Urban canyon positioning optimization method and system

Abstract

The invention provides an urban canyon positioning optimization method and system, and the method comprises the steps: controlling a vehicle-mounted laser radar and a camera to collect the information of buildings around a vehicle when the GNSS signal intensity of the vehicle is continuously lower than a preset threshold value, and judging whether the vehicle is in an urban canyon region or not according to the height, number and density of the buildings; and if the vehicle is in the urban canyon area, switching the positioning mode identifier of the vehicle, estimating the position of the vehicle through a Kalman filtering algorithm based on data acquired by a vehicle IMU, a wheel speed sensor and a steering wheel angle sensor, and displaying the vehicle positioning error range by dynamically adjusting the size of a positioning precision icon. According to the scheme, a user can conveniently know the vehicle positioning state in time, misjudgment of a driver under complex road conditions is avoided, and the user experience is improved.

Description

Technical Field

[0001] This invention belongs to the field of vehicle positioning technology, and in particular relates to a method and system for optimizing positioning in urban canyons. Background Technology

[0002] Global Navigation Satellite System (GNSS) is currently the main means of vehicle positioning. However, in the "urban canyon" environment, satellite signals are severely affected by building obstruction and multipath effects, resulting in a sharp drop in positioning accuracy and positioning drift of tens to hundreds of meters, which seriously affects the navigation experience.

[0003] Currently, positioning in urban canyon areas mainly relies on multi-sensor fusion, such as combining inertial measurement units (IMUs) and wheel speed sensors for dead reckoning, to compensate for short-term GNSS signal gaps. However, these positioning methods focus on optimizing the underlying algorithms. When the positioning status changes or accuracy deteriorates, users cannot promptly perceive the changes, resulting in a noticeable delay in perception. When navigation information does not match the actual vehicle status, it can easily lead to driver misjudgment in complex road conditions. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an urban canyon positioning optimization method and system to solve the problem that users cannot obtain positioning status in a timely manner and the perception is relatively lagging in the current urban canyon positioning, which is prone to driving misjudgment.

[0005] In a first aspect of the present invention, an urban canyon positioning optimization method is provided, comprising: When the vehicle's GNSS signal strength is consistently below a preset threshold, the vehicle's lidar and camera are controlled to collect information about buildings around the vehicle, and the vehicle is determined to be in an urban canyon area based on the height, number, and density of the buildings. If the vehicle is in an urban canyon area, the vehicle's positioning mode is switched, and the vehicle's position is estimated using a Kalman filter algorithm based on data collected by the vehicle's IMU, wheel speed sensors, and steering wheel angle sensors. The positioning accuracy icon size is dynamically adjusted to display the vehicle's positioning error range.

[0006] In a second aspect of the present invention, an urban canyon positioning optimization system is provided, comprising: The detection and judgment module is used to control the vehicle-mounted lidar and camera to collect information about buildings around the vehicle when the vehicle's GNSS signal strength is continuously lower than a preset threshold, and to determine whether the vehicle is in an urban canyon area based on the height, number and density of the buildings. The position estimation module estimates the vehicle's position using a Kalman filter algorithm based on data collected from the vehicle's IMU, wheel speed sensors, and steering wheel angle sensor. The visualization module is used to switch the vehicle's positioning mode identifier if the vehicle is in an urban canyon area, and to display the vehicle's positioning error range by dynamically adjusting the size of the positioning accuracy icon.

[0007] In a third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect of the present invention.

[0008] In a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method provided in the first aspect of the present invention.

[0009] In this embodiment of the invention, building information is collected to determine whether a vehicle is in an urban canyon area. In urban canyon mode, the vehicle's positioning mode identifier is switched, the vehicle's position is estimated, and the size of the positioning accuracy icon is dynamically adjusted to display the vehicle's positioning error range. This not only allows users to promptly obtain information about changes in positioning status but also avoids driver misjudgments caused by complex road conditions.

[0010] By integrating GNSS, IMU, wheel speedometer, LiDAR, and camera data, positioning accuracy and continuity in urban canyon environments can be significantly improved, avoiding drastic drift of vehicle icons on the map and ensuring real-time vehicle perception. By employing methods such as switching positioning mode indicators and adjusting positioning accuracy icons, the previously invisible positioning status and accuracy range within the system are made transparent and visible, enhancing user experience and trust in the navigation system. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating an urban canyon positioning optimization method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the interface effect provided in one embodiment of the present invention; Figure 3 This is another schematic diagram of the interface effect provided in one embodiment of the present invention; Figure 4This is a schematic diagram of the structure of an urban canyon positioning optimization system provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0014] It should be understood that the terms "comprising" and other similar expressions in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, or apparatus that includes a series of steps or units and is not limited to the listed steps or units. Furthermore, "first" and "second" are used to distinguish different objects and are not intended to describe a specific order.

[0015] Please see Figure 1 The present invention provides a flowchart illustrating an urban canyon positioning optimization method, comprising: S101. When the vehicle's GNSS signal strength is continuously lower than the preset threshold, control the vehicle's lidar and camera to collect information about buildings around the vehicle, and determine whether the vehicle is in an urban canyon area based on the height, number and density of the buildings. The vehicle GNSS positioning module is installed inside the shark fin antenna on the roof. It is generally used to receive satellite signals from multiple systems such as GPS, Beidou, and Galileo to obtain basic vehicle location data. Its signal sensitivity is usually less than -158dBm, and its positioning accuracy (open area) is less than 2m.

[0016] By default, only the GNSS module maintains low power consumption (e.g., power consumption ≤ 50mW). When the vehicle's GNSS signal strength is continuously lower than a preset threshold, for example, when the GNSS signal strength is continuously lower than -130dBm for 3 seconds, the vehicle controller automatically wakes up the lidar and camera, while the wheel speed sensor and IMU enter standby mode.

[0017] The lidar and camera scan the surrounding environment every certain period of time (e.g., 0.5 seconds) to obtain building information. The building information can include the building location, height, number and density of buildings, etc. Based on the building information, a coordinate set of building location-height can be constructed.

[0018] Whether a vehicle is in an urban canyon area can be determined by the height, number, and density of buildings within a certain distance. For example, if there are ≥10 buildings with a height ≥50m within a 1km range and the building distribution density is ≥2 buildings / 1000㎡, then the vehicle can be determined to be in an urban canyon area.

[0019] In some embodiments, it can be determined whether a vehicle is in an urban canyon area based on GNSS positioning deviation or GNSS signal strength, combined with building information.

[0020] Optionally, when the vehicle is within a predetermined driving distance, the system can determine whether the vehicle is in an urban canyon area based on the height, number, density of buildings and the positioning deviation of the GNSS signal. Alternatively, the height, number, density of buildings, and the strength of GNSS signals can be used to determine whether a vehicle is in an urban canyon area.

[0021] For example, if there are 10 buildings with a height of ≥50m within a 1km radius, the building distribution density is ≥2 buildings / 1000㎡, and the GNSS positioning deviation exceeds 10m for 5 consecutive seconds or the signal strength is continuously below -130dBm, then the vehicle is determined to be located in an urban canyon area.

[0022] By actively scanning building density using lidar and cameras, and combining this with GNSS signal strength analysis, we can not only improve the accuracy of our assessments, but also make advance predictions about urban canyon environments, allowing time for mode switching.

[0023] S102. If the vehicle is in an urban canyon area, the vehicle's positioning mode is switched, and the vehicle's position is estimated using a Kalman filter algorithm based on data collected by the vehicle's IMU, wheel speed sensor, and steering wheel angle sensor. The positioning error range is displayed by dynamically adjusting the size of the positioning accuracy icon.

[0024] The vehicle's positioning mode can be divided into standard mode, estimated mode, and correction mode, and the corresponding display icons are also different for each mode. When the vehicle is in an urban canyon area, it can be determined that the vehicle's positioning is in estimated mode, and the positioning mode icon on the vehicle's infotainment system needs to be actively switched to estimated mode. For example, a satellite icon is displayed in standard mode, while in estimated mode, it switches to a special icon with the outline of tall buildings, and is distinguished by color (e.g., blue changes to amber).

[0025] In some embodiments, when a vehicle's GNSS signal is interrupted for a long period of time, such as when the vehicle passes through a tunnel and does not receive a GNSS signal for 10 consecutive seconds, it can be determined that the vehicle is in the prediction mode.

[0026] In prediction mode, the vehicle's position and trajectory are estimated using a Kalman filter algorithm based on data collected from the vehicle's IMU, wheel speed sensors, and steering wheel angle sensor. In standard mode, where the GNSS signal strength is ≥-120dBm, the positioning deviation is ≤5m, and no urban canyons are detected, the GNSS module can be used for positioning, assisted by the wheel speed sensors. In correction mode, where a brief recovery of the GNSS signal is detected in prediction mode, the predicted trajectory can be corrected using the current precise GNSS position, and the algorithm parameters can be updated.

[0027] Kalman filtering is an algorithm that uses the state equations of a linear system to make an optimal estimate of the system state using system input and output observation data.

[0028] In this embodiment, the initial state of the Kalmar filter is set as follows: the last precise GNSS position before entering the urban canyon (set as coordinate P0(x0,y0)) is used as the initial value, and the current time t0 and vehicle speed v0 are recorded. The state prediction is as follows: Based on the real-time speed v(t) and steering wheel angle θ(t) collected by the wheel speed sensor, the predicted position P1(x1,y1) at time t is calculated: Then, x1 = x0 + ∫(t0 to t) v(t) × cos[θ(t)] dt; y1 = y0+∫(t0 to t) v(t)×sin[θ(t)] dt; The error correction process is as follows: by collecting the acceleration a(t) and angular velocity ω(t) from the IMU, the error covariance matrix of the predicted position is calculated. Combined with the building boundary constraints detected by the lidar and camera (such as vehicles being unable to penetrate buildings, correcting lateral offset), the final estimated position P(x,y) is output, with the error controlled within ≤3m. A semi-transparent circle is rendered to represent the positioning accuracy icon. The size of the icon is dynamically adjusted to display the vehicle's positioning error range. The positioning error range refers to the area where positioning errors may exist. For example, if a vehicle is traveling through a section of road in an urban canyon, the positioning accuracy icon can be displayed to indicate that this section is an area with positioning errors. As the vehicle travels through the urban canyon area, the size of the positioning accuracy icon can be gradually adjusted to indicate that the vehicle is within the positioning error range and that errors are accumulating. For example, as shown... Figure 2 As shown in the figure, label 1 represents the positioning mode indicator, label 2 represents the positioning accuracy icon, and the adjusted positioning accuracy icon is as follows. Figure 3 As shown.

[0029] In this embodiment, the triggered wake-up mechanism activates the high-power sensor only after the GNSS signal strength decreases, effectively reducing the overall system energy consumption. By fusing GNSS, IMU, wheel speedometer, lidar, and camera data, the positioning accuracy and continuity in urban canyon environments are significantly improved, avoiding the phenomenon of drastic drift of vehicle icons on the map. The uniquely designed positioning status and accuracy icons make the previously invisible positioning status and accuracy range transparent and visible, allowing users to understand their current positioning status and the reliable range of the estimated location, greatly enhancing user trust in the navigation system and improving the user experience.

[0030] In one embodiment, if the density of tall buildings does not exceed a preset threshold for a continuous period of time based on the lidar and camera, and the GNSS signal recovers to a preset strength and stabilizes for a longer time than a set value, then the lidar, camera, wheel speed sensor and IMU are turned off, while the GNSS module remains operational.

[0031] For example, if the lidar and camera do not detect excessive building density for 10 consecutive seconds (e.g., fewer than 5 buildings over 50m in height within a 1km radius), and the GNSS signal strength recovers to above -120dBm and remains stable for 5 seconds, the system automatically shuts down the lidar, camera, wheel speed sensor, and IMU, leaving only the GNSS module operational.

[0032] In this embodiment, after the vehicle leaves the urban canyon area and the GNSS signal strength is restored, the lidar, camera, wheel speed sensor and IMU and other equipment are actively turned off, thereby reducing the power consumption of the whole vehicle (new energy vehicles can reduce the range loss by about 3%).

[0033] In one embodiment, step S102 further includes: When the vehicle is in an urban canyon area and the detected GNSS signal strength is greater than a first preset value and the signal strength stabilization time is greater than a second preset value, the vehicle position and trajectory are corrected according to the position provided by the current GNSS signal, and the initial parameters of the Kalman filter algorithm are updated.

[0034] In prediction mode, if the GNSS signal is briefly restored (e.g., while driving through a gap between tall buildings), the system immediately compares the current GNSS position (denoted as P2) with the predicted position P and calculates the deviation value. If Δ > 2m, then update the initial position to P2 and recalibrate the initial parameters of the Kalman filter algorithm, such as x0, y0, t0 and v0, to avoid error accumulation.

[0035] In one embodiment, step S102 further includes: When a vehicle is detected to have left the urban canyon, the predicted trajectory of the urban canyon area is compared with the trajectory information recovered by GNSS. An error correction model is constructed, which compares the predicted trajectory with the actual trajectory recovered by GNSS to determine the optimization ratio of each coefficient of the Kalman filter. The coefficients of the Kalman filter algorithm are then optimized based on the error correction model.

[0036] After the vehicle leaves the urban canyon, the system compares the entire predicted trajectory data (including a location point every 10 seconds) with the accurate trajectory recovered by GNSS, builds an error correction model, and optimizes the algorithm coefficients of the next prediction mode, such as adjusting the weight ratio of IMU and wheel speed sensors.

[0037] In one embodiment, when the vehicle is in an urban canyon area, after the vehicle's infotainment system switches the positioning mode identifier, as the vehicle travels a distance, the positioning accuracy icon rendered in the infotainment system is gradually enlarged until it reaches the maximum pixel limit, and the transparency of the positioning accuracy icon is gradually increased until it reaches the maximum transparency limit.

[0038] A semi-transparent circle is rendered around the vehicle icon. In prediction mode, the circle dynamically enlarges, and its radius is positively correlated with the positioning error probability range, intuitively displaying the uncertainty of the current position.

[0039] For example, the precision circle around the vehicle icon dynamically adjusts according to the map scale. In standard mode, R is approximately 5-10 pixels; in estimated mode, R increases with the travel distance, reaching a maximum of 50 pixels, and is rendered with an amber semi-transparent effect. The transparency also gradually increases as the icon grows, such as... Figure 2 and Figure 3 As shown in the figure, mark 2 represents the positioning accuracy circle. Increasing the accuracy circle can gradually increase its transparency, thus avoiding it being too obtrusive on the vehicle interface.

[0040] In some embodiments, when entering the prediction mode for the first time from the standard mode, the system plays a short sound effect and the status icon flashes once and remains in the new state to indicate to the user that the mode has been switched.

[0041] When the location mode changes, the user is notified in a gentle, non-intrusive way (such as a change in the status bar icon or a slight sound effect), and a detailed explanation is provided when the user asks for clarification. This reduces user distraction and improves the user experience.

[0042] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0043] Figure 4This is a schematic diagram of a city canyon positioning optimization system provided in an embodiment of the present invention. The system includes: The detection and judgment module 410 is used to control the vehicle-mounted lidar and camera to collect information on buildings around the vehicle when the vehicle's GNSS signal strength is continuously lower than a preset threshold, and to determine whether the vehicle is in an urban canyon area based on the height, number and density of the buildings. Optionally, the detection and judgment module includes: The sensor control unit is used to shut down the lidar, camera, wheel speed sensor, and IMU when the lidar and camera have not detected a high-rise building density exceeding a preset threshold for a continuous period of time, and the GNSS signal recovers to a preset strength and stabilizes for a longer time than a set value, while keeping the GNSS module working.

[0044] Preferably, when the vehicle is within a predetermined driving distance, the system determines whether the vehicle is in an urban canyon area based on the height, number, density of buildings, and the positioning deviation of the GNSS signal. Alternatively, the height, number, density of buildings, and the strength of GNSS signals can be used to determine whether a vehicle is in an urban canyon area.

[0045] The position estimation module 420 is used to estimate the vehicle's position when the vehicle is in an urban canyon area, based on data collected by the vehicle's IMU, wheel speed sensors, and steering wheel angle sensor, using a Kalman filter algorithm. The visualization module 430 is used to switch the vehicle's positioning mode identifier if the vehicle is in an urban canyon area, and to display the vehicle's positioning error range by dynamically adjusting the size of the positioning accuracy icon.

[0046] The visualization module 430 includes: The dynamic adjustment unit is used to gradually increase the size of the positioning accuracy icon rendered in the vehicle's infotainment system as the vehicle travels further after switching the positioning mode indicator when the vehicle is in an urban canyon area, until it reaches the maximum pixel limit, and gradually increase the transparency of the positioning accuracy icon until it reaches the maximum transparency limit.

[0047] In one embodiment, the urban canyon positioning optimization system further includes: The real-time correction module is used to correct the vehicle's position and trajectory based on the position provided by the current GNSS signal when the vehicle is in an urban canyon area and the detected GNSS signal strength is greater than a first preset value and the signal strength stabilization time is greater than a second preset value, and also to update the initial parameters of the Kalman filter algorithm.

[0048] In one embodiment, the urban canyon positioning optimization system further includes: The offline correction module is used to compare the estimated trajectory of the urban canyon area with the trajectory information recovered by GNSS when the vehicle is detected to have left the urban canyon, construct an error correction model, and optimize the coefficients of the Kalman filter algorithm based on the error correction model.

[0049] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0050] Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device is used for vehicle positioning. Figure 5 As shown, the electronic device 5 of this embodiment includes: a memory 510, a processor 520, and a system bus 530. The memory 510 includes an executable program 5101 stored thereon. As those skilled in the art will understand, Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0051] The following is combined Figure 5 A detailed introduction to each component of the electronic device: The memory 510 can be used to store software programs and modules. The processor 520 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 510. The memory 510 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as cached data), etc. In addition, the memory 510 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0052] The memory 510 contains an executable program 5101 for signal processing methods. This executable program 5101 can be divided into one or more modules / units, which are stored in the memory 510 and executed by the processor 520 to achieve vehicle positioning optimization, etc. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, describing the execution process of the executable program 5101 in the electronic device 5. For example, the executable program 5101 can be divided into functional modules such as a detection and judgment module, a position estimation module, and a visualization module.

[0053] The processor 520 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 510, and by calling data stored in the memory 510, it performs various functions and processes data, thereby monitoring the overall status of the electronic device. Optionally, the processor 520 may include one or more processing units; preferably, the processor 520 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, application programs, etc., and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 520.

[0054] The system bus 530 is used to connect various functional components inside the computer, transmitting data, address, and control information. Its type can be, for example, a PCI bus, an ISA bus, or a CAN bus. Instructions from the processor 520 are transmitted to the memory 510 via the bus, and the memory 510 sends data back to the processor 520. The system bus 530 is responsible for data and instruction exchange between the processor 520 and the memory 510. Of course, the system bus 530 can also connect to other devices, such as network interfaces and display devices.

[0055] In this embodiment of the invention, the executable program executed by the processor 520 included in the electronic device includes: When the vehicle's GNSS signal strength is consistently below a preset threshold, the vehicle's lidar and camera are controlled to collect information about buildings around the vehicle, and the vehicle is determined to be in an urban canyon area based on the height, number, and density of the buildings. If the vehicle is in an urban canyon area, the vehicle's positioning mode is switched, and the vehicle's position is estimated using a Kalman filter algorithm based on data collected by the vehicle's IMU, wheel speed sensors, and steering wheel angle sensors. The positioning accuracy icon size is dynamically adjusted to display the vehicle's positioning error range.

[0056] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0057] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0058] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the positioning of urban canyons, characterized in that, include: When the vehicle's GNSS signal strength is consistently below a preset threshold, the vehicle's lidar and camera are controlled to collect information about buildings around the vehicle, and the vehicle is determined to be in an urban canyon area based on the height, number, and density of the buildings. If the vehicle is in an urban canyon area, the vehicle's positioning mode is switched, and the vehicle's position is estimated using a Kalman filter algorithm based on data collected by the vehicle's IMU, wheel speed sensors, and steering wheel angle sensors. The positioning accuracy icon size is dynamically adjusted to display the vehicle's positioning error range.

2. The method according to claim 1, characterized in that, The process of controlling the vehicle-mounted lidar and camera to collect information about buildings around the vehicle, and determining whether the vehicle is in an urban canyon area based on the height, number, and density of the buildings, includes: If the LiDAR and camera do not detect a building density exceeding a preset threshold for a continuous period of time, and the GNSS signal recovers to a preset strength and stabilizes for a longer time than a set value, then the LiDAR, camera, wheel speed sensor, and IMU are turned off, while the GNSS module remains operational.

3. The method according to claim 1, characterized in that, The method of determining whether a vehicle is located in an urban canyon area based on the height, number, and density of buildings includes: When the vehicle is within the predetermined driving distance, the system determines whether the vehicle is in an urban canyon area based on the height, number, density of buildings and the positioning deviation of the GNSS signal. Alternatively, the height, number, density of buildings, and the strength of GNSS signals can be used to determine whether a vehicle is in an urban canyon area.

4. The method according to claim 1, characterized in that, The estimation of vehicle position using the Kalman filter algorithm also includes: When the vehicle is in an urban canyon area and the detected GNSS signal strength is greater than a first preset value and the signal strength stabilization time is greater than a second preset value, the vehicle position and trajectory are corrected according to the position provided by the current GNSS signal, and the initial parameters of the Kalman filter algorithm are updated.

5. The method according to claim 1, characterized in that, The estimation of vehicle position using the Kalman filter algorithm also includes: When a vehicle is detected to have left the urban canyon, the predicted trajectory of the urban canyon area is compared with the trajectory information recovered by GNSS, an error correction model is constructed, and the coefficients of the Kalman filter algorithm are optimized based on the error correction model.

6. The method according to claim 1, characterized in that, The method of dynamically adjusting the size of the positioning accuracy icon to display the vehicle positioning error range includes: When the vehicle is in an urban canyon area, after the vehicle's infotainment system switches to the positioning mode indicator, as the vehicle travels a distance, the positioning accuracy icon rendered in the infotainment system gradually increases in size until it reaches the maximum pixel limit, and the transparency of the positioning accuracy icon gradually increases until it reaches the maximum transparency limit.

7. A city canyon positioning optimization system, characterized in that, include: The detection and judgment module is used to control the vehicle-mounted lidar and camera to collect information about buildings around the vehicle when the vehicle's GNSS signal strength is continuously lower than a preset threshold, and to determine whether the vehicle is in an urban canyon area based on the height, number and density of the buildings. The position estimation module is used to estimate the vehicle's position when it is in an urban canyon area, based on data collected by the vehicle's IMU, wheel speed sensors, and steering wheel angle sensor, using a Kalman filter algorithm. The visualization module is used to switch the vehicle's positioning mode identifier if the vehicle is in an urban canyon area, and to display the vehicle's positioning error range by dynamically adjusting the size of the positioning accuracy icon.

8. The system according to claim 7, characterized in that, The detection and judgment module includes: The sensor control unit is used to shut down the lidar, camera, wheel speed sensor, and IMU when the lidar and camera have not detected a high-rise building density exceeding a preset threshold for a continuous period of time, and the GNSS signal recovers to a preset strength and stabilizes for a longer time than a set value, while keeping the GNSS module working.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the urban canyon positioning optimization method as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the steps of the urban canyon positioning optimization method as described in any one of claims 1 to 6.

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