Distraction early warning method, system and equipment based on driver cognitive state monitoring and medium

Abstract

The invention discloses a distraction early warning method, system and device based on driver cognitive state monitoring and a medium, and relates to the technical field of intelligent driving assistance. The method comprises the following steps: acquiring multi-modal data and calculating a cognitive load index; determining a VATS cumulative time based on the eye movement tracking data; constructing a cognitive control evaluation model based on the cognitive load index, and determining whether the current driving state is a high cognitive load state or not by using the cognitive control evaluation model; if the accumulation time of the VATS is greater than or equal to a set limit value, triggering a first-level warning; wherein the set limit value is dynamically adjusted according to a driver behavior mode; and if the current driving state is a high cognitive load state and the VATS accumulation time is greater than or equal to a set limit value, triggering a secondary warning and suggesting to take over driving. The problems that in the prior art, the VATS warning effect is insufficient, and cognitive state monitoring is not accurate can be solved, and the cognitive and response efficiency of a driver to distraction behaviors is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving assistance technology, and in particular to a distraction warning method, system, device and medium based on driver cognitive state monitoring. Background Art

[0002] Existing driver monitoring systems (DMS) mainly detect distracted behavior by the duration of gaze deviation (such as long distraction LD) or cumulative short-term distraction (such as visual attention time sharing VATS). However, they still have the following problems:

[0003] First, VATS warnings are ineffective: drivers lack awareness of the VATS triggering mechanism, and generic warnings fail to guide behavioral improvements. Second, cognitive status monitoring is inaccurate: traditional DMS relies on a single behavioral indicator (such as gaze direction), making it difficult to distinguish between levels of cognitive control. Finally, feedback mechanisms are rigid: existing systems lack dynamic visualization and graded warnings, making them unable to adapt to drivers' individual needs.

[0004] Therefore, there is an urgent need for a new monitoring and early warning method for intelligent driving assistance. Summary of the Invention

[0005] The object of the present invention is to provide a distraction warning method, system, device and medium based on driver cognitive status monitoring, aiming to solve or improve at least one of the above-mentioned technical problems.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A distraction warning method based on driver cognitive status monitoring, comprising:

[0008] Acquiring multimodal data during driving and calculating a cognitive load index; the multimodal data includes eye tracking data and MEG neuroimaging data;

[0009] determining a VATS cumulative time based on the eye tracking data;

[0010] Constructing a cognitive control evaluation model based on the cognitive load index, and using the cognitive control evaluation model to determine whether the current driving state is a high cognitive load state;

[0011] If the VATS cumulative time is greater than or equal to a set limit, a level 1 warning is triggered; wherein the set limit is dynamically adjusted according to the driver's behavior pattern;

[0012] If the current driving state is a high cognitive load state and the VATS cumulative time is greater than or equal to the set limit, a level 2 warning is triggered and it is recommended to take over driving.

[0013] Optionally, the acquiring of multimodal data and calculating of a cognitive load index specifically includes:

[0014] Collect multimodal data based on an on-board eye tracker and a MEG-compatible driving simulator;

[0015] Extracting the multimodal data to obtain key parameters; the key parameters include fixation count, search range, and frontal midline theta wave;

[0016] A cognitive load index is calculated based on the key parameters.

[0017] Optionally, the calculation formula of the cognitive load index is:

[0018] CLI = α × FMT energy + β × normalized eye movement index value calculation

[0019] The FMT energy was determined by the frontal midline θ wave, α + β = 1 and α ≥ 0.5.

[0020] Optionally, the cognitive control evaluation model is expressed as: when the eye movement index is less than a set frequency, it is determined that there is an eye movement index abnormality, and when the FMT energy is greater than or equal to a set threshold and there is an eye movement index abnormality, it is determined that the current driving state is a high cognitive load state; wherein, the FMT energy is determined by the frontal midline theta wave; and the eye movement index includes the number of fixations.

[0021] Optionally, the method further includes: collecting statistics on the multimodal data and warning feedback, analyzing the warning triggering cause using a psychological model, and generating a personalized report.

[0022] The present invention also provides a distraction warning system based on driver cognitive status monitoring, comprising:

[0023] A data acquisition and index calculation unit, configured to acquire multimodal data during driving and calculate a cognitive load index; the multimodal data includes eye tracking data and MEG neuroimaging data;

[0024] a VATS accumulation calculation unit, configured to determine a VATS accumulation time based on the eye tracking data;

[0025] a state judgment unit, configured to construct a cognitive control evaluation model based on the cognitive load index, and determine whether the current driving state is a high cognitive load state using the cognitive control evaluation model;

[0026] a first-level warning triggering unit, configured to trigger a first-level warning if the accumulated VATS time is greater than or equal to a set limit; wherein the set limit is dynamically adjusted according to the driver's behavior pattern;

[0027] The secondary warning triggering unit is used to trigger a secondary warning and suggest taking over driving if the current driving state is a high cognitive load state and the VATS cumulative time is greater than or equal to a set limit.

[0028] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned distraction warning method based on driver cognitive status monitoring.

[0029] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the distraction warning method based on driver cognitive state monitoring as described above.

[0030] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0031] The present invention discloses a distraction warning method, system, device, and medium based on driver cognitive state monitoring. The method includes acquiring multimodal data and calculating a cognitive load index; determining VATS cumulative time based on eye tracking data; constructing a cognitive control assessment model based on the cognitive load index, and using the cognitive control assessment model to determine whether the current driving state is a high cognitive load state; triggering a level one warning if the VATS cumulative time is greater than or equal to a set limit; wherein the set limit is dynamically adjusted based on the driver's behavior pattern; and triggering a level two warning and suggesting taking over driving if the current driving state is a high cognitive load state and the VATS cumulative time is greater than or equal to the set limit. The present invention can overcome the problems of insufficient VATS warning effectiveness and inaccurate cognitive state monitoring in the prior art, and improve the driver's recognition and response efficiency to distraction behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below 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 any creative work.

[0033] Figure 1 Schematic diagram of the flow of the distraction warning method based on driver cognitive status monitoring of the present invention;

[0034] Figure 2 This is a schematic diagram of the overall operation logic of this embodiment;

[0035] Figure 3 This is a logic diagram for triggering hierarchical warnings in this embodiment;

[0036] Figure 4 This is a schematic diagram of the psychological model training interface in this embodiment. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The object of the present invention is to provide a distraction warning method, system, device and medium based on driver cognitive status monitoring, aiming to solve or improve at least one of the above-mentioned technical problems.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] like Figures 1-4 As shown, the present invention provides a distraction warning method based on driver cognitive state monitoring, comprising:

[0041] Multimodal data during driving is acquired and a cognitive load index is calculated; the multimodal data includes eye tracking data and MEG neuroimaging data.

[0042] The VATS cumulative time is determined based on the eye tracking data; a cognitive control evaluation model is constructed based on the cognitive load index, and the cognitive control evaluation model is used to determine whether the current driving state is a high cognitive load state.

[0043] If the accumulated VATS time is greater than or equal to the set limit, a level one warning is triggered; wherein the set limit is dynamically adjusted according to the driver's behavior pattern; if the current driving state is a high cognitive load state and the accumulated VATS time is greater than or equal to the set limit, a level two warning is triggered and it is recommended to take over the driving.

[0044] As a specific implementation method, the specific processing process of the above steps is as follows.

[0045] For multimodal data fusion:

[0046] Integrate eye tracking (1000Hz sampling) with MEG neuroimaging data to capture fixation count, search range, and frontal midline theta (FMT) activity in real time.

[0047] A cognitive control assessment model was constructed: when the FMT energy ≥ the threshold and the eye movement index (such as the number of fixations <10 times / 4 seconds) was abnormal, it was determined to be a high cognitive load state.

[0048] For dynamic visualization and graded warnings:

[0049] VATS cumulative progress bar: The dashboard displays the cumulative distraction time within 30 seconds (such as "8 / 10 seconds"), and triggers a level 1 warning (flashing icon + voice prompt "Attention multiple distractions") when the set limit is reached.

[0050] Cognitive state-related warning: If VATS is detected under high cognitive load and the accumulated VATS reaches the set limit, a level 2 warning (full-screen red prompt + voice message "Cognitive overload, slow down recommended") will be triggered.

[0051] For mental model training:

[0052] Interactive simulation training: A driving scenario simulation is embedded in the vehicle's central control screen to guide the driver to identify the difference between LD and VATS, and provide real-time feedback on the cause of the warning trigger (such as "You triggered the warning due to multiple short-term distractions").

[0053] Personalized report generation: Generate weekly reports based on historical data (such as "VATS triggers decreased by 30%"), combined with a safe driving points reward mechanism.

[0054] In this embodiment, the psychological model is constructed based on a neural network.

[0055] For adaptive optimization algorithms with set limits:

[0056] Dynamically adjust the VATS threshold based on driver behavior patterns: For drivers who frequently trigger high-cognition warnings, gradually lower the VATS cumulative threshold (e.g., from 10 seconds to 8 seconds) until the distracted behavior improves.

[0057] MEG data were integrated to optimize the eye movement-cognition correlation model: when the correlation between FMT activity and eye movement indicators was <0.7, the detection parameters were automatically recalibrated.

[0058] As a specific embodiment, a specific deployment and application process of the above method content is provided.

[0059] 1. Hardware deployment

[0060] The in-vehicle eye tracker (SR Research EyeLink 1000) is linked to a MEG-compatible driving simulator, and the data is transmitted to the in-vehicle ECU via the CAN bus.

[0061] The central control screen integrates a training module and supports touch interaction and voice feedback.

[0062] 2. Software Process

[0063] The data fusion module analyzes eye movement and MEG data in real time and calculates the cognitive load index (CLI = 0.6 × FMT energy + 0.4 × normalized number of fixations).

[0064] When CLI ≥ 0.8 and VATS cumulative ≥ 7 seconds, a level 2 warning is triggered and it is recommended to take over driving.

[0065] 3. Adaptive Optimization Example

[0066] If the driver triggers the VATS warning three times in a row when CLI ≥ 0.8, the system will reduce the VATS threshold from 10 seconds to 9 seconds, and prompt on the central control screen that "Increased distraction risk has been detected and the warning sensitivity has been adjusted."

[0067] Therefore, this technical solution improves the effectiveness of VATS warnings by associating VATS visualization with cognitive status warnings, and combines it with neuroimaging data to improve the accuracy of distraction detection. It also further improves the driver's cognitive accuracy of the VATS mechanism through psychological model training.

[0068] In summary, this invention addresses the inadequacy of existing DMS warnings for VATS by integrating MEG neuroimaging data with eye movement metrics to assess driver cognitive load in real time. This system, combined with dynamic visualization and a graded warning mechanism, addresses the issue of ineffective VATS warnings from existing DMS. The system integrates psychological model training and adaptive algorithm optimization to significantly improve the driver's cognitive accuracy and response efficiency to distracting behaviors, reducing driving risks caused by cognitive errors.

[0069] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0070] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A distraction warning method based on driver cognitive status monitoring, characterized in that: include: Acquiring multimodal data during driving and calculating a cognitive load index; the multimodal data includes eye tracking data and MEG neuroimaging data; determining a VATS cumulative time based on the eye tracking data; Constructing a cognitive control evaluation model based on the cognitive load index, and using the cognitive control evaluation model to determine whether the current driving state is a high cognitive load state; If the VATS cumulative time is greater than or equal to a set limit, a level 1 warning is triggered; wherein the set limit is dynamically adjusted according to the driver's behavior pattern; If the current driving state is a high cognitive load state and the VATS cumulative time is greater than or equal to the set limit, a level 2 warning is triggered and it is recommended to take over driving.

2. The distraction warning method based on driver cognitive status monitoring according to claim 1 is characterized in that: The obtaining of multimodal data and calculating of cognitive load index specifically includes: Collect multimodal data based on an on-board eye tracker and a MEG-compatible driving simulator; Extracting the multimodal data to obtain key parameters; the key parameters include fixation count, search range, and frontal midline theta wave; A cognitive load index is calculated based on the key parameters.

3. The distraction warning method based on driver cognitive status monitoring according to claim 2, characterized in that: The calculation formula of the cognitive load index is: CLI = α × FMT energy + β × normalized eye movement index value calculation The FMT energy was determined by the frontal midline θ wave, α + β = 1 and α ≥ 0.

5.

4. The distraction warning method based on driver cognitive status monitoring according to claim 1, characterized in that: The cognitive control evaluation model is expressed as follows: when the eye movement index is less than a set frequency, it is determined that there is an eye movement index abnormality, and when the FMT energy is greater than or equal to a set threshold and the eye movement index is abnormal, the current driving state is determined to be a high cognitive load state; wherein the FMT energy is determined by the frontal midline theta wave; and the eye movement index includes the number of fixations.

5. The distraction warning method based on driver cognitive status monitoring according to claim 1, characterized in that: Also includes: The multimodal data and warning feedback are collected, and the psychological model is used to analyze the warning triggering cause and generate a personalized report.

6. A distraction warning system based on driver cognitive status monitoring, characterized in that: include: A data acquisition and index calculation unit, configured to acquire multimodal data during driving and calculate a cognitive load index; the multimodal data includes eye tracking data and MEG neuroimaging data; a VATS accumulation calculation unit, configured to determine a VATS accumulation time based on the eye tracking data; a state judgment unit, configured to construct a cognitive control evaluation model based on the cognitive load index, and determine whether the current driving state is a high cognitive load state using the cognitive control evaluation model; a first-level warning triggering unit, configured to trigger a first-level warning if the accumulated VATS time is greater than or equal to a set limit; wherein the set limit is dynamically adjusted according to the driver's behavior pattern; The secondary warning triggering unit is used to trigger a secondary warning and suggest taking over driving if the current driving state is a high cognitive load state and the VATS cumulative time is greater than or equal to a set limit.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the distraction warning method based on driver cognitive state monitoring according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the distraction warning method based on driver cognitive state monitoring as described in any one of claims 1 to 5.

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