Vehicle positioning method, device, vehicle and storage medium based on environment matching

Abstract

A vehicle localization method, apparatus, vehicle, and storage medium based on environment matching are disclosed, relating to the field of autonomous driving technology. The method includes: Step S10: acquiring current environmental data and current road images of the location of the vehicle to be located; Step S20: searching for a set of environmentally impacted road images that match the current environmental data; Step S30: matching the current road image with the environmentally impacted road images in the set to obtain a target environmentally impacted road image that matches the current road image; Step S40: determining the current location information of the vehicle to be located based on the target environmentally impacted road image. Compared to existing technologies that often use high-precision maps created under clear daytime conditions for vehicle localization, ignoring positioning errors caused by weather and time of day, the above steps fully consider the mapping effects caused by different environmental factors, reduce positioning errors caused by environmental interference, and improve vehicle localization accuracy under different environmental conditions.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a vehicle localization method, device, vehicle, and storage medium based on environment matching. Background Technology

[0002] With the development and iteration of autonomous driving technology, the day when mass production of autonomous vehicles is closer than ever. During the automatic operation of an autonomous driving system, high-precision positioning is essential for better understanding its own posture and for precise behavior planning and vehicle control. Currently, the industry recognizes two main methods for achieving high-precision positioning. One is a high-precision positioning system based on a combination of satellite positioning and inertial navigation systems. This system relies on fixed-frequency multi-satellite communication for positioning. In areas where satellite communication is unavailable, such as tunnels and overpasses, inertial navigation is used for position estimation, thus achieving high-precision positioning from different directions. The other method involves map acquisition using single sensors such as lasers or vision, or multi-sensor fusion (vision, laser, millimeter wave, etc.), while simultaneously using a mapping toolchain for Simultaneous Localization and Mapping (SLAM). When the autonomous driving system is running on the road, it uses sensors for feature matching to achieve high-precision positioning.

[0003] In practical implementation, the creation of high-precision maps (Map for Highly Automated Driving, HAD Map) requires map data collection. Currently, map data collection both domestically and internationally primarily employs two methods: 1. Dedicated map collection fleets collect or update map information. 2. Data crowdsourcing for map collection or updates. Regardless of the method, current mapping practices involve first establishing a high-precision map, and then matching the real-time environmental perception with the high-precision map for localization during autonomous driving. However, this approach introduces an information gap. While the high-precision map is built upon collected map information, the perceived environment during real-time localization varies significantly, influenced by factors such as sunny days, cloudy days, daytime, nighttime, rainy days, snowy days, and foggy days, as well as different times of day. Therefore, improving vehicle localization accuracy under various environmental conditions is a pressing issue.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a vehicle positioning method, device, vehicle, and storage medium based on environment matching, aiming to solve the technical problem of how to improve vehicle positioning accuracy under different environmental conditions.

[0006] To achieve the above objectives, the present invention provides a vehicle localization method based on environment matching, the method comprising the following steps:

[0007] Obtain the current environmental data and current road image of the location of the vehicle to be located;

[0008] Find a set of environmental impact road images that match the current environmental data;

[0009] The current road image is matched with the environmental impact road images in the environmental impact road image set to obtain the target environmental impact road image that matches the current road image.

[0010] The current location information of the vehicle to be located is determined based on the road image affected by the target environment.

[0011] Optionally, before the step of finding a set of environmental impact road images that match the current environmental data, the method further includes:

[0012] A set of road images under a preset environmental mode is obtained, and environmental impact removal processing is performed on the road image set to obtain a basic road image set;

[0013] The basic road images are injected with environmental impact feature information corresponding to different environmental impacts to generate environmental impact road image sets under different environmental impacts.

[0014] Optionally, the step of acquiring a road image set under a preset environmental mode and performing environmental impact removal processing on the road image set to obtain a basic road image set includes:

[0015] Road image sets under different weather conditions and time periods are acquired, and image recognition and feature labeling are performed on the road image sets to obtain road feature labels;

[0016] Environmental impact removal processing is performed on the road image set based on the road feature markers to obtain a basic road image set.

[0017] Optionally, the step of injecting the basic road images with environmental impact feature information corresponding to different environmental impacts to generate environmental impact road image sets under different environmental impacts includes:

[0018] Obtain weather impact factors and time-period impact factors from environmental characteristic information corresponding to different environmental impacts;

[0019] The weather influencing factors and time period influencing factors are combined according to a preset combination rule to obtain different environmental composite factors;

[0020] The environmental composite factors are injected into the base road image to generate a set of environmental impact road images under different environmental influences.

[0021] Optionally, the step of injecting the environmental composite factors into the base road image to generate an environmental impact road image set under different environmental influences includes:

[0022] Obtain the road components from the basic road image set;

[0023] The road components in the basic road image set are rendered according to the environmental composite factors to generate environmental impact road image sets under different environmental influences.

[0024] Optionally, the step of finding a set of environmental impact road images that match the current environmental data includes:

[0025] Obtain the historical GPS location information of the vehicle to be located, and determine the corresponding image set of the area based on the historical GPS location information;

[0026] Search the image set of environmental impact roads in the image set of the region that matches the current environmental data.

[0027] Optionally, the step of determining the current location information of the vehicle to be located based on the road image influenced by the target environment includes:

[0028] The current location information of the vehicle to be located is determined based on the historical GPS positioning information and the location information corresponding to the road image affected by the target environment.

[0029] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle positioning device based on environment matching, the vehicle positioning device based on environment matching comprising:

[0030] The data acquisition module is used to acquire the current environmental data and current road image of the location of the vehicle to be located;

[0031] The image set search module is used to search for an image set of environmental impact roads that matches the current environmental data;

[0032] The image matching module is used to match the current road image with the environmental impact road images in the environmental impact road image set to obtain a target environmental impact road image that matches the current road image.

[0033] The vehicle positioning module is used to determine the current location information of the vehicle to be located based on the road image affected by the target environment.

[0034] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle comprising: a memory, a processor, and an environment-matching-based vehicle localization program stored in the memory and executable on the processor, the environment-matching-based vehicle localization program being configured to implement the steps of the environment-matching-based vehicle localization method as described above.

[0035] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing an environment-matching-based vehicle positioning program, wherein when the environment-matching-based vehicle positioning program is executed by a processor, it implements the steps of the environment-matching-based vehicle positioning method described above.

[0036] In this invention, the current environmental data and current road image of the location of the vehicle to be located are acquired. A set of environmentally impactable road images matching the current environmental data is searched. The current road image is then matched with the environmentally impactable road images in the set to obtain a target environmentally impactable road image matching the current road image. The current location information of the vehicle to be located is determined based on the target environmentally impactable road image. Compared to existing technologies that often use high-precision maps created under clear daytime conditions for vehicle positioning, ignoring positioning errors caused by weather and time of day, this invention finds the environmentally impactable road image set corresponding to the current environmental data of the location of the vehicle to be located, matches the current road image of the location of the vehicle to be located with the environmentally impactable road images in the set to obtain a target environmentally impactable road image matching the current road image, and then determines the current location information of the vehicle to be located based on the target environmentally impactable road image. This fully considers the mapping effects caused by different environmental factors, reduces positioning errors caused by environmental interference, and improves vehicle positioning accuracy under different environmental conditions. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the structures shown in these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the vehicle structure in the hardware operating environment involved in the embodiments of the present invention;

[0039] Figure 2This is a flowchart illustrating the first embodiment of the vehicle positioning method based on environment matching of the present invention.

[0040] Figure 3 This is a flowchart illustrating the second embodiment of the vehicle positioning method based on environment matching of the present invention;

[0041] Figure 4 This is a schematic diagram of dual positioning involved in the second embodiment of the vehicle positioning method based on environment matching of the present invention;

[0042] Figure 5 This is a flowchart illustrating the third embodiment of the vehicle positioning method based on environment matching of the present invention.

[0043] Figure 6 This is a structural block diagram of the first embodiment of the vehicle positioning device based on environment matching of the present invention.

[0044] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0045] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0046] Reference Figure 1 , Figure 1 This is a schematic diagram of the vehicle structure of the hardware operating environment involved in the embodiments of the present invention.

[0047] like Figure 1 As shown, the vehicle may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0048] Those skilled in the art will understand that Figure 1The structure shown does not constitute a limitation on the vehicle and may include more or fewer parts than shown, or combine certain parts, or have different arrangements of parts.

[0049] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a vehicle positioning program based on environment matching.

[0050] exist Figure 1 In the vehicle shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the vehicle of the present invention can be installed in the vehicle, and the vehicle calls the vehicle positioning program based on environment matching stored in the memory 1005 through the processor 1001 and executes the vehicle positioning method based on environment matching provided in the embodiment of the present invention.

[0051] This invention provides a vehicle localization method based on environment matching, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the vehicle positioning method based on environment matching of the present invention.

[0052] In this embodiment, the vehicle positioning method based on environment matching includes the following steps:

[0053] Step S10: Obtain the current environmental data and current road image of the location of the vehicle to be located;

[0054] It is easy to understand that the execution entity of this embodiment can be the processor 1001 mentioned above. In specific implementation, the current road image of the location of the vehicle to be located can be collected by a single sensor such as laser or vision, or by multi-sensor fusion (vision, laser, millimeter wave, etc.) connected to the processor 1001. The current road image can be understood as the image corresponding to the road where the vehicle to be located is currently located. In specific implementation, since there are also unplanned roads, it can also be the image corresponding to the area where the vehicle to be located is currently located. The size of the area can be set according to actual needs, and this embodiment does not limit it.

[0055] In specific implementation, in order to improve the accuracy of data collection and further improve the accuracy of vehicle positioning, the current environmental data of the location of the vehicle to be located can also be obtained. The current environmental data includes current weather data and current time period data. The current weather data can reflect the current weather conditions of the location of the vehicle to be located, such as sunny, cloudy, rainy, snowy, foggy, etc.; the current time period data can reflect the current time period of the location of the vehicle to be located, such as early morning, noon, evening, late at night, etc.

[0056] Step S20: Locate a set of environmental impact road images that match the current environmental data;

[0057] It should be noted that after obtaining the current environmental data, the environmental impact road image set that matches the current environmental data can be searched in the preset road image library. The environmental impact road image set can be understood as a dataset containing road images corresponding to different environmental impacts, such as road images corresponding to rainy nights, foggy mornings, and cloudy evenings. The preset road image library can be understood as a database that is updated in real time and stores image sets corresponding to different roads (or different areas) under different environmental impacts.

[0058] Step S30: Match the current road image with the environmental impact road images in the environmental impact road image set to obtain the target environmental impact road image that matches the current road image;

[0059] Step S40: Determine the current location information of the vehicle to be located based on the road image affected by the target environment.

[0060] It is easy to understand that when obtaining a set of environmental impact road images that match the current environmental data of the location of the vehicle to be located, the current road image of the location of the vehicle to be located can be matched with the environmental impact road images in the set of environmental impact road images to obtain a target environmental impact road image that matches the current road image. The current location information of the vehicle to be located can be determined based on the target environmental impact road image. In a specific implementation, a high-precision map can be drawn based on the set of environmental impact road images using synchronous positioning and mapping technology. Then, vehicle positioning can be performed based on the obtained high-precision map. Furthermore, the location information of the target environmental impact road image in the high-precision map can be used as the current location information of the vehicle to be located. For example, if the location coordinates of the target environmental impact road image are (a, b, c), then (a, b, c) can be used as the current location information of the vehicle to be located.

[0061] In this embodiment, the current environmental data and current road image of the location of the vehicle to be located are acquired. A set of environmentally impactable road images matching the current environmental data is searched. The current road image is then matched with the environmentally impactable road images in the set to obtain a target environmentally impactable road image matching the current road image. The current location information of the vehicle to be located is determined based on the target environmentally impactable road image. Compared to existing technologies that often use high-precision maps created under clear daytime conditions for vehicle positioning, ignoring positioning errors caused by weather and time of day, this embodiment searches for an environmentally impactable road image set corresponding to the current environmental data of the location of the vehicle to be located, matches the current road image of the location of the vehicle to be located with the environmentally impactable road images in the set to obtain a target environmentally impactable road image matching the current road image, and then determines the current location information of the vehicle to be located based on the target environmentally impactable road image. This approach fully considers the mapping effects caused by different environmental factors, reduces positioning errors caused by environmental interference, and improves vehicle positioning accuracy under different environmental conditions.

[0062] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the vehicle positioning method based on environment matching of the present invention.

[0063] Based on the first embodiment described above, in this embodiment, before step S20, the method further includes:

[0064] Step S01: Obtain a road image set under a preset environment mode, and perform environmental impact removal processing on the road image set to obtain a basic road image set;

[0065] It should be noted that, in order to obtain road image sets under different environmental influences, road image sets under different weather conditions (e.g., sunny, cloudy, rainy, snowy, foggy, etc.) and different time periods (e.g., early morning, noon, evening, late night, etc.) can be acquired first. Image recognition is then performed on these road image sets to obtain image recognition results. Next, feature labeling is applied to these image recognition results to obtain road feature labels. These feature labels can be understood as marking the feature information in the image recognition results and can be used to identify weather, time period, road components, and the category to which the road components belong. For example, the image recognition results of the road components of a certain road... If the streetlight is on a dimly lit road, the corresponding road feature label would be: Evening / Late Night, Streetlight. Similarly, if the image recognition result of a road component element is a warning sign covered in snow, the corresponding road feature label would be: Snowy Day, Warning Sign. Here, "road component elements" can be understood as different elements that make up a road, such as road traffic sign elements (e.g., warning signs, prohibitory signs, directional signs, etc.), road traffic marking elements (e.g., directional markings, prohibitory markings, warning markings, etc.), traffic facility elements (e.g., traffic lights, streetlights, guardrails, etc.), and building facility elements (e.g., residences, schools, hospitals, etc.). In specific implementations, to improve feature labeling accuracy, user-inputted labeling correction information can be received, and the image feature labels can be added / deleted / modified based on this correction information; or, the user-inputted road feature labels can be received directly.

[0066] It is easy to understand that when obtaining the road feature markers, environmental impact removal processing can be performed on the road image set based on the road feature markers to obtain a basic road image set. The environmental impact removal can be understood as applying corresponding post-processing effects to the road image set affected by environmental factors according to the different environmental impacts, to obtain a basic road image set without environmental impact. In specific implementation, corresponding effect processing plugins can be selected according to the different environmental impacts, and then different effect processing plugins can be used to perform corresponding post-processing effects on the road image set to obtain the basic road image set. For example, if the weather marker in the road feature markers is "snowy day," then the effect processing plugin corresponding to "snowy day" will be invoked. Similarly, if the time marker in the road feature markers is "evening / late night," then the effect processing plugin corresponding to "evening / late night" will be invoked. Furthermore, the corresponding effect processing plugins can be retrieved by combining the tags of the identifier elements in the feature tags to remove environmental impacts. For example, if the road feature tags are: evening / late night, streetlights, then the effect processing plugins corresponding to evening / late night and streetlights, or the effect plugins corresponding to the element categories to which evening / late night and streetlights belong (i.e., traffic facility elements), can be retrieved. As another example, if the road feature tags are: snowy weather, warning signs, then the effect processing plugins corresponding to snowy weather and warning signs, or the effect plugins corresponding to the element categories to which snowy weather and warning signs belong (i.e., road traffic sign elements), can be retrieved.

[0067] In specific implementation, a corresponding basic high-precision map can be drawn based on the basic road image set using synchronous positioning and mapping technology. Then, the vehicle can be initially located based on the obtained basic high-precision map. Furthermore, a corresponding high-precision map can be drawn based on the subsequent road image set affected by environmental factors using synchronous positioning and mapping technology. Then, dual positioning can be performed based on the obtained basic high-precision map and high-precision map to further improve the vehicle positioning accuracy and the user's riding safety.

[0068] Step S02: Inject the basic road images into the environmental impact feature information corresponding to different environmental impacts to generate environmental impact road image sets under different environmental impacts.

[0069] It should be noted that, in order to improve the image accuracy of the obtained road image sets under different environmental influences, weather influence factors and time period influence factors can be obtained from the environmental feature information corresponding to different environmental influences. The weather influence factors can be understood as image influencing factors corresponding to different weather conditions, such as rain, wind, snow, fog, and dust storms. The time period influence factors can be understood as image influencing factors corresponding to different time periods, such as temperature, humidity, and light intensity. Then, the weather influence factors and time period influence factors are combined according to a preset combination rule to obtain different environmental composite factors. The preset combination rule can be set according to actual needs, such as combining each weather influence factor with each time period influence factor separately. This embodiment does not limit this. The environmental composite factors are then injected into the base road image to generate a set of environmentally affected road images under different environmental influences. In a specific implementation, a corresponding effect processing plugin can be matched according to the environmental composite factors, and the environmental composite factors can be injected into the base road image through the effect processing plugin to generate a set of environmentally affected road images under different environmental influences. For example, if the environmental composite factor is a combination of snow and low light intensity, the matched effect processing plugin can be the effect processing plugin corresponding to snowy days and evening / late night.

[0070] It is easy to understand that, in order to improve the image accuracy of the obtained road image sets under different environmental influences, road component elements in the basic road image set can also be obtained, and the road component elements in the basic road image set can be rendered according to the environmental composite factor to generate road image sets under different environmental influences. In specific implementation, a corresponding effect processing plugin can be matched according to the environmental composite factor, and the road component elements in the basic road image set can be rendered respectively through the effect processing plugin to generate road image sets under different environmental influences. Furthermore, to improve the accuracy of the obtained road image sets under different environmental influences, corresponding effect processing plugins can be matched with road component elements. For example, if the environmental composite factor is a combination of snow and low light intensity, and the road component element is a crash barrier, then the effect processing plugin corresponding to snowy weather, evening / late night, and the crash barrier can be retrieved and rendered using that plugin; or, the effect plugin corresponding to the element category (i.e., traffic facility element) of snowy weather, evening / late night, and the crash barrier can be retrieved and rendered using that plugin. In this way, by traversing the road component elements in the basic road image set, road image sets under different environmental influences can be obtained. Further, a high-precision map can be drawn based on the road image sets using simultaneous localization and mapping (SLAM) technology, and vehicle positioning can then be performed based on the obtained high-precision map.

[0071] refer to Figure 4 , Figure 4 This is a schematic diagram of dual positioning involved in the second embodiment of the vehicle positioning method based on environment matching of the present invention.

[0072] Figure 4 In this process, after collecting map data (i.e., the road image set under the aforementioned preset environment mode), environmental impact stripping (i.e., the aforementioned environmental impact removal processing) can be performed on the map data to obtain a basic road image set. Then, based on the basic road image set, a corresponding basic high-precision map is drawn using synchronous positioning and mapping technology. Next, environmental impact injection is performed on the basic high-precision map (i.e., the basic road images are injected with environmental impact feature information corresponding to different environmental impacts respectively) to generate environmental impact road image sets under different environmental impacts. Based on the environmental impact road image sets, a corresponding high-precision map is drawn using synchronous positioning and mapping technology. Finally, real-time dual positioning is performed by combining the matched high-precision map under the current environmental impact and the basic high-precision map to further improve vehicle positioning accuracy and user ride safety.

[0073] In this embodiment, a road image set under a preset environmental mode is acquired, and environmental impact removal processing is performed on the road image set to obtain a basic road image set. Based on the environmental impact feature information corresponding to different environmental impacts, the basic road images are then injected to generate environmental impact road image sets under different environmental impacts. By performing environmental impact removal processing on the road image set under the preset environmental mode to obtain the basic road image set, and then injecting the basic road images based on the environmental impact feature information corresponding to different environmental impacts to generate environmental impact road image sets under different environmental impacts, the accuracy of the obtained environmental impact road image sets under different environmental impacts and the accuracy of the high-precision map subsequently drawn based on the environmental impact road image sets under different environmental impacts are improved. Furthermore, the accuracy of vehicle positioning based on the obtained high-precision map is also improved.

[0074] refer to Figure 5 , Figure 5 This is a flowchart illustrating the third embodiment of the vehicle positioning method based on environment matching of the present invention.

[0075] Based on the above embodiments, in this embodiment, step S20 includes:

[0076] Step S201: Obtain the historical GPS positioning information of the vehicle to be located, and determine the corresponding image set of the area based on the historical GPS positioning information;

[0077] Step S202: Search the image set of environmental impact roads in the image set of the region that matches the current environmental data.

[0078] It is easy to understand that, in order to improve the efficiency of image set search, the historical GPS positioning information of the vehicle to be located can be obtained first, and the corresponding regional image set can be determined based on the historical GPS positioning information. Then, the environmental impact road image set matching the current environmental data can be searched in the regional image set. It is easy to understand that, during vehicle operation, there may be situations where vehicle positioning is difficult due to weak GPS signals. In this case, historical GPS positioning information can be obtained, and the corresponding regional image set can be determined based on the historical GPS positioning information. The regional image set can be understood as different image sets divided according to different regions stored in a preset road image library. The preset road image library can be understood as a database that is updated in real time and stores image sets corresponding to different roads (or different regions) under different environmental influences. In specific implementations, the region size can be set according to actual needs, and this embodiment does not limit it. In addition, it should be noted that the information obtained in this embodiment is not limited to the historical GPS positioning information of the vehicle to be located, but can also be inertial navigation positioning information, etc., and this embodiment does not limit it.

[0079] Accordingly, step S40 includes:

[0080] Step S401: Determine the current location information of the vehicle to be located based on the historical GPS positioning information and the location information corresponding to the target environment-affected road image.

[0081] In this embodiment, to improve vehicle positioning accuracy, the current GPS positioning information can be predicted based on the historical GPS positioning information. Then, the current position information of the vehicle to be located can be determined based on the predicted current GPS positioning information and the position information corresponding to the road image affected by the target environment. In a specific implementation, it can also be determined whether the error rate between the predicted current GPS positioning information and the position information corresponding to the road image affected by the target environment in different scenarios is less than or equal to a preset error rate. If it is less than or equal to the preset error rate, the weights corresponding to the predicted current GPS positioning information and the position information corresponding to the road image affected by the target environment in different scenarios are set, and the current position information of the vehicle to be located is determined based on the two and their corresponding weights. The preset error rate can be set according to actual needs, and this embodiment does not limit it. In a specific implementation, a high-precision map can be drawn based on the environmental impact road image set using synchronous positioning and mapping technology. Then, the location information of the target environmental impact road image in the high-precision map is obtained. The current location information of the vehicle to be located is determined based on the location information of the target environmental impact road image in the high-precision map and the predicted GPS positioning information at the current time. For example, if the location coordinates of the target environmental impact road image are (a, b, c) with a weight of 0.7, and the predicted GPS positioning information at the current time is (d, e, f) with a weight of 0.3, then the current location information of the vehicle to be located can be (0.7a+0.3d, 0.7b+0.3e, 0.7c+0.3f).

[0082] In this embodiment, historical GPS positioning information of the vehicle to be located is obtained, and a corresponding regional image set is determined based on the historical GPS positioning information. Then, an environmental impact road image set matching the current environmental data is searched within the regional image set. By determining the corresponding regional image set based on the historical GPS positioning information of the vehicle to be located and searching for an environmental impact road image set matching the current environmental data within that regional image set, the image set search efficiency is improved, further enhancing the efficiency of subsequent vehicle positioning based on the image set. Furthermore, in this embodiment, the current location information of the vehicle to be located is determined based on the historical GPS positioning information and the location information corresponding to the target environmental impact road image. By combining the historical GPS positioning information of the vehicle to be located and the location information corresponding to the target environmental impact road image to determine the current location information of the vehicle to be located, vehicle positioning errors are minimized, vehicle positioning accuracy is improved, and dual protection is provided for vehicle positioning, thereby enhancing user safety.

[0083] Furthermore, this embodiment of the invention also proposes a storage medium storing an environment-matching-based vehicle positioning program, which, when executed by a processor, implements the steps of the environment-matching-based vehicle positioning method described above.

[0084] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the vehicle positioning device based on environment matching of the present invention.

[0085] like Figure 6 As shown, the vehicle positioning device based on environment matching proposed in this embodiment of the invention includes:

[0086] The data acquisition module 10 is used to acquire the current environmental data and current road image of the location of the vehicle to be located;

[0087] Image set search module 20 is used to search for an image set of environmental impact roads that matches the current environmental data;

[0088] Image matching module 30 is used to match the current road image with the environmental impact road images in the environmental impact road image set respectively, and obtain a target environmental impact road image that matches the current road image;

[0089] The vehicle positioning module 40 is used to determine the current location information of the vehicle to be located based on the road image affected by the target environment.

[0090] In this embodiment, the current environmental data and current road image of the location of the vehicle to be located are acquired. A set of environmentally impactable road images matching the current environmental data is searched. The current road image is then matched with the environmentally impactable road images in the set to obtain a target environmentally impactable road image matching the current road image. The current location information of the vehicle to be located is determined based on the target environmentally impactable road image. Compared to existing technologies that often use high-precision maps created under clear daytime conditions for vehicle positioning, ignoring positioning errors caused by weather and time of day, this embodiment searches for an environmentally impactable road image set corresponding to the current environmental data of the location of the vehicle to be located, matches the current road image of the location of the vehicle to be located with the environmentally impactable road images in the set to obtain a target environmentally impactable road image matching the current road image, and then determines the current location information of the vehicle to be located based on the target environmentally impactable road image. This approach fully considers the mapping effects caused by different environmental factors, reduces positioning errors caused by environmental interference, and improves vehicle positioning accuracy under different environmental conditions.

[0091] Based on the first embodiment of the vehicle positioning device based on environment matching of the present invention, a second embodiment of the vehicle positioning device based on environment matching of the present invention is proposed.

[0092] In this embodiment, the data acquisition module 10 is further configured to acquire a road image set under a preset environmental mode, and perform environmental impact removal processing on the road image set to obtain a basic road image set.

[0093] The data acquisition module 10 is further configured to inject the basic road images into the images according to the environmental impact feature information corresponding to different environmental impacts, so as to generate environmental impact road image sets under different environmental impacts.

[0094] The data acquisition module 10 is also used to acquire road image sets under different weather conditions and different time periods, and to perform image recognition and feature marking on the road image sets to obtain road feature markings;

[0095] The data acquisition module 10 is further configured to perform environmental impact removal processing on the road image set based on the road feature markers to obtain a basic road image set.

[0096] The data acquisition module 10 is also used to acquire weather impact factors and time period impact factors from the environmental characteristic information corresponding to different environmental impacts;

[0097] The data acquisition module 10 is also used to combine the weather influencing factors and time period influencing factors according to a preset combination rule to obtain different environmental composite factors.

[0098] The data acquisition module 10 is further configured to inject the environmental composite factors into the basic road image respectively, so as to generate a set of environmental impact road images under different environmental influences.

[0099] The data acquisition module 10 is also used to acquire road component elements in the basic road image set;

[0100] The data acquisition module 10 is further configured to render the road components in the basic road image set according to the environmental composite factors, so as to generate an environmental impact road image set under different environmental influences.

[0101] The image set search module 20 is also used to obtain the historical GPS positioning information of the vehicle to be located, and to determine the corresponding image set of the area based on the historical GPS positioning information.

[0102] The image set search module 20 is also used to search for an environmental impact road image set that matches the current environmental data in the image set of the region.

[0103] The vehicle positioning module 40 is further configured to determine the current location information of the vehicle to be located based on the historical GPS positioning information and the location information corresponding to the target environment-affected road image.

[0104] Other embodiments or specific implementations of the vehicle positioning device based on environment matching of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0105] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0106] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0108] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A vehicle localization method based on environment matching, characterized in that, The vehicle localization method based on environment matching includes the following steps: Obtain the current environmental data and current road image of the location of the vehicle to be located; A set of road images under a preset environmental mode is obtained, and environmental impact removal processing is performed on the road image set to obtain a basic road image set; The basic road images are injected with environmental impact feature information corresponding to different environmental impacts to generate environmental impact road image sets under different environmental impacts. Find a set of environmental impact road images that match the current environmental data; The current road image is matched with the environmental impact road images in the environmental impact road image set to obtain the target environmental impact road image that matches the current road image. The current location information of the vehicle to be located is determined based on the road image affected by the target environment.

2. The vehicle positioning method based on environment matching as described in claim 1, characterized in that, The step of acquiring a road image set under a preset environmental mode and performing environmental impact removal processing on the road image set to obtain a basic road image set includes: Road image sets under different weather conditions and time periods are acquired, and image recognition and feature labeling are performed on the road image sets to obtain road feature labels; Environmental impact removal processing is performed on the road image set based on the road feature markers to obtain a basic road image set.

3. The vehicle positioning method based on environment matching as described in claim 1, characterized in that, The step of injecting the basic road images with environmental impact feature information corresponding to different environmental impacts to generate environmental impact road image sets under different environmental impacts includes: Obtain weather impact factors and time-period impact factors from environmental characteristic information corresponding to different environmental impacts; The weather influencing factors and time period influencing factors are combined according to a preset combination rule to obtain different environmental composite factors; The environmental composite factors are injected into the base road image to generate a set of environmental impact road images under different environmental influences.

4. The vehicle positioning method based on environment matching as described in claim 3, characterized in that, The step of injecting the environmental composite factors into the base road image to generate an environmental impact road image set under different environmental influences includes: Obtain the road components from the basic road image set; The road components in the basic road image set are rendered according to the environmental composite factors to generate environmental impact road image sets under different environmental influences.

5. The vehicle positioning method based on environment matching as described in any one of claims 1 to 4, characterized in that, The step of finding a set of environmental impact road images that match the current environmental data includes: Obtain the historical GPS location information of the vehicle to be located, and determine the corresponding image set of the area based on the historical GPS location information; Search the image set of environmental impact roads in the image set of the region that matches the current environmental data.

6. The vehicle positioning method based on environment matching as described in claim 5, characterized in that, The step of determining the current location information of the vehicle to be located based on the road image affected by the target environment includes: The current location information of the vehicle to be located is determined based on the historical GPS positioning information and the location information corresponding to the road image affected by the target environment.

7. A vehicle positioning device based on environment matching, characterized in that, The environment-matching-based vehicle positioning device includes: The data acquisition module is used to acquire the current environmental data and current road image of the location of the vehicle to be located; The image set search module is used to search for an image set of environmental impact roads that matches the current environmental data; The image matching module is used to match the current road image with the environmental impact road images in the environmental impact road image set to obtain a target environmental impact road image that matches the current road image. The vehicle positioning module is used to determine the current location information of the vehicle to be located based on the road image affected by the target environment.

8. A vehicle, characterized in that, The vehicle includes: a memory, a processor, and an environment-matching vehicle localization program stored in the memory and executable on the processor, the environment-matching vehicle localization program being configured to implement the steps of the environment-matching vehicle localization method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a vehicle localization program based on environment matching, which, when executed by a processor, implements the steps of the vehicle localization method based on environment matching as described in any one of claims 1 to 6.

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