Navigation method and device, electronic equipment, medium and autonomous vehicle

Abstract

The invention provides a navigation method and device, electronic equipment, a medium and an automatic driving vehicle, and relates to the technical field of maps, in particular to map navigation, an intelligent cabin and an automatic driving technology. According to the implementation scheme, point cloud data of a front road surface of a vehicle are obtained; fitting a pavement plane based on the point cloud data; obtaining candidate points from the point cloud data, wherein the distances between the candidate points and the pavement plane are within a preset distance interval, and forming a non-pavement point set; screening ice surface points based on the reflection intensity of each candidate point in the non-pavement point set to form an ice surface point set; based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface, determining the ice layer thickness of the front road surface; and outputting navigation information corresponding to the thickness of the ice layer.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of maps, in particular to map navigation, intelligent cockpit and automatic driving technology, and specifically to a navigation method, device, electronic device, computer readable storage medium, computer program product and automatic driving vehicle. BACKGROUND

[0002] In winter, various types of icy and snowy road surfaces are often formed, such as snow, ice, semi-melted snow, semi-melted ice and the like. Road icing can significantly reduce the road friction coefficient, and vehicles are prone to traffic accidents when driving on such road surfaces.

[0003] The methods described in this section can not have been previously conceived or made. Unless otherwise indicated herein, the methods described in this section are not to be assumed to have been previously conceived or made, merely because they are described in this section. Similarly, any problems mentioned in this section should not be assumed to have been recognized in the art, unless otherwise indicated. SUMMARY

[0004] The present disclosure provides a navigation method, device, electronic device, computer readable storage medium, computer program product and automatic driving vehicle.

[0005] According to an aspect of the present disclosure, a navigation method is provided, comprising: obtaining point cloud data of a front road surface of a vehicle; fitting a road surface plane based on the point cloud data; obtaining candidate points in the point cloud data that are within a preset distance interval from the road surface plane to form a non-road surface point set; screening ice surface points based on the reflection intensity of each candidate point in the non-road surface point set to form an ice surface point set; determining the ice layer thickness of the front road surface based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface; and outputting navigation information corresponding to the ice layer thickness.

[0006] According to another aspect of the present disclosure, a navigation device is provided, comprising: a first obtaining unit configured to obtain point cloud data of a front road surface of a vehicle; a fitting unit configured to fit a road surface plane based on the point cloud data; a second obtaining unit configured to obtain candidate points in the point cloud data that are within a preset distance interval from the road surface plane to form a non-road surface point set; a screening unit configured to screen ice surface points based on the reflection intensity of each candidate point in the non-road surface point set to form an ice surface point set; a first determining unit configured to determine the ice layer thickness of the front road surface based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface; and an output unit configured to output navigation information corresponding to the ice layer thickness.

[0007] According to another aspect of the disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the navigation method of the disclosure.

[0008] According to another aspect of the disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are for causing a computer to perform the navigation method of the disclosure.

[0009] According to another aspect of the disclosure, there is provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the navigation method of the disclosure.

[0010] According to another aspect of the disclosure, there is provided an autonomous vehicle comprising: the electronic device of the disclosure.

[0011] It is to be understood that the details described in this section are not intended to identify key or critical features of the embodiments of the disclosure or to limit the scope of the disclosure. Other features of the disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the written description, the drawings serve to explain exemplary implementations of the embodiments. The illustrated embodiments are merely examples and do not limit the scope of the claims. In all the drawings, like reference numerals refer to like but not necessarily identical elements.

[0013] Figure 1 A schematic diagram of an exemplary system in which the various methods described herein can be implemented according to embodiments of the disclosure is shown; Figure 2 A flowchart of a navigation method according to embodiments of the disclosure is shown; Figure 3 A flowchart of fitting a road surface plane based on point cloud data according to embodiments of the disclosure is shown; Figure 4 A flowchart of fitting a road surface plane based on a first set of points according to embodiments of the disclosure is shown; Figure 5 A flowchart of outputting navigation information corresponding to an ice layer thickness according to embodiments of the disclosure is shown; Figure 6 A flowchart of a navigation method according to exemplary embodiments of the disclosure is shown; Figure 7 A block diagram of the structure of a navigation device according to embodiments of the disclosure is shown; Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0014] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding them. These should be considered in their context only. Thus, those of ordinary skill in the art will recognize the various examples described herein can be practiced with variations of those examples without departing from the scope of the present disclosure. Similarly, to avoid obscuring the present disclosure, the description that follows refrains from describing all possible combinations of features that can come within the scope of the present disclosure.

[0015] In the present disclosure, the terms "first", "second", and the like are used to describe various elements only and do not intend to limit the positional relationship, the time relationship, or the importance of the elements, and such terms are only used to distinguish one element from another. In some examples, the first element and the second element can refer to the same instance of the element, and in some cases, based on the context of the description, they can also refer to different instances.

[0016] The terms used in the description of various described examples in the present disclosure are only for the purpose of describing particular examples and are not intended to be limiting. Unless the context clearly indicates otherwise, the term, if not specifically defined, can be one or more. In addition, the term "and / or" used in the present disclosure encompasses any one of the listed items and all possible combinations thereof.

[0017] In the related art, the vehicle-mounted navigation system usually only provides a wide-area icing warning based on meteorological data in winter driving scenarios, and lacks real-time detection and passability judgment capability for the ice layer thickness of specific road sections. This leads to the fact that the driver cannot accurately determine whether it is safe to pass the icy road section when facing it, which may cause a skidding accident and reduce driving safety and efficiency.

[0018] Embodiments of the present disclosure provide a navigation method, which obtains point cloud data of the road surface in front of the vehicle, fits the road surface plane according to the point cloud data, further screens out a non-road surface point set higher than the road surface, accurately identifies an ice surface point set from the non-road surface points through the reflection intensity, and finally determines the ice layer thickness by comparing the ice surface point height with the historical ground height and outputs navigation information, so as to realize efficient and accurate detection of the road ice layer thickness, provide navigation decision support for the driver based on objective measurement data, and effectively improve the driving safety in the icing risk area.

[0019] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0020] Figure 1A schematic diagram illustrating an example system 100 in which various methods and apparatus described herein can be implemented in accordance with embodiments of the present disclosure is shown. Reference is made to Figure 1 The system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 coupling the motor vehicle 110 to the server 120.

[0021] In embodiments of the present disclosure, the motor vehicle 110 can include a computing device in accordance with embodiments of the present disclosure and / or be configured to perform methods in accordance with embodiments of the present disclosure.

[0022] The server 120 can run one or more services or software applications that enable methods of detecting a thickness of an ice layer on a road ahead and outputting navigation information in accordance with the thickness of the ice layer. In certain embodiments, the server 120 can also provide other services or software applications, which can include non-virtual environments and virtual environments. In Figure 1 In the illustrated configuration, the server 120 can include one or more components that implement the functionality performed by the server 120. These components can include software components, hardware components, or a combination thereof, executable by one or more processors. A user of the motor vehicle 110 can in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that various different system configurations are possible, which can differ from the system 100. Thus, Figure 1 The system 100 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0023] The server 120 can include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframe computers, server clusters, or any other appropriate arrangement and / or combination. The server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for the server). In various embodiments, the server 120 can run one or more services or software applications that provide the functionality described below.

[0024] The computing units in the server 120 can run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0025] In some embodiments, the server 120 can include one or more applications to analyze and consolidate data feeds and / or event updates received from the motor vehicles 110. The server 120 can also include one or more applications to display the data feeds and / or real-time events via one or more display devices of the motor vehicles 110.

[0026] The network 130 can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of available protocols, including without limitation TCP / IP, SNA, IPX, etc. As examples, one or more of the networks 110 can be a satellite communications network, a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (including, for example, Bluetooth, WiFi), and / or any combination of these and other networks.

[0027] The system 100 can also include one or more databases 150. In certain embodiments, these databases can be used to store data and other information. For example, one or more of the databases 150 can be used to store information such as audio files and video files. The data stores 150 can reside at various locations. For example, a data store used by the server 120 can be local to the server 120 or can be remote from the server 120 and can communicate with the server 120 via a network- or application-specific connection. The data stores 150 can be of different types. In certain embodiments, a data store used by the server 120 can be a database, such as a relational database. One or more of these databases can store, update, and retrieve data to and from the database in response to commands.

[0028] In certain embodiments, one or more of the databases 150 can also be used by applications to store application data. Databases used by applications can be different types of databases, such as key-value stores, object stores, or regular stores supported by file systems.

[0029] The motor vehicle 110 can comprise sensors 111 for perceiving the surrounding environment. The sensors 111 can comprise one or more of the following sensors: visual camera, infrared camera, ultrasonic sensor, millimeter wave radar, and laser radar (LiDAR). Different sensors can provide different detection accuracy and range. The camera can be installed at the front, rear or other positions of the vehicle. The visual camera can capture the situation inside and outside the vehicle in real time and present it to the driver and / or passenger. In addition, by analyzing the pictures captured by the visual camera, information such as traffic signal indication, intersection situation, other vehicle operating state, etc. can be obtained. The infrared camera can capture objects in night vision conditions. The ultrasonic sensor can be installed around the vehicle to measure the distance from the vehicle to the object outside the vehicle by taking advantage of the strong directivity of ultrasonic waves. The millimeter wave radar can be installed at the front, rear or other positions of the vehicle to measure the distance from the vehicle to the object outside the vehicle by taking advantage of the characteristics of electromagnetic waves. The laser radar can be installed at the front, rear or other positions of the vehicle to detect the edge and shape information of the object, thereby performing object recognition and tracking. Due to the Doppler effect, the radar device can also measure the speed change of the vehicle and the moving object.

[0030] The motor vehicle 110 can also comprise a communication device 112. The communication device 112 can comprise a satellite positioning module capable of receiving satellite positioning signals (e.g. Beidou, GPS, GLONASS and GALILEO) from satellites 141 and generating coordinates based on these signals. The communication device 112 can also comprise a module for communicating with mobile communication base stations 142, and the mobile communication network can implement any suitable communication technology, such as GSM / GPRS, CDMA, LTE, etc. current or developing wireless communication technology (e.g. 5G technology). The communication device 112 can also have a vehicle-to-everything (V2X) module configured to enable communication between the vehicle and the outside world, such as vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. In addition, the communication device 112 can also have a module configured to communicate with user terminals 145 (including but not limited to smartphones, tablets or wearable devices such as watches) by using, for example, wireless local area networks or Bluetooth based on IEEE 802.11 standards. With the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.

[0031] The motor vehicle 110 can also include a control device 113. The control device 113 can include a processor, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other specialized processors, in communication with various types of computer readable storage devices or media. The control device 113 can include an autonomous driving system for automatically controlling various actuators in the vehicle. The autonomous driving system is configured to control the motor vehicle 110 (not shown) powertrain, steering system, and braking system, etc. via a plurality of actuators to control acceleration, steering, and braking, respectively, in response to inputs from a plurality of sensors 111 or other input devices, without or with limited human intervention. Part of the processing function of the control device 113 can be implemented through cloud computing. For example, some processing can be performed using an on-board processor, while other processing can be performed using the computing resources of the cloud. The control device 113 can be configured to perform the methods according to the present disclosure. In addition, the control device 113 can be implemented as one example of a motor vehicle side (client) computing device according to the present disclosure.

[0032] Figure 1 The system 100 can be configured and operated in various ways to enable the application of various methods and devices described according to the present disclosure.

[0033] According to embodiments of the present disclosure, as shown in Figure 2 A navigation method is provided, including: step S201, acquiring point cloud data of a front road surface of a vehicle; step S202, fitting a road surface plane based on the point cloud data; step S203, acquiring candidate points in the point cloud data that are within a preset distance interval from the road surface plane to form a non-road surface point set; step S204, screening ice surface points based on the reflection intensity of each candidate point in the non-road surface point set to form an ice surface point set; step S205, determining the ice layer thickness of the front road surface based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface; and step S206, outputting navigation information corresponding to the ice layer thickness.

[0034] Thus, by acquiring point cloud data of a front road surface of a vehicle, fitting a road surface plane therefrom, screening a non-road surface point set higher than the road surface, accurately identifying an ice surface point set from the non-road surface points based on reflection intensity, and finally determining the ice layer thickness by comparing the ice surface point height with the historical ground height and outputting navigation information, the road ice layer thickness can be efficiently and accurately detected, navigation decision support based on objective measurement data can be provided for the driver, and the driving safety in the icing risk area can be effectively improved.

[0035] In some embodiments, the point cloud data of the road surface in front of the vehicle can be obtained by a point cloud data acquisition device equipped on the vehicle, where the point cloud data acquisition device can be a laser radar or a depth camera (RGB-D camera) for example.

[0036] In some embodiments, fitting the road surface plane based on the point cloud data can be based on a least square method.

[0037] In some embodiments, as shown in FIG. 3, Figure 3 Based on the point cloud data, fitting the road surface plane can include: step S301, converting the first coordinates of each point in the point cloud data in the point cloud data acquisition device coordinate system to the corresponding vehicle coordinate system of the vehicle to obtain the second coordinates of each point; step S302, screening the points with the second coordinates within a preset coordinate range to form a first point set; and step S303, fitting the road surface plane based on the first point set.

[0038] Thus, by first converting the point cloud data in the laser radar coordinate system to the more stable vehicle coordinate system and screening the points in the effective area to fit the road surface plane, the influence of the vehicle attitude change on the detection can be eliminated, ensuring the accuracy and stability of the subsequent road surface plane fitting and providing a more reliable basis for the ice layer thickness calculation.

[0039] In some embodiments, the point cloud data acquisition device can be installed at the front of the vehicle or any other position.

[0040] Taking the laser radar as an example, the point cloud data acquisition device coordinate system is centered on the laser radar. If the coordinates in the point cloud data acquisition device coordinate system are directly used, the installation angle error of the laser radar (for example, the radar is slightly tilted downward by 2°) can cause the points on the road surface to be misjudged as high-altitude points, thereby affecting the accuracy of the road surface plane fitting. Therefore, before fitting the road surface plane, the point cloud data can be converted to the vehicle coordinate system by coordinate system conversion, thereby improving the accuracy of the road surface plane fitting and the accuracy of the ice layer thickness detection.

[0041] In some example embodiments, the conversion process of converting the point cloud data from the point cloud data acquisition device coordinate system to the vehicle coordinate system can be represented by the following formula: =R· + T wherein, represents the point coordinates after converting the coordinate system (i.e., the second coordinates), represents the original point coordinates, R is a 3x3 rotation matrix, which is determined based on the installation angle of the laser radar, if the installation angle of the X axis of the point cloud data collection device coordinate system relative to the X axis of the vehicle coordinate system is 0°, it is considered that the point cloud data collection device coordinate system is completely aligned with the vehicle coordinate system, at this time R is a unit matrix: R=

[0042] In addition, T = [0, 0, ] in the above formula represents a translation vector (that is, the radar height from the ground, for example, 1.5m). Taking the radar point as an example, the installation angle is 0°, the radar height , , and the above formula is calculated: R· = · =

[0043] =R· + T = =

[0044] Then the point coordinates converted to the vehicle coordinate system are .

[0045] In some embodiments, after the coordinate system conversion, the second coordinates of each point can be based on the second coordinates of each point, and points whose second coordinates are within a preset coordinate range are screened to form a first point set. For example, points within 10 meters in front and within a range of ±3 meters in width can be screened to form a first point set, that is, the point cloud is traversed, and only points satisfying the conditions 0≤x≤10, -3≤y≤3, -0.5≤z≤0.5 are retained. In this way, the points far away or high in the point cloud data can be further screened out, thereby further improving the accuracy and efficiency of the road surface plane fitting.

[0046] In some embodiments, the points in the above first point set can be fitted based on the least squares method to obtain a road surface plane.

[0047] In some embodiments, as Figure 4As shown, based on the first point set, fitting the road surface plane can include: step S401, performing a first operation multiple times on the first point set to obtain a plurality of candidate planes and an inlier point number corresponding to each candidate plane, the first operation including: step S4011, randomly selecting three points in the first point set to determine a candidate plane based on the three points; and step S4012, counting the number of inlier points in the first point set that are less than a first preset distance from the candidate plane as the inlier point number of the candidate plane; step S402, determining the candidate plane with the largest inlier point number in the plurality of candidate planes as the target plane to obtain an inlier point set of the target plane; and step S403, performing plane fitting based on the inlier point set to obtain the road surface plane.

[0048] Thus, by randomly selecting points multiple times to generate candidate planes and evaluate their inlier point numbers, the optimal plane is ultimately selected and fine fitting is performed, thereby being able to effectively and robustly fit the most real road surface plane from point cloud data containing noise and abnormal points (such as bumps and debris), greatly improving the anti-interference ability and accuracy of the algorithm.

[0049] In some example embodiments, for the first point set obtained by the above method , }3 points can be randomly selected in the first point set as samples, the above 3 points can be represented as , , , , then the vector =( , , ), =( , , ).

[0050] On this basis, the normal vector of the plane (candidate plane) where the above 3 points are located can be represented as = · .

[0051] Substituting the point d=-(a +b ), the equation of the candidate plane can be obtained: ax+by+cz+d=0.

[0052] ​​​​​​Subsequently, an inlier point statistic can be performed based on the candidate plane. First, for each point in the first point set, a distance between the point and the candidate plane is calculated. The distance of any point The distance of any point to the candidate plane can be expressed as:

[0053] wherein the numerator in the above formula is the absolute value of the point substituted into the candidate plane equation, and the denominator is the length of the normal vector, which ensures the distance calculation is correct.

[0054] Based on this, it can be determined whether the point is an inlier point of the candidate plane by determining whether the distance of the point to the candidate plane is less than a first preset distance. In some example embodiments, the first preset distance can be determined according to the general characteristics of the ground, for example, the road surface is usually a continuous plane with small fluctuations, and the first preset distance can be set to 0.05 m. This threshold value can tolerate small unevenness of the road surface, such as asphalt texture, small stones, etc., to avoid misjudgment and mistakenly deleting road surface points.

[0055] Based on the above method, for the candidate plane, each point in the first point set is traversed to detect all inlier points of the candidate plane and to count the number of inlier points.

[0056] Through similar operations as described above, the candidate plane and the number of inlier points are obtained multiple times. In some embodiments, the above operation can be performed a preset number of times to obtain a preset number of candidate planes and their corresponding inlier point sets and inlier point numbers. In some example embodiments, the preset number can be set to 1000 times, which is not limited herein.

[0057] After obtaining a preset number of candidate planes and their corresponding inlier point sets and inlier point numbers, the candidate plane with the largest number of inlier points can be selected as a reference, and the inlier point set corresponding to the candidate plane can be used to perform road surface plane fitting.

[0058] In some embodiments, the points in the inlier point set can be fitted into a plane based on the least squares method to obtain a road surface plane.

[0059] After obtaining the road surface plane, the non-road surface points can be screened based on the road surface plane to obtain a non-road surface point set.

[0060] In some embodiments, each point in the point cloud data can be traversed, and the distance of each point to the road surface plane can be calculated. If the distance of the point to the road surface plane is within a preset distance interval, the point is determined as a candidate point and is added to the non-road surface point set. ​​​​

[0061] Generally, the point cloud data can include ground points, ice surface points, snow points and obstacle points. The point cloud data is further filtered by setting a preset distance interval to retain as many points as possible in the point cloud data which are neither road surface points nor obstacle points.

[0062] In some example embodiments, the preset distance interval can be set as [0.05m, 0.1m], so that the ground points close to the ground and the obstacle points higher than the ground can be filtered out, and the non-road surface point set is obtained.

[0063] Subsequently, the ice surface points can be further filtered from the non-road surface point set. Each point in the point cloud data includes a reflection intensity feature. Generally, the reflection intensity of the ice surface is high due to its smoothness and transparency, the reflection intensity of the ground (such as asphalt) is low, and the reflection intensity of the snow or debris is lower.

[0064] In some embodiments, a reflection intensity threshold can be set to filter the candidate points in the non-road surface point set. In some example embodiments, in response to determining that the reflection intensity of a candidate point is greater than the reflection intensity threshold, the candidate point is determined to be an ice surface point and is added to the ice surface point set.

[0065] In some example embodiments, the reflection intensity of each candidate point can be normalized and compared with the reflection intensity threshold. The reflection intensity threshold can be set to 0.7, for example.

[0066] In some embodiments, based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface, the ice layer thickness of the front road surface can be determined by first calculating the mean value of the original height coordinates of the ice surface points in the ice surface point set, and subtracting the corresponding historical reference ground height of the front road surface, thereby obtaining the ice layer thickness of the front road surface.

[0067] In some embodiments, based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface, the ice layer thickness of the front road surface can include determining the ice layer thickness based on the mean value of the height coordinates in the second coordinates of the ice surface points in the ice surface point set and the historical reference ground height.

[0068] Thus, by calculating the average value of the ice surface point height coordinates and subtracting the historical reference ground height to determine the ice layer thickness, the single-point measurement error and the fluctuation caused by the unevenness of the ice surface can be reduced by data smoothing (taking the mean value), so that the final output ice layer thickness value is more reliable and more representative.

[0069] In some example embodiments, the ice layer thickness The ice layer thickness can be calculated based on the following formula:

[0070] wherein, The historical reference ground height can be data obtained by the vehicle automatically in a non-icing season (such as summer), or can be data obtained by calling a related geographic data service interface, which is not limited herein. The mean value of the height coordinates (i.e., z coordinates) in the second coordinates of the ice surface points in the ice surface click.

[0071] In some embodiments, when a user is driving a vehicle, the user initiates navigation through a vehicle-mounted navigation system of the vehicle, and in the navigation process, a cloud server in communication connection with the vehicle-mounted navigation system can control the vehicle to perform the collection and uploading of the point cloud data in real time through the vehicle-mounted navigation system. After the server receives the point cloud data, the server can determine the ice layer thickness of the road in front based on the above method, and determine the navigation information to be output through the vehicle-mounted navigation system based on the ice layer thickness, such as including risk prompt information, etc. In some embodiments, the above method can also be executed through a vehicle terminal, and the vehicle terminal determines the navigation information to be output according to the detected ice layer thickness, which is not limited herein.

[0072] In some embodiments, outputting the navigation information corresponding to the ice layer thickness can include: in response to detecting that the ice layer thickness is greater than a preset thickness threshold, triggering re-planning of a route to obtain a detour route for avoiding the icy road section in front; and outputting the detour route.

[0073] Thus, by automatically triggering the route re-planning function when the ice layer thickness exceeds the safety threshold, a detour route that can avoid the icy road section is generated and output, so that the high-risk road section can be actively avoided, and a safe alternative solution is provided for the driver, thereby fundamentally avoiding traffic accidents that can occur when the vehicle enters a thick ice area.

[0074] In some example embodiments, the above-mentioned preset thickness threshold can be, for example, 5 mm. If the current ice layer thickness is greater than 5 mm, it means that the road section in front is a high-risk slippery road section, and the server or the client terminal can automatically trigger the re-planning of the navigation route to plan the route based on the user's destination and the current position, and obtain at least one detour route that can avoid the icy road section in front. After obtaining the detour route, the recommended detour route can be displayed on the user interface in the form of a pop-up window or a card, so that the user can select and switch the route.

[0075] In some embodiments, outputting the navigation information corresponding to the ice layer thickness can also include: adjusting the display mode of the map in the user interface according to the ice layer thickness.

[0076] Therefore, by dynamically adjusting the map display method in the user interface based on the real-time detected ice thickness, the way map information is presented can be matched with the current road hazard level, significantly improving the efficiency of hazard information transmission and driver alertness.

[0077] In some embodiments, adjusting the way the map is displayed in the user interface may include at least one of the following: adjusting the map display scale; focusing the map view on the area where the icy road section is located; and highlighting the icy road section with a marker style corresponding to the ice thickness.

[0078] Therefore, by adjusting the map scale to display more details, focusing the view on dangerous road sections, or highlighting icy road sections with special styles corresponding to ice thickness, drivers' attention can be directly guided to dangerous areas through a variety of intuitive visual means, ensuring that key navigation and warning information is obtained quickly and accurately.

[0079] In some exemplary embodiments, different ice thicknesses correspond to different risk levels. For example, an ice thickness greater than 2 mm and less than or equal to 5 mm corresponds to a medium risk level, an ice thickness less than or equal to 2 mm corresponds to a low risk level, and an ice thickness greater than 5 mm corresponds to a high risk level. Different risk levels can correspond to different map display methods.

[0080] In some exemplary embodiments, if the ice thickness indicates a low risk level, the map display method need not be adjusted; if the ice thickness indicates a medium risk level, the map scale can be reduced, and the displayed map view can be focused on the map area where the icy road section is located. Simultaneously, the icy road section can be highlighted using a marking style corresponding to the medium risk level, for example, by marking the icy road section ahead with a prominent flashing yellow line; if the ice thickness indicates a high risk level, the map scale can be reduced, and the displayed map view can be focused on the map area where the icy road section is located. Simultaneously, the icy road section can be highlighted using a marking style corresponding to the high risk level, for example, by marking the icy road section with a prominent red line. It is understood that those skilled in the art can set the risk level determination method and map display method as they see fit, and no restrictions are imposed here.

[0081] In some embodiments, such as Figure 5 As shown, the output navigation information corresponding to the ice thickness may further include: step S501, determining the driving risk level based on the ice thickness; step S502, determining the output strategy of the prompt information based on the driving risk level, wherein the output strategy includes at least one of the display style of the prompt information and the voice features of the prompt information; and step S503, outputting the prompt information according to the output strategy.

[0082] Thus, by determining the risk level according to the ice layer thickness, and automatically adjusting the display style (such as color, size) and voice features (such as volume, speed) of the prompt information, the graded warning can be realized, so that the intensity of the warning is proportional to the degree of danger, and the driver is reminded in the most effective way to avoid insufficient warning or excessive interference.

[0083] In some embodiments, the driving risk level can be determined in a similar manner as described above, which will not be repeated here.

[0084] In some embodiments, the display style may, for example, include the font color or size of the prompt information, the color or size of the pop-up window, etc.; the voice features may, for example, include the voice, tone, speed, etc.

[0085] In some exemplary embodiments, if the ice layer thickness indicates that the current risk level is a low risk level, the user can be shown the prompt information “The road ahead is suspected to be icy, please drive carefully”, and the prompt information is broadcasted based on a relaxed tone (for example, 400 Hz) with a speed of 150 words per minute; if the ice layer thickness indicates that the current risk level is a medium risk level, the prompt information “There is thin ice on the road ahead, please drive carefully” can be displayed through a pop-up window, and the prompt information is broadcasted based on a neutral tone (for example, 300 Hz) with a speed of 120 words per minute; if the ice layer thickness indicates that the current risk level is a high risk level, the prompt information “The ice ahead is too thick, please choose other routes immediately” can be displayed through a pop-up window, and the prompt information is broadcasted based on a low tone (for example, 200 Hz) with a speed of 90 words per minute, while a semi-transparent red warning layer that flashes constantly is overlaid on the map interface. It can be understood that the related technical personnel can set the determination method of the risk level and the output strategy of the prompt information by themselves, which is not limited here.

[0086] In some embodiments, the above navigation method can further include: obtaining a vehicle passability parameter of the vehicle, the vehicle passability parameter including at least one of a tire grip coefficient and a chassis height; and wherein determining the driving risk level based on the ice layer thickness can include: determining the driving risk level based on the vehicle passability parameter and the ice layer thickness.

[0087] Thus, by combining the passability parameters (such as grip, chassis height) of the vehicle itself and the external ice layer thickness to comprehensively judge the driving risk level, individualized risk assessment can be realized, and risk level prompts that match different performance vehicles are provided, making the warning more accurate and practical.

[0088] In some example embodiments, determining the driving risk level based on the vehicle passability parameter and the ice layer thickness may, for example, comprise determining the driving risk level as a lower risk level in response to the ice layer thickness being greater than 2 mm and less than or equal to 5 mm and the tire grip coefficient of the vehicle being greater than or equal to a first grip coefficient threshold (e.g., 0.7); and determining the driving risk level as a higher risk level in response to the ice layer thickness being greater than 2 mm and less than or equal to 5 mm and the tire grip coefficient of the vehicle being less than a second grip coefficient threshold (e.g., 0.4). Different prompt information can be set for the above two levels, but the same voice features are broadcasted, for example, the prompt information for the lower risk level can be "the ice thickness in front is 3 mm, please reduce the speed to 30 km / h", and the prompt information for the higher risk level can be "there is thin ice on the road ahead, the current tire anti-slip ability is insufficient, it is recommended to detour".

[0089] In some example embodiments, determining the driving risk level based on the vehicle passability parameter and the ice layer thickness may, for example, further comprise determining the driving risk level as a high-risk level in response to the ice layer thickness being greater than 5 mm and the ground clearance of the vehicle being less than a preset height threshold, and further outputting the prompt information "there may be ice ridges on the ice surface in front, your vehicle has low ground clearance, and the risk of passing through is high".

[0090] In some embodiments, the above navigation method may, before acquiring the point cloud data of the road surface in front of the vehicle, further comprise: acquiring environmental data in real time, the environmental data comprising at least one of real-time weather data and at least one of a road surface temperature and a road surface reflectivity of the road surface in front; determining an icing probability of the road surface in front based on the environmental data; and in response to detecting that the icing probability is greater than a preset probability threshold, controlling the point cloud data acquisition device of the vehicle to acquire the point cloud data of the road surface in front.

[0091] Thus, by using environmental data (weather, road surface temperature, etc.) to predict the icing probability before starting the high-energy-consuming laser radar for accurate detection, and only starting the accurate detection when the probability is high, a hierarchical detection system can be constructed, and the system energy consumption can be effectively saved.

[0092] In some embodiments, the road surface temperature can be acquired by an infrared sensor of the vehicle.

[0093] In some embodiments, the acquisition of the road surface temperature may, for example, comprise: acquiring a thermal spectrum of the road surface in front, wherein each pixel point in the thermal spectrum comprises a pixel point temperature; and determining the road surface temperature based on the pixel point temperature of each pixel point in the thermal spectrum.

[0094] Thus, by acquiring the thermal spectrum and analyzing the pixel temperature, the road surface temperature can be determined, thereby realizing non-contact and large-area remote road surface temperature measurement, and providing more accurate temperature data support for early judgment of icing probability.

[0095] In some example embodiments, the infrared thermal imaging device can scan the road surface within a preset range (e.g., within 10 meters in front) to obtain a thermal spectrum of the road surface (e.g., the resolution can be 160x120, and each pixel point contains temperature information, and the temperature range can be -20°C to 120°C).

[0096] In some example embodiments, the road surface temperature The road surface temperature can be calculated based on the following formula:

[0097] wherein, T i is the pixel temperature of the i th pixel point, and N is the number of pixel points.

[0098] In some embodiments, the road surface reflection intensity can be obtained by the millimeter wave radar of the vehicle.

[0099] In some embodiments, the weather data can be obtained by calling a related weather service interface.

[0100] In some embodiments, the weather data can include at least one of a temperature factor, a humidity factor, and a snowfall amount factor. Thus, by using weather data including factors such as temperature, humidity, and snowfall amount, the key meteorological elements that cause road icing can be grasped, the most direct and effective input for the calculation of icing probability can be provided, and the scientificity and accuracy of the prediction results can be ensured.

[0101] In some embodiments, based on the environmental data, determining the icing probability of the road surface in front can include inputting one or more of the above environmental data into a trained icing probability prediction model after encoding them as feature vectors, to obtain the icing probability output by the model.

[0102] In some embodiments, based on the environmental data, determining the icing probability of the road surface in front can include: respectively determining a first probability, a second probability, and a third probability based on the road surface temperature, the road surface reflection intensity, and the weather data; and performing weighted summation on the first probability, the second probability, and the third probability to obtain the icing probability of the road surface in front.

[0103] Thus, by respectively calculating the probabilities based on the road surface temperature, the reflection intensity, and the weather data, and performing weighted fusion to determine the final icing probability, the advantages of multi-source heterogeneous data can be comprehensively utilized, the unreliability of a single data source can be compensated for, and the prediction results of the icing probability can be more comprehensive and accurate. Thus, by respectively calculating the probabilities based on the road surface temperature, the reflection intensity, and the weather data, and performing weighted fusion to determine the final icing probability, the advantages of multi-source heterogeneous data can be comprehensively utilized, the unreliability of a single data source can be compensated for, and the prediction results of the icing probability can be more comprehensive and accurate.

[0104] In some exemplary embodiments, the three types of environmental data—road surface temperature (or thermal spectrum), road surface reflection intensity, and multiple weather data—can be feature-encoded and then input into the temperature-based icing probability prediction model, the road surface reflection intensity-based icing probability prediction model, and the weather data-based icing probability prediction model, respectively, to obtain the first probability, second probability, and third probability output by the corresponding models. Subsequently, the above three probabilities can be weighted and summed to obtain the icing probability of the road surface ahead.

[0105] In some exemplary embodiments, the first probability It can also be calculated based on the following formula: = max(0, ) in, This represents the road surface temperature. According to the formula above, if... (That is, the icing threshold, determined based on the SAE J2945 standard), then .

[0106] In some exemplary embodiments, the second probability It can also be calculated based on the following formula: =

[0107] =

[0108] in: Indicates the road surface reflection intensity. and Settings are used for Normalization is performed, where, For example, it could be 0.2. For example, it could be 0.9. According to the formula above, if... If the value is greater than 0.6, it can be considered as suspected icing.

[0109] In some exemplary embodiments, the third probability It can also be calculated based on the following formula: = ·T + ·H + ·S =

[0110] Where: T∈[-20,0], H∈[0,100], S∈[0,10], , are weights corresponding to different factors, which can be 0.5, 0.3, 0.2 respectively. The above second formula is used to normalize to [0, 1], outputting the weather icing probability . .

[0111] In some example embodiments, the icing probability can be calculated based on the following formula: =

[0112] wherein, w1, w2, w3 are weights of the first probability, the second probability and the third probability respectively, which can be 0.4, 0.3, 0.3 respectively, without limitation. , , .

[0113] In some example embodiments, the preset probability threshold can be set as 0.5, that is, if the calculated , it can be considered that the road surface has been iced, and then the point cloud data acquisition device is controlled to acquire the point cloud data of the front road surface, if the calculated , the subsequent process is not continued.

[0114] Figure 6 A flowchart of a navigation method according to an example embodiment of the present disclosure is shown.

[0115] In some example embodiments, as shown in Figure 6 , a navigation method is provided, comprising: step S601, acquiring environment data of an environment in which a vehicle currently locates in real time; step S602, determining an icing probability based on the environment data; step S603, judging whether the icing probability is greater than a preset probability threshold, and in response to judging that the icing probability is not greater than the preset probability threshold, returning to step S601; step S604, in response to judging that the icing probability is greater than the preset probability threshold, controlling a point cloud data acquisition device to acquire point cloud data of a front road surface; step S605, fitting a road surface plane of the front road surface according to the point cloud data; step S606, determining an ice layer thickness of the front road surface based on the point cloud data; and step S607, outputting navigation information corresponding to the ice layer thickness.

[0116] In some embodiments, as shown in Figure 7As shown, a navigation device 700 is provided, comprising: a first obtaining unit 710 configured to obtain point cloud data of a front road surface of a vehicle; a fitting unit 720 configured to fit a road surface plane based on the point cloud data; a second obtaining unit 730 configured to obtain candidate points in the point cloud data which are within a preset distance interval from the road surface plane to form a non-road surface point set; a screening unit 740 configured to screen ice surface points based on the reflection intensity of each candidate point in the non-road surface point set to form an ice surface point set; a first determining unit 750 configured to determine an ice layer thickness of the front road surface based on the height of each ice surface point in the ice surface point set and a historical reference ground height of the front road surface; and an output unit 760 configured to output navigation information corresponding to the ice layer thickness.

[0117] The operations performed by the units 710-760 in the navigation device 700 and the effects that can be achieved are similar to steps S201-S206 of the navigation method described above, and will not be repeated here.

[0118] In some embodiments, the fitting unit comprises: a conversion sub-unit configured to convert a first coordinate of each point in the point cloud data in a coordinate system of a point cloud data acquisition device into a second coordinate of each point in a vehicle coordinate system corresponding to the vehicle; a screening sub-unit configured to screen points whose second coordinates are within a preset coordinate range to form a first point set; and a fitting sub-unit configured to fit the road surface plane based on the first point set.

[0119] In some embodiments, the fitting sub-unit can be further configured to: perform a first operation multiple times on the first point set to obtain multiple candidate planes and an inlier point number corresponding to each candidate plane, the first operation comprising: randomly selecting three points in the first point set to determine a candidate plane based on the three points; and counting the number of inlier points in the first point set that are less than a first preset distance from the candidate plane as the inlier point number of the candidate plane; determining the candidate plane with the largest inlier point number from the multiple candidate planes as the target plane to obtain an inlier point set of the target plane; and performing plane fitting based on the inlier point set to obtain the road surface plane.

[0120] In some embodiments, the first determining unit can be further configured to: determine the ice layer thickness based on the mean value of the height coordinates in the second coordinates of the ice surface points in the ice surface point set and the historical reference ground height.

[0121] In some embodiments, the output unit can comprise: a triggering sub-unit configured to trigger re-planning of a route to obtain a detour route for avoiding the front icy road section in response to detecting that the ice layer thickness is greater than a preset thickness threshold; and a first output sub-unit configured to output the detour route.

[0122] In some embodiments, the output unit can further include an adjusting subunit configured to adjust a display manner of the map in the user interface according to the ice layer thickness.

[0123] In some embodiments, the adjusting the display manner of the map in the user interface can include at least one of the following: adjusting a scale of the map display; focusing a map view on an area where the icy road section is located; and highlighting the icy road section in a marking style corresponding to the ice layer thickness.

[0124] In some embodiments, the output unit can further include a first determining subunit configured to determine a driving risk level based on the ice layer thickness; a second determining subunit configured to determine an output strategy of the prompt information based on the driving risk level, wherein the output strategy can include at least one of a display style of the prompt information and a voice feature of playing the prompt information; and a second output subunit configured to output the prompt information according to the output strategy.

[0125] In some embodiments, the navigation device described above can further include a third obtaining unit configured to obtain a vehicle passability parameter of the vehicle, the vehicle passability parameter including at least one of a tire grip coefficient and a chassis height; and wherein the first determining subunit can be further configured to determine the driving risk level based on the vehicle passability parameter and the ice layer thickness.

[0126] In some embodiments, the navigation device described above can further include a fourth obtaining unit configured to obtain environmental data in real time before obtaining the point cloud data of the front road surface of the vehicle, the environmental data including at least one of real-time weather data and a road surface temperature, a road surface reflection intensity of the front road surface; a second determining unit configured to determine an icing probability of the front road surface based on the environmental data; and a control unit configured to control the point cloud data collection device of the vehicle to collect the point cloud data of the front road surface in response to detecting that the icing probability is greater than a preset probability threshold.

[0127] In some embodiments, the second determining unit can include a third determining subunit configured to determine a first probability, a second probability and a third probability based on the road surface temperature, the road surface reflection intensity and the weather data, respectively; and an obtaining subunit configured to obtain the icing probability of the front road surface by weighted sum of the first probability, the second probability and the third probability.

[0128] In some embodiments, the weather data can include at least one of a temperature factor, a humidity factor and a snowfall amount factor.

[0129] In some embodiments, the obtaining of the road surface temperature can include: obtaining a thermal spectrum of the front road surface, wherein each pixel point in the thermal spectrum includes a pixel point temperature; and determining the road surface temperature based on the pixel point temperature of each pixel point in the thermal spectrum.

[0130] According to embodiments of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0131] Reference Figure 8 A block diagram of an electronic device 800, which is an example of a hardware device that can be applied to aspects of the present disclosure, will now be described, which can be a server or a client of the present disclosure. The electronic device is intended to represent a wide variety of digital electronic computer devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computer devices. The electronic device can also represent a wide variety of mobile devices such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other like computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0132] As Figure 8 shown, the electronic 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 electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0133] A plurality of components in the electronic device 800 are connected to the I / O interface 805, including an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 can be any type of device capable of inputting information to the electronic device 800, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device, and can include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a jog wheel, a microphone, and / or a remote control. The output unit 807 can be any type of device capable of presenting information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 808 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0134] The computing unit 801 can be various general and / or special purpose processing components having 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 special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs various methods and processes described above, such as the navigation method of the present disclosure. For example, in some embodiments, the navigation method of the present disclosure can be implemented as a computer software program tangibly embodied 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 electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded onto the RAM 803 and executed by the computing unit 801, one or more steps of the navigation method of the present disclosure described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the navigation method of the present disclosure by any other appropriate means, such as by means of firmware.

[0135] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0136] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0137] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The 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 an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical conductors, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0138] To provide for interaction with a user, the systems and techniques described here 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 be used to provide for interaction with a user as well; 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.

[0139] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or 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 communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0140] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server is generally established by computer programs running on the respective computers and having a client-server relationship to each other. The servers can be cloud servers, servers of a distributed system, or servers combined with a blockchain.

[0141] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technology disclosed in the present disclosure are achieved, which is not limited herein.

[0142] While embodiments or examples of this disclosure have been described with reference to the figures, it will be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the application is not limited to these embodiments or examples. Various elements of the embodiments or examples can be omitted or substituted by equivalents thereof. Furthermore, the steps can be performed in a different order than described in the disclosure. Further, various elements of the embodiments or examples can be combined in various ways. It is important that as technology evolves, many of the elements described herein can be substituted by equivalents which serve the same function.

Claims

1. A navigation method, comprising: obtaining point cloud data of a front road surface of a vehicle; fitting a road surface plane based on the point cloud data; obtaining candidate points in the point cloud data that are within a preset distance range from the road surface plane to form a non-road surface point set; screening ice surface points from the non-road surface point set based on reflection intensity of each candidate point in the non-road surface point set to form an ice surface point set; determining an ice layer thickness of the front road surface based on a height of each ice surface point in the ice surface point set and a historical reference ground height of the front road surface; and outputting navigation information corresponding to the ice layer thickness. The fitting of the road surface plane based on the point cloud data comprises:

2. The method of claim 1, wherein, converting a first coordinate of each point in the point cloud data in a point cloud data collection device coordinate system into a second coordinate of each point in a vehicle coordinate system corresponding to the vehicle; screening points with the second coordinate within a preset coordinate range to form a first point set; and fitting the road surface plane based on the first point set. The fitting of the road surface plane based on the first point set comprises:

3. The method of claim 2, wherein, performing a first operation multiple times on the first point set to obtain multiple candidate planes and an inlier point number corresponding to each candidate plane, the first operation comprising: randomly selecting three points in the first point set to determine a candidate plane based on the three points; and counting the number of inlier points in the first point set that are less than a first preset distance from the candidate plane as the inlier point number of the candidate plane; determining a candidate plane with the largest inlier point number from the multiple candidate planes as a target plane to obtain an inlier point set of the target plane; and performing plane fitting based on the inlier point set to obtain the road surface plane. The determination of the ice layer thickness of the front road surface based on the height of each ice surface point in the ice surface point set and the historical reference ground height of the front road surface comprises:

4. The method of claim 2 or 3, wherein, determining the ice layer thickness based on a mean value of the height coordinate in the second coordinate of the ice surface points in the ice surface point set and the historical reference ground height. The outputting of the navigation information corresponding to the ice layer thickness comprises:

5. The method of any one of claims 1 to 4, wherein, in response to detecting that the ice layer thickness is greater than a preset thickness threshold, triggering a route re-planning to obtain a detour route for avoiding a front icy road section; and outputting the detour route. The outputting of the navigation information corresponding to the ice layer thickness further comprises:

6. The method of claim 5, wherein, adjusting a display mode of a map in a user interface according to the ice layer thickness. The adjustment of the display mode of the map in the user interface comprises at least one of the following:

7. The method of claim 6, wherein, adjusting a scale of the map display; focusing a map view on an area where the icy road section is located; and highlighting the icy road section in a marking style corresponding to the ice layer thickness. The outputting of the navigation information corresponding to the ice layer thickness further comprises: determining a driving risk level based on the ice layer thickness; 8. The method of any one of claims 5-7, wherein, determining an output strategy of prompt information based on the driving risk level, wherein the output strategy comprises at least one of a display style of the prompt information and a voice feature of playing the prompt information; and outputting the prompt information according to the output strategy.

9. The method of claim 8, further comprising: ​ ​ obtaining a vehicle passability parameter of the vehicle, the vehicle passability parameter comprising at least one of a tire grip coefficient and a chassis height; and wherein, the determining the driving risk level based on the ice layer thickness comprises: determining the driving risk level based on the vehicle passability parameter and the ice layer thickness.

10. The method of any one of claims 1-9, further comprising: obtaining environmental data in real time before obtaining the point cloud data of the front road surface of the vehicle, the environmental data comprising at least one of real-time weather data and a road surface temperature, a road surface reflectivity of the front road surface; determining an icing probability of the front road surface based on the environmental data; and controlling a point cloud data collection device of the vehicle to collect the point cloud data of the front road surface in response to detecting that the icing probability is greater than a preset probability threshold.

11. The method of claim 10, wherein, the determining the icing probability of the front road surface based on the environmental data comprises: determining a first probability, a second probability and a third probability based on the road surface temperature, the road surface reflectivity and the weather data, respectively; and performing a weighted summation on the first probability, the second probability and the third probability to obtain the icing probability of the front road surface.

12. The method of claim 10 or 11, wherein, the weather data comprises at least one of a temperature factor, a humidity factor and a snowfall amount factor.

13. The method of any one of claims 10-12, wherein, the obtaining of the road surface temperature comprises: obtaining a thermal spectrum of the front road surface, wherein each pixel point in the thermal spectrum comprises a pixel point temperature; and determining the road surface temperature based on the pixel point temperature of each pixel point in the thermal spectrum.

14. A navigation device, comprising: a first obtaining unit configured to obtain point cloud data of a front road surface of a vehicle; a fitting unit configured to fit a road surface plane based on the point cloud data; a second obtaining unit configured to obtain candidate points in the point cloud data that are within a preset distance range from the road surface plane to form a non-road surface point set; a screening unit configured to screen ice surface points based on a reflectivity of each candidate point in the non-road surface point set to form an ice surface point set; a first determining unit configured to determine an ice layer thickness of the front road surface based on a height of each ice surface point in the ice surface point set and a historical reference ground height of the front road surface; and an output unit configured to output navigation information corresponding to the ice layer thickness.

15. An electronic device, 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-13. The computer instructions are used to enable a computer to perform the method of any one of claims 1-13.

16. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer program, when executed by a processor, implements the method of any one of claims 1-13.

17. A computer program product comprising a computer program, wherein, The electronic device of claim 15.

18. An autonomous vehicle, comprising: The electronic device of claim 15.

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