Navigation information generation method and device, equipment and storage medium

Abstract

The invention provides a navigation information generation method, and relates to the technical field of artificial intelligence, in particular to the technical fields of map navigation, automatic driving, intelligent transportation and the like. The method comprises the steps of determining whether an illumination intensity value in front of a target vehicle is within a preset light intensity range or not; in response to determining that the illumination intensity value is within the preset light intensity range, acquiring point cloud data of a road in front of the target vehicle by using a radar sensor on the target vehicle; and generating local road condition information according to the road point cloud data, and overlapping and displaying the local road condition information on global navigation information displayed by the target vehicle. According to the method, the road condition in front of the vehicle at night is reconstructed by using the point cloud data collected by the radar sensor, and driving safety is guaranteed.

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 more particularly to a method, apparatus, device, and storage medium for generating navigation information. Background Art

[0002] When a vehicle is driving at night, if the oncoming vehicle does not switch to low beam, it will cause severe dazzle to the driver, making it impossible for him to see the road conditions ahead and make timely judgments based on the road conditions, which will seriously affect driving safety. Summary of the Invention

[0003] The present disclosure provides a method, apparatus, device and storage medium for generating navigation information.

[0004] According to a first aspect of the present disclosure, a method for generating navigation information is provided, including: determining whether a light intensity value in front of a target vehicle is within a preset light intensity range; in response to determining that the light intensity value is within the preset light intensity range, using a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle; generating local road condition information based on the road point cloud data, and superimposing the local road condition information on the global navigation information displayed by the target vehicle.

[0005] According to a second aspect of the present disclosure, a navigation information generating device is provided, including: a determination module, configured to determine whether the light intensity value in front of a target vehicle is within a preset light intensity range; a collection module, configured to, in response to determining that the light intensity value is within the preset light intensity range, collect road point cloud data in front of the target vehicle using a radar sensor on the target vehicle; and a display module, configured to generate local road condition information based on the road point cloud data, and superimpose the local road condition information on the global navigation information displayed on the target vehicle.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising: 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 in the first aspect.

[0007] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause 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, a computer program product is provided, comprising a computer program, which implements the method described in any implementation manner of the first aspect when executed by a processor.

[0009] According to a sixth aspect of the present disclosure, a vehicle is provided, comprising: at least one light intensity sensor for collecting light intensity values ​​in front of a target vehicle; at least one radar sensor for collecting road point cloud data in front of the target vehicle; and the electronic device as described in the third aspect.

[0010] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure. Figure 1 is an exemplary system architecture diagram in which the present disclosure may be applied; Figure 2 is a flowchart of a first embodiment of a method for generating navigation information according to the present disclosure; Figure 3 is a flow chart of a second embodiment of a method for generating navigation information according to the present disclosure; Figure 4 is a flowchart of a third embodiment of a method for generating navigation information according to the present disclosure; Figure 5 is a flowchart of a fourth embodiment of a method for generating navigation information according to the present disclosure; Figure 6 is a flowchart of a fifth embodiment of the method for generating navigation information according to the present disclosure; Figure 7 is a flowchart of a sixth embodiment of the method for generating navigation information according to the present disclosure; Figure 8-1 This is a schematic diagram of the display style of local road condition information when there are no obstacles; Figure 8-2 It is a schematic diagram of the display style of local road condition information when there are obstacles; Figure 9 is a structural diagram of an embodiment of a device for generating navigation information according to the present disclosure; Figure 10 It is a block diagram of an electronic device used to implement the method for generating navigation information according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0013] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0014] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of the method or apparatus for generating navigation information disclosed herein may be applied.

[0015] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, and 104, a network 105, and a server 106. Network 105 is a medium for providing communication links between terminal devices 101, 102, 103, and 104 and server 106. Network 105 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0016] Users can use terminal devices 101, 102, 103, 104 to interact with server 106 via network 105 to receive or send information, etc. Various client applications can be installed on terminal devices 101, 102, 103, 104.

[0017] Terminal devices 101, 102, 103, and 104 can be hardware or software. When terminal devices 101, 102, 103, and 104 are hardware, they can be various electronic devices, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers. When terminal devices 101, 102, 103, and 104 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules, or as a single software program or software module. This is not specifically limited here.

[0018] The server 106 may provide various services. For example, the server 106 may analyze and process the light intensity values ​​in front of the target vehicle obtained from the terminal devices 101, 102, 103, and 104, and generate processing results (e.g., generating local road condition information and overlaying the local road condition information on the global navigation information displayed by the target vehicle).

[0019] It should be noted that server 106 can be either hardware or software. When server 106 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 106 is software, it can be implemented as multiple software programs or software modules (for example, to provide distributed services), or as a single software program or software module. This is not specifically limited here.

[0020] It should be noted that the method for generating navigation information provided in the embodiment of the present disclosure is generally executed by the server 106 , and accordingly, the device for generating navigation information is generally provided in the server 106 .

[0021] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0022] Continue to refer Figure 2 , which shows a process 200 of a first embodiment of a method for generating navigation information according to the present disclosure. The method for generating navigation information includes the following steps: Step 201: Determine whether the light intensity value in front of the target vehicle is within a preset light intensity range.

[0023] In this embodiment, the execution subject of the method for generating navigation information (eg Figure 1 The server 106 shown first obtains the light intensity value in front of the target vehicle and determines whether the light intensity value is within a preset light intensity range.

[0024] Specifically, one or more light intensity sensors can be pre-installed on the target vehicle. High-sensitivity photoelectric sensors, such as photodiodes or CMOS (Complementary Metal Oxide Semiconductor) sensors, are typically used. Three to five light intensity sensors are typically installed. The target vehicle in this case is the ego vehicle. The light intensity sensors can be placed above the vehicle's windshield in an array layout, with evenly spaced horizontal arrangements (for example, along the instrument panel on the windshield), or in a triangular arrangement, providing more accurate direction detection. The light intensity sensors are typically spaced 10-20 cm apart, and the sampling frequency is typically set to 100 Hz (Hertz) to ensure real-time response to changes in light intensity. The execution entity obtains the light intensity values ​​in front of the target vehicle, as captured by the light intensity sensors.

[0025] Experimental measurements have shown that in normal nighttime driving conditions, the light intensity ahead of a vehicle typically ranges from 0.5 to 50 lux. However, when oncoming vehicles use their high beams, the light intensity can reach 300 to 500 lux. Based on this, a preset light intensity range of 250 to 550 lux is pre-set, meaning the glare threshold G is set to 250 lux based on practical experience. Since the maximum light intensity from oncoming vehicles is generally 500 lux, the maximum value of this light intensity range is set to 550 lux. After obtaining the light intensity value ahead of the target vehicle, the aforementioned execution entity determines whether the light intensity value falls within the preset light intensity range, i.e., whether the light intensity value falls within the range of 250 to 550 lux. Of course, the preset light intensity range and glare threshold can be set to other values ​​depending on actual circumstances, and this embodiment does not impose specific limitations on this.

[0026] It should be noted that glare refers to the phenomenon where strong or high-brightness light directly enters the field of vision, or interferes with visual comfort through reflection or scattering. It can lead to decreased visual clarity, eye discomfort, and even temporary blindness or safety hazards. It is commonly seen in scenes such as direct sunlight, nighttime car lights, and screen reflections.

[0027] Step 202 : In response to determining that the light intensity value is within a preset light intensity range, using a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle.

[0028] In this embodiment, if the light intensity value is determined to be within a preset light intensity range, the execution entity will use the radar sensor installed on the target vehicle to collect point cloud data of the road ahead of the target vehicle. For example, if the light intensity sensor detects a light intensity value of 320 lx for a preset duration (e.g., 200 milliseconds), the radar sensor will be activated and used to collect point cloud data of the road ahead of the target vehicle. The radar sensor is typically a millimeter-wave radar sensor.

[0029] If the light intensity value is within the preset light intensity range, the radar sensor activates and scans the road ahead of the vehicle at a sampling frequency of 50Hz to generate initial point cloud data. In addition, the above-mentioned execution entity also uses a point cloud filtering algorithm to remove interference noise from the initial point cloud data, thereby improving data accuracy.

[0030] In addition, the above-mentioned execution entity will also obtain data from the inertial measurement unit (IMU) on the target vehicle, namely IMU data, and fuse the initial point cloud data with the IMU data to obtain road point cloud data, thereby improving the stability of the radar point cloud and ensuring that the road can still be accurately reconstructed under bumpy road conditions.

[0031] Step 203 : generating local road condition information based on the road point cloud data, and overlaying the local road condition information on the global navigation information displayed by the target vehicle.

[0032] In this embodiment, the execution entity generates local road condition information based on the collected road point cloud data. Specifically, the execution entity converts the collected radar data (i.e., road point cloud data) into high-precision 2D (two-dimensional) or 3D (three-dimensional) views using deep learning algorithms (such as the PointNet++ point cloud processing model or the VoxelNet point cloud-based 3D detection network). This information is then combined with existing high-precision map data to generate local road condition information. This local road condition information refers to a view of the road ahead of the target vehicle. Finally, the execution entity overlays the generated local road condition view on the target vehicle's in-vehicle map.

[0033] Since the navigation information displayed on the in-vehicle map is generally global, it will display the complete geographic information near the vehicle. However, the navigation information displayed on the in-vehicle map is generally generated based on pre-collected map data. It can display the complete navigation route, but it cannot display the real-time data of the route, or in other words, it cannot display the real-time data at each moment on the navigation route. In particular, when the driver is temporarily blinded by the oncoming vehicle without switching on the low beam, the driver cannot obtain the real-time traffic information ahead from the navigation information displayed on the in-vehicle map. At this time, the above-mentioned execution subject generates real-time local traffic information through the aforementioned steps, and superimposes the local traffic information on the navigation information on the in-vehicle map, so that the driver can obtain the traffic information ahead in a timely manner in the case of glare, thereby ensuring driving safety.

[0034] In addition, the above-mentioned execution entity will also display information such as whether there are obstacles blocking the road ahead on the view. If there are no obstacles blocking the road ahead, the road ahead information will be directly drawn and displayed, prompting you to drive slowly; if there are obstacles ahead, a corresponding prompt warning message will be generated and highlighted to achieve the purpose of warning.

[0035] The method for generating navigation information provided in the disclosed embodiment first determines whether the light intensity value in front of a target vehicle is within a preset light intensity range; then, in response to determining that the light intensity value is within the preset light intensity range, uses a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle; finally, generates local road condition information based on the road point cloud data, and overlays the local road condition information on the global navigation information displayed by the target vehicle. The method for generating navigation information in this embodiment uses point cloud data collected by the radar sensor to reconstruct the road conditions ahead at night, thereby providing the driver with clear and visual road information even in the presence of high beam interference, thereby ensuring the timeliness and accuracy of navigation information and further ensuring driving safety.

[0036] In addition, in the technical solutions involved in this disclosure, the acquisition, storage, use, processing, transportation, provision and disclosure of the user personal information involved (such as the light intensity value in front of the target vehicle involved in this disclosure and the global navigation information displayed by the target vehicle) are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0037] Continue to refer Figure 3 , Figure 3 The flowchart 300 of the second embodiment of the method for generating navigation information according to the present disclosure is shown. The method for generating navigation information includes the following steps: Step 301: Obtain the current speed value of the target vehicle and determine whether the current speed value is within a preset vehicle speed range.

[0038] In this embodiment, the execution subject of the method for generating navigation information (eg Figure 1 The server 106 shown in the figure obtains the current speed of the target vehicle and determines whether the current speed is within a preset speed range. This speed range is pre-set: a speed between 0 and 30 km / h (kilometers per hour) is considered low speed, and a speed above 60 km / h is considered high speed. After obtaining the current speed of the target vehicle, the execution entity determines whether the current speed is within these two speed ranges, i.e., whether the vehicle is currently traveling at a low or high speed. If the speed is between 30 km / h and 60 km / h, the vehicle is considered to be at a normal speed.

[0039] Step 302 : Determine whether the light intensity value in front of the target vehicle is within a preset light intensity range corresponding to the current speed value.

[0040] In this embodiment, the preset light intensity range is set for vehicles traveling at normal speeds (i.e., 30 km / h-60 km / h). Different preset light intensity ranges are set for low and high speeds. Considering that urban streetlights also generate a certain amount of light intensity during nighttime driving, a higher light intensity threshold is allowed at low speeds to avoid interference from urban streetlights. For example, the preset light intensity range is set to 350 lx-550 lx at low speeds. At high speeds, the light intensity threshold is lowered to improve detection sensitivity. For example, the preset light intensity range is set to 200 lx-550 lx at high speeds.

[0041] After obtaining the current speed of the vehicle, the execution entity will determine the speed range corresponding to the current speed and determine the light intensity range corresponding to the speed range.

[0042] Step 303 : In response to determining that the current speed value is within a preset vehicle speed range and the light intensity value is within a preset light intensity range corresponding to the current speed value, a radar sensor on the target vehicle is used to collect road point cloud data in front of the target vehicle.

[0043] In this embodiment, if it is determined that the current speed value is within the preset vehicle speed range and the light intensity value is within the preset light intensity range corresponding to the current speed value, the above-mentioned execution entity will activate the radar sensor on the target vehicle and use the radar sensor to collect road point cloud data in front of the target vehicle.

[0044] Step 304 : Generate local road condition information based on the road point cloud data, and overlay the local road condition information on the global navigation information displayed by the target vehicle.

[0045] Step 304 is basically the same as step 203 in the aforementioned embodiment. For the specific implementation method, reference can be made to the aforementioned description of step 203 and will not be repeated here.

[0046] from Figure 3 It can be seen that Figure 2 Compared with the corresponding embodiments, the method for generating navigation information in this embodiment highlights the step of combining the vehicle speed to determine whether the light intensity value in front of the vehicle is within a preset light intensity range. By setting different light intensity ranges for different vehicle speeds, the current light intensity is determined to be within the corresponding light intensity range based on the current vehicle speed. This avoids the light intensity interference caused by city street lights when the vehicle is driving at low speed, and improves the light intensity detection sensitivity when the vehicle is driving at high speed, thereby improving the triggering accuracy of the radar sensor.

[0047] Continue to refer Figure 4 , Figure 4A process 400 of a third embodiment of a method for generating navigation information according to the present disclosure is shown. The method for generating navigation information includes the following steps: Step 401 : collecting multiple light intensity values ​​in front of the target vehicle through multiple light intensity sensors installed on the target vehicle.

[0048] In this embodiment, the execution subject of the method for generating navigation information (eg Figure 1 The server 106 as shown collects multiple light intensity values ​​in front of the target vehicle through multiple light intensity sensors installed on the target vehicle.

[0049] Multiple light intensity sensors (also known as photoelectric sensors or light-sensitive sensors) are pre-installed on the target vehicle. High-sensitivity photoelectric sensors, such as photodiodes and CMOS (Complementary Metal Oxide Semiconductor) sensors, are typically selected, and three to five light intensity sensors are typically installed. These sensors are typically located above the vehicle's windshield. They can be arranged in an array, evenly spaced horizontally (for example, along the instrument panel on the windshield), or in a triangular configuration, providing more accurate direction detection. The light intensity sensors are typically spaced 10-20 cm apart, and the sampling frequency is typically set to 100 Hz (Hertz) to ensure real-time response to changes in lighting conditions. Each light intensity sensor collects light intensity data in real time, and the aforementioned execution entity obtains the light intensity values ​​in front of the target vehicle collected by the light intensity sensors.

[0050] Step 402: Calculate the light source angle according to the multiple light intensity values.

[0051] In this embodiment, the above-mentioned execution entity will calculate the light source angle based on multiple light intensity values ​​collected by the light intensity sensor. The light source angle is used to characterize the incident direction or angle of light relative to the target vehicle, for example, the light source angle is directly in front of the vehicle, on the left side of the vehicle, or on the right side of the vehicle.

[0052] Step 403: Based on the light source angle and the preset light source angle range, determine whether the light intensity value is within the preset light intensity range.

[0053] In this embodiment, the execution entity determines whether the light intensity value is within the preset light intensity range based on the relationship between the light source angle and the preset light source angle range. Specifically, the execution entity first determines whether the calculated light source angle is within the preset light source angle range. If so, the execution entity determines that the light intensity value is within the preset light intensity range.

[0054] For example, the preset light source angle range is 0-15°, and the light source angle calculated by multiple light intensity values ​​is 10°. At this time, the light source angle is within the preset light source angle range, then it can be determined that the light intensity value is within the preset light intensity range, and it is determined that the current situation is high beam interference.

[0055] When there are multiple light intensity sensors, the light source angle is calculated based on multiple light intensity values, and further based on the light source angle, it is determined whether the light intensity value is within the preset light intensity range, and then it is determined whether to start the radar sensor to collect data, thereby improving the accuracy of judging interference in high beam conditions.

[0056] Step 404 : In response to determining that the light intensity value is within a preset light intensity range, using a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle.

[0057] Step 404 is basically the same as step 202 in the aforementioned embodiment. For the specific implementation method, reference can be made to the aforementioned description of step 202 and will not be repeated here.

[0058] Step 405: Generate local road condition information based on the road point cloud data and the pre-built high-precision map.

[0059] In this embodiment, the above-mentioned execution entity will convert the collected radar data (i.e., road point cloud data) into high-precision 2D or 3D views through deep learning algorithms (such as PointNet++ point cloud processing model, VoxelNet point cloud-based 3D detection network, etc.), and then combine it with pre-built high-precision map data to generate local road condition information. Local road condition information refers to the road condition view of the road ahead of the target vehicle.

[0060] Step 406: Overlay and display the local road condition information on the global navigation information displayed by the target vehicle in a preset style.

[0061] In this embodiment, the execution entity will overlay the local road condition information on the global navigation information displayed by the target vehicle in a preset style. Here, the preset style can be highlighted, displayed with a larger resolution, etc.

[0062] Local road condition information is generated by combining high-precision maps and collected point cloud data, and displayed in a preset prominent style, so that drivers can obtain current road condition information more intuitively and make decisions more timely, thereby improving driving safety.

[0063] This embodiment uses the VoxelNet point cloud target detection algorithm for spatial reconstruction and combines it with the Unity engine for real-time 3D visualization. This allows the driver to drive according to the real-time simulated road image ahead when being disturbed by the high beams of oncoming vehicles, ensuring driving safety.

[0064] from Figure 4 It can be seen that Figure 2 Compared to the corresponding embodiments, the navigation information generation method in this embodiment highlights the steps of determining whether the light intensity value is within a preset light intensity range when multiple light intensity sensors are used, as well as the steps of generating and displaying local road condition information. It further determines whether the light intensity value is within the preset light intensity range based on the light source angle, and then determines whether to activate the radar sensor for data collection, thereby improving the accuracy of high-beam interference judgment. Furthermore, by combining high-precision maps and collected point cloud data to generate local road condition information and displaying it in a preset prominent style, drivers can more intuitively obtain current road condition information, making more timely decisions and thus improving driving safety.

[0065] Continue to refer Figure 5 , Figure 5 The flowchart 500 of the fourth embodiment of the method for generating navigation information according to the present disclosure is shown. The method for generating navigation information includes the following steps: Step 501 : collecting multiple light intensity values ​​in front of the target vehicle through multiple light intensity sensors installed on the target vehicle.

[0066] Step 501 is basically the same as step 401 in the aforementioned embodiment. For the specific implementation method, please refer to the aforementioned description of step 401, which will not be repeated here.

[0067] Step 502: Determine a maximum illumination intensity value and a minimum illumination intensity value from a plurality of illumination intensity values.

[0068] In this embodiment, the execution subject of the method for generating navigation information (eg Figure 1 The server 106 shown in FIG. 1 determines the highest value of the light intensity from the multiple light intensity values. and the minimum light intensity .

[0069] Step 503: Determine the distance between the light intensity sensor that collects the highest light intensity value and the light intensity sensor that collects the lowest light intensity value.

[0070] In this embodiment, the execution subject will first determine the collection Sensors and collection and determine the distance d between the two sensors. The distance d may be preset. After the two target sensors are determined, the distance d between the two sensors is directly obtained or calculated.

[0071] Step 504 : Calculate the light source angle according to the maximum light intensity value, the minimum light intensity value, and the distance.

[0072] In this embodiment, the execution subject calculates the light source angle according to formula (1): . (1) By calculating the light source angle based on the maximum and minimum values ​​of multiple light intensity values ​​and the distance between two sensors corresponding to the maximum and minimum light intensity values, the calculation efficiency and accuracy of the light source angle are improved.

[0073] Step 505 , calculating the ratio of the light intensity value collected by the light intensity sensor to the sensitivity of the light intensity sensor.

[0074] In this embodiment, for any light intensity sensor among the multiple light intensity sensors, the execution subject will obtain the light intensity value collected by the light intensity sensor. And the sensitivity of the light intensity sensor , and calculate the ratio of the two, that is, / , where n is the number of light intensity sensors.

[0075] Step 506 , calculating the ambient light intensity according to the multiple ratios corresponding to the multiple light intensity sensors, and determining the light source direction according to the ambient light intensity.

[0076] In this embodiment, the execution entity calculates the ambient light intensity E based on multiple ratios corresponding to multiple light intensity sensors. For example, the execution entity first calculates the average of the multiple ratios and uses this average as the ambient light intensity. The ambient light intensity E is used to represent the current average ambient light level. Finally, the light source direction is determined based on the ambient light intensity. The light source direction can be a light source located directly in front of the vehicle or a light source located to the side of the vehicle. The light sources located to the side of the vehicle can also include a light source located on the left side of the vehicle and a light source located on the right side of the vehicle.

[0077] Using triangulation, with a fixed spacing d between sensors, the sensor that first receives the highest light intensity will see a noticeable change in intensity, assuming the light source is coming from the left or right. By comparing the light intensity received by each sensor, the incident direction (i.e., the source direction) can be determined, improving the efficiency and accuracy of determining the light source's angle.

[0078] Step 507 : Determine whether the light intensity value is within a preset light intensity range based on the light source direction, the light source angle, and the preset light source angle range.

[0079] In this embodiment, the execution entity determines whether the light intensity value is within the preset light intensity range by combining the light source direction, light source angle, and preset light source angle range. Specifically, different light source directions correspond to different preset light source angle ranges. After determining the light source direction, the execution entity determines whether the light source angle is within the light source angle range corresponding to the light source direction, thereby determining whether the light intensity value is within the preset light intensity range.

[0080] For example, assuming the light source direction is directly in front of the vehicle, and the light source angle corresponding to the light source direction is (-15°, 15°), the calculated light source angle is 10°. Then it can be determined that the light source angle is within the light source angle range of (-15°, 15°), and the light intensity value can be determined to be within the preset light intensity range. At this time, the radar sensor is activated.

[0081] For another example, assuming that the direction of the light source is to the side of the vehicle, and the light source angles corresponding to the light source direction are (15°, 30°) and (-30°, -15°), the calculated light source angle is 35°. Then it can be determined that the light source angle is not within the two light source angle ranges of (15°, 30°) and (-30°, -15°). It can be determined that the light intensity value is not within the preset light intensity range, and the radar sensor is not triggered.

[0082] By combining the light source direction, light source angle and preset light source angle range, it is determined whether the light intensity value is within the preset light intensity range, further improving the triggering sensitivity and accuracy of the radar sensor.

[0083] Step 508 : In response to determining that the light intensity value is within the preset light intensity range, using a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle.

[0084] Step 509: Generate local road condition information based on the road point cloud data and the pre-built high-precision map.

[0085] Step 510: Overlay and display the local road condition information on the global navigation information displayed by the target vehicle in a preset style.

[0086] Steps 508-510 are basically the same as steps 404-406 of the aforementioned embodiment. For specific implementation methods, reference may be made to the aforementioned description of steps 404-406, which will not be repeated here.

[0087] from Figure 5 It can be seen that Figure 4Compared to the corresponding embodiments, the navigation information generation method in this embodiment emphasizes the step of calculating the light source angle. By comparing the light intensity received by each sensor, the incident direction of the light (i.e., the light source direction) is determined, thereby improving the efficiency and accuracy of determining the light source angle. Furthermore, by combining the light source direction, light source angle, and a preset light source angle range to determine whether the light intensity value is within the preset light intensity range, the triggering sensitivity and accuracy of the radar sensor are further improved.

[0088] Continue to refer Figure 6 , Figure 6 The flowchart 600 of the fifth embodiment of the method for generating navigation information according to the present disclosure is shown. The method for generating navigation information includes the following steps: Step 601 : collecting multiple light intensity values ​​in front of the target vehicle through multiple light intensity sensors installed on the target vehicle.

[0089] Step 601 is basically the same as step 401 in the aforementioned embodiment. For the specific implementation method, reference can be made to the aforementioned description of step 401 and will not be repeated here.

[0090] Step 602: Calculate the total light intensity according to the multiple light intensity values.

[0091] In this embodiment, the execution subject of the method for generating navigation information (eg Figure 1 The server 106 shown in FIG. 106 will calculate the total light intensity, assuming that the multiple light intensity values ​​are 、 … , then the total light intensity is .

[0092] Step 603: Determine the angle values ​​of the positions of the plurality of light intensity sensors relative to the target vehicle.

[0093] In this embodiment, the execution subject determines the angle value of each light intensity sensor in the plurality of light intensity sensors relative to the target vehicle. The angle value refers to the placement angle on the target vehicle relative to the center of the vehicle. For example, the angle value can be -30°, -15°, 0°, 15°, or 30°. The angle value can be pre-set, meaning the sensor is placed on the target vehicle at that angle. The angle value can also be measured after the sensor is installed on the target vehicle.

[0094] Step 604 : Calculate the light source angle according to the multiple light intensity values, the angle values ​​corresponding to the multiple light intensity sensors, and the total light intensity.

[0095] In this embodiment, the execution subject calculates the light source angle according to formula (2): : . (2) The light source angle is calculated by using the angle value of the light intensity sensor relative to the target vehicle, multiple light intensity values, and the sum of the light intensities of the multiple light intensity values, thereby improving the accuracy of the calculated light source angle.

[0096] Step 605: Calculate the ratio of the light intensity value collected by the light intensity sensor to the sensitivity of the light intensity sensor.

[0097] Step 606 , calculating the ambient light intensity according to the multiple ratios corresponding to the multiple light intensity sensors, and determining the light source direction according to the ambient light intensity.

[0098] Step 607 : Determine whether the light intensity value is within a preset light intensity range based on the light source direction, the light source angle, and the preset light source angle range.

[0099] Step 608 : In response to determining that the light intensity value is within the preset light intensity range, using a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle.

[0100] Step 609: Generate local road condition information based on the road point cloud data and the pre-built high-precision map.

[0101] Step 610: Overlay and display the local road condition information on the global navigation information displayed by the target vehicle in a preset style.

[0102] Steps 605-610 are basically the same as steps 505-510 of the aforementioned embodiment. For specific implementation methods, reference can be made to the aforementioned description of steps 505-510, which will not be repeated here.

[0103] from Figure 6 It can be seen that Figure 5 Compared with the corresponding embodiments, the method for generating navigation information in this embodiment calculates the light source angle through the angle value of the position of the light intensity sensor relative to the target vehicle, multiple light intensity values ​​and the sum of the light intensities of multiple light intensity values, thereby improving the accuracy of the calculated light source angle.

[0104] Continue to refer Figure 7 , Figure 7 The sixth embodiment of the method for generating navigation information according to the present disclosure is shown in process 700. The method for generating navigation information includes the following steps: Step 701: Determine whether the light intensity value in front of the target vehicle is within a preset light intensity range.

[0105] Step 702 : In response to determining that the light intensity value is within a preset light intensity range, using a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle.

[0106] Steps 701-702 are basically the same as steps 201-202 of the aforementioned embodiment. For specific implementation methods, reference can be made to the aforementioned description of steps 201-202, which will not be repeated here.

[0107] Step 703: Generate local road condition information based on the road point cloud data and the pre-built high-precision map.

[0108] Step 704: Overlay and display the local road condition information on the global navigation information displayed by the target vehicle in a preset style.

[0109] Steps 701-702 are basically the same as steps 609-610 of the aforementioned embodiment. For specific implementation methods, reference can be made to the aforementioned description of steps 609-610, which will not be repeated here.

[0110] Step 705: Determine whether there is an obstacle ahead of the road based on the road point cloud data and obtain a determination result.

[0111] In this embodiment, the execution subject of the method for generating navigation information (eg Figure 1 The server 106 shown in the figure will also determine whether there is an obstacle ahead of the road based on the road point cloud data, and obtain a determination result, that is, a determination result of whether there is an obstacle or not.

[0112] Step 706: Change the display style of the local traffic condition information according to the judgment result.

[0113] In this embodiment, the execution entity sets corresponding display styles for different judgment results. After determining the judgment result, it determines the target display style corresponding to the judgment result and uses the target display style to change the original display style of the local road condition information. By setting different display styles for different judgment results, the current obstacle information can be displayed more vividly and intuitively.

[0114] Further references Figure 8-1 , Figure 8-1 It shows the display style of local road condition information when there are no obstacles. If the above-mentioned execution body determines that there are no obstacles ahead of the road, it will directly draw the road ahead information and display it in green, thereby indicating to the driver that the road ahead is unobstructed and can be passed slowly.

[0115] Further references Figure 8-2 , Figure 8-2 The display style of local road condition information when there is an obstacle is shown. If the above-mentioned execution entity determines that there is an obstacle ahead of the road, it will generate a prompt message while drawing the information ahead of the road, such as "slow down immediately", and it will appear in red, thereby indicating to the driver that there is an obstacle ahead of the road and the driver needs to slow down to ensure driving safety.

[0116] In some optional implementations of this embodiment, the above-mentioned method for generating navigation information further includes: in response to determining that there is an obstacle ahead on the road, determining category information of the obstacle; and performing a prompt operation corresponding to the category information.

[0117] In this implementation, when it is determined that there is an obstacle ahead on the road, the above-mentioned execution entity will further determine the category information of the obstacle, for example, using the YOLO visual target detection algorithm or the Faster R-CNN target detection algorithm to perform object detection, thereby determining the category information of the obstacle, which may include: vehicles, pedestrians, animals, etc., and performing prompt operations corresponding to the category information.

[0118] For example, if the obstacle is determined to be a vehicle, the vehicle horn will be automatically triggered and the driver will be prompted to brake. If the obstacle is determined to be a pedestrian or an animal, a message will be generated and displayed (or a voice reminder) to prompt the driver to brake.

[0119] Therefore, different reminder operations can be performed for different types of obstacles, further improving driving safety.

[0120] In some optional implementations of this embodiment, the above-mentioned method for generating navigation information further includes: in response to detecting that the light intensity value is not within a preset light intensity range, hiding the local road condition information.

[0121] In this implementation, after displaying local road condition information, if the execution entity detects that the light intensity value in front of the target vehicle gradually falls out of the preset light intensity range, it means that there is no glare in front of the target vehicle. At this time, the driver can clearly see the road ahead with the naked eye. In this case, the local road condition information is hidden, and the road condition view in the in-vehicle map gradually disappears. After the glare disappears, the normal navigation map display is restored.

[0122] from Figure 7 It can be seen that Figure 6 Compared with the corresponding embodiments, the method for generating navigation information in this embodiment highlights the step of changing the display style of local road condition information according to the type of obstacle. By setting different display styles for different judgment results, the current obstacle information can be displayed more vividly and intuitively, thereby further improving driving safety.

[0123] Further references Figure 9 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for generating navigation information. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0124] like Figure 9 As shown, the navigation information generation device 900 of this embodiment includes: a determination module 901, a collection module 902, and a display module 903. The determination module 901 is configured to determine whether the light intensity value in front of the target vehicle is within a preset light intensity range; the collection module 902 is configured to, in response to determining that the light intensity value is within the preset light intensity range, use a radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle; and the display module 903 is configured to generate local road condition information based on the road point cloud data, and superimpose the local road condition information on the global navigation information displayed on the target vehicle.

[0125] In this embodiment, in the navigation information generating device 900, the specific processing of the determining module 901, the collecting module 902 and the display module 903 and the technical effects thereof can be referred to in the respective Figure 2 The relevant descriptions of steps 201-203 in the corresponding embodiment are not repeated here.

[0126] In some optional implementations of this embodiment, the above-mentioned navigation information generating device 900 also includes: an acquisition module, configured to obtain the current speed value of the target vehicle; a vehicle speed determination module, configured to determine whether the current speed value is within a preset vehicle speed range; and the acquisition module is further configured to: in response to determining that the current speed value is within the preset vehicle speed range and the light intensity value is within the preset light intensity range corresponding to the current speed value, use the radar sensor on the target vehicle to collect road point cloud data in front of the target vehicle.

[0127] In some optional implementations of this embodiment, the above-mentioned navigation information generating device 900 also includes: a sensor acquisition module, configured to collect multiple light intensity values ​​in front of the target vehicle through multiple light intensity sensors installed on the target vehicle; and the determination module includes: an angle calculation submodule, configured to calculate the light source angle based on the multiple light intensity values; a judgment submodule, configured to determine whether the light intensity value is within a preset light intensity range based on the light source angle and a preset light source angle range.

[0128] In some optional implementations of this embodiment, the angle calculation submodule is further configured to: determine the maximum light intensity value and the minimum light intensity value from multiple light intensity values; determine the distance between the light intensity sensor that collects the maximum light intensity value and the light intensity sensor that collects the minimum light intensity value; and calculate the light source angle based on the maximum light intensity value, the minimum light intensity value and the distance.

[0129] In some optional implementations of this embodiment, the angle calculation submodule is further configured to: calculate the sum of light intensities based on multiple light intensity values; determine the angle values ​​of the positions of multiple light intensity sensors relative to the target vehicle respectively; and calculate the light source angle based on multiple light intensity values, the angle values ​​corresponding to the multiple light intensity sensors, and the sum of light intensities.

[0130] In some optional implementations of this embodiment, the above-mentioned navigation information generating device 900 also includes: a first calculation module, configured to calculate the ratio of the light intensity value collected by the light intensity sensor and the sensitivity of the light intensity sensor; a second calculation module, configured to calculate the ambient light intensity based on multiple ratios corresponding to multiple light intensity sensors; a light source direction determination module, configured to determine the light source direction based on the ambient light intensity; and the judgment submodule is further configured to: determine whether the light intensity value is within a preset light intensity range based on the light source direction, the light source angle and the preset light source angle range.

[0131] In some optional implementations of this embodiment, the display module includes: a generation submodule, configured to generate local road condition information based on road point cloud data and a pre-built high-precision map; and a display submodule, configured to superimpose the local road condition information in a preset style on the global navigation information displayed by the target vehicle.

[0132] In some optional implementations of this embodiment, the above-mentioned navigation information generating device 900 also includes: an obstacle judgment module, configured to judge whether there is an obstacle ahead of the road based on the road point cloud data, and obtain a judgment result; a style change module, configured to change the display style of the local road condition information based on the judgment result.

[0133] In some optional implementations of this embodiment, the navigation information generating device 900 further includes: an execution module configured to determine category information of the obstacle in response to determining that there is an obstacle ahead on the road; and perform a prompt operation corresponding to the category information.

[0134] In some optional implementations of this embodiment, the navigation information generating device 900 further includes: a hiding module configured to hide the local road condition information in response to detecting that the light intensity value is not within a preset light intensity range.

[0135] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, a computer program product, and a vehicle.

[0136] Figure 10A schematic block diagram of an example electronic device 1000 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as 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 personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0137] like Figure 10 As shown, device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. RAM 1003 may also store various programs and data required for the operation of device 1000. Computing unit 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to bus 1004.

[0138] Multiple components in device 1000 are connected to I / O interface 1005, including: an input unit 1006, such as a keyboard, mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a magnetic disk, optical disk, etc.; and a communication unit 1009, such as a network card, modem, wireless communication transceiver, etc. The communication unit 1009 allows device 1000 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0139] The computing unit 1001 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized 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 1001 performs the various methods and processes described above, such as the method for generating navigation information. For example, in some embodiments, the method for generating navigation information can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the method for generating navigation information described above can be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the method for generating navigation information in any other appropriate manner (eg, by means of firmware).

[0140] The vehicle provided by the present disclosure may include at least one light intensity sensor, at least one radar sensor, and Figure 10 The electronic device shown above can implement the method for generating navigation information described in any of the above embodiments when executed by its processor. The light intensity sensor is used to collect the light intensity value in front of the target vehicle, and the radar sensor is used to collect road point cloud data in front of the target vehicle.

[0141] Various embodiments of the systems and techniques described herein 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), system-on-chip systems (SOCs), 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 that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. Such program code may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] To provide 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 input, voice input, or tactile input).

[0145] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, 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 communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0146] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0147] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0148] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on 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 scope of protection of this disclosure.

Claims

1. A method for generating navigation information, comprising: Determine whether the light intensity value in front of the target vehicle is within a preset light intensity range; In response to determining that the light intensity value is within the preset light intensity range, collecting road point cloud data in front of the target vehicle using a radar sensor on the target vehicle; Local road condition information is generated based on the road point cloud data, and the local road condition information is superimposed and displayed on the global navigation information displayed by the target vehicle.

2. The method according to claim 1, further comprising: Obtaining the current speed value of the target vehicle; Determining whether the current speed value is within a preset vehicle speed range; as well as In response to determining that the light intensity value is within the preset light intensity range, collecting road point cloud data in front of the target vehicle using a radar sensor on the target vehicle includes: In response to determining that the current speed value is within a preset vehicle speed range and the light intensity value is within a preset light intensity range corresponding to the current speed value, a radar sensor on the target vehicle is used to collect road point cloud data in front of the target vehicle.

3. The method according to claim 1, further comprising: Collecting multiple light intensity values ​​in front of the target vehicle by using multiple light intensity sensors installed on the target vehicle; as well as Determining whether the light intensity value in front of the target vehicle is within a preset light intensity range includes: Calculating a light source angle according to the plurality of light intensity values; Based on the light source angle and the preset light source angle range, it is determined whether the light intensity value is within the preset light intensity range.

4. The method according to claim 3, wherein: Calculating the light source angle according to the multiple light intensity values ​​includes: Determining a maximum light intensity value and a minimum light intensity value from the plurality of light intensity values; Determine the distance between the light intensity sensor that collects the highest light intensity value and the light intensity sensor that collects the lowest light intensity value; The light source angle is calculated according to the maximum light intensity value, the minimum light intensity value and the distance.

5. The method according to claim 3, wherein: Calculating the light source angle according to the multiple light intensity values ​​includes: Calculating a light intensity sum according to the multiple light intensity values; respectively determining angle values ​​of positions of the plurality of light intensity sensors relative to the target vehicle; The light source angle is calculated according to the multiple light intensity values, the angle values ​​corresponding to the multiple light intensity sensors, and the total light intensity.

6. The method according to claim 4 or 5, further comprising: Calculating the ratio of the light intensity value collected by the light intensity sensor to the sensitivity of the light intensity sensor; calculating the ambient light intensity according to the multiple ratios corresponding to the multiple light intensity sensors; determining a light source direction according to the ambient light intensity; as well as The determining, based on the light source angle and the preset light source angle range, whether the light intensity value is within a preset light intensity range includes: Based on the light source direction, the light source angle, and the preset light source angle range, it is determined whether the light intensity value is within a preset light intensity range.

7. The method according to claim 1, wherein The generating of local road condition information according to the road point cloud data, and superimposing and displaying the local road condition information on the global navigation information displayed by the target vehicle, comprises: Generating the local road condition information based on the road point cloud data and a pre-built high-precision map; The local road condition information is superimposed and displayed in a preset style on the global navigation information displayed by the target vehicle.

8. The method according to claim 7, further comprising: Determine whether there is an obstacle ahead of the road based on the road point cloud data, and obtain a determination result; The display style of the local road condition information is changed according to the judgment result.

9. The method according to claim 8, further comprising: In response to determining that an obstacle exists ahead of the road, determining category information of the obstacle; Execute a prompt operation corresponding to the category information.

10. The method according to any one of claims 1 to 9, further comprising: In response to detecting that the light intensity value is not within the preset light intensity range, hiding the local road condition information.

11. A navigation information generating device, comprising: a determination module configured to determine whether the light intensity value in front of the target vehicle is within a preset light intensity range; A collection module is configured to collect road point cloud data in front of the target vehicle using a radar sensor on the target vehicle in response to determining that the light intensity value is within the preset light intensity range; The display module is configured to generate local road condition information based on the road point cloud data, and superimpose the local road condition information on the global navigation information displayed by the target vehicle.

12. 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 10.

13. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to cause the computer to execute the method according to any one of claims 1 to 10.

14. 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 10.

15. A vehicle comprising: At least one light intensity sensor, used to collect light intensity values ​​in front of the target vehicle; at least one radar sensor, configured to collect point cloud data of the road ahead of the target vehicle; And the electronic device as claimed in claim 12.

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