Navigation voice broadcasting method and device, equipment and storage medium

Abstract

The invention provides a navigation voice broadcasting method, and relates to the technical field of artificial intelligence, in particular to the technical fields of map navigation, automatic driving, intelligent traffic and the like. The method comprises the following steps: generating road condition grade information based on road condition data corresponding to a navigation path; generating object level information based on the state data of the target object; generating comprehensive grade information based on the road condition grade information and the object grade information; and determining navigation voice parameters according to the comprehensive level information, and broadcasting voice for navigation by using the navigation voice parameters. According to the method, the navigation voice parameters can be dynamically adjusted according to the road condition and the driver state, so that the navigation experience of a user is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to technical fields such as map navigation, autonomous driving, and intelligent transportation, and particularly relates to a method, device, equipment, and storage medium for playing navigation voice. Background Art

[0002] With the development of intelligent transportation and mobile Internet technologies, voice navigation systems have become an important auxiliary tool for daily travel. Traditional voice navigation systems rely on pre-recorded voice segments to provide navigation services, and these segments are usually recorded by professional voice actors and cover common navigation instructions such as "Please turn left" and "Please turn right". Summary of the Invention

[0003] The present disclosure provides a method, device, equipment, and storage medium for playing navigation voice.

[0004] According to a first aspect of the present disclosure, there is provided a method for playing navigation voice, including: generating road condition level information based on road condition data corresponding to a navigation path; generating object level information based on status data of a target object; generating comprehensive level information based on the road condition level information and the object level information; determining navigation voice parameters according to the comprehensive level information, and playing the voice for navigation by using the navigation voice parameters.

[0005] According to a second aspect of the present disclosure, there is provided a device for playing navigation voice, including: a road condition information generation module configured to generate road condition level information based on road condition data corresponding to a navigation path; an object information generation module configured to generate object level information based on status data of a target object; a comprehensive information generation module configured to generate comprehensive level information based on the road condition level information and the object level information; a parameter information generation module configured to determine navigation voice parameters according to the comprehensive level information, and play the voice for navigation by using the navigation voice parameters.

[0006] According to a third aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any implementation manner of the first aspect.

[0007] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method described in any implementation manner of the first aspect.

[0008] According to a fifth aspect of the present disclosure, there is provided a computer program product including a computer program which, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0009] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0011] Figure 1 is an exemplary system architecture diagram to which the present disclosure can be applied;

[0012] Figure 2 is a flowchart of a first embodiment of the navigation voice broadcast method according to the present disclosure;

[0013] Figure 3 is a flowchart of a second embodiment of the navigation voice broadcast method according to the present disclosure;

[0014] Figure 4 is a flowchart of a third embodiment of the navigation voice broadcast method according to the present disclosure;

[0015] Figure 5 is a flowchart of a fourth embodiment of the navigation voice broadcast method according to the present disclosure;

[0016] Figure 6 is a flowchart of a fifth embodiment of the navigation voice broadcast method according to the present disclosure;

[0017] Figure 7 is a schematic structural diagram of an embodiment of the navigation voice broadcast device according to the present disclosure;

[0018] Figure 8 is a block diagram of an electronic device for implementing the navigation voice broadcast method of the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following describes exemplary embodiments of the present disclosure in conjunction with the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist in understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted below.

[0020] It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other. The following will describe the present disclosure in detail with reference to the accompanying drawings and in combination with the embodiments.

[0021] Figure 1 An exemplary system architecture 100 showing an embodiment of a method for broadcasting navigation voice or a device for broadcasting navigation voice to which the present disclosure can be applied is shown.

[0022] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, 104, a network 105, and a server 106. The network 105 is used as a medium for providing a communication link between the terminal devices 101, 102, 103, 104 and the server 106. The network 105 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0023] Users can use the terminal devices 101, 102, 103, 104 to interact with the server 106 through the network 105 to receive or send information, etc. Various client applications may be installed on the terminal devices 101, 102, 103, 104.

[0024] The terminal devices 101, 102, 103, 104 may be hardware or software. When the terminal devices 101, 102, 103, 104 are hardware, they may be various electronic devices, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc. When the terminal devices 101, 102, 103, 104 are software, they may be installed in the above-mentioned electronic devices. They may be implemented as multiple software or software modules, or may be implemented as a single software or software module. No specific limitation is made here.

[0025] The server 106 may provide various services. For example, the server 106 may analyze and process the road condition data and the status data of the target object obtained from the terminal devices 101, 102, 103, 104, and generate a processing result (such as navigation voice parameters).

[0026] It should be noted that the server 106 may be hardware or software. When the server 106 is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server. When the server 106 is software, it may be implemented as multiple software or software modules (such as for providing distributed services), or may be implemented as a single software or software module. No specific limitation is made here.

[0027] It should be noted that the method for broadcasting navigation voice provided by the embodiments of the present disclosure is generally executed by the server 106. Correspondingly, the device for broadcasting navigation voice is generally set in the server 106.

[0028] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in [[ ]] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers.

[0029] Continuing to refer to [[ ]] Figure 2 which shows the process 200 of the first embodiment of the navigation voice announcement method according to the present disclosure. The navigation voice announcement method includes the following steps:

[0030] Step 201, generating road condition level information based on the road condition data corresponding to the navigation path.

[0031] In this embodiment, the execution subject of the navigation voice announcement method (such as Figure 1 the server 105 shown) will generate road condition level information according to the road condition data corresponding to the navigation path. Understandably, after the user inputs or selects the navigation start point information and the navigation end point information, at least one navigation route can be generated for the user, and the user can select the target navigation path according to actual needs. Or a default navigation route can be directly generated according to the navigation start point information and the navigation end point information, for example, directly using the navigation route with the 'least time consumption' as the default navigation route.

[0032] After determining the navigation path, the above-mentioned execution subject will obtain the road condition data corresponding to the navigation path in real time through the map API (Application Programming Interface), and the road condition data here can include but is not limited to: vehicle dimension data, traffic dimension data, and road feature dimension data. Among them, the vehicle dimension data can refer to the traffic flow data on the path and the congestion data of the path, the traffic dimension data can refer to the distance and time data of traffic accidents occurring on the path, and the road feature dimension data can refer to data such as the curvature and slope of the road.

[0033] Since the road condition data may include data of one or more dimensions, when the road condition data includes data of multiple dimensions, the above-mentioned execution entity may generate road condition level information based on the road condition data of each dimension respectively. For example, when the road condition data includes data of three dimensions, one road condition level information is generated according to the data of the vehicle dimension, one road condition level information is generated according to the data of the traffic dimension, and one road condition level information is generated according to the data of the road feature dimension. Then, the road condition level information with the highest level among these three road condition level information is used as the final road condition level information. For example, if the road condition level information generated according to the data of the vehicle dimension is a high level, the road condition level information generated according to the data of the traffic dimension is a low level, and the road condition level information generated according to the data of the road feature dimension is also a low level, then the final road condition level information is a high level. It can be understood that the higher the level of the generated road condition level information, the worse the road condition, and the worse the road condition, the greater the driving risk.

[0034] Step 202: Generate object level information based on the status data of the target object.

[0035] In this embodiment, the above-mentioned execution entity generates object level information based on the status data of the target object. Here, the target object refers to the driver. The above-mentioned execution entity may collect the video or image of the target object through the camera in the vehicle, and determine the status data of the target object by analyzing the collected video or image. The status data may include, but is not limited to: the status data of the eye dimension and / or the posture data of the head dimension. Among them, the eye status data may include data such as the blink frequency, the duration of continuous eye closure, and the line of sight direction; the head posture data may include the head drooping angle data.

[0036] Since the status data of the target object may include data of one or more dimensions, when the status data may include data of multiple dimensions, the above-mentioned execution entity may generate object level information based on the status data of each dimension respectively. For example, when the status data may include data of two dimensions, one object level information may be generated according to the eye status data, and one object level information may be generated according to the head posture data; then, the object level information with the highest level among these two object level information is used as the final object level information. For example, if the object level information generated according to the eye status data is a high level, and the object level information generated according to the head posture data is a low level, then the final object level information is a high level.

[0037] It can be understood that the road condition level information and the object level information in this embodiment can be information representing specific levels, such as high level, medium level, and low level, or can also be score information capable of characterizing levels. For example, a score of 0.8 - 1 (representing high level), a score of 0.5 - 0.8 (representing medium level), and a score of 0 - 0.5 (representing low level). The higher the level information, the higher the driving risk. For example, the higher the road condition level information, the more complex the road condition or the more congested the traffic, that is, the worse the road condition; the higher the object level information, the higher the driving risk of the target object, such as inattentiveness or drowsiness.

[0038] This embodiment does not limit the execution order of step 201 and step 202. That is, step 201 can be executed before step 202, step 201 can also be executed after step 202, and even step 201 can be executed simultaneously with step 202.

[0039] Step 203: Generate comprehensive level information based on the road condition level information and the object level information.

[0040] In this embodiment, the above-mentioned execution entity can generate comprehensive level information according to the road condition level information and the object level information, that is, determine the current driving risk level, that is, the comprehensive level information, by integrating the current road condition information and the driver status information.

[0041] In one example, the level information with a higher level in the road condition level information and the object level information can be used as the comprehensive level information. For example, if the road condition level information is high level and the object level information is low level, then the comprehensive level information is high level.

[0042] In one example, when the level information is score information used to characterize the level, the road condition level information and the object level information can be weighted and calculated, so as to determine the comprehensive level information according to the calculation result. For example, the road condition level information is 0.9 points, the object level information is 0.5 points, the weight of the road condition level information is 0.7, and the weight of the object level information is 0.3. Then, through weighted calculation, the final score can be obtained: 0.9 * 0.7 + 0.5 * 0.3 = 0.78 points. Then 0.78 points is the comprehensive level information. According to the foregoing correspondence relationship: a score of 0.8 - 1 (representing high level), a score of 0.5 - 0.8 (representing medium level), and a score of 0 - 0.5 (representing low level), it can be determined that the comprehensive level is medium level, that is, the current driving risk is medium level.

[0043] Step 204: Determine the navigation voice parameters according to the comprehensive level information, and use the navigation voice parameters to broadcast the voice for navigation.

[0044] In this embodiment, the above-mentioned execution entity will preset a correspondence table between different levels of information and navigation voice parameters in advance, so that after determining the comprehensive level information, the navigation voice parameters corresponding to the comprehensive level information can be determined according to this correspondence table. The navigation voice parameters here may include but are not limited to: volume, pitch, speech rate, tone, etc.

[0045] For example, it can be preset in advance that when the risk level is high risk, the volume level is adjusted to 100%, the speech rate is adjusted to 1.5 times, the pitch is adjusted to a high pitch, and the tone of the voice is adjusted to a serious and urgent tone, so that when driving at high risk, the user can perceive the current driving risk through the serious tone.

[0046] For another example, it can be preset in advance that when the risk level is medium risk, the volume level is adjusted to 60%, the speech rate is adjusted to 1.2 times, the pitch is adjusted to a slightly higher pitch, and the tone of the voice is adjusted to a tone of reminding attention, so that when driving at medium risk, the user can perceive that there is a certain driving risk currently through the reminder, but the user will not feel overly nervous.

[0047] For another example, it can be preset in advance that when the risk level is low risk, the volume level is adjusted to 30%, the speech rate is adjusted to 1.0 times, the pitch is adjusted to a normal pitch, and the tone of the voice is adjusted to a gentle and stable tone, so that when driving at low risk, that is, on a comfortable section, a relaxed voice broadcast is provided for the user.

[0048] The navigation voice broadcast method provided by the embodiments of the present disclosure first generates road condition level information based on the road condition data corresponding to the navigation path; then generates object level information based on the status data of the target object; then generates comprehensive level information based on the road condition level information and the object level information; finally determines the navigation voice parameters according to the comprehensive level information, and uses the navigation voice parameters to broadcast the voice for navigation. In the navigation voice broadcast method in this embodiment, by analyzing the road condition data and the driver's status data, and comprehensively determining the current driving risk level based on the analysis results of the two dimensions, and then dynamically adjusting parameters such as the tone color, pitch, and speech rate of the navigation voice according to the driving risk level, so as to associate the broadcast voice with information such as road conditions dynamically, enabling the user to perceive the current driving risk according to the broadcast voice, thereby improving driving safety and also improving the user's navigation experience.

[0049] In addition, in the technical solutions involved in the present disclosure, the acquisition, storage, use, processing, transportation, provision, and disclosure of the user's personal information (such as the road condition data and the status data of the target object involved in the present disclosure) and other processes all comply with the regulations of relevant laws and regulations and do not violate public order and good customs.

[0050] Continue to refer to Figure 3 ,Figure 3 Flowchart 300 of the second embodiment of the method for playing navigation voice according to the present disclosure is shown. The method for playing navigation voice includes the following steps:

[0051] Step 301, generating road condition level information based on traffic flow, road fault information, and / or road feature information.

[0052] In this embodiment, the execution subject of the method for playing navigation voice (such as Figure 1 the server 105 shown) generates road condition level information based on traffic flow, road fault information, and / or road feature information. That is, the road condition data in this embodiment includes one or more of traffic flow, road fault information, and road feature information. Any of the above-dimensional data can represent the road conditions at that time. Of course, if multiple-dimensional data can represent the road conditions at that time more comprehensively, so the above execution subject can generate road condition level information according to the obtained road condition data (one or more of traffic flow, road fault information, and road feature information).

[0053] In some optional implementation manners of this embodiment, step 301 includes: generating the road condition level information based on traffic flow and the driving speed of the vehicle; and / or generating the road condition level information based on road fault information and the position information of the vehicle; and / or generating the road condition level information based on road feature information, where the road feature information includes: road curvature, road slope, and / or the density of road forks.

[0054] In this implementation manner, traffic flow is the data of the vehicle dimension, road fault information is the data of the road traffic dimension, and road feature information is the data of the road dimension. The above execution subject generates road condition level information according to the data of each dimension respectively, that is, generates road condition level information according to the data of the vehicle dimension (traffic flow), and / or generates road condition level information according to the data of the road traffic dimension (road fault information), and / or generates road condition level information according to the data of the road dimension (road feature information).

[0055] Further, when generating road condition level information according to traffic flow, the driving speed of the vehicle is also obtained, so as to generate road condition level information by comprehensively considering the driving speed of the vehicle and the current traffic flow.

[0056] When generating road condition level information based on road fault information, the above-mentioned execution entity will receive accident information within a preset time period or a preset distance, such as accident reports within the last 1 hour or within 1 kilometer. Then, it obtains the current position of the vehicle. If the distance between the current position of the vehicle and the accident point is less than 500 meters, it is considered that the current road condition level is of high risk. Further, a risk score can be set for the current road condition level, for example, set to 0.8. In addition, if the accident point is located at a continuous bend or a road intersection of the road, the accident will have a greater impact on road traffic. At this time, it is considered that the current road condition level is of high risk, and the risk score for the current road condition level can be further set to 1.

[0057] When generating road condition level information based on road characteristic information, the above-mentioned execution entity will extract road section information through the map API, including the curvature of the bend, the slope, and the density of road forks. Among them, the curvature of the bend = 1 / radius of the bend. When the curvature of the bend > 0.2, it is regarded as a sharp bend. If the current bend is a sharp bend and the length of the bend > 50 meters, it is considered that the current road condition level is of high risk. Further, a risk score can be set for the current road condition level to 0.8. If the slope > 10% (that is, the height change exceeds 10 meters per 100 meters), it is considered that the current road condition level is of medium risk, and a risk score can be set for the current road condition level to 0.6. It should be noted that when the bend risk is of high risk and the slope risk is of medium risk, the road condition level generated based on the road characteristic information is the higher of the two risks, that is, the road condition level information is of high risk.

[0058] Thus, the current road condition is analyzed based on road condition data from different dimensions to more accurately evaluate the current road condition, thereby improving the accuracy of generating road condition level information.

[0059] In some alternative implementation manners of this embodiment, generating road condition level information based on traffic flow and the driving speed of the vehicle includes: determining whether the vehicle density on the road is of high density according to the traffic flow and a preset flow threshold; in response to determining that the vehicle density is of high density, determining the congestion degree of the road according to the driving speed and the speed limit value of the road; and determining the road condition level information of the road according to the congestion degree.

[0060] In this implementation, when the above-mentioned execution entity generates road condition level information based on the traffic flow and the driving speed of vehicles, it will first determine whether the current road section is of high density (or high traffic flow) according to the traffic flow and the flow threshold. If the current traffic flow exceeds the flow threshold (for example, 100 vehicles per kilometer), it is determined to be of high density. Then, in the case of high density, according to the driving speed of the vehicle and the speed limit value of the road, the congestion degree (or congestion level) of the road is determined. The congestion level = 1 - current vehicle speed / road speed limit. For example, on a road with a speed limit of 60 km / h and a current vehicle speed of 20 km / h, the congestion level is 1 - 20 / 60 = 0.67. Finally, according to the congestion degree, the road condition level information of the road is determined. In the case of high density, if the congestion level > 0.5, it is considered that the road condition level is of high risk. Correspondingly, the risk score is set to 0.8.

[0061] Thus, first determine whether the current road is of high density according to the traffic flow. If so, further determine the congestion level of the road in combination with the current vehicle speed. Finally, determine the road condition level information according to the congestion level of the road, improving the accuracy of the road condition level information.

[0062] In some alternative implementations of this embodiment, in response to determining that multiple road condition level information is generated, the target road condition level information that meets the preset requirements among the multiple road condition level information is used as the final road condition level information.

[0063] In this implementation, if multiple road condition level information is generated, that is, the road conditions include information in multiple dimensions, the above-mentioned execution entity will generate a road condition level information according to the road condition data of each dimension respectively, so as to obtain multiple road condition level information. At this time, the above-mentioned execution entity will use the target road condition level information that meets the preset requirements among the multiple road condition level information as the final road condition level information. For example, the road condition level information with the highest level is used as the final road condition level information. Thus, the final road condition level information can more accurately reflect the current road conditions.

[0064] For example, if the road condition level information generated according to the vehicle dimension data is of high level, the road condition level information generated according to the traffic dimension data is of low level, and the road condition level information generated according to the road feature dimension data is also of low level, then the final road condition level information is of high level.

[0065] In some alternative implementations of this embodiment, in response to determining that the number of target road condition level information is multiple, a weighted calculation is performed on the multiple target road condition level information, and the calculation result is used as the final road condition level information.

[0066] In this implementation, if multiple target road condition level information is finally determined, that is, multiple target road level information that meets the preset conditions is obtained. At this time, corresponding scores and corresponding weights can be set for these multiple target road condition level information respectively, so that these multiple target road condition level information can be weighted and calculated, and the calculation result is used as the final road condition level information. Thus, the final road condition level information is determined through weighted calculation, further improving the accuracy and comprehensiveness of the road condition level information.

[0067] Step 302: Generate object level information based on eye data and / or head pose data.

[0068] In this embodiment, the above-mentioned execution entity generates object level information based on eye data and / or head pose data, that is, the state data of the target object in this embodiment includes one or both of eye dimension data and head dimension pose data. The data of any of the above dimensions can represent the state of the target object (driver). Of course, if the data of the two dimensions can represent the state of the driver at that time more comprehensively. Therefore, the above-mentioned execution entity can generate object level information according to the obtained state data (one or both of eye dimension data and head dimension data).

[0069] In some alternative implementation manners of this embodiment, step 302 includes: generating object level information based on eye data, where the eye data includes: blink frequency, eye closure duration, and / or line of sight direction; and / or generating object level information based on head pose data, where the head pose data includes head drooping angle.

[0070] In this implementation manner, the above-mentioned execution entity generates object level information according to the data of each dimension respectively, that is, generates object level information based on the data of the eye dimension, and / or generates object level information based on the pose data of the head dimension.

[0071] Furthermore, the above-mentioned execution entity determines the blink frequency of the driver according to the image or video collected by the in-vehicle camera. Under normal circumstances, the blink frequency is 15 - 20 times per minute. Therefore, if the blink frequency is less than 10 times or higher than 30 times, it is regarded as a fatigued state, and the object level information at this time is determined to be a high risk, and the corresponding risk score can be set to 0.8. The eye closure duration of the driver can also be further determined: if the duration > 2 seconds, it is regarded as a fatigued state, and the object level information at this time is determined to be a high risk, and the corresponding risk score can be set to 1. In addition, if the driver's line of sight direction leaves the road for more than 5 seconds, it is regarded as distracted, and the object level information at this time is determined to be a high risk, and the corresponding risk score can be set to 0.8.

[0072] In addition, if it is determined that the head drooping angle of the driver exceeds 15° and lasts for greater than or equal to 2 seconds, it is regarded as fatigue, and the object level information at this time is determined to be a high risk, and the corresponding risk score can be set to 1.

[0073] Thus, the current state of the driver is analyzed based on the state data of the driver in different dimensions, so as to more accurately evaluate the current state, thereby improving the accuracy of generating the object level information.

[0074] In some alternative implementation manners of this embodiment, in response to determining that multiple object level information is generated, the object level information that meets the preset requirements among the multiple object level information is used as the final object level information.

[0075] In this implementation manner, if multiple object level information is generated, that is, the state data of the target object includes information in multiple dimensions, the above-mentioned execution entity will respectively generate an object level information according to the state data of each dimension, so as to obtain multiple object level information. At this time, the above-mentioned execution entity will use the object level information that meets the preset requirements among the multiple object level information as the final object level information. For example, the object level information with the highest level is used as the final object level information. Thus, the final object level information can more accurately reflect the current state of the driver.

[0076] For example, if the object level information generated according to the data of the eye dimension is a high level, and the object level information generated according to the data of the head dimension is a low level, then the final object level information is a high level.

[0077] Step 303, generate comprehensive level information based on the road condition level information and the object level information.

[0078] Step 304, determine the navigation voice parameters according to the comprehensive level information, and use the navigation voice parameters to broadcast the voice for navigation.

[0079] Steps 303-304 are basically the same as steps 203-204 of the foregoing embodiment, and the specific implementation manner can refer to the description of steps 203-204 above, and will not be elaborated here.

[0080] From Figure 3 it can be seen that compared with the Figure 2 corresponding embodiment, in the method for broadcasting the navigation voice in this embodiment, this method highlights the steps of determining the road condition level information and the object level information, so as to analyze the current road condition according to the road condition data in different dimensions, and analyze the current state of the driver according to the state data of the driver in different dimensions, so as to more accurately evaluate the current road condition and the current state of the driver, and improve the accuracy of the generated road condition level information and object level information.

[0081] Continue to refer to Figure 4 , Figure 4 shows the process 400 of the third embodiment of the navigation voice announcement method according to the present disclosure. The navigation voice announcement method includes the following steps:

[0082] Step 401, generating road condition level information based on the road condition data corresponding to the navigation path.

[0083] Step 402, generating object level information based on the status data of the target object.

[0084] Steps 401-402 are basically the same as steps 201-202 of the foregoing embodiment. The specific implementation manner can refer to the description of steps 201-202 above and will not be elaborated here.

[0085] Step 403, generating weather level information based on the weather data.

[0086] In this embodiment, the execution subject of the navigation voice announcement method (such as Figure 1 the server 105 shown) will generate weather level information according to the weather data. The weather data here can include meteorological data, such as rainfall, haze, low temperature and other data, and can also include the Air Quality Index (AQI) and visibility data, and can also include real-time temperature and humidity data, etc. The above execution subject will generate corresponding weather level information according to the obtained weather data.

[0087] In some optional implementation manners of this embodiment, the weather data includes: meteorological information, air quality information and / or temperature information; and step 403 includes: generating weather level information based on the meteorological information, air quality information and / or temperature information.

[0088] In this implementation manner, the weather data includes one or more of meteorological data, air quality information and temperature information. Therefore, the above execution subject can generate weather level information according to one or more of the obtained meteorological information, air quality information and temperature information.

[0089] In some optional implementation manners of this embodiment, generating weather level information based on the meteorological information, air quality information and / or temperature information includes: generating weather level information based on the meteorological information; and / or generating weather level information based on the air quality information and visibility; and / or generating weather level information based on the temperature information and humidity information.

[0090] In this implementation manner, the above-mentioned execution entity generates weather level information respectively according to one or more of meteorological information, air quality information, and temperature information, that is, generates weather level information according to meteorological information, and / or generates weather level information according to air quality information and visibility, and / or generates weather level information according to temperature information and humidity information.

[0091] Furthermore, the above-mentioned execution entity obtains weather information through a meteorological service, including conditions such as rainfall, haze, and low-temperature icing, and obtains the rainfall intensity (millimeters per hour): if the rainfall intensity < 2.5 mm / h, it is determined as light rain, and at this time, it is considered that there is no additional danger, and the risk score is set to 0; if the rainfall intensity is 2.5 - 7.5 mm / h, it is moderate rain, and at this time, there is a risk of slippery road surface, and it is considered that the weather level information at this time is medium risk, and the risk score is set to 0.5; if the rainfall intensity > 7.5 mm / h, it is heavy rain, and at this time, it is considered that the weather level information is high risk, and the risk score is set to 1; in addition, if the road slope > 5% and the rainfall intensity > 5 mm / h, there is a risk of water accumulation on the road, and at this time, it is considered that the weather level information is also high risk, and the risk score is set to 1.

[0092] The above-mentioned execution entity also obtains the air quality index and visibility (meters) from the API. If the visibility < 500 meters, it is considered that the weather level information is high risk, and the risk score is set to 1; if 500 meters ≤ visibility < 1000 meters, it is considered that the weather level information is medium risk, and the risk score is set to 0.5; if the air quality index > 200 (severe pollution), it is considered that the weather level information is medium risk, and the risk score is set to 0.5.

[0093] The above-mentioned execution entity also obtains the real-time temperature and humidity. When the temperature < 0°C and the humidity > 80%, it is marked as an icing risk, and it is considered that the weather level information is high risk, and the risk score is set to 1.

[0094] Thus, the current weather is analyzed based on weather data from different dimensions to more accurately evaluate the current weather risk, thereby improving the accuracy of generating weather level information.

[0095] In some optional implementation manners of this embodiment, in response to determining that multiple weather level information is generated, the weather level information that meets the preset requirements among the multiple weather level information is used as the final weather level information.

[0096] In this implementation, if multiple weather level information is generated, that is, the weather data includes information in multiple dimensions, the above-mentioned execution entity will generate a weather level information according to the weather data of each dimension respectively, so as to obtain multiple weather level information. At this time, the above-mentioned execution entity will use the weather level information that meets the preset requirements among the multiple weather level information as the final weather level information. For example, the weather level information with the highest level is used as the final weather level information. This makes the final weather level information more accurately reflect the current weather conditions.

[0097] For example, if the weather level information generated according to meteorological information is a high level, and the weather level information generated according to air quality information and visibility is a medium level, then the final weather level information is a high level.

[0098] Step 404: Generate comprehensive level information based on the road condition level information, object level information, and weather level information.

[0099] In this embodiment, the above-mentioned execution entity can generate comprehensive level information according to the road condition level information, object level information, and weather level information, that is, determine the current driving risk level (i.e., comprehensive level information) by integrating the current road condition information, driver status information, and weather information.

[0100] Thus, by integrating the current road condition information, driver status information, and weather information to determine the current driving risk level (i.e., comprehensive level information), the accuracy and comprehensiveness of the comprehensive level information are improved.

[0101] In an example, the level information with a high level among the road condition level information, object level information, and weather level information can be used as the comprehensive level information. For example, if the road condition level information is a high level, the object level information is a low level, and the weather level information is a medium level, then the comprehensive level information is a high level.

[0102] Step 405: Determine the navigation voice parameters according to the comprehensive level information, and use the navigation voice parameters to broadcast the voice for navigation.

[0103] Step 405 is basically the same as step 204 in the foregoing embodiment. The specific implementation method can refer to the description of step 204 above and will not be elaborated here.

[0104] From Figure 4 it can be seen that compared with the Figure 3 corresponding embodiment, in the navigation voice broadcast method in this embodiment, on the basis of considering the road condition information and driver status information, the weather information is also integrated, so as to integrate the current road condition information, driver status information, and weather information to determine the current driving risk level (i.e., comprehensive level information), improving the accuracy and comprehensiveness of the comprehensive level information.

[0105] Continue to refer to Figure 5 , Figure 5 which shows the flow 500 of the fourth embodiment of the navigation voice broadcast method according to the present disclosure. The navigation voice broadcast method includes the following steps:

[0106] Step 501, generating road condition level information based on the road condition data corresponding to the navigation path.

[0107] Step 502, generating object level information based on the status data of the target object.

[0108] Steps 501-502 are basically the same as steps 201-202 of the foregoing embodiment. The specific implementation manner can refer to the description of steps 201-202 above, and will not be elaborated here.

[0109] Step 503, generating vehicle level information based on vehicle sensing data.

[0110] In this embodiment, the execution subject of the navigation voice broadcast method (such as Figure 1 the server 105 shown) will generate vehicle level information according to the vehicle sensing data. The vehicle sensing data here is the data obtained through in-vehicle sensors, such as the distance between the obstacle in front of the vehicle and the own vehicle and the speed information of the obstacle obtained through radar and ultrasonic sensors, and for another example, the number of vehicles in the surrounding lanes detected by the radar sensor. The above execution subject will generate the corresponding vehicle level information according to the obtained sensor data.

[0111] In some alternative implementation manners of this embodiment, the vehicle sensing data includes: the number of obstacles, the distance between the obstacle and the vehicle, and / or the speed information of the obstacle; and step 503 includes: generating vehicle level information based on the number of obstacles, the distance between the obstacle and the vehicle, and / or the speed information of the obstacle.

[0112] In this implementation manner, the vehicle sensing data includes one or more of the number of obstacles around the own vehicle, the distance between the obstacle and the vehicle, and the speed information of the obstacle. Therefore, the above execution subject can generate vehicle level information according to one or more of the obtained number of obstacles, the distance between the obstacle and the vehicle, and the speed information of the obstacle.

[0113] In some alternative implementation manners of this embodiment, generating vehicle level information based on the number of obstacles, the distance between the obstacle and the vehicle, and / or the speed information of the obstacle includes: generating vehicle level information according to the distance between the obstacle and the vehicle and the speed information of the obstacle; and / or generating vehicle level information according to the number of obstacles and the information of the lane where the obstacle is located.

[0114] In this implementation manner, the above-mentioned execution entity generates vehicle level information respectively according to one or more of the number of obstacles, the distance between the obstacles and the vehicle, and the speed information of the obstacles, that is, generates vehicle level information according to the distance between the obstacles and the vehicle and the speed information of the obstacles, and / or generates vehicle level information according to the number of obstacles and the information of the lane where the obstacles are located.

[0115] Further, the above-mentioned execution entity obtains the distance (in meters) between the front obstacle detected by the radar and ultrasonic wave and the vehicle itself and the speed (m / s) of the obstacle. If the distance between the obstacle and the vehicle itself is < 20 meters and the relative speed > 5 m / s, it is considered that the vehicle level information is of high risk at this time, and the risk score is set to 1. Here, the relative speed is determined according to the speed of the obstacle and the speed of the vehicle itself. If the danger coefficient > 0.7, it is considered that the vehicle level information is of high risk at this time, and the risk score is set to 0.8. Here, the danger coefficient = 1 - (distance between the obstacle and the vehicle itself / safety distance), and the safety distance = current speed * reaction time (usually taken as 2 seconds).

[0116] The above-mentioned execution entity also obtains the number of vehicles in the surrounding lanes detected by the radar sensor. If in the surrounding lanes, the vehicle density per lane > 5 vehicles / 100 meters, which is regarded as high traffic flow, it is considered that the vehicle level information is of medium risk at this time, and the risk score is set to 0.8.

[0117] In addition, the above-mentioned execution entity also obtains the curvature of the road ahead detected by the camera or radar. If the curvature is greater than 0.3 (sharp turn) and the current speed of the vehicle itself > 40 km / h, it is considered that the vehicle level information is of high risk at this time, and the risk score is set to 1.

[0118] Thus, the risk situation of the current vehicle is analyzed according to vehicle sensor data in different dimensions to more accurately evaluate the risk of the current vehicle, thereby improving the accuracy of generating vehicle level information.

[0119] In some optional implementation manners of this embodiment, in response to determining that multiple vehicle level information is generated, the vehicle level information that meets the preset requirements among the multiple vehicle level information is used as the final vehicle level information.

[0120] If multiple vehicle level information is generated, that is, the vehicle data includes multiple sensor information, the above-mentioned execution entity generates a vehicle level information respectively according to the sensor data of each dimension, so as to obtain multiple vehicle level information. At this time, the above-mentioned execution entity will use the vehicle level information that meets the preset requirements among the multiple vehicle level information as the final vehicle level information. For example, the vehicle level information with the highest level is used as the final vehicle level information. Thus, the final vehicle level information can more accurately reflect the current vehicle risk situation.

[0121] For example, if the vehicle level information generated based on the distance between the obstacle and the vehicle and the speed information of the obstacle is a high level, and the vehicle level information generated based on the number of obstacles and the information of the lane where the obstacle is located is a medium level, then the final vehicle level information is a high level.

[0122] Step 504: Generate comprehensive level information based on the road condition level information, object level information, and vehicle level information.

[0123] In this embodiment, the above-mentioned execution entity can generate comprehensive level information according to the road condition level information, object level information, and vehicle level information, that is, determine the current driving risk level, namely the comprehensive level information, by integrating the current road condition information, driver status information, and vehicle information.

[0124] Thus, by integrating the current road condition information, driver status information, and vehicle sensing information to determine the current driving risk level (i.e., the comprehensive level information), the accuracy and comprehensiveness of the comprehensive level information are improved.

[0125] In an example, the level information with a higher level among the road condition level information, object level information, and vehicle level information can be used as the comprehensive level information. For example, if the road condition level information is a high level, the object level information is a low level, and the vehicle level information is a medium level, then the comprehensive level information is a high level.

[0126] Step 505: Determine the navigation voice parameters according to the comprehensive level information, and use the navigation voice parameters to broadcast the voice for navigation.

[0127] Step 505 is basically the same as step 204 in the foregoing embodiment, and the specific implementation manner can refer to the description of step 204 above, which will not be elaborated here.

[0128] From Figure 5 it can be seen that compared with the Figure 3 corresponding embodiment, in the navigation voice broadcast method in this embodiment, on the basis of considering the road condition information and driver status information, the vehicle sensor information is also integrated, so as to integrate the current road condition information, driver status information, and vehicle obstacle information to determine the current driving risk level (i.e., the comprehensive level information), improving the accuracy and comprehensiveness of the comprehensive level information.

[0129] Continue to refer to Figure 6 , Figure 6 shows the flow 600 of the fifth embodiment of the navigation voice broadcast method according to the present disclosure. The navigation voice broadcast method includes the following steps:

[0130] Step 601: Generate road condition level information based on the road condition data corresponding to the navigation path.

[0131] Step 602: Generate object level information based on the status data of the target object.

[0132] Step 603: Generate weather level information based on the weather data.

[0133] Steps 601 - 603 are basically the same as steps 401 - 403 of the foregoing embodiment. The specific implementation manners can refer to the description of steps 401 - 403 above, and will not be elaborated here.

[0134] Step 604: Generate vehicle level information based on the vehicle sensing data.

[0135] Step 604 is basically the same as step 503 of the foregoing embodiment. The specific implementation manners can refer to the description of step 503 above, and will not be elaborated here.

[0136] Step 605: Generate comprehensive level information based on the road condition level information, object level information, weather level information, and vehicle level information.

[0137] In this embodiment, the execution subject of the navigation voice broadcast method (such as Figure 1 the server 105 shown) can generate comprehensive level information according to the road condition level information, object level information, weather level information, and vehicle level information, that is, determine the current driving risk level, i.e., the comprehensive level information, by integrating the current road condition information, driver status information, current weather information, and vehicle sensor information.

[0138] Thus, by integrating the current road condition information, driver status information, current weather information, and vehicle sensing information to determine the current driving risk level (i.e., the comprehensive level information), the accuracy and comprehensiveness of the comprehensive level information are improved.

[0139] In one example, the level information with a higher level among the road condition level information, object level information, weather level information, and vehicle level information can be used as the comprehensive level information. For example, if the road condition level information is at a high level, the object level information is at a low level, the weather level information is at a low level, and the vehicle level information is at a medium level, then the comprehensive level information is at a high level.

[0140] In some optional implementation manners of this embodiment, step 605 includes: determining a first weight corresponding to the road condition level information, a second weight corresponding to the object level information, a third weight corresponding to the weather level information, and a fourth weight corresponding to the vehicle level information; performing weighted calculation using the road condition level information and the first weight, the object level information and the second weight, the weather level information and the third weight, and the vehicle level information and the fourth weight, and taking the calculation result as the comprehensive level information.

[0141] In this implementation manner, the above-mentioned execution entity will set weights for each level of information respectively. For example, set the weight of the road condition level information, that is, the first weight is 0.3; the weight of the object level information, that is, the second weight is 0.2; the weight of the weather level information, that is, the third weight is 0.3; the weight of the vehicle level information, that is, the fourth weight is 0.2. Then, perform weighted calculation based on the road condition level information and the first weight, the object level information and the second weight, the weather level information and the third weight, and the vehicle level information and the fourth weight, and use the calculation result as the comprehensive level information.

[0142] That is, obtain the score corresponding to each level of information, and then through weighted calculation based on their respective corresponding weights, obtain the final comprehensive level information.

[0143] For example, the score of the road condition level information is 0.8, its corresponding first weight is 0.3, the score of the object level information is 1 point, its corresponding second weight is 0.2, the score of the weather level information is 0.5, its corresponding third weight is 0.3, and the score of the vehicle level information is 0.8, its corresponding fourth weight is 0.2. Through weighted calculation, the comprehensive score M can be obtained, M = 0.8 * 0.3 + 1 * 0.2 + 0.5 * 0.3 + 0.8 * 0.2, that is, the comprehensive score M is 0.75.

[0144] Finally, determine the corresponding comprehensive level information according to the comprehensive score. Here, it can be preset that when the comprehensive score is between 0 - 0.4, it is a low risk; when the comprehensive score is between 0.4 - 0.52, it is a medium risk; when the comprehensive score is between 0.52 - 1, it is a high risk; that is, it is determined that the comprehensive score 0.75 corresponds to a high risk.

[0145] Of course, the numerical ranges of different risks can be set according to the actual situation, and this embodiment does not make specific limitations on this.

[0146] It should be noted that if a certain level of information has no score (such as no risk), then set its corresponding risk score to 0.4.

[0147] Thus, by setting corresponding weights for each level of information and determining the final comprehensive score through weighted calculation, the comprehensiveness and accuracy of the determined comprehensive score (comprehensive level information) are further improved.

[0148] Step 606, determine the navigation voice parameters corresponding to the comprehensive level information from the preset comparison table, and use the navigation voice parameters to broadcast the voice for navigation.

[0149] In this embodiment, the above-mentioned execution entity determines the navigation voice parameters corresponding to the comprehensive level information from a preset comparison table, where the navigation voice parameters include: volume, speech rate, pitch, and / or tone. Among them, the preset comparison table shows the volume level, speech rate, pitch, and tone corresponding to different risk levels. The preset comparison table can be referred to as shown in Table 1:

[0150] Table 1 Preset Comparison Table

[0151] Risk level Volume level Speech rate Pitch Tone Low risk 30％ 1.0 times Normal Gentle and steady Medium risk 60％ 1.2 times Slightly higher Attention High risk 100％ 1.5 times High Serious and urgent

[0152] Therefore, after determining the comprehensive level information, the voice parameters such as the volume level, speech rate, pitch, and tone corresponding to the comprehensive level information can be determined through the preset comparison table, and the navigation voice can be broadcast according to this parameter, so that the user can perceive the current driving risk according to the broadcast voice, thereby improving driving safety and also improving the user's navigation experience.

[0153] In some optional implementation manners of this embodiment, the above method further includes: in response to the navigation path indicating that the vehicle changes the driving direction, determining the target driving direction of the vehicle; determining the target speaker corresponding to the target driving direction from multiple speakers in the vehicle; using the target speaker to broadcast the voice for indicating the driving direction of the vehicle.

[0154] In this implementation manner, the above-mentioned execution entity will automatically detect the positions of the speakers in the vehicle in advance (such as the left front, right front, left rear, and right rear), map them to the navigation directions (left, right, front, and rear), and save the volume and direction parameters of each speaker in the system for subsequent dynamic adjustment.

[0155] Then, when the navigation route interface shows that the vehicle is about to change the driving direction, determine the target driving direction of the vehicle. For example, if the navigation shows that the vehicle is about to turn right, then the target driving direction can be determined as the right side. Then determine the target speaker corresponding to the target driving direction from multiple speakers in the vehicle, that is, determine the target speaker for the navigation right direction from the system, and finally use the target speaker to broadcast the voice for indicating the driving direction of the vehicle.

[0156] In an example, when the navigation prompts "turn right", the target driving direction is right, and the target speaker is the right front speaker. That is, at this time, the navigation voice is mainly played through the right front speaker, and at the same time, the volume of the left and rear speakers is reduced, so as to simulate the effect of the sound coming from the right side.

[0157] In an example, when the navigation prompts "continuous left turn", the target driving direction is left, and the target speaker is the left speaker. Then the voice will start from the left speaker and gradually switch to the right front speaker, simulating the effect of the sound moving, and helping the driver quickly perceive the direction change.

[0158] In addition, at complex intersections, the above-mentioned execution entity will also dynamically adjust the volume according to the vehicle driving trajectory. For example, when turning right after going straight, the voice prompt will gradually switch from the front speaker to the right speaker, accompanied by a gradual change in volume, so that the driver can intuitively perceive the direction change.

[0159] Thus, by utilizing the spatial positioning ability of the in-vehicle speakers, the voice broadcast is combined with the actual orientation to establish an intuitive spatial perception for the driver, further enhancing the user's driving experience. Especially in the case of multi-lane or intersections, the spatial perception experience of the driver is improved.

[0160] From Figure 6 it can be seen that compared with the corresponding embodiment of Figure 4 in this embodiment, the method for broadcasting navigation voice takes into account road condition information, driver status information, and weather information, and also integrates vehicle sensor data, so as to comprehensively determine the current driving risk level (i.e., comprehensive level information) based on the current road condition information, driver status information, weather information, and vehicle information, improving the accuracy and comprehensiveness of the comprehensive level information. In addition, by utilizing the spatial positioning ability of the in-vehicle speakers, the voice broadcast is combined with the actual orientation to establish an intuitive spatial perception for the driver, further enhancing the user's driving experience. Especially in the case of multi-lane or intersections, the spatial perception experience of the driver is improved.

[0161] Further referring to Figure 7 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for broadcasting navigation voice. This device embodiment corresponds to the method embodiment shown in Figure 2 and can be specifically applied to various electronic devices.

[0162] As Figure 7 shown, the navigation voice broadcast device 700 of this embodiment includes: a road condition information generation module 701, an object information generation module 702, a comprehensive information generation module 703, and a parameter information generation module 704. Among them, the road condition information generation module 701 is configured to generate road condition level information based on the road condition data corresponding to the navigation path; the object information generation module 702 is configured to generate object level information based on the status data of the target object; the comprehensive information generation module 703 is configured to generate comprehensive level information based on the road condition level information and the object level information; the parameter information generation module 704 is configured to determine navigation voice parameters according to the comprehensive level information and use the navigation voice parameters to broadcast the voice for navigation.

[0163] In this embodiment, in the navigation voice broadcast device 700: For the specific processing of the traffic condition information generation module 701, the object information generation module 702, the comprehensive information generation module 703, and the parameter information generation module 704 and the technical effects brought thereby, reference can be made respectively to Figure 2 the relevant descriptions of steps 201-204 in the corresponding embodiment, which will not be elaborated here.

[0164] In some alternative implementation manners of this embodiment, the traffic condition data includes: traffic flow, road fault information, and / or road feature information; and the traffic condition information generation module includes: a traffic condition information generation sub-module configured to generate traffic condition level information based on the traffic flow, road fault information, and / or road feature information; the above navigation voice broadcast device 700 further includes: a first determination module configured to, in response to determining that a plurality of traffic condition level information is generated, use the target traffic condition level information that meets the preset requirements among the plurality of traffic condition level information as the final traffic condition level information.

[0165] In some alternative implementation manners of this embodiment, the above navigation voice broadcast device 700 further includes: a second determination module configured to, in response to determining that the number of the target traffic condition level information is multiple, perform a weighted calculation on the multiple target traffic condition level information, and use the calculation result as the final traffic condition level information.

[0166] In some alternative implementation manners of this embodiment, the traffic condition information generation sub-module includes: a first generation unit configured to generate traffic condition level information based on the traffic flow and the driving speed of the vehicle; and / or a second generation unit configured to generate traffic condition level information based on the road fault information and the position information of the vehicle; and / or a third generation unit configured to generate traffic condition level information based on the road feature information, where the road feature information includes: road curvature, road slope, and / or the density of road forks.

[0167] In some alternative implementation manners of this embodiment, the first generation unit is further configured to: determine whether the vehicle density on the road is high density according to the traffic flow and a preset flow threshold; in response to determining that the vehicle density is high density, determine the congestion degree of the road according to the driving speed and the speed limit value of the road; and determine the traffic condition level information of the road according to the congestion degree.

[0168] In some alternative implementation manners of the present embodiment, the status data includes: eye data and / or head pose data; and the object information generation module includes: an object information generation sub-module configured to generate object level information based on the eye data and / or the head pose data; the above navigation voice broadcast device 700 further includes: a third determination module configured to, in response to determining that multiple object level information is generated, use the object level information that meets the preset requirements among the multiple object level information as the final object level information.

[0169] In some alternative implementation manners of the present embodiment, the object information generation sub-module is further configured to: generate object level information based on the eye data, where the eye data includes: blink frequency, eye closure duration, and / or line of sight direction; and / or generate object level information based on the head pose data, where the head pose data includes the head drooping angle.

[0170] In some alternative implementation manners of the present embodiment, the above navigation voice broadcast device 700 further includes: a weather information generation module configured to generate weather level information based on weather data; and the comprehensive information generation module includes: a first comprehensive information generation sub-module configured to generate comprehensive level information based on the road condition level information, the object level information, and the weather level information.

[0171] In some alternative implementation manners of the present embodiment, the weather data includes: meteorological information, air quality information, and / or temperature information; and the weather information generation module includes: a weather information generation sub-module configured to generate weather level information based on the meteorological information, the air quality information, and / or the temperature information; the above navigation voice broadcast device 700 further includes: a fourth determination module configured to, in response to determining that multiple weather level information is generated, use the weather level information that meets the preset requirements among the multiple weather level information as the final weather level information.

[0172] In some alternative implementation manners of the present embodiment, the weather information generation sub-module is further configured to: generate weather level information based on the meteorological information; and / or generate the weather level information based on the air quality information and visibility; and / or generate the weather level information based on the temperature information and humidity information.

[0173] In some alternative implementation manners of the present embodiment, the above navigation voice broadcast device 700 further includes: a vehicle information generation module configured to generate vehicle level information based on vehicle sensing data; and the comprehensive information generation module includes: a second comprehensive information generation sub-module configured to generate comprehensive level information based on the road condition level information, the object level information, and the vehicle level information.

[0174] In some alternative implementation manners of this embodiment, the vehicle sensing data includes: the number of obstacles, the distance between the obstacles and the vehicle, and / or the speed information of the obstacles; and the vehicle information generation module includes: a vehicle information generation sub-module configured to generate vehicle level information based on the number of obstacles, the distance between the obstacles and the vehicle, and / or the speed information of the obstacles; the above navigation voice broadcast device 700 further includes: a fifth determination module configured to, in response to determining the generation of the vehicle level information, use the vehicle level information that meets the preset requirements among the multiple vehicle level information as the final vehicle level information.

[0175] In some alternative implementation manners of this embodiment, the vehicle information generation sub-module is further configured to: generate vehicle level information according to the distance between the obstacles and the vehicle and the speed information of the obstacles; and / or generate vehicle level information according to the number of obstacles and the information of the lane where the obstacles are located.

[0176] In some alternative implementation manners of this embodiment, the above navigation voice broadcast device 700 further includes: a vehicle information generation module configured to generate vehicle level information based on the vehicle sensing data; and the comprehensive information generation module includes: a third comprehensive information generation sub-module configured to generate comprehensive level information based on the road condition level information, the object level information, the weather level information, and the vehicle level information.

[0177] In some alternative implementation manners of this embodiment, the third comprehensive information generation sub-module is further configured to: determine a first weight corresponding to the road condition level information, a second weight corresponding to the object level information, a third weight corresponding to the weather level information, and a fourth weight corresponding to the vehicle level information; perform weighted calculation using the road condition level information and the first weight, the object level information and the second weight, the weather level information and the third weight, and the vehicle level information and the fourth weight, and use the calculation result as the comprehensive level information.

[0178] In some alternative implementation manners of this embodiment, the parameter information generation module is further configured to: determine the navigation voice parameters corresponding to the comprehensive level information from a preset comparison table, where the navigation voice parameters include: volume, speech rate, pitch, and / or tone.

[0179] In some alternative implementation manners of this embodiment, the above navigation voice broadcast device 700 further includes: a broadcast module configured to, in response to the navigation path indicating that the vehicle changes its driving direction, determine the target driving direction of the vehicle; determine the target speaker corresponding to the target driving direction from multiple speakers in the vehicle; and use the target speaker to broadcast the voice for indicating the driving direction of the vehicle.

[0180] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0181] Figure 8 FIG. shows a schematic block diagram of an exemplary electronic device 800 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0182] As Figure 8 shown, the device 800 includes a computing unit 801 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0183] Multiple components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0184] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above, such as the method for playing navigation voice. For example, in some embodiments, the method for playing navigation voice can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for playing navigation voice described above can be executed. Alternatively, in other embodiments, the computing unit 801 can be configured to execute the method for playing navigation voice in any other suitable manner (e.g., by means of firmware).

[0185] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0186] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0187] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] For purposes of providing an interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0189] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of a communication network include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0190] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0191] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0192] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for broadcasting navigation voice, comprising: Generate traffic level information based on traffic data corresponding to the navigation path; generating object level information based on the state data of the target object; generating comprehensive level information based on the road level information and the object level information; Navigation voice parameters are determined according to the comprehensive level information, and the navigation voice parameters are used to broadcast the voice used for navigation.

2. The method according to claim 1, wherein: The road condition data includes: vehicle flow, road fault information and / or road feature information; and The generating of the traffic level information based on the traffic data corresponding to the navigation path includes: Generate the road condition level information based on the vehicle flow, the road fault information and / or the road feature information; The method further comprises: In response to determining that a plurality of the road condition level information are generated, target road condition level information that meets preset requirements among the plurality of the road condition level information is used as final road condition level information.

3. The method according to claim 2, further comprising: In response to determining that the number of the target road condition level information is multiple, weighted calculation is performed on the multiple target road condition level information, and the calculation result is used as the final road condition level information.

4. The method according to claim 2, wherein: The generating the road condition level information based on the vehicle flow, the road fault information and / or the road feature information includes: Generating the road condition level information based on the vehicle flow and the vehicle's driving speed; and / or Generate the road condition level information based on the road fault information and the vehicle location information; and / or The road condition level information is generated based on the road characteristic information, wherein the road characteristic information includes: road curvature, road slope and / or density of road forks.

5. The method according to claim 4, wherein: The generating the road condition level information based on the vehicle flow and the vehicle driving speed includes: Determining whether the vehicle density of the road is high density according to the vehicle flow and a preset flow threshold; In response to determining that the vehicle density is the high density, determining a congestion degree of the road according to the driving speed and a speed limit value of the road; The traffic level information of the road is determined according to the congestion level.

6. The method according to claim 1, wherein: The state data includes: eye data and / or head posture data; and The generating object level information based on the state data of the target object includes: generating the object level information based on the eye data and / or the head posture data; The method further comprises: In response to determining that a plurality of the object level information are generated, the object level information that meets a preset requirement among the plurality of the object level information is used as the final object level information.

7. The method according to claim 6, wherein: The generating the object level information based on the eye data and / or the head posture data comprises: Generating the object level information based on the eye data, wherein the eye data includes: blink frequency, duration of eye closure and / or gaze direction; and / or The object level information is generated based on the head posture data, wherein the head posture data includes a head drooping angle.

8. The method according to claim 1, further comprising: Generate weather level information based on weather data; as well as The generating comprehensive level information based on the road condition level information and the object level information includes: The comprehensive level information is generated based on the road condition level information, the object level information and the weather level information.

9. The method according to claim 8, wherein: The weather data includes: meteorological information, air quality information and / or temperature information; and The generating weather level information based on weather data includes: generating the weather level information based on the meteorological information, the air quality information and / or the temperature information; The method further comprises: In response to determining that a plurality of weather level information are generated, the weather level information that meets preset requirements among the plurality of weather level information is used as final weather level information.

10. The method according to claim 9, wherein: The generating the weather level information based on the meteorological information, the air quality information and / or the temperature information comprises: Generate the weather level information based on the meteorological information; and / or generating the weather level information based on the air quality information and visibility; and / or The weather level information is generated based on the temperature information and the humidity information.

11. The method according to claim 1, further comprising: generating vehicle class information based on vehicle sensing data; as well as The generating comprehensive level information based on the road condition level information and the object level information includes: The integrated level information is generated based on the road condition level information, the object level information and the vehicle level information.

12. The method according to claim 11, wherein: The vehicle sensing data includes: the number of obstacles, the distance between the obstacles and the vehicle and / or the speed information of the obstacles; and The generating of vehicle grade information based on vehicle sensing data comprises: generating the vehicle level information based on the number of obstacles, the distance between the obstacles and the vehicle, and / or the speed information of the obstacles; The method further comprises: In response to determining that a plurality of vehicle grade information are generated, the vehicle grade information that meets a preset requirement among the plurality of vehicle grade information is used as the final vehicle grade information.

13. The method according to claim 12, wherein: The generating the vehicle level information based on the number of obstacles, the distance between the obstacle and the vehicle and / or the speed information of the obstacle includes: generating the vehicle level information according to the distance between the obstacle and the vehicle and the speed information of the obstacle; and / or The vehicle level information is generated according to the number of obstacles and information about the lanes where the obstacles are located.

14. The method according to claim 8, further comprising: generating vehicle class information based on vehicle sensing data; as well as The generating of comprehensive level information based on the road level information and the object level information further includes: The integrated level information is generated based on the road level information, the object level information, the weather level information, and the vehicle level information.

15. The method according to claim 14, wherein: The generating the comprehensive level information based on the road condition level information, the object level information, the weather level information and the vehicle level information comprises: Determine a first weight corresponding to the road condition level information, a second weight corresponding to the object level information, a third weight corresponding to the weather level information, and a fourth weight corresponding to the vehicle level information; A weighted calculation is performed using the road condition level information and the first weight, the object level information and the second weight, the weather level information and the third weight, and the vehicle level information and the fourth weight, and the calculation result is used as the comprehensive level information.

16. The method according to any one of claims 1 to 15, wherein: The determining of the navigation voice parameters according to the comprehensive level information includes: The navigation voice parameters corresponding to the comprehensive level information are determined from a preset comparison table, wherein the navigation voice parameters include: volume, speech speed, pitch and / or tone.

17. The method according to claim 16, further comprising: In response to the navigation path indicating that the vehicle changes its driving direction, determining a target driving direction of the vehicle; determining a target speaker corresponding to the target driving direction from a plurality of speakers in the vehicle; The target loudspeaker is used to broadcast a voice indicating the driving direction of the vehicle.

18. A navigation voice broadcasting device, comprising: A traffic information generation module is configured to generate traffic level information based on traffic data corresponding to the navigation path; an object information generation module configured to generate object level information based on the state data of the target object; a comprehensive information generating module, configured to generate comprehensive level information based on the road level information and the object level information; The parameter information generating module is configured to determine the navigation voice parameters according to the comprehensive level information, and broadcast the voice used for navigation using the navigation voice parameters.

19. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 17.

20. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method of any one of claims 1-17.

21. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 17.

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