.
[0101] In one alternative implementation, after generating the first type of interactive educational content or the second type of interactive educational content, the method further includes:
[0102] Collect children's behavioral data;
[0103] The pre-trained child behavior recognition model analyzes the child behavior data in real time to obtain the child behavior type, which includes focused behavior type and distracted behavior type.
[0104] When a child's behavior is identified as a focused behavior type, continue with the current interactive educational content;
[0105] When a child's behavior is identified as distracted, a pause command for the educational content is triggered, a prompt voice is generated, and the child's behavior is monitored in real time. When the child's behavior is detected as returning to focused behavior, the interactive educational content is resumed according to the interruption point.
[0106] This implementation method collects children's behavioral data and analyzes it in real time using a pre-trained children's behavior recognition model. It can accurately distinguish between children's focused and distracted behaviors, and can monitor children's learning status in real time during in-vehicle education. This avoids the drawbacks of traditional in-vehicle education, which involves "blindly playing and ignoring feedback." Differentiated management strategies are adopted for different behavior types to ensure that children can continue to learn efficiently when focused and to pause in time when distracted, avoiding ineffective playback and significantly improving the actual effect and time utilization of fragmented in-vehicle education.
[0107] For example, this implementation adopts a child distraction behavior recognition mode that combines millimeter-wave radar and vision: based on a child behavior recognition model, it identifies six types of distraction behaviors in real time, such as looking out the window, leaving the seat, holding non-educational devices, and having attention continuously deviating from the screen for ≥5 seconds. This triggers an intervention mechanism that pauses the educational content and sends a safety reminder. Once the child regains focus, the educational content is intelligently resumed based on the interruption point, forming a closed loop of monitoring-intervention-resumption of education. The distraction behavior recognition response time is ≤0.5 seconds; the intervention prompt voice volume is ≤65dB to avoid startling the child.
[0108] In one optional implementation, after generating the first type of interactive educational content or the second type of interactive educational content, the method further includes: collecting and summarizing children's behavior data and learning progress data within a preset time period, performing fusion analysis on the children's behavior data and the learning progress data, and generating learning tasks adapted to children's fragmented time during mobile travel.
[0109] This implementation method collects and aggregates children's behavioral and learning progress data within a preset time frame, enabling a comprehensive understanding of children's learning status, interests, and knowledge acquisition during multiple in-vehicle trips. Through fusion analysis, it breaks the limitations of single-trip education, generating learning tasks tailored to fragmented time, connecting scattered in-vehicle learning moments, avoiding disorganized learning content, and ensuring efficient use of fragmented in-vehicle time, thus improving the systematic and coherent nature of children's in-vehicle learning. Furthermore, based on children's behavioral data, it can identify their attention span, interests, and distractibility scenarios; based on learning progress data, it can clarify their knowledge weaknesses and learning pace. The combination of these two aspects creates a comprehensive learning profile of the child. The resulting learning tasks are adapted to the duration of fragmented in-vehicle time and meet the individual learning needs of children, avoiding problems such as tasks being too difficult, too easy, or unsuitable in duration, further enhancing the personalization and effectiveness of in-vehicle education.
[0110] For example, the specific process of generating learning tasks is as follows:
[0111] Data collection and preprocessing: Collect children's behavior data and learning progress data within a preset time period, and perform noise reduction, normalization and timestamp alignment on the two types of data to eliminate data dimensional differences and time sequence deviations, forming standardized input data.
[0112] Feature extraction: behavioral features such as attention duration, number of distractions, response delay, and interaction activity are extracted from standardized children's behavior data; progress features such as learning completion rate, knowledge mastery rate, answer accuracy rate, and content dwell time are extracted from learning progress data.
[0113] Feature weighted fusion: The behavioral features and progress features are weighted according to preset weights, and the multi-dimensional features are fused into a single comprehensive learning status indicator to achieve a quantitative and unified evaluation of behavioral performance and learning effect.
[0114] Learning status assessment and profile construction: Based on comprehensive learning status indicators, determine the child's current learning focus, knowledge gaps, learning preferences and learning pace, and generate a corresponding in-vehicle learning profile.
[0115] Learning task generation: Based on the learning profile and combined with the characteristics of vehicle driving scenarios and duration, personalized learning tasks are generated to suit children's fragmented time during mobile travel, achieving precise matching of learning content, difficulty, duration and interaction methods.
[0116] In one embodiment, see Figure 2 As shown in the figure, this application provides an in-vehicle interactive educational device for children, the device comprising:
[0117] Data acquisition module 10 is configured to acquire vehicle motion parameters and road condition type;
[0118] The stability confirmation module 20 is configured to confirm the vehicle stability based on the vehicle motion parameters and road condition type.
[0119] The content generation module 30 is configured to generate a first type of interactive educational content when the vehicle stability is greater than or equal to a preset stability threshold and the duration exceeds a preset time threshold. The interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction, and tactile interaction. Otherwise, it generates a second type of interactive educational content. The interaction method of the second type of interactive educational content includes language interaction and / or tactile interaction, but does not include visual interaction.
[0120] In one alternative implementation, see Figure 3 As shown, the stability confirmation module 20 includes:
[0121] Normalization unit 21 is configured to normalize the vehicle motion parameters to obtain normalized parameter values;
[0122] Mapping unit 22 is configured to perform nonlinear feature mapping on normalized parameter values to obtain mapping results;
[0123] The correction unit 23 is configured to multiply the mapping result by the road condition correction coefficient to obtain the vehicle stability; wherein the road condition correction coefficient is determined according to the road condition type.
[0124] In one embodiment, this application provides an in-vehicle interactive education system for children, which includes: an in-vehicle interactive education device for children, an intelligent driving system interface, a data acquisition element, a core processing element, and an interactive terminal element;
[0125] The intelligent driving system interface establishes communication with the intelligent driving domain controller via the vehicle's Ethernet interface, transmitting in real time: vehicle motion parameters, intelligent driving VLA large model scene, and intent recognition result signals. The data refresh rate of the intelligent driving system interface is ≥10Hz, and the signal transmission delay is ≤300ms.
[0126] All sensors in the data acquisition unit complete time synchronization and spatial coordinate calibration, with a calibration error of <0.5cm, ensuring consistent and accurate data acquisition. Specifically, this includes:
[0127] Intelligent driving data acquisition component: Onboard IMU (Inertial Measurement Unit) sensor, with a sampling frequency of 10Hz, is installed at the geometric center of the center console to collect vehicle motion parameters.
[0128] Millimeter-wave radar with a detection range of 0.5~5m and a horizontal detection angle. It is installed inside the headrest of the front seat, facing the rear passenger area, for monitoring children's behavioral profiles.
[0129] A visual camera with a resolution of no less than 1080P and a frame rate of no less than 30fps is installed in the rearview mirror position inside the car to capture the child's facial and limb movements.
[0130] The noise-canceling microphone has a signal-to-noise ratio of ≥60dB and a frequency response range of 300-8000Hz. It is installed on the back of the rear seat and supports voice collection for multiple mainstream dialects.
[0131] Core processing unit: The edge computing module is used as the core processing unit with a computing power of ≥21 TOPS. The intelligent driving system adopts an interpretable VLA model, and 70% of the computing power is dedicated to the VLA model and safety-related modules to ensure the real-time performance of core functions.
[0132] Intelligent driving VLA model initialization: Load the interpretability judgment rules and model parameters for scene recognition, child intention recognition, and driving intention recognition. All recognition logic is stored locally, and the linkage calibration between the model and the intelligent driving system and sensors is completed.
[0133] Stability algorithm module initialization: preload stability calculation formula, retrieve normalized baseline threshold, first weight coefficient, second weight coefficient, third weight coefficient, preset stability threshold and preset time threshold, and set road condition correction coefficient according to urban, highway and scenic road levels;
[0134] Initialization of the child behavior recognition module: Import the large model recognition model, preset 6 types of distraction behavior recognition labels, and set the intervention to be triggered only when the probability of recognizing distraction behavior is ≥90%.
[0135] Privacy protection module initialization: Initialize the AES-256 encryption algorithm, configure the local storage path on the vehicle's 128GB encrypted solid-state drive, and disable the cloud upload permission for raw voice and behavioral data. A closed-loop process is adopted, consisting of local feature extraction, encrypted vector upload, cloud model optimization, and local model update. Only the acoustic feature vectors of children's dialect voices are extracted and encrypted for cloud upload (frequency ≤ 1 time / week). Raw voice data is stored locally on the vehicle's encrypted terminal (storage time can be set to 15 days, 30 days, or permanently). The dialect adaptation model optimized in the cloud is transmitted back to the local machine through an encrypted channel, achieving accurate recognition of children's voices while protecting their privacy.
[0136] Dynamic knowledge mapping library initialization: Load five categories of age-appropriate learning content: traffic rules, vehicle cognition, English learning, popular science, and safety knowledge, and complete the binding configuration with the scene recognition results of the intelligent driving VLA large model.
[0137] The interactive terminal unit includes an AR display terminal, in-vehicle zoned speakers, a parent-side APP communication module, and a personalized education generation module.
[0138] AR display terminal: Options include rear screen, rear window (5×3 inches), or rooftop screen (8×5 inches), field of view. ,brightness Designate a dedicated display area for children's education; the fusion error between AR annotations and real road elements is ≤0.1°.
[0139] In-vehicle zoned speakers: rear speaker volume limited to ≤65dB, safety warning sounds only played in the rear, and front speakers muted;
[0140] The parent-side APP communication module establishes communication with the mobile APP based on the MQTT protocol, and only pushes three types of data: distraction behavior reminders, learning progress summaries, and hardware failure prompts.
[0141] Personalized Education Generation Module: Completes initialization for linkage with the GBDT algorithm and sets feature weights for intelligent driving scenario data. .
[0142] In one optional implementation, the system triggers a safe exit procedure when any of the following occurs: vehicle power failure, the intelligent driving VLA model determines an abnormal driving state, the child is out of seat for more than 5 seconds, a collision / sudden braking occurs, or the parent's app manually disables the educational function. Upon exit, the core processing unit automatically saves the child's current learning progress, behavioral data, and personalized learning plan using an incremental saving method; subsequently, it shuts down the AR display terminal, speakers, and other interactive terminals, retaining only the basic status monitoring function of the intelligent driving VLA model; when the vehicle restarts and meets the preset vehicle stability conditions, the system can directly resume pushing educational content from the last interruption point.
[0143] In one embodiment, see Figure 4 As shown, this application discloses a vehicle 100 that includes an in-vehicle interactive educational device for children according to any of the above embodiments.
[0144] It should be noted that the vehicle 100 can be, but is not limited to, a pure electric vehicle (PEV / BEV), a hybrid electric vehicle (HEV), a range-extended electric vehicle (REEV), a plug-in hybrid electric vehicle (PHEV), a new energy vehicle, or a gasoline vehicle.
[0145] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.
Claims
1. A vehicle-mounted interactive educational method for children, characterized in that, include: Obtain vehicle motion parameters and road condition type, and confirm vehicle stability based on the vehicle motion parameters and road condition type; When the vehicle stability is greater than or equal to a preset stability threshold and the duration exceeds a preset time threshold, a first type of interactive educational content is generated. The interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction, and tactile interaction. Otherwise, a second type of interactive educational content is generated. The interaction method of the second type of interactive educational content includes language interaction and / or tactile interaction, but does not include visual interaction.
2. The in-vehicle interactive education method for children according to claim 1, characterized in that: Determining vehicle stability based on the vehicle motion parameters and road condition type includes: The vehicle motion parameters are normalized to obtain normalized parameter values; The normalized parameter values are subjected to nonlinear feature mapping to obtain the mapping result; The vehicle stability is obtained by multiplying the mapping result by the road condition correction factor; the road condition correction factor is determined according to the road condition type.
3. The in-vehicle interactive education method for children according to claim 1, characterized in that: The vehicle motion parameters include longitudinal acceleration, lateral acceleration, and angular velocity.
4. The in-vehicle interactive education method for children according to claim 1, characterized in that: Before obtaining vehicle motion parameters and road condition type, the following is also included: Real-time acquisition of driving status assessment results; When the driving status assessment result indicates that the current vehicle driving status is abnormal, child status monitoring is performed, and the steps to confirm vehicle stability based on the vehicle motion parameters and road condition type and subsequent steps are suspended. When the driving status assessment result indicates that the current vehicle driving status is stable, continue to execute the steps of obtaining vehicle motion parameters and road condition type, and the steps of confirming vehicle stability based on the vehicle motion parameters and road condition type.
5. The in-vehicle interactive education method for children according to claim 1, characterized in that, Generating the first type of interactive educational content or the second type of interactive educational content includes: filtering and matching interactive educational content based on a pre-built dynamic knowledge mapping library, wherein the interactive educational content includes the first type of interactive educational content or the second type of interactive educational content; the dynamic knowledge mapping library includes age group dimension, road condition type dimension and knowledge type dimension; Interactive educational content is filtered and matched based on a pre-built dynamic knowledge mapping base, including: Real-time identification of the vehicle's current road condition type and matching of the corresponding road condition type dimension parameters; Obtain children's age information and match the corresponding age group dimension parameters; Based on the vehicle stability assessment results and the preset educational objectives, the corresponding knowledge type dimension parameters are determined; By combining the age group dimension parameter, the road condition dimension parameter, and the knowledge type dimension parameter, appropriate interactive educational content is retrieved from the dynamic knowledge mapping library.
6. The in-vehicle interactive education method for children according to claim 1, characterized in that: After generating the first type of interactive educational content or the second type of interactive educational content, it also includes: Collect children's behavioral data; The pre-trained child behavior recognition model analyzes the child behavior data in real time to obtain the child behavior type, which includes focused behavior type and distracted behavior type. When a child's behavior is identified as a focused behavior type, continue with the current interactive educational content; When a child's behavior is identified as distracted, a pause command for the educational content is triggered, a prompt voice is generated, and the child's behavior is monitored in real time. When the child's behavior is detected as returning to focused behavior, the interactive educational content is resumed according to the interruption point.
7. The in-vehicle interactive education method for children according to claim 1, characterized in that, After generating the first type or the second type of interactive educational content, the method further includes: collecting and summarizing children's behavior data and learning progress data within a preset time period, performing fusion analysis on the children's behavior data and the learning progress data, and generating learning tasks adapted to children's fragmented time during mobile travel.
8. A vehicle-mounted interactive educational device for children, characterized in that, include: The data acquisition module is configured to acquire vehicle motion parameters and road condition types; The stability confirmation module is configured to confirm the vehicle stability based on the vehicle motion parameters and road condition type; The content generation module is configured to generate a first type of interactive educational content when the vehicle stability is greater than or equal to a preset stability threshold and the duration exceeds a preset time threshold. The interaction method of the first type of interactive educational content includes at least one of visual interaction, language interaction, and tactile interaction. Otherwise, it generates a second type of interactive educational content. The interaction method of the second type of interactive educational content includes language interaction and / or tactile interaction, but does not include visual interaction.
9. The in-vehicle interactive educational device for children according to claim 8, characterized in that, The stability confirmation module includes: The normalization unit is configured to normalize the vehicle motion parameters to obtain normalized parameter values; The mapping unit is configured to perform nonlinear feature mapping on the normalized parameter values to obtain the mapping result; The correction unit is configured to multiply the mapping result by a road condition correction coefficient to obtain the vehicle stability; wherein the road condition correction coefficient is determined based on the road condition type.
10. A vehicle, characterized in that: Includes the in-vehicle interactive educational device for children as described in claim 8 or 9.