Human factors intelligent cockpit driver experience evaluation system and method

Abstract

The present application relates to a human-factor intelligent cockpit driver experience evaluation system and method, wherein the system includes: at least one single-vehicle evaluation system, which is used to collect sensor data generated by at least one evaluation driver based on corresponding evaluation content, and identify the driver status of at least one evaluation driver based on the sensor data; an evaluation management system, which is used to generate evaluation content corresponding to at least one evaluation driver, and receive sensor data and driver status uploaded by at least one single-vehicle evaluation system to obtain experience evaluation data of the human-factor intelligent cockpit. The embodiment of the present application can use the generated evaluation content to conduct experience tests on drivers, and obtain experience evaluation data of the human-factor intelligent cockpit based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for driver experience evaluation, meeting the actual evaluation needs of the human-factor intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation.

Description

Technical Field

[0001] The present application relates to the field of smart cockpit technology, and in particular to a human factors smart cockpit driver experience evaluation system and method. Background Art

[0002] With the rapid development of automobile intelligence, people's requirements for automobile comfort are getting higher and higher. In particular, the driver experience of the car's smart cockpit is becoming more and more important, and the driver experience test and evaluation of the car's smart cockpit has become an important way to measure the driver experience effect.

[0003] In the related technology, it is possible to select an experience test scenario based on travel needs, analyze and process the data in the scenario to obtain the experience test results of the personnel, and evaluate the cockpit to obtain the experience test evaluation results of the cockpit.

[0004] However, when conducting driver experience tests in the vehicle's cockpit, there is a lack of objective evaluation basis for the driver's experience, which cannot meet the actual evaluation needs of the smart cockpit, affecting the driver's experience during the evaluation process. In addition, the work efficiency and accuracy of the evaluation are poor, which urgently needs to be improved. Summary of the invention

[0005] The present application provides a human factors intelligent cockpit driver experience evaluation system and method to solve the problems of lack of objective evaluation basis of driver experience when conducting driver experience testing in the vehicle cockpit, failure to meet the actual evaluation needs of the intelligent cockpit, affecting the driver experience during the evaluation process, and poor work efficiency and accuracy of the evaluation.

[0006] The first aspect of the present application provides a human factors intelligent cockpit driver experience evaluation system, including: at least one single-vehicle evaluation system, used to collect sensor data generated by at least one evaluation driver based on corresponding evaluation content, and identify the driver status of the at least one evaluation driver based on the sensor data; an evaluation management system, used to generate evaluation content corresponding to the at least one evaluation driver, and send the evaluation content to the at least one single-vehicle evaluation system, and receive the sensor data and the driver status uploaded by the at least one single-vehicle evaluation system, so as to obtain experience evaluation data of the human factors intelligent cockpit based on the sensor data and the driver status, wherein the experience evaluation data is used to evaluate the interactivity between the human factors intelligent cockpit and the driver.

[0007] According to the above-mentioned technical means, the embodiment of the present application can use the generated evaluation content to conduct experience tests on drivers, and obtain experience evaluation data of the human-factor intelligent cockpit based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for the driver's experience evaluation, meeting the actual evaluation needs of the human-factor intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation.

[0008] Exemplarily, in one embodiment of the present application, each of the at least one single-vehicle evaluation system includes: a collection component for collecting at least one of the eye movement data, physiological data, behavioral data and voice data of the at least one evaluation driver as the sensor data; an evaluation component for evaluating the fatigue level and / or emotional state of the at least one evaluation driver based on the sensor data as the driver state.

[0009] According to the above-mentioned technical means, the embodiments of the present application can use sensor data to evaluate the driver's fatigue level, or use sensor data to evaluate the driver's emotional state, and can also use sensor data to evaluate the driver's fatigue level and emotional state, so as to analyze the different states of the driver during driving in a targeted manner, and provide a comprehensive and accurate evaluation system specification for the driver's experience evaluation, meeting the actual needs of the human-factor intelligent cockpit.

[0010] Exemplarily, in one embodiment of the present application, the acquisition component is also used to time synchronize multiple items of the eye movement data, physiological data, behavioral data and voice data of the at least one driver being evaluated.

[0011] According to the above-mentioned technical means, the embodiment of the present application can also time synchronize multiple items of the driver's eye movement data, physiological data, behavioral data and voice data, so as to monitor and evaluate the driver's driving behavior and driving status in real time, effectively improving the work efficiency and accuracy of the evaluation.

[0012] Exemplarily, in one embodiment of the present application, the evaluation management system includes: a system management module, which is used to execute at least one of the test project management tasks, vehicle model management tasks, test management tasks and main test management tasks of the evaluation system; a resource library module, which is used to execute the resource library management tasks of the evaluation system; an evaluation design module, which is used to call the resource library module to generate an evaluation process and corresponding evaluation content based on the timeline; a data analysis module, which is used to execute the data analysis tasks of the evaluation system according to the sensor data and the driver status; an indicator system and weight management module, which is used to construct an indicator system for the evaluation system, and obtain the corresponding weights of the sensor data and the driver status based on the indicator system, so as to use the weights to perform tasks.

[0013] According to the above-mentioned technical means, the embodiments of the present application can execute test project management tasks, vehicle model management tasks, test management tasks and main test management tasks, etc., so as to realize the integrated management of smart cockpit real vehicle test projects, improve the efficiency of managing the test process, and use weights to perform tasks, and then screen the indicators to be used and assign weights, which is more reliable.

[0014] Exemplarily, in one embodiment of the present application, the test project management task includes at least one of a project creation task, a personnel assignment task, a vehicle model association task, and a project progress display task; the vehicle model management task includes at least one of a vehicle model information creation task and a management task; the test subject management task includes at least one of a test subject addition task, a test subject demographic information addition task, a demographic information custom addition task, a test subject history record task, a test subject import and export task, and a demographic information statistics task; the main tester management task includes at least one of a main tester addition task and a main tester history record task.

[0015] According to the above-mentioned technical means, the embodiments of the present application can create project tasks, personnel allocation tasks, vehicle type association tasks and project progress display tasks, so as to realize the integrated management of the smart cockpit real vehicle test project based on the complete evaluation project workflow, maintain the comprehensive information of the subjects to ensure effective participation and management, and fully record and manage the information of each examiner through the examiner management task, so as to provide a comprehensive and accurate evaluation system specification for the driver experience evaluation, and effectively improve the work efficiency and accuracy of the evaluation.

[0016] Exemplarily, in one embodiment of the present application, the resource library management task includes one or more of the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask and state assessment algorithm model management subtask in the objective evaluation task.

[0017] According to the above-mentioned technical means, the embodiments of the present application can ensure the comprehensiveness of the evaluation based on the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask, state assessment algorithm model management subtask in the objective evaluation task, so as to meet the actual evaluation needs of the human factors intelligent cockpit.

[0018] Exemplarily, in one embodiment of the present application, the data analysis task includes at least one of a video behavior data analysis task, an eye movement data analysis task, a physiological data analysis task, a speech data analysis task, and a visualization report task.

[0019] According to the above-mentioned technical means, the embodiments of the present application can improve the data collection rate through video behavior data analysis tasks, eye movement data analysis tasks, physiological data analysis tasks, voice data analysis tasks and visual report tasks, ensure the reliability of the analysis, and ultimately effectively improve the work efficiency and accuracy of the assessment.

[0020] A second aspect of the present application provides a method for evaluating the driver experience of a human-factor intelligent cockpit, comprising the following steps: collecting sensor data generated by at least one evaluated driver based on corresponding evaluation content, and identifying the driver status of the at least one evaluated driver based on the sensor data; and obtaining experience evaluation data of the human-factor intelligent cockpit based on the sensor data and the driver status, wherein the experience evaluation data is used to evaluate the interactivity between the human-factor intelligent cockpit and the driver.

[0021] According to the above-mentioned technical means, the embodiment of the present application can use the generated evaluation content to conduct experience tests on drivers, and obtain experience evaluation data of the human-factor intelligent cockpit based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for the driver's experience evaluation, meeting the actual evaluation needs of the human-factor intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation.

[0022] Exemplarily, in one embodiment of the present application, the collecting of sensor data generated by at least one evaluation driver based on corresponding evaluation content includes: collecting at least one of the eye movement data, physiological data, behavioral data and voice data of the at least one evaluation driver as the sensor data; and evaluating the fatigue level and / or emotional state of the at least one evaluation driver based on the sensor data as the driver state.

[0023] According to the above-mentioned technical means, the embodiments of the present application can use sensor data to evaluate the driver's fatigue level, or use sensor data to evaluate the driver's emotional state, and can also use sensor data to evaluate the driver's fatigue level and emotional state, so as to analyze the different states of the driver during driving in a targeted manner, and provide a comprehensive and accurate evaluation system specification for the driver's experience evaluation, meeting the actual needs of the human-factor intelligent cockpit.

[0024] Exemplarily, in one embodiment of the present application, the collecting of at least one of the eye movement data, physiological data, behavioral data and voice data of the at least one driver being evaluated as the sensor data includes: time synchronization of multiple items of the eye movement data, physiological data, behavioral data and voice data of the at least one driver being evaluated.

[0025] According to the above-mentioned technical means, the embodiment of the present application can also time synchronize multiple items of the driver's eye movement data, physiological data, behavioral data and voice data, so as to monitor and evaluate the driver's driving behavior and driving status in real time, effectively improving the work efficiency and accuracy of the evaluation.

[0026] Exemplarily, in one embodiment of the present application, the experience evaluation data of the human factors intelligent cockpit is obtained according to the sensor data and the driver status, including: executing at least one of the test project management tasks, vehicle model management tasks, test management tasks and main test management tasks; executing resource library management tasks; generating an evaluation process and corresponding evaluation content based on the timeline; executing the data analysis task of the evaluation system according to the sensor data and the driver status; and obtaining the corresponding weights of the sensor data and the driver status based on the indicator system, so as to use the weights to perform tasks.

[0027] According to the above-mentioned technical means, the embodiments of the present application can execute test project management tasks, vehicle model management tasks, test management tasks and main test management tasks, etc., so as to realize the integrated management of smart cockpit real vehicle test projects, improve the efficiency of managing the test process, and use weights to perform tasks, and then screen the indicators to be used and assign weights, which is more reliable.

[0028] Exemplarily, in one embodiment of the present application, the test project management task includes at least one of a project creation task, a personnel assignment task, a vehicle model association task, and a project progress display task; the vehicle model management task includes at least one of a vehicle model information creation task and a management task; the test subject management task includes at least one of a test subject addition task, a test subject demographic information addition task, a demographic information custom addition task, a test subject history record task, a test subject import and export task, and a demographic information statistics task; the main tester management task includes at least one of a main tester addition task and a main tester history record task.

[0029] According to the above-mentioned technical means, the embodiments of the present application can create project tasks, personnel allocation tasks, vehicle type association tasks and project progress display tasks, so as to realize the integrated management of the smart cockpit real vehicle test project based on the complete evaluation project workflow, maintain the comprehensive information of the subjects to ensure effective participation and management, and fully record and manage the information of each examiner through the examiner management task, so as to provide a comprehensive and accurate evaluation system specification for the driver experience evaluation, and effectively improve the work efficiency and accuracy of the evaluation.

[0030] Exemplarily, in one embodiment of the present application, the resource library management task includes one or more of the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask and state assessment algorithm model management subtask in the objective evaluation task.

[0031] According to the above-mentioned technical means, the embodiments of the present application can ensure the comprehensiveness of the evaluation based on the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask, state assessment algorithm model management subtask in the objective evaluation task, so as to meet the actual evaluation needs of the human factors intelligent cockpit.

[0032] Exemplarily, in one embodiment of the present application, the data analysis task includes at least one of a video behavior data analysis task, an eye movement data analysis task, a physiological data analysis task, a speech data analysis task, and a visualization report task.

[0033] According to the above-mentioned technical means, the embodiments of the present application can improve the data collection rate through video behavior data analysis tasks, eye movement data analysis tasks, physiological data analysis tasks, voice data analysis tasks and visual report tasks, ensure the reliability of the analysis, and ultimately effectively improve the work efficiency and accuracy of the assessment.

[0034] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the human factors intelligent cockpit driver experience evaluation method as described in the above embodiment.

[0035] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned human factors intelligent cockpit driver experience evaluation method.

[0036] The embodiment of the present application can use the generated evaluation content to conduct experience tests on drivers, and obtain experience evaluation data of the human-factor intelligent cockpit based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for driver experience evaluation, meeting the actual evaluation needs of the human-factor intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation. As a result, the lack of objective evaluation basis for the driver's experience when conducting driver experience tests in the vehicle's cockpit is solved, which cannot meet the actual evaluation needs of the intelligent cockpit, affects the driver's experience during the evaluation process, and the work efficiency and accuracy of the evaluation are poor.

[0037] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0039] Figure 1 A schematic diagram of the structure of a human factors intelligent cockpit driver experience evaluation system provided according to an embodiment of the present application;

[0040] Figure 2 A schematic diagram of event and segment marking of different types of data in a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0041] Figure 3 A schematic diagram of clock synchronization of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0042] Figure 4 A schematic diagram of a wearable eye tracker of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0043] Figure 5 A schematic diagram of physiological sensors of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0044] Figure 6 A schematic diagram of a comprehensive video analysis of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0045] Figure 7 Schematic diagram of eye movement data analysis of a human factors intelligent cockpit driver experience evaluation system according to one embodiment of the present application;

[0046] Figure 8 A schematic diagram of physiological data analysis of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0047] Fig. 9 A schematic diagram of a questionnaire scale report of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0048] Fig.10 A schematic diagram of a behavior experiment report of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0049] Fig.11 A schematic diagram of the structure of a human factors intelligent cockpit driver experience evaluation system according to an embodiment of the present application;

[0050] Fig.12 A flowchart of a method for evaluating driver experience in a human factors intelligent cockpit provided according to an embodiment of the present application;

[0051] Fig.13 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0053] The following describes the human factors intelligent cockpit driver experience evaluation system and method of the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that when the driver experience test is carried out in the cockpit of the vehicle, there is a lack of objective evaluation basis for the driver experience, which cannot meet the actual evaluation needs of the intelligent cockpit, affects the driver experience during the evaluation process, and the work efficiency and accuracy of the evaluation are poor, the present application provides a human factors intelligent cockpit driver experience evaluation system, in which the driver can be tested for experience using the generated evaluation content, and the experience evaluation data of the human factors intelligent cockpit can be obtained according to the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for the driver experience evaluation, meeting the actual evaluation needs of the human factors intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation. Thus, the problem that when the driver experience test is carried out in the cockpit of the vehicle, there is a lack of objective evaluation basis for the driver experience, which cannot meet the actual evaluation needs of the intelligent cockpit, affects the driver experience during the evaluation process, and the work efficiency and accuracy of the evaluation are poor is solved.

[0054] Specifically, Figure 1 A schematic diagram of the structure of a human factors intelligent cockpit driver experience evaluation system provided in an embodiment of the present application.

[0055] like Figure 1 As shown, the human factor intelligent cockpit driver experience evaluation system 10 includes:

[0056] Specifically, at least one single-vehicle evaluation system 100 is used to collect sensor data generated by at least one evaluation driver based on corresponding evaluation content, and identify the driver status of at least one evaluation driver based on the sensor data.

[0057] It can be understood that at least one bicycle evaluation system 100 in the embodiment of the present application can be a sub-server, and efficient data communication and task management can be achieved between the main server and the on-site sub-server. The on-site evaluation terminal can perform professional and systematic evaluation operations according to the received evaluation journey requirements. After the evaluation work is completed, the data is automatically or manually uploaded to the main server.

[0058] During the actual implementation process, the embodiments of the present application can collect sensor data generated by at least one evaluated driver based on corresponding evaluation content through at least one single-vehicle evaluation system 100, and identify the driving state of at least one evaluated driver based on the sensor data. For example, the embodiments of the present application can collect the driver's blinking frequency, pupil state, etc., and when the blinking frequency is 11 blinks per minute and the pupil size remains relatively stable, the driver's driving state is identified as a focused driving state; for another example, the embodiments of the present application can identify the driver's driving state as a fatigued driving state when the blinking frequency is 6 blinks per minute and the pupil is constricted.

[0059] The embodiment of the present application can use the generated evaluation content to conduct experience tests on the driver, thereby identifying the driver's driving status, and then providing support for subsequent driver experience evaluation, conducting targeted evaluations, meeting the actual evaluation needs of the human-factor intelligent cockpit, and ensuring that the evaluation system is intelligent and reliable.

[0060] Exemplarily, in one embodiment of the present application, each of the at least one single-vehicle evaluation system 100 includes: a collection component 101, used to collect at least one of the eye movement data, physiological data, behavioral data and voice data of at least one evaluation driver as sensor data; an evaluation component 102, used to evaluate the fatigue level and / or emotional state of at least one evaluation driver based on the sensor data as the driver state.

[0061] It is understandable that the embodiment of the present application can perform real-time synchronous collection when accessing the eye movement data, physiological data, video behavior data and voice data during the evaluation execution process. The synchronization time interval can be but is not limited to less than or equal to 10ms, thereby ensuring the consistency of the time points of the multimodal data, and can perform event and segment marking of different types of data, such as Figure 1 As shown in the figure, the NTP network time protocol can be used to synchronize the computer clock to UTC (Universal Time Coordinated). The timestamp and calibration algorithm are used to achieve clock synchronization to ensure that the clocks of multiple devices or systems are consistent. Figure 2 shown.

[0062] In the actual implementation process, the embodiment of the present application can collect eye movement data of at least one driver being evaluated through the collection component 101. For example, a wearable eye tracker can be used to collect eye movement data in real time during the execution of the single-vehicle evaluation, including but not limited to collecting data indicators such as pupil, gaze and blinking, so as to further analyze the driver's eye movement state and cognitive processing ability during driving, such as Figure 3 shown.

[0063] In the embodiment of the present application, the collection component 101 can collect at least one physiological data of the driver for evaluation, for example, the physiological indicators of the driver can be collected in real time through the physiological sensor, including but not limited to the collection of physiological data such as electrocardiogram, pulse, skin electricity, myoelectricity and breathing, so as to facilitate further statistical analysis of the changes in the driver's psychological state during driving, etc., wherein the supporting hardware equipment such as Figure 4 shown.

[0064] In the embodiment of the present application, the collection component 101 can collect the behavior data of at least one driver being evaluated. For example, the behavior observation camera can be used to collect the behavior data of the single-vehicle evaluation process in real time, record the operation behavior in the form of a video, and customize the classification behavior based on the characteristics and rules and make comprehensive statistics. Table 1 is a table of supporting hardware equipment for behavior data collection, where, as shown in Table 1:

[0065] Table 1

[0066] Serial number Hardware Name 1 First person perspective recorder 2 Stylus 3 Handheld Terminal 4 Behavior recording camera 5 High-speed camera

[0067] In the embodiment of the present application, the collection component 101 can collect the voice data of at least one driver to be evaluated. For example, the voice interaction data of the actual vehicle can be collected in real time through the voice device, and the voice data of the entire test process can be collected. The voice data content includes but is not limited to voice commands, voice emotions and conversation content, etc. The collected voice data can be marked and classified afterwards, and the voice interaction events and background noise can be extracted to facilitate further analysis later. Table 2 is a table of supporting hardware equipment for voice data collection, where, as shown in Table 2:

[0068] Table 2

[0069]

[0070]

[0071] The embodiments of the present application can use sensor data to evaluate the driver's fatigue level, or use sensor data to evaluate the driver's emotional state, and can also use sensor data to evaluate the driver's fatigue level and emotional state, so as to analyze the different states of the driver during driving in a targeted manner, provide a comprehensive and accurate evaluation system specification for the driver's experience evaluation, and meet the actual needs of the cockpit.

[0072] Exemplarily, in one embodiment of the present application, the acquisition component 101 is also used to time synchronize multiple items of eye movement data, physiological data, behavioral data, and voice data of at least one driver being evaluated.

[0073] It can be understood that the eye movement data in the embodiments of the present application include but are not limited to pupil, gaze and blinking data, the physiological data include but are not limited to electrocardiogram, pulse, electrodermal, electromyography and breathing data, the behavioral data include but are not limited to behavioral data collected by the camera, and the voice data include but are not limited to voice commands, voice emotions and conversation content data.

[0074] During the actual implementation process, the embodiment of the present application can time synchronize multiple items of the eye movement data, physiological data, behavioral data and voice data of at least one evaluated driver through the acquisition component 101. For example, the embodiment of the present application can time synchronize the eye movement data when the driver blinks, the physiological data when breathing, and the voice data when issuing voice commands, thereby ensuring the consistency of the time points of the multimodal data.

[0075] The embodiment of the present application can time synchronize multiple items of the driver's eye movement data, physiological data, behavioral data, and voice data, so as to monitor and evaluate the driver's driving behavior and driving status in real time, effectively improving the work efficiency and accuracy of the evaluation.

[0076] The evaluation management system 200 is used to generate evaluation content corresponding to at least one evaluation driver, and send the evaluation content to at least one single-vehicle evaluation system 100, and receive sensor data and driver status uploaded by at least one single-vehicle evaluation system 100, so as to obtain experience evaluation data of the human-factor intelligent cockpit based on the sensor data and driver status, wherein the experience evaluation data is used to evaluate the interactivity between the human-factor intelligent cockpit and the driver.

[0077] It is understandable that the present application can conduct a human-factor intelligent cockpit driver experience evaluation, wherein human-factor intelligence is based on human-factor engineering technology combined with artificial intelligence (AI) technology, human-factor engineering technology is the application of human psychological and physiological principles to the engineering and design of products, processes and systems, in other words, it is the technology of designing and improving the human-machine-environment system according to human characteristics, and combining this technology with artificial intelligence technology can enable machines to interact with people more intelligently. The driver's state in the embodiment of the present application includes but is not limited to driving fatigue state, driving concentration state, emotional positive state, emotional neutral state and emotional negative state, and the experience evaluation data of the human-factor intelligent cockpit includes but is not limited to satisfaction, neutrality and dissatisfaction; sensor data can be but is not limited to being collected by sensors.

[0078] For example, an embodiment of the present application can generate at least one evaluation content for evaluating a driver's driving satisfaction, and send the evaluation content about driving satisfaction to at least one single-vehicle evaluation system 100, so as to facilitate meeting the actual evaluation needs of the human-factor intelligent cockpit. An embodiment of the present application can collect sensor data of the driver's steady pulse and the driver's positive emotional state through physiological sensors, and obtain experience evaluation data of the human-factor intelligent cockpit as satisfactory based on the sensor data and the positive emotional state; for another example, an embodiment of the present application can generate at least one evaluation content for evaluating a driver's driving satisfaction, and send the evaluation content about driving satisfaction to at least one single-vehicle evaluation system 100, collect sensor data of the driver's panic behavior and the driver's negative emotional state through a camera, and obtain experience evaluation data of the human-factor intelligent cockpit based on the sensor data and the negative emotional state, thereby obtaining experience evaluation data of the human-factor intelligent cockpit as unsatisfactory, wherein the experience evaluation data is used to evaluate the interactivity between the human-factor intelligent cockpit and the driver.

[0079] The embodiment of the present application can obtain experience evaluation data of the human-factor intelligent cockpit based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for the driver's experience evaluation, meeting the actual evaluation needs of the human-factor intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation.

[0080] Exemplarily, in one embodiment of the present application, the evaluation management system 200 includes: a system management module 201, which is used to execute at least one of the test project management tasks, vehicle model management tasks, test management tasks and main test management tasks of the evaluation system 100; a resource library module 202, which is used to execute the resource library management tasks of the evaluation system; an evaluation design module 203, which is used to call the resource library module 202 to generate an evaluation process and corresponding evaluation content based on the timeline; a data analysis module 204, which is used to execute the data analysis tasks of the evaluation system 100 according to the sensor data and the driver's status; an indicator system and weight management module 205, which is used to construct an indicator system for the evaluation system, and obtain the corresponding weights of the sensor data and the driver's status based on the indicator system, so as to use the weights to perform tasks.

[0081] In the actual execution process, the embodiment of the present application can execute the test project management tasks, vehicle model management tasks, test management tasks and main test management tasks of the evaluation system 100 through the system management module 201 in the evaluation management system 200, so as to realize the integrated management of the human factors intelligent cockpit real vehicle test project; the embodiment of the present application can execute the resource library management tasks of the evaluation system through the resource library module 202 to ensure the comprehensiveness of management; the embodiment of the present application can call the resource library module 202 through the evaluation design module 203, add the evaluation task content and set the evaluation method based on the evaluation process designed on the timeline, call the subjective evaluation resources and the objective evaluation resources, publish the evaluation tasks, call the use cases, questionnaire scales, behavioral experiments and journeys in the resource library as the evaluation task content, support the issuance of evaluation tasks to the client terminal, and carry out testing. The use case management subtask in the subjective evaluation task can define and edit the evaluation use cases of the intelligent cockpit system, and can efficiently manage the test process through visual display, execution history and flexible parameter configuration, which can include:

[0082] (1) Test case library subtask: supports adding, editing, and deleting test cases;

[0083] (2) Advanced filtering subtask: Test cases can be filtered according to the set rules.

[0084] Furthermore, the embodiment of the present application can be based on the data analysis module 204 to perform the data analysis task of the evaluation system 100 according to the sensor data and the driver's status, which is more reliable; the embodiment of the present application can construct an indicator system for the evaluation system through the indicator system and weight management module 205, and obtain the corresponding weights of the sensor data and the driver's status based on the indicator system, so as to use the weights to perform tasks, thereby screening the indicators to be used and allocating weights.

[0085] Among them, in the index system and weight management module 205, the index system is constructed based on the components of man-machine-environment. The index system for driver experience can be divided into, but not limited to, safety, efficiency and pleasure, corresponding to the components of the two-machine-environment system in the interaction process and the driver and passenger, respectively. The platform supports the screening of the indicators to be used and the allocation of weights according to the index system. Table 3 is an index system table, wherein, as shown in Table 3:

[0086] Table 3

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] The embodiments of the present application can execute test project management tasks, vehicle model management tasks, test management tasks and main test management tasks, etc., so as to realize the integrated management of smart cockpit real vehicle test projects, improve the efficiency of managing the test process, and use weights to execute tasks, and then screen the indicators to be used and assign weights, which is more reliable.

[0093] Exemplarily, in one embodiment of the present application, the test project management task includes at least one of a project creation task, a personnel assignment task, a vehicle model association task, and a project progress display task; the vehicle model management task includes at least one of a vehicle model information creation task and a management task; the test subject management task includes at least one of a test subject addition task, a test subject demographic information addition task, a demographic information custom addition task, a test subject history record task, a test subject import and export task, and a demographic information statistics task; the main tester management task includes at least one of a main tester addition task and a main tester history record task.

[0094] The test project management tasks in the embodiments of the present application may include but are not limited to:

[0095] (1) Create project tasks: support custom creation of local assessment projects, editable project names and project storage paths; support one-click opening of local storage projects and viewing of historical project records;

[0096] (2) Personnel allocation task: select the main tester and test subjects of the evaluation project, and call the personnel information added by the test subject management module and the main tester management module;

[0097] (3) Vehicle type association task: Select the vehicle type to be evaluated, call the vehicle information added by the vehicle type management module, and then view and compare the evaluation results based on the vehicle type;

[0098] (4) Project progress display task: Break down the project into key milestones in the form of a timeline and use visualization tools to display the progress of goal completion.

[0099] In an embodiment of the present application, the vehicle model management task can create and manage vehicle model information: add a test vehicle, fill in the vehicle model information, such as the manufacturer, model number, VIN (Vehicle identification number), vehicle number, production date, software version and other information.

[0100] The management tasks to be tested in the embodiments of the present application may include but are not limited to:

[0101] (1) Add subject task: support manual adding of subjects, filling in the subject's name and demographic information;

[0102] (2) Adding demographic information of the subjects: including gender, age, height, weight, ID number, contact number, and email address;

[0103] (3) Customized demographic information adding task: supports adding options, text, numbers and other different types of information such as driving age, expression ability, main vehicle type, etc., and can encrypt or hide sensitive information;

[0104] (4) Subject history recording task: recording the subject’s participation history, including the items they participated in and the models they have evaluated;

[0105] (5) Subject import and export tasks: support batch import and export of subject information to facilitate cross-project testing;

[0106] (6) Demographic information statistics task: supports filtering and counting the subject's demographic information based on options, text content, and numeric range.

[0107] The main test management tasks in the embodiment of the present application may include but are not limited to:

[0108] (1) Main tester task: support adding a main tester and filling in the main tester's name or other information;

[0109] (2) Examiner history recording task: record and display the assessment items in which the relevant examiners have participated.

[0110] The embodiments of the present application can create project tasks, personnel allocation tasks, vehicle type association tasks, and project progress display tasks, etc., so as to realize the integrated management of the smart cockpit real vehicle test project based on the complete evaluation project workflow, and maintain the comprehensive information of the subjects by adding subject tasks, subject demographic information adding tasks, demographic information custom adding tasks, subject history recording tasks, subject import and export tasks, and demographic information statistics tasks, etc., through the test management tasks, the main test tasks and the main test history recording tasks are added, so as to ensure effective participation and management, and the information of each main tester can be fully recorded and managed by adding the main tester tasks and the main tester history recording tasks, etc., to ensure the system's comprehensive understanding of the main tester.

[0111] Exemplarily, in one embodiment of the present application, the resource library management task includes one or more of the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask and state assessment algorithm model management subtask in the objective evaluation task.

[0112] In the embodiment of the present application, the questionnaire scale management subtask can manage and maintain subjective questionnaires and scales to ensure that the system can comprehensively and accurately collect data such as driver experience, including:

[0113] (1) Scale library subtask: The system has built-in driver experience-related scales. Each scale is equipped with a detailed page introduction, including theoretical background, question examples, scoring rules, etc. Table 4 is a scale, as shown in Table 4:

[0114] Table 4

[0115] Serial number name 1 System Usability Scale (SUS) 2 Driver Interface Satisfaction Questionnaire (QUIS) 3 Effectiveness Satisfaction and Usability Questionnaire USE 4 PSSUQ: Overall Usability Assessment Questionnaire 5 Likert five-level satisfaction questionnaire scale 6 Ten-level satisfaction and acceptance scale SAE 7 Post-Scene Questionnaire ASQ 8 Mission Load Index NASA-TLX

[0116] (2) Custom scale subtask: supports modifying the questions and score calculations of the scales in the scale library, and supports creating new scales using questionnaire scale templates or question-and-answer scale templates;

[0117] (3) Search and filter subtasks: Supports searching and filtering based on fields such as scale name and type.

[0118] In an embodiment of the present application, the behavioral experiment management subtask can manage and maintain behavioral experiments that can be used for the driving subtask to evaluate the cognitive and behavioral responses of the subject under specific conditions, which may include but are not limited to:

[0119] (1) Behavioral experiment library subtasks: The system has built-in behavioral experiments suitable for driving subtasks, such as the GO-NOGO task (a dual-task paradigm for studying reaction-stopping ability) and the Odd-ball task (a classic experimental paradigm for ERP (Event-related Potentials)).

[0120] (2) Customized experimental subtasks: support modification of experimental parameters in the behavioral experiment library, and support creation of new experiments through reaction time paradigm templates or experimental paradigm templates;

[0121] (3) Search and filter subtasks: Supports searching and filtering based on fields such as experiment name and type.

[0122] In the embodiment of the present application, the journey management subtask can define and create different driver journeys to simulate the complete operation process of the driver in the smart cockpit system, which may include but is not limited to:

[0123] (1) Trip library subtask: The system will have built-in driver trips in multiple different scenarios, with the number exceeding 10;

[0124] (2) Customized journey subtasks: Supports modification of journeys in the journey library, and supports calling the use case library and scale library content to create new journeys;

[0125] (3) Search and filter subtasks: Supports searching and filtering based on fields such as journey name and type.

[0126] In the embodiment of the present application, the voice evaluation management subtask can manage the corpus and voice used for voice interaction evaluation when the real vehicle is installed, which can include but is not limited to:

[0127] (1) Corpus subtask: The system has built-in corpora of no less than 2,000 pieces of data covering all functional modules of vehicle-machine voice interaction;

[0128] (2) Voice library subtask: The system has no less than 20,000 human voice commands built in;

[0129] (3) Voice interaction evaluation subtask: supports calling corpus and voice library content;

[0130] (4) Search subtask: supports searching the contents in the corpus and speech library;

[0131] (5) Import subtask: supports batch import of voice commands in the voice library.

[0132] In the embodiment of the present application, the evaluation tool management subtask in the objective evaluation task can use different types of measurement tools to objectively and quantitatively evaluate the driver's operating behavior, physiological changes, eye movement status, voice interaction, etc. during the interactive evaluation task, so as to manage and count various data indicators, which may include but are not limited to:

[0133] (1) Eye movement evaluation tool: It provides rich eye movement data such as pupil diameter, blink frequency, and fixation time to evaluate the design and usability of the smart cockpit driver interface. By measuring the driver's eye movement status, we can understand their attention distribution, browsing patterns, and interaction paths. In the process of human-computer interaction in the smart cockpit, eye movement data can be used to understand the driver's reaction and performance behavior when interacting with the cockpit, which helps to improve the design of the human-computer interaction interface;

[0134] (2) Physiological evaluation tools: These tools provide data on changes in physiological characteristics such as HRV (heart rate variability), EDA (electronic design automation), RESP (respiration), and EMG (electromyography). These tools are used to evaluate the driver's emotional state, psychological stress, fatigue level, anxiety level, etc. during the actual vehicle evaluation process, and to understand the impact of the intelligent cockpit system on the driver's psychological state.

[0135] (3) Behavior evaluation tools: provide operation performance data such as the number of behavior occurrences, interaction reaction time, and accuracy, which are used to evaluate the operation performance in interactive processes such as finger touch. For example, when the driver performs touch operation on the interactive interface of the smart cockpit central control screen, the total movement distance of the operating fingertip, the frequency of change in the direction of the operating fingertip, the operation time, the number of operation steps, etc. are measured and recorded;

[0136] (4) Speech evaluation tools: These tools provide objective data on speech interaction and language expression, such as speech rate, pitch characteristics, noise level, and recognition accuracy. They are used to evaluate the speech recognition and speech synthesis performance in smart cockpits, measure the cockpit system’s perception of the driver’s voice emotions and its emotion regulation effects, and evaluate the smart cockpit system’s accuracy in recognizing voice commands and the quality of the interactive experience.

[0137] In the embodiment of the present application, the state assessment algorithm model management subtask in the objective assessment task can support the assessment of the subject's psychological state during the assessment process based on the multimodal physiological data collected from the subject in real time and the algorithm model deployed by the system, which may include but is not limited to:

[0138] (1) Load state recognition model: It can predict at least two classification states of the subject's high load level / low load level in real time, including but not limited to the fusion calculation of data sources such as HRV heart rate variability, SKT skin temperature, EDA skin charge, etc. It is a classic state recognition model in the industry. The model is verified by scale results or actual state classification, and the model accuracy is greater than 70%;

[0139] (2) Fatigue state recognition model: It can predict at least two classification states of fatigue / non-fatigue of the subject in real time, including but not limited to the fusion calculation of data sources such as HRV heart rate variability, SKT skin temperature, EDA skin charge, etc. It belongs to the classic state recognition model. The model effect is verified by scale results or actual state classification, and the model accuracy is greater than 70%;

[0140] (3) Emotion recognition model: It can predict at least three categories of the subject's positive / negative / neutral state in real time, including but not limited to the fusion calculation of data sources such as HRV heart rate variability, SKT skin temperature, EDA skin charge, etc. It is a classic state recognition model in the industry. The model is verified by scale results or actual state classification results, and the model accuracy is greater than 70%.

[0141] The embodiments of the present application can ensure the comprehensiveness of the evaluation based on the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask, state assessment algorithm model management subtask in the objective evaluation task, so as to meet the actual evaluation needs of the human factors intelligent cockpit.

[0142] Exemplarily, in one embodiment of the present application, the data analysis task includes at least one of a video behavior data analysis task, an eye movement data analysis task, a physiological data analysis task, a speech data analysis task, and a visualization report task.

[0143] It can be understood that the video behavior data analysis task in the embodiment of the present application can support the processing and analysis of the interactive operation behavior data recorded in the video during the evaluation process, the eye movement data analysis task can support the batch processing and visualization analysis of the eye movement data during the evaluation process, the physiological data analysis task can support the batch processing and visualization analysis of the physiological data during the evaluation process, the voice data analysis task can support the batch processing and visualization analysis of the voice interaction data during the evaluation process, and the visualization report task can support the automatic generation or customized compilation of the evaluation result report in the form of text or graphics, provide a visual interactive editing function to adjust the report content, and support the export of the result report.

[0144] In the actual implementation process, the video behavior data analysis tasks in the embodiments of the present application may include but are not limited to:

[0145] (1) Video Synchronous Playback: Supports synchronous playback of two or more videos, and can customize the switching of video channels; supports timeline positioning, video playback fast forward and rewind, and video player settings;

[0146] (2) Automatically obtain task time points: The system can automatically obtain the start and end time points of the test task and the current time of the time axis positioning, and automatically calculate the task duration;

[0147] (3) Cross-screen operation behavior statistics: record the driver's operation data on the screen, and perform custom classification based on the characteristics and rules of the operation behavior, including long press, short press, sliding, dragging and other operation categories, count the number of each operation behavior, and intuitively present the density of operation actions through visualization methods such as heat maps, bee swarm maps, and trajectory maps. Further explore and analyze the differences between different devices or driver groups;

[0148] (4) Feedback and analysis and judgment records: Record the subjects' operational feedback through questionnaires or verbal retelling, associate it with the operational data, and combine it with the video analysis and judgment results of the experimenter to conduct a comprehensive video analysis, such as Figure 5 shown.

[0149] The eye movement data analysis tasks in the embodiments of the present application may include:

[0150] (1) Eye movement data processing: Extract fixation points, blinks and saccades based on the I-VT algorithm; support custom settings for processing parameters, including noise reduction level, data interpolation, fixation time difference, fixation angle difference and other benchmarks;

[0151] (2) Visualized eye movement data chart: including raw data, processed data, X and Y coordinates of the gaze point, angular velocity, and left and right eye pupil data;

[0152] (3) Gaze Mapping: Supports local gaze mapping automatic overlay and manual coding functions. Provides visualization of Assisted Mapping confidence interval results and automatic processing of multi-task data;

[0153] (4) Visual analysis of eye movements: providing visualization and export of heat maps, gaze trajectory maps, gaze trajectory playback videos, and other visualization results;

[0154] (5) AOI Area of ​​Interest Analysis: Supports customizing sequence settings based on a single AOI or AOI Group, and supports statistical analysis of eye movement data within the created Snapshot AOI and Dynamic AOI;

[0155] (6) TOI event sequence analysis: You can customize metrics, visualize the interval onset and offset of the results, and export event-based sequence length, TTF, number of visits, behavior count, TOI ratio and other data in Excel format;

[0156] (7) Eye movement data export: Supports exporting all raw data, processed data, and analyzed data in Excel, CSV, TSV, XLSX, and PLOF formats, including gyroscope, accelerometer, magnetometer, and pupil data, as well as fixation, visit, and saccade statistical indicators based on the area of ​​interest, such as Figure 6It provides visualization and export of heat maps, gaze track maps, gaze track playback videos, etc. It supports the creation and editing of Snapshot AOI and Dynamic AOI, and supports the use of AOI Tag grouping to obtain statistical indicators based on AOI or AOI group. It supports local Assisted Mapping automatic overlay and manual coding functions, provides Assisted Mapping confidence interval visualization results, and automatically processes multi-task data. It provides TOI event sequence analysis function, and can customize Metrics and Interval of visualization results. onset and offset, can export event-based sequence length, TTF, number of visits, behavior count, TOI ratio and other data and export them in Excel format; support the export of gyroscope, accelerometer, magnetometer and pupil data; preset Attention and Fixation two visualization data filtering standards, support the creation of custom data filtering standards, including noise reduction level, data interpolation, fixation time difference, fixation angle difference and other benchmarks; support I-VT data filter, support visual angular velocity visualization result presentation; support the export of fixation (Fixation), visit (Visit), saccade (Saccade) statistical indicators based on interest areas; the exported raw data formats are TSV, XLSX, PLOF.

[0157] The physiological data analysis tasks in the embodiments of the present application may include but are not limited to:

[0158] (1) Physiological data processing: supports batch processing of HRV signals, EDA signals, RESP signals, EMG and other signals. Customize the processing parameters in advance or use the system default parameters to perform batch processing of multiple subject data. The most authentic and reliable physiological data can be obtained through filtering and other parameters.

[0159] (2) HRV heart rate variability data analysis: supports IBI point detection, HRV time domain analysis, frequency domain analysis, difference scatter plot analysis, etc., to quantitatively evaluate sympathetic and parasympathetic nerve activity;

[0160] (3) EDA skin conductance data analysis: supports automatic recognition of SCR skin conductance response, ER event correlation analysis, time domain analysis, ER overall statistics and visual chart display;

[0161] (4) RESP respiratory data analysis: supports time domain analysis and frequency domain analysis of respiratory signals;

[0162] (5) EMG electromyography data analysis: supports signal normalization, EMG processing data analysis, rectification data analysis, normalization data analysis, frequency domain analysis, and dynamic periodic force identification;

[0163] (6) Visual Chart and Data Export: Supports exporting raw data, processed data, analyzed data, overall data report, downsampled data, relative time data, absolute time data, etc. in .excel and csv formats. Figure 7 shown.

[0164] The speech data analysis tasks in the embodiments of the present application may include but are not limited to:

[0165] (1) Voice data processing: supports the use of filters to clean voice data and extract voice features;

[0166] (2) Visual analysis of speech data: extracting audio information from the video and visually displaying the audio track, which can quickly determine whether there is speech activity in a certain period of time;

[0167] (3) Speech segment sequence analysis: Through voice activity detection, the speech segments and corpus order are marked, and speech group segment analysis can be performed based on the order;

[0168] (4) Voice interaction analysis: Analyze and collect voice interaction data during the evaluation process, including voice interaction accuracy, reaction time, number of voice interactions, etc.

[0169] (5) Voice data export: supports exporting raw voice data, processed data, analyzed data, and overall data reports.

[0170] The visualization report tasks in the embodiment of the present application may include:

[0171] (1) Questionnaire scale report: It can provide scores of different dimensions of the scale and specific options for the subjects, such as Figure 8 As shown;

[0172] (2) Behavioral experiment report: It can provide the overall results of the experiment and the detailed results of each trial, such as Fig. 9 As shown;

[0173] (3) Physiological data report: It can provide comprehensive statistical results and visualization charts of various physiological indicators based on events or segments and different analysis methods;

[0174] (4) Eye movement data report: It can provide comprehensive statistical results and visual charts of various eye movement indicators based on different records, clip types and different analysis methods;

[0175] (5) Behavioral data report: It can provide comprehensive statistical results and visual charts of various behavioral indicators based on different records, fragment types, behavior types and different analysis methods.

[0176] The embodiments of the present application can improve the data collection rate through video behavior data analysis tasks, eye movement data analysis tasks, physiological data analysis tasks, voice data analysis tasks and visual report tasks, ensure the reliability of the analysis, and ultimately effectively improve the work efficiency and accuracy of the assessment.

[0177] Specifically, it can be combined Fig.11 As shown, the working principle of the human factors intelligent cockpit driver experience evaluation system in the embodiment of the present application is described in detail with a specific embodiment.

[0178] like Fig.11 As shown, the embodiment of the present application includes at least one bicycle evaluation system 100 and an evaluation management system 20.

[0179] Among them, at least one bicycle evaluation system 100 includes: an acquisition component 101, an eye movement data acquisition unit 1011, a physiological data acquisition unit 1012, a behavior data acquisition unit 1013 and a voice data acquisition unit 1014; an evaluation component 102, a fatigue level evaluation unit 1021 and an emotional state evaluation unit 1022.

[0180] The evaluation management system 200 includes: a system management module 201, a project management unit 2011, a vehicle model management unit 2012, a subject management unit 2013 and a test management unit 2014; a resource library module 202, a subjective evaluation unit 2021, a questionnaire unit 2022, a behavioral test unit 2023, a use case management unit 2024, a journey unit 2025, an objective evaluation unit 2026, an eye movement evaluation unit 2027, a physiological evaluation unit 2028, a behavioral evaluation unit 2029, a language evaluation unit 2030, and a language management unit 2031. Audio evaluation unit 2030, intelligent evaluation unit 2031; evaluation design module 203, timeline establishment unit 2031, resource library calling unit 2032, subjective and objective evaluation unit 2033, test content publishing unit 2034; data analysis module 204, video behavior data analysis unit 2041, eye movement data analysis unit 2042, physiological data analysis unit 2043, voice data analysis unit 2044 and visual report export unit 2045; indicator system and weight management module 205.

[0181] According to the human factors intelligent cockpit driver experience evaluation system proposed in the embodiment of the present application, the driver can be tested for experience using the generated evaluation content, and the experience evaluation data of the human factors intelligent cockpit can be obtained based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for the driver experience evaluation, meeting the actual evaluation needs of the human factors intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation. Thus, the problem of lack of objective evaluation basis for the driver experience when conducting a driver experience test in the cockpit of a vehicle is solved, which cannot meet the actual evaluation needs of the cockpit, affects the driver experience during the evaluation process, and the work efficiency and accuracy of the evaluation are both poor.

[0182] Next, the human factors intelligent cockpit driver experience evaluation method proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0183] Fig.12 It is a flow chart of the human factors intelligent cockpit driver experience evaluation method of the embodiment of the present application.

[0184] like Fig.12 As shown, the human factor intelligent cockpit driver experience evaluation method includes the following steps:

[0185] In step S1201, sensor data generated by at least one evaluation driver based on corresponding evaluation content is collected, and the driver status of at least one evaluation driver is identified based on the sensor data.

[0186] In step S1202, experience evaluation data of the human-centric intelligent cockpit is obtained based on the sensor data and the driver's status, wherein the experience evaluation data is used to evaluate the interactivity between the human-centric intelligent cockpit and the driver.

[0187] Exemplarily, in one embodiment of the present application, sensor data generated by at least one evaluated driver based on corresponding evaluation content is collected, including: collecting at least one of the eye movement data, physiological data, behavioral data and voice data of at least one evaluated driver as sensor data; and evaluating the fatigue level and / or emotional state of at least one evaluated driver based on the sensor data as the driver's state.

[0188] Exemplarily, in one embodiment of the present application, at least one of the eye movement data, physiological data, behavioral data and voice data of at least one evaluated driver is collected as sensor data, including: time synchronization of multiple items of the eye movement data, physiological data, behavioral data and voice data of at least one evaluated driver.

[0189] Exemplarily, in one embodiment of the present application, experience evaluation data of the human factors intelligent cockpit is obtained according to the sensor data and the driver's status, including: executing at least one of the test project management tasks, vehicle model management tasks, test management tasks and main test management tasks; executing the resource library management task; generating the evaluation process and the corresponding evaluation content based on the timeline; executing the data analysis task of the evaluation system according to the sensor data and the driver's status; obtaining the corresponding weights of the sensor data and the driver's status based on the indicator system, and using the weights to perform tasks.

[0190] Exemplarily, in one embodiment of the present application, the test project management task includes at least one of a project creation task, a personnel assignment task, a vehicle model association task, and a project progress display task; the vehicle model management task includes at least one of a vehicle model information creation task and a management task; the test subject management task includes at least one of a test subject addition task, a test subject demographic information addition task, a demographic information custom addition task, a test subject history record task, a test subject import and export task, and a demographic information statistics task; the main tester management task includes at least one of a main tester addition task and a main tester history record task.

[0191] Exemplarily, in one embodiment of the present application, the resource library management task includes one or more of the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask and state assessment algorithm model management subtask in the objective evaluation task.

[0192] Exemplarily, in one embodiment of the present application, the data analysis task includes at least one of a video behavior data analysis task, an eye movement data analysis task, a physiological data analysis task, a speech data analysis task, and a visualization report task.

[0193] It should be noted that the aforementioned explanation of the embodiment of the human factors intelligent cockpit driver experience evaluation system is also applicable to the human factors intelligent cockpit driver experience evaluation method of this embodiment, and will not be repeated here.

[0194] According to the human-factor intelligent cockpit driver experience evaluation method proposed in the embodiment of the present application, the driver can be tested for experience using the generated evaluation content, and the experience evaluation data of the human-factor intelligent cockpit can be obtained based on the driver's sensor data and driver status feedback, thereby providing a comprehensive and accurate evaluation system specification for the driver experience evaluation, meeting the actual evaluation needs of the human-factor intelligent cockpit, and effectively improving the work efficiency and accuracy of the evaluation. Thus, the problem of lack of objective evaluation basis for the driver's experience when conducting a driver experience test in the cockpit of a vehicle is solved, which cannot meet the actual evaluation needs of the cockpit, affects the driver's experience during the evaluation process, and the work efficiency and accuracy of the evaluation are both poor.

[0195] Fig.13 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0196] A memory 1301 , a processor 1302 , and a computer program stored in the memory 1301 and executable on the processor 1302 .

[0197] When the processor 1302 executes the program, the human factors intelligent cockpit driver experience evaluation method provided in the above embodiment is implemented.

[0198] Furthermore, the electronic device further comprises:

[0199] The communication interface 1303 is used for communication between the memory 1301 and the processor 1302 .

[0200] The memory 1301 is used to store computer programs that can be executed on the processor 1302 .

[0201] The memory 1301 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0202] If the memory 1301, the processor 1302 and the communication interface 1303 are implemented independently, the communication interface 1303, the memory 1301 and the processor 1302 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.13 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0203] Optionally, in a specific implementation, if the memory 1301, the processor 1302 and the communication interface 1303 are integrated on a chip, the memory 1301, the processor 1302 and the communication interface 1303 can communicate with each other through an internal interface.

[0204] The processor 1302 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0205] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned human factors intelligent cockpit driver experience evaluation method.

[0206] 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 application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N 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, without contradiction.

[0207] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0208] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0209] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0210] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0211] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0212] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0213] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A human factors intelligent cockpit driver experience evaluation system, characterized in that: include: At least one single vehicle evaluation system, used to collect sensor data generated by at least one evaluation driver based on corresponding evaluation content, and identify the driver status of the at least one evaluation driver according to the sensor data; Wherein, each of the at least one bicycle evaluation system comprises: A collection component, used to collect physiological data, behavioral data and voice data of the at least one driver being evaluated as the sensor data, and to perform time synchronization on multiple items of the physiological data, behavioral data and voice data of the at least one driver being evaluated; an evaluation component, configured to evaluate the fatigue level and / or emotional state of the at least one evaluated driver according to the sensor data as the driver state; Assessment management system, including: A system management module, used to execute at least one of the test item management tasks, vehicle type management tasks, test subject management tasks and main test management tasks of the evaluation system; A resource library module, used to perform resource library management tasks of the evaluation system; An evaluation design module is used to call the resource library module to generate an evaluation process and corresponding evaluation content based on a timeline; A data analysis module, used to perform a data analysis task of the evaluation system according to the sensor data and the driver status; An indicator system and weight management module, used to construct an indicator system of the evaluation system, and obtain corresponding weights of the sensor data and the driver's state based on the indicator system, so as to perform tasks using the weights; The evaluation management system is used to generate evaluation content corresponding to the at least one evaluation driver, and send the evaluation content to the at least one single-vehicle evaluation system, and receive the sensor data and the driver status uploaded by the at least one single-vehicle evaluation system, so as to obtain experience evaluation data of the human-factor intelligent cockpit based on the sensor data and the driver status, wherein the experience evaluation data is used to evaluate the interactivity between the human-factor intelligent cockpit and the driver.

2. The human factors intelligent cockpit driver experience evaluation system according to claim 1 is characterized in that: in, The test project management task includes at least one of a project creation task, a personnel allocation task, a vehicle model association task, and a project progress display task; The vehicle type management task includes at least one of a vehicle type information creation task and a vehicle type information management task; The test management task includes at least one of a test subject adding task, a test subject demographic information adding task, a demographic information custom adding task, a test subject history recording task, a test subject importing and exporting task, and a demographic information statistics task; The main test management task includes at least one of a main test adding task and a main test history recording task.

3. The human factors intelligent cockpit driver experience evaluation system according to claim 1 is characterized in that: The resource library management task includes one or more of the use case management subtask, questionnaire scale management subtask, behavioral experiment management subtask, journey management subtask, voice evaluation management subtask in the subjective evaluation task and the evaluation tool management subtask and state evaluation algorithm model management subtask in the objective evaluation task.

4. The human factors intelligent cockpit driver experience evaluation system according to claim 1 is characterized in that: The data analysis task includes at least one of a video behavior data analysis task, an eye movement data analysis task, a physiological data analysis task, a voice data analysis task, and a visualization report task.

5. A method for evaluating driver experience in a human-factor intelligent cockpit, wherein the human-factor intelligent cockpit driver experience evaluation system according to any one of claims 1 to 4 is used to implement the method for evaluating driver experience in a human-factor intelligent cockpit, characterized in that: The following steps are involved: Acquire sensor data generated by at least one evaluated driver based on corresponding evaluation content and a driver status of at least one evaluated driver identified based on the sensor data; and Experience evaluation data of the human-factors intelligent cockpit is obtained according to the sensor data and the driver's status, wherein the experience evaluation data is used to evaluate the interactivity between the human-factors intelligent cockpit and the driver.

6. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the human factors intelligent cockpit driver experience evaluation method as described in claim 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the human factors intelligent cockpit driver experience evaluation method as described in claim 5.

Images