Data acquisition method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to a data acquisition method and device, electronic equipment, a storage medium and a vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the driving state data of a driver; determining the driving state of the driver according to the driving state data; the driving state represents whether the emotion of the driver is abnormal or not; and according to the driving state, driving related data of the vehicle in the driving process are collected. The subjective driving experience of the driver is fully considered, the driving related data in the vehicle driving process are collected in combination with the subjective driving experience of the driver, the method is not limited to a specific driving scene, the operation can be executed as long as the vehicle is in the driving process, and the driving experience of the driver is improved. The data acquisition amount and the scene covered by the acquired data are greatly improved, and the data acquisition efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to a data collection method, device, electronic device, storage medium and vehicle. Background Art

[0002] As people's demand for vehicle driving continues to increase, autonomous driving plays an increasingly important role in vehicle driving. Autonomous driving often requires the use of massive amounts of data for machine learning and algorithm model training, so data acquisition is crucial to the development of autonomous driving technology.

[0003] At present, most manufacturers usually simulate specific driving scenarios by building their own test fleets, and test engineers collect data from them to obtain data for learning and training autonomous driving. However, this method does not take into account the driver's subjective driving experience during the data collection process, and because it simulates specific driving scenarios, the scenarios covered by the collected data are also very limited. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a data collection method, device, electronic device, storage medium and vehicle.

[0005] According to a first aspect of an embodiment of the present disclosure, a data collection method is provided, the method comprising: acquiring driving status data of a driver; determining the driving status of the driver based on the driving status data; the driving status characterizes whether the driver has an abnormal emotion; and based on the driving status, collecting driving-related data of a vehicle during driving.

[0006] Optionally, determining the driving state of the driver based on the driving state data includes: when the driving state data is within a preset parameter range, determining that the driving state of the driver is an abnormal emotional state.

[0007] Optionally, determining the driving state of the driver based on the driving state data includes: determining a target parameter corresponding to the driving state data; and when the target parameter is greater than or equal to a preset parameter threshold, determining that the driving state of the driver is an abnormal emotional state.

[0008] Optionally, in the case where the driving status data includes multiple driving status data, determining the target parameter corresponding to the driving status data includes: determining, for each driving status data, a state parameter corresponding to the driving status data; the state parameter is used to characterize whether the driving status data is within a preset parameter range; and determining the target parameter based on the multiple state parameters.

[0009] Optionally, the method further includes: obtaining a weight value corresponding to each driving status data; and determining the target parameter based on the multiple status parameters includes: performing weighted summation of the status parameters according to the weight values ​​to obtain the target parameter.

[0010] Optionally, the driving status data includes at least one of the following data: facial feature data; steering wheel grip data; physiological feature data; driving posture data; and voice feature data.

[0011] Optionally, the driving status data includes voice audio of the driver, and determining the driving status of the driver based on the driving status data includes: identifying the voice audio to obtain a corresponding voice text; and determining the driving status of the driver based on the voice text.

[0012] Optionally, determining the driving state of the driver based on the voice text includes: when there are preset keywords in the voice text, determining that the driving state of the driver is an abnormal emotional state.

[0013] Optionally, determining the driving state of the driver based on the voice text includes: determining the number of preset keywords in the voice text; when the number of keywords is greater than or equal to a preset number threshold, determining that the driving state of the driver is an abnormal emotional state.

[0014] Optionally, collecting driving-related data of the vehicle during driving according to the driving state includes: when the driving state is an abnormal emotional state, collecting driving-related data of the vehicle within a preset time period after a specified moment, and the specified moment includes the current moment or any moment in historical time.

[0015] Optionally, the driving-related data includes at least one of the following data: vehicle surrounding environment data; vehicle driving data; driver status data; and vehicle driving route.

[0016] According to a second aspect of an embodiment of the present disclosure, a data collection device is provided, the device comprising: an acquisition module, configured to acquire driving status data of a driver; a determination module, configured to determine the driving status of the driver based on the driving status data; the driving status represents whether the driver has an abnormal emotion; and the acquisition module, configured to collect driving-related data of a vehicle during driving according to the driving status.

[0017] Optionally, the determination module is configured to determine that the driver's driving state is an abnormal emotional state when the driving state data is within a preset parameter range.

[0018] Optionally, the determination module is configured to determine a target parameter corresponding to the driving status data; when the target parameter is greater than or equal to a preset parameter threshold, determine that the driver's driving status is an abnormal emotional state.

[0019] Optionally, in the case where the driving status data includes multiple ones, the determination module is configured to determine, for each driving status data, a state parameter corresponding to the driving status data; the state parameter is used to characterize whether the driving status data is within a preset parameter range; and the target parameter is determined based on the multiple state parameters.

[0020] Optionally, the acquisition module is further configured to acquire a weight value corresponding to each driving status data; the determination module is configured to perform weighted summation on the status parameters according to the weight value to obtain the target parameter.

[0021] Optionally, the driving status data includes at least one of the following data: facial feature data; steering wheel grip data; physiological feature data; driving posture data; and voice feature data.

[0022] Optionally, the driving status data includes voice audio of the driver, and the determination module is configured to recognize the voice audio and obtain corresponding voice text; and determine the driving status of the driver according to the voice text.

[0023] Optionally, the determination module is configured to determine that the driver's driving state is an abnormal emotional state when there are preset keywords in the voice text.

[0024] Optionally, the determination module is configured to determine the number of preset keywords in the voice text; when the number of keywords is greater than or equal to a preset number threshold, determine that the driver's driving state is an abnormal emotional state.

[0025] Optionally, the acquisition module is configured to collect driving-related data of the vehicle within a preset time period after a specified time when the driving state is an abnormal emotional state, and the specified time includes the current time or any time in historical time.

[0026] Optionally, the driving-related data includes at least one of the following data: vehicle surrounding environment data; vehicle driving data; driver status data; and vehicle driving route.

[0027] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory for storing processor executable instructions; wherein the processor is configured to implement the steps of the data collection method provided in the first aspect of the present disclosure when calling the executable instructions stored on the memory.

[0028] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the data collection method provided in the first aspect of the present disclosure are implemented.

[0029] According to a fifth aspect of an embodiment of the present disclosure, a vehicle is provided, comprising the electronic device provided by the third aspect of the present disclosure.

[0030] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: First, the driving state data of the driver is obtained. Secondly, the driving state of the driver is determined based on the driving state data. Finally, the driving-related data of the vehicle during driving is collected based on the driving state. Through the above method, the driving state of the driver, that is, the subjective driving experience of the driver, can be determined based on the driving state data of the driver during the driving of the vehicle. And based on the driving state of the driver, the driving-related data of the vehicle during driving is collected. The present disclosure fully considers the subjective driving experience of the driver, and collects driving-related data during the driving process of the vehicle in combination with the subjective driving experience of the driver. It is not limited to specific driving scenarios. As long as the vehicle is in driving, the above operations can be performed, which greatly increases the amount of data collected and the scenarios covered by the collected data, and improves the efficiency of data collection.

[0031] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0033] Figure 1 The figure is a flow chart of a data collection method according to an exemplary embodiment.

[0034] Figure 2 The figure is a flow chart of another data collection method according to an exemplary embodiment.

[0035] Figure 3 The figure is a flow chart of another data collection method according to an exemplary embodiment.

[0036] Figure 4 The figure is a block diagram of a data collection device according to an exemplary embodiment.

[0037] Figure 5 is a block diagram of a vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0039] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0040] The terms "first", "second", etc. in the specification and claims of this application and the above drawings are used to distinguish similar objects and do not have to be understood as a specific order or sequence. In addition, in the description with reference to the drawings, the same symbols in different drawings represent the same elements.

[0041] In the description of the present disclosure, unless otherwise specified, "multiple" means two or more than two, and other quantifiers are similar thereto; "at least one item", "one item or multiple items" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one item a can represent any number of a; for another example, one item or multiple items among a, b and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple; "and / or" is a kind of description of the association relationship of associated objects, indicating that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " indicates that the associated objects before and after are in an "or" relationship.

[0042] Although operations or steps are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or to perform all the operations or steps shown to obtain the desired results. In the embodiments of the present disclosure, these operations or steps can be performed in series; these operations or steps can also be performed in parallel; or some of these operations or steps can be performed.

[0043] Before introducing the data collection method, device, electronic device, storage medium and vehicle provided by the present disclosure, the application scenarios involved in each embodiment of the present disclosure are first introduced. In one implementation method, some manufacturers simulate specific driving scenarios by building their own test fleets, and test collection engineers collect all or part of the data (for example, data in specific scenarios, such as U-turn scenarios, climbing scenarios, etc.) to obtain data for learning and training autonomous driving. In other words, the entire data collection process is not based on the user's actual usage environment, but on experimental test scenarios. It can be seen that this method has the problems of limited number of vehicles in the test fleet, small scale, limited scenarios that can be covered, low collection efficiency, and high overall collection cost. Moreover, this method does not take into account the driver's subjective driving experience during the data collection process.

[0044] In another implementation, some manufacturers use crowdsourcing collection solutions based on mass-produced cars actually used by users, that is, data collection is based on the actual driving environment of users. In this scenario, there is another "clone brain" in addition to the main brain of the vehicle, which can also obtain various sensor data of the vehicle, make predictions about the driving conditions, and output driving decision instructions. When the vehicle is in a manned state, the system including sensors is still running, but it does not participate in the actual control of the vehicle, but only verifies the decision algorithm. The decision is compared with the driver's driving behavior. Once there is a discrepancy between the two, the scene is judged as an "extreme condition", which triggers data collection. Although this method can overcome the problems of small scale, high cost, limited coverage of scenarios, and low efficiency in the previous implementation, this method also does not take into account the driver's subjective driving experience during data collection.

[0045] Based on the above data collection method, the inventor found that, on the basis of giving priority to safety, the user experience is the ultimate evaluation standard for whether the automatic driving function is implemented well or not. For example, for automatic driving scenarios, some automatic driving vehicles have strong ability to turn at large curvatures, but the turning speed is very fast. From the perspective of driving safety, their turning speed is safe. But the actual effect is that the driver is in a very nervous state during this process. In other words, from the perspective of user experience, this technical solution may not be the best strategy, and the user experience is poor. For non-automatic driving scenarios, that is, the vehicle is actually controlled by the driver, although the driver's operation is consistent with the control result of the driving decision instruction predicted by the vehicle, the driver still has a strong sense of tension. For example, in some dangerous scenarios, due to the high tension of the driver, a better operation strategy was not adopted, but at the same time, the control strategy predicted by the vehicle also failed to effectively identify the scene and take reasonable operations. In other words, although the scene is very dangerous, the nervous user and the poor automatic driving control strategy have the same action performance. In this case, it is believed that the vehicle's predicted control strategy is consistent with the user's actual operation, and the algorithm does not need additional optimization. As a result, the dangerous scenario that causes stress to the driver is not identified, and the opportunity for optimization and improvement is missed.

[0046] It can be seen from this that both of the above two methods do not take into account the driver's actual subjective driving experience, and are not user-centered data collection logic that uses user experience as a measurement and judgment standard.

[0047] In order to solve the above technical problems, the present invention provides a data collection method, device, electronic device, storage medium and vehicle, which can determine the driver's driving state, that is, the driver's subjective driving experience, according to the driver's driving state data during the vehicle driving process. And according to the driver's driving state, the driving-related data of the vehicle during driving is collected. The present disclosure fully considers the driver's subjective driving experience, and collects driving-related data during the vehicle driving process in combination with the driver's subjective driving experience. It is not limited to specific driving scenarios. As long as the vehicle is in the process of driving, the above operations can be performed, which greatly increases the amount of data collected and the scenarios covered by the collected data, and improves the efficiency of data collection.

[0048] The specific implementation modes of the present invention are described in detail below with reference to the accompanying drawings.

[0049] Figure 1 is a flow chart of a data collection method according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps.

[0050] In step S101 , the driving state data of the driver is acquired.

[0051] This embodiment can be applied to an autonomous driving scenario, or a semi-autonomous driving scenario (that is, the vehicle assisted driving mode, when a dangerous accident is detected, the corresponding obstacle avoidance operation is performed), or a non-autonomous driving scenario (that is, the driver drives the vehicle). In some scenarios, although no dangerous accident has occurred, the driver feels nervous and uncomfortable during the driving of the vehicle, which seriously affects the user experience. For example, when there are large trucks running parallel in the adjacent road, it will cause a great sense of oppression to the user of the vehicle. Therefore, in this step, no matter which driving scenario the vehicle is in, the driver's driving status data can be monitored and recorded in real time. That is, the driver's driving status data is obtained while the vehicle is in motion.

[0052] Among them, the driving state data may include, for example but not limited to, one or more of the following data: facial feature data (such as pupil size, facial expression (it can be used to determine whether the driver has frowning / staring expressions, etc.), etc.); steering wheel grip data (such as steering wheel grip or grip change); physiological feature data (heart rate, blood pressure, respiratory rate and pulse, etc.); driving posture data (such as the driver's body movement distance, which can be used to determine whether the driver has body leaning forward, shaking and other actions); and voice feature data (such as sound intensity, tone, pitch, etc.). Through the driving state data, it can be determined whether the driver has emotional changes during the driving process of the vehicle. Considering that for drivers with different personalities, when facing the same driving scene that makes them feel nervous and uncomfortable, the reactions they produce are also different. Therefore, in order to collect all driving scene data that make users feel nervous and uncomfortable as much as possible, in some scenes, the driving state data may also include the driver's voice audio. Through the driver's voice audio, the driver's current driving state can also be further determined.

[0053] For example, the above driving status data, such as heart rate, blood pressure, respiratory rate and pulse, etc., can be obtained through wearable devices such as bracelets, watches, headphones, etc. connected to the vehicle. The above driving status data, such as steering wheel grip or grip change, sweating, etc., can also be obtained through a steering wheel grip detection sensor. The above driving status data, such as pupil size, facial expression, driver body information, etc., can also be obtained through a camera set in the vehicle (which can be located at the steering wheel or other locations in the cockpit). The above driving status data, such as voice audio, sound intensity, tone, pitch, etc., can also be obtained through a voice acquisition device set in the vehicle.

[0054] In step S102, the driving state of the driver is determined according to the driving state data.

[0055] The driving status is used to indicate whether the driver is in an abnormal mood.

[0056] After obtaining the driving status data, the driving status data can be sent to the vehicle's acquisition trigger judgment module through a preset communication method. The preset communication method can be, for example but not limited to, CAN (English: Controller Area Network; Chinese: Controller Local Area Network), ETH (English: Ethernet; Chinese: Ethernet) and other wired or BT (English: Bluetooth; Chinese: Bluetooth), WiFi and other wireless methods.

[0057] The acquisition trigger determination module determines whether the current driver's driving state is an abnormal emotional state based on the received driving state data. Among them, the abnormal emotional state can be understood as the current driver being in a state of emotional tension or anxiety. Specifically, the state data corresponding to the driving state being an abnormal emotional state can be pre-set (for example, it can be a parameter range, or it can also be a keyword). And the acquired driving state data is compared with the state data corresponding to the pre-set driving state being an abnormal emotional state, and then it is determined whether the current driver's driving state is an abnormal emotional state.

[0058] In step S103, driving-related data of the vehicle during driving is collected according to the driving state.

[0059] For example, when the driving state is an abnormal emotional state, driving-related data of the vehicle within a preset time period after a specified time may be collected, and the specified time may include the current time or any time in the historical time. The preset time period may be flexibly set according to actual needs, and the present disclosure does not specifically limit this.

[0060] The driving-related data includes at least one of the following data: vehicle surrounding environment data (such as obstacle data, lane line data, sidewalk data, and driving road condition data, etc.); vehicle driving data (such as vehicle speed, vehicle direction, vehicle acceleration, steering wheel angular velocity, and accelerator pedal depth, etc.); driver status data; and vehicle driving route. In some embodiments, the above driving-related data can be collected by on-board sensors (such as cameras, millimeter wave radars, laser radars, ultrasonic radars, voice collection devices, speed sensors, pressure sensors, etc.) and external sensors (such as watches, bracelets, headphones, and other wearable devices) installed on the vehicle.

[0061] After the driving-related data is collected, it can be uploaded to the cloud in real time, or temporarily stored in the vehicle's storage device, or part of it can be uploaded in real time and part of it can be temporarily stored and then uploaded at a later time.

[0062] It is understandable that drivers with different experience levels and personalities will have different feelings when facing the same driving scene. For example, some drivers may not feel certain scenes, acceleration and deceleration levels, and the degree of proximity to the adjacent vehicles, while other drivers may already be in a very nervous state. The same driver will have different tolerance levels at different stages of driving experience, and the degree of preference will change with the improvement of experience and ability. Therefore, in this embodiment, the driver's driving state data is obtained to determine the driver's driving state, that is, the driver's subjective driving experience. Based on this, the driving-related data of the vehicle is collected. In this way, scenes that cause tension, discomfort or pressure to the driver can be automatically identified, which is more targeted and plays a very important role in improving the user experience of autonomous driving in the future and has a beneficial data supplement effect.

[0063] After obtaining driving-related data, if the driving scenario includes a semi-automatic driving scenario or an automatic driving scenario, the driving decision instructions generated by the vehicle in the semi-automatic driving scenario or the automatic driving scenario can also be uploaded to the cloud at the same time, so that the technicians can optimize the vehicle's automatic driving capabilities in a more targeted manner based on the driving-related data and the driving decision instructions generated by the vehicle. It is also possible to determine the tension level of different drivers based on the driving-related data, and optimize the vehicle's automatic driving capabilities based on the tension level of different types of drivers and their corresponding scenarios, which will help implement personalized grading strategies in the subsequent assisted driving process and further improve the user experience.

[0064] By adopting the above method, the driving state of the driver, that is, the subjective driving experience of the driver, can be determined according to the driving state data of the driver during the driving process of the vehicle. And according to the driving state of the driver, the driving-related data of the vehicle during the driving process is collected. The present disclosure fully considers the subjective driving experience of the driver, and collects the driving-related data during the driving process of the vehicle in combination with the subjective driving experience of the driver. It is not limited to a specific driving scene. As long as the vehicle is in the driving process, the above operation can be performed, which greatly increases the amount of data collected and the scenes covered by the collected data, and improves the efficiency of data collection.

[0065] The above step S102 is described in detail below.

[0066] In a possible implementation, the above step S102, according to the driving state data, determining the driving state of the driver may include:

[0067] When the driving state data is within a preset parameter range, it is determined that the driver's driving state is an abnormal emotional state.

[0068] For example, in the case where the driving state parameter includes pupil size, the preset parameter range may include a preset pupil range, that is, if the current driver's pupil size is within the preset pupil range, it indicates that the driver's current driving state is an abnormal emotional state. In the case where the driving state parameter includes steering wheel grip, the preset parameter range may include a preset grip range, that is, if the current driver's steering wheel grip is within the preset grip range, it indicates that the driver's current driving state is an abnormal emotional state. In the case where the driving state parameter includes heart rate, the preset parameter range may include a preset heart rate range, that is, if the current driver's heart rate is within the preset heart rate range, it indicates that the driver's current driving state is an abnormal emotional state. In the case where the driving state parameter includes the driver's body movement distance, the preset parameter range may include a preset distance range, that is, if the current driver's body movement distance is within the preset distance range, it indicates that the driver's current driving state is an abnormal emotional state. In the case where the driving state parameter includes the driver's voice intensity, the preset parameter range may include a preset intensity range, that is, if the current driver's voice intensity is within the preset intensity range, it indicates that the driver's current driving state is an abnormal emotional state. Methods for determining other driving state parameters are not listed here one by one, and can refer to the above examples.

[0069] In this step, if the driving state data includes multiple data, the driving state of the driver can be determined to be an abnormal emotional state when any one of the driving state data is within the preset parameter range. It can also be determined that the driving state of the driver is an abnormal emotional state when a preset number of driving state data are within the preset parameter range. It can also be determined that the driving state of the driver is an abnormal emotional state when at least the specified driving state data is within the preset parameter range.

[0070] In another possible implementation, Figure 2 As shown, the above step S102, based on the driving state data, determines the driving state of the driver, which may include the following steps:

[0071] In step S1021, the target parameter corresponding to the driving status data is determined.

[0072] For example, first, for each driving state data, a state parameter corresponding to the driving state data can be determined. The state parameter is used to characterize whether the driving state data is within a preset parameter range. For example, when the driving state data is within the preset parameter range, the state parameter can be determined to be 1. When the driving state data is outside the preset parameter range, the state parameter can be determined to be 0. The determination of whether the driving state data is within the preset parameter range can refer to the above example, which will not be repeated here. Then, the target parameter can be determined based on multiple state parameters. In one embodiment, the sum of multiple state parameters can be directly used as the target parameter. In another embodiment, corresponding weight values ​​can be set for different driving state data, and the target parameter can be determined based on the weight value and the state parameter. For example, the weight value corresponding to each driving state data can be obtained. Afterwards, the state parameter is weighted and summed according to the weight value to obtain the target parameter.

[0073] In step S1022, when the target parameter is greater than or equal to the preset parameter threshold, it is determined that the driver's driving state is an abnormal emotional state.

[0074] In another possible implementation, when the driving status data includes the driver's voice audio, such as Figure 3 As shown, the above step S102, according to the driving state data, determines the driving state of the driver, which may include the following steps:

[0075] In step S1023, the voice audio is recognized to obtain the corresponding voice text.

[0076] In this step, the speech audio may be recognized by a relevant speech recognition model to obtain the speech text corresponding to the speech audio.

[0077] In step S1024, the driving status of the driver is determined based on the voice text.

[0078] In some embodiments, if there are preset keywords in the voice text, the driver's driving state can be determined to be an abnormal emotional state. The preset keywords can be words that can express that the driver is in a state of emotional tension, discomfort, or high pressure, such as words such as being scared, afraid, scared, nervous, etc.

[0079] In other embodiments, considering that the driver may occasionally carry the above-mentioned preset keywords during speech due to personal habits, in order to improve the accuracy of data collection and avoid accidental, the number of keywords containing the preset keywords in the voice text may be determined first. And when the number of keywords is greater than or equal to the preset number threshold, it is determined that the driver's driving state is an abnormal emotional state. In other words, when the number of keywords containing the preset keywords in the voice text is greater than or equal to the preset number threshold, it is determined that the driver is in an abnormal emotional state, and then the driving-related data of the vehicle is collected, thereby improving the accuracy of data collection.

[0080] By adopting the above method, the driving state of the driver, that is, the subjective driving experience of the driver, can be determined according to the driving state data of the driver during the driving process of the vehicle. And according to the driving state of the driver, the driving-related data of the vehicle during the driving process is collected. The present disclosure fully considers the subjective driving experience of the driver, and collects the driving-related data during the driving process of the vehicle in combination with the subjective driving experience of the driver. It is not limited to a specific driving scene. As long as the vehicle is in the driving process, the above operation can be performed, which greatly increases the amount of data collected and the scenes covered by the collected data, and improves the efficiency of data collection.

[0081] Figure 4 is a block diagram of a data collection device according to an exemplary embodiment. Figure 4 As shown, the device 200 includes:

[0082] An acquisition module 201 is configured to acquire driving status data of a driver;

[0083] The determination module 202 is configured to determine the driving state of the driver according to the driving state data; the driving state indicates whether the driver has abnormal emotions;

[0084] The collection module 203 is configured to collect driving-related data of the vehicle during driving according to the driving state.

[0085] Optionally, the determination module 202 is configured to determine that the driver's driving state is an abnormal emotional state when the driving state data is within a preset parameter range.

[0086] Optionally, the determination module 202 is configured to determine a target parameter corresponding to the driving status data; when the target parameter is greater than or equal to a preset parameter threshold, it is determined that the driver's driving status is an abnormal emotional state.

[0087] Optionally, in the case where the driving status data includes multiple ones, the determination module 202 is configured to determine, for each of the driving status data, a state parameter corresponding to the driving status data; the state parameter is used to characterize whether the driving status data is within a preset parameter range; and the target parameter is determined based on the multiple state parameters.

[0088] Optionally, the acquisition module 201 is further configured to acquire a weight value corresponding to each driving state data; the determination module 202 is configured to perform weighted summation on the state parameters according to the weight value to obtain the target parameter.

[0089] Optionally, the driving status data includes at least one of the following data: facial feature data; steering wheel grip data; physiological feature data; driving posture data; and voice feature data.

[0090] Optionally, the driving status data includes voice audio of the driver, and the determination module 202 is configured to recognize the voice audio and obtain corresponding voice text; and determine the driving status of the driver according to the voice text.

[0091] Optionally, the determination module 202 is configured to determine that the driver's driving state is an abnormal emotional state when there are preset keywords in the voice text.

[0092] Optionally, the determination module 202 is configured to determine the number of preset keywords in the voice text; when the number of keywords is greater than or equal to a preset number threshold, determine that the driver's driving state is an abnormal emotional state.

[0093] Optionally, the collection module 203 is configured to collect driving-related data of the vehicle within a preset time period after a specified time when the driving state is an abnormal emotional state, and the specified time includes the current time or any time in historical time.

[0094] Optionally, the driving-related data includes at least one of the following data: vehicle surrounding environment data; vehicle driving data; driver status data; and vehicle driving route.

[0095] By adopting the above device, the driving state of the driver, that is, the subjective driving experience of the driver, can be determined according to the driving state data of the driver during the driving of the vehicle. And according to the driving state of the driver, the driving-related data of the vehicle during the driving process is collected. The present disclosure fully considers the subjective driving experience of the driver, and collects the driving-related data during the driving process of the vehicle in combination with the subjective driving experience of the driver. It is not limited to a specific driving scene. As long as the vehicle is in the driving process, the above operation can be performed, which greatly increases the amount of data collected and the scenes covered by the collected data, and improves the efficiency of data collection.

[0096] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0097] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the data collection method provided by the present disclosure are implemented.

[0098] Figure 5 is a block diagram of a vehicle 300 according to an exemplary embodiment. For example, the vehicle 300 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. The vehicle 300 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0099] Reference Figure 5 , the vehicle 300 may include various subsystems, for example, an infotainment system 310, a perception system 320, a decision control system 330, a drive system 340, and a computing platform 350. The vehicle 300 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 300 may be interconnected by wire or wireless means.

[0100] In some embodiments, the infotainment system 310 may include a communication system, an entertainment system, and a navigation system, among others.

[0101] The perception system 320 may include several sensors for sensing information about the environment around the vehicle 300. For example, the perception system 320 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0102] The decision control system 330 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0103] The drive system 340 may include components that provide power movement for the vehicle 300. In one embodiment, the drive system 340 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0104] Some or all functions of the vehicle 300 are controlled by a computing platform 350. The computing platform 350 may include at least one processor 351 and a memory 352, and the processor 351 may execute instructions 353 stored in the memory 352.

[0105] The processor 351 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0106] The memory 352 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0107] In addition to instructions 353 , memory 352 may also store data, such as road maps, route information, vehicle location, direction, speed, etc. The data stored in memory 352 may be used by computing platform 350 .

[0108] In the embodiment of the present disclosure, the processor 351 may execute the instruction 353 to complete all or part of the steps of the above-mentioned data collection method.

[0109] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned data collection method when executed by the programmable device.

[0110] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0111] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data collection method, characterized in that: The method comprises: Acquire the driver's driving status data; Determining the driving state of the driver according to the driving state data; the driving state indicates whether the driver is in an abnormal mood; According to the driving state, driving related data of the vehicle during driving is collected.

2. The method according to claim 1, characterized in that Determining the driving state of the driver according to the driving state data includes: When the driving state data is within a preset parameter range, it is determined that the driver's driving state is an abnormal emotional state.

3. The method according to claim 1, characterized in that: Determining the driving state of the driver according to the driving state data includes: determining a target parameter corresponding to the driving state data; When the target parameter is greater than or equal to a preset parameter threshold, it is determined that the driver's driving state is an abnormal emotional state.

4. The method according to claim 3, characterized in that In the case where the driving state data includes a plurality of data, determining the target parameter corresponding to the driving state data includes: For each driving state data, determining a state parameter corresponding to the driving state data; the state parameter is used to indicate whether the driving state data is within a preset parameter range; The target parameter is determined according to the plurality of state parameters.

5. The method according to claim 4, characterized in that The method further comprises: Obtaining a weight value corresponding to each driving state data; Determining the target parameter according to the plurality of state parameters comprises: The state parameters are weighted and summed according to the weight values ​​to obtain the target parameters.

6. The method according to any one of claims 1 to 5, characterized in that The driving status data includes at least one of the following data: Facial feature data; Steering wheel grip data; Physiological characteristic data; Driving posture data; and Voice feature data.

7. The method according to claim 1, characterized in that The driving state data includes a voice audio of the driver, and determining the driving state of the driver according to the driving state data includes: Recognize the voice audio and obtain the corresponding voice text; The driving status of the driver is determined according to the voice text.

8. The method according to claim 7, characterized in that Determining the driving state of the driver according to the voice text includes: When there are preset keywords in the voice text, it is determined that the driving state of the driver is an abnormal emotional state.

9. The method according to claim 7, characterized in that: Determining the driving state of the driver according to the voice text includes: Determine the number of preset keywords in the voice text; When the number of the keywords is greater than or equal to a preset number threshold, it is determined that the driving state of the driver is an abnormal emotional state.

10. The method according to claim 1, characterized in that The collecting of driving-related data of the vehicle during driving according to the driving state includes: In the case where the driving state is an abnormal emotional state, driving-related data of the vehicle within a preset time period after a specified time is collected, and the specified time includes the current time or any time in historical time.

11. The method according to claim 1, characterized in that: The driving related data includes at least one of the following data: Vehicle surrounding environment data; Vehicle driving data; Driver status data; and The vehicle's route.

12. A data collection device, characterized in that: The device comprises: An acquisition module, configured to acquire driving status data of a driver; A determination module is configured to determine the driving state of the driver according to the driving state data; the driving state indicates whether the driver has abnormal emotions; The collection module is configured to collect driving-related data of the vehicle during driving according to the driving state.

13. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the steps of the method described in any one of claims 1 to 11 when calling the executable instructions stored in the memory.

14. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method described in any one of claims 1 to 11 are implemented.

15. A vehicle, characterized in that: The electronic device comprising the electronic device described in claim 13 above.

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