Control method and device of vehicle, storage medium and electronic equipment

Abstract

The present disclosure relates to a vehicle control method and device, storage medium and electronic equipment, and relates to the technical field of automatic driving. The method is applied to a target vehicle and includes: collecting target information by a processing module; sending the target information to a cloud control platform by a control module, so that the cloud control platform determines shared information according to the target information and other information sent by other vehicles; receiving target shared information sent by the cloud control platform by the control module, the target shared information belonging to the shared information and being determined by the cloud control platform according to driving information of the target vehicle; if the target shared information includes to-be-updated information, updating a positioning map stored in the target vehicle and / or a to-be-detected signal lamp list according to the to-be-updated information by the processing module; and if the target shared information includes an unsafe area, controlling the target vehicle to drive according to the unsafe area by the processing module. The present disclosure can realize sharing of various information between vehicles, and improve the processing efficiency and accuracy of vehicle control.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and more specifically, to a vehicle control method, apparatus, storage medium, and electronic device. Background Technology

[0002] With the continuous increase in the number of cars in my country, the situation regarding road traffic safety is becoming increasingly severe. To reduce traffic accidents, improve the driving experience, and increase traffic efficiency, autonomous driving technology has received widespread attention. The realization of autonomous driving technology relies on the accuracy of high-precision maps. However, high-precision maps occupy a large amount of space and have a long update cycle, making it difficult to reflect the real-time road conditions for complex and ever-changing real-world roads. Typically, technicians create lightweight dynamic layers based on periodically collected environmental and road condition information and distribute these dynamic layers to the vehicle, allowing the vehicle to display them on the high-precision map in a layered manner. However, the display of dynamic layers is based on high-precision maps, thus it is strongly correlated with them. Furthermore, the creation of dynamic layers cannot meet the requirements of real-time updates and high accuracy, reducing the processing efficiency and accuracy of vehicle control. Summary of the Invention

[0003] The purpose of this disclosure is to provide a vehicle control method, apparatus, storage medium, and electronic device to partially solve the aforementioned problems existing in the related art.

[0004] According to a first aspect of the present disclosure, a vehicle control method is provided, applied to a target vehicle, the target vehicle including a control module and a processing module, the method comprising:

[0005] The processing module collects target information, which includes at least one of traffic light information, location information, and environmental information.

[0006] The control module sends the target information to the cloud control platform, so that the cloud control platform can determine the shared information based on the target information and other information sent by other vehicles; the other vehicles are vehicles other than the target vehicle.

[0007] The control module receives target shared information sent by the cloud control platform. This target shared information is part of the shared information and is determined by the cloud control platform based on the driving information of the target vehicle.

[0008] If the target shared information includes information to be updated, the processing module updates the positioning map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated.

[0009] If the target shared information includes an unsafe area, the processing module controls the target vehicle to drive based on the unsafe area.

[0010] Optionally, the processing module may be multiple, including: a traffic light module, a positioning module, a sensing module, and a planning module;

[0011] The process of collecting target information through the processing module includes:

[0012] The traffic light module collects traffic light information, the positioning module collects positioning information, and the sensing module collects environmental information.

[0013] The step of updating the location map stored in the target vehicle and / or the list of traffic lights to be detected by the processing module according to the information to be updated includes:

[0014] The positioning module updates the positioning map based on the local map included in the information to be updated, and the traffic light module deletes abnormal traffic lights included in the information to be updated from the list of traffic lights to be detected.

[0015] The step of controlling the target vehicle's movement based on the unsafe area via the processing module includes:

[0016] The planning module adjusts the driving path included in the driving information so that the adjusted driving path does not include the unsafe area.

[0017] Optionally, the target information further includes: traffic flow speed information;

[0018] The process of collecting target information through the processing module also includes:

[0019] The traffic flow speed information is collected through the planning module.

[0020] The method further includes:

[0021] If the target shared information includes the traffic flow speed on the driving path, the planning module adjusts the driving path according to the traffic flow speed on the driving path.

[0022] Optionally, the step of collecting the traffic light information through the traffic light module includes:

[0023] Acquire multiple consecutive image frames corresponding to the target traffic light;

[0024] Identify the color of the target traffic light in each of the image frames;

[0025] The state of the target traffic light is determined based on the color of the target traffic light in multiple image frames;

[0026] Generate the traffic light information used to indicate the status of the target traffic light.

[0027] Optionally, determining the state of the target traffic light based on the color of the target traffic light in the plurality of image frames includes:

[0028] If the color of the target traffic light in multiple image frames satisfies the first constraint and the second constraint, but does not satisfy the third constraint, the state of the target traffic light is determined to be normal.

[0029] If the color of the target traffic light in multiple image frames satisfies the first constraint, the second constraint, and the third constraint, the state of the target traffic light is determined to be an abnormal state.

[0030] The first constraint is that the number of image frames is greater than a first threshold; the second constraint is that the number of image frames in which the target traffic light is a valid color is greater than a second threshold; and the third constraint is that the ratio of the number of image frames in which the target traffic light is black to the number of image frames in which the target traffic light is a valid color is greater than a third threshold. The valid colors include black, red, yellow, and green.

[0031] Optionally, the step of collecting the location information through the positioning module includes:

[0032] The current location of the target vehicle is determined using multiple positioning methods;

[0033] If the locations corresponding to multiple positioning methods do not meet the first preset condition, the positioning information used to indicate that the current positioning of the target vehicle is inaccurate is generated;

[0034] The process of collecting environmental information through the sensing module includes:

[0035] The environmental status of the target vehicle's current environment is collected, and the environmental status includes at least one of the following: the strength of the positioning signal, the environmental image, and environmental parameters.

[0036] If the environmental state does not meet the second preset condition, the environmental information is generated.

[0037] The first preset condition includes at least one of the following:

[0038] The difference between the locations corresponding to multiple positioning methods is less than or equal to the fourth threshold;

[0039] The confidence level of the location corresponding to each positioning method is greater than the fifth threshold;

[0040] The second preset condition includes at least one of the following:

[0041] The strength of the positioning signal is greater than the sixth threshold;

[0042] The difference between the environmental image and the positioning map is less than or equal to the seventh threshold.

[0043] The environmental parameters are matched with preset autonomous driving environmental parameters.

[0044] According to a second aspect of the present disclosure, a vehicle control method is provided, applied to a cloud control platform, the method comprising:

[0045] The system receives reporting information from the control module of each of multiple vehicles. The reporting information is collected by the processing module of the corresponding vehicle and includes at least one of traffic light information, location information, and environmental information.

[0046] Based on the reported information sent by multiple vehicles, the shared information is determined;

[0047] Based on the driving information of the target vehicle, the target shared information corresponding to the target vehicle is determined from the shared information;

[0048] The target shared information is sent to the control module of the target vehicle, so that if the target shared information includes information to be updated, the target vehicle's processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected according to the information to be updated. If the target shared information includes unsafe areas, the target vehicle's processing module controls the target vehicle to drive according to the unsafe areas. The target vehicle is any one of the multiple vehicles.

[0049] Optionally, determining the shared information based on the reported information sent by the multiple vehicles includes:

[0050] When the reported information includes traffic light information, a first number of traffic light information indicating that any traffic light is in a normal state within a preset time range and a second number of traffic light information indicating that the traffic light is in an abnormal state within the preset time range are determined.

[0051] If the sum of the first quantity and the second quantity is greater than a preset threshold for the number of traffic lights, and the ratio of the second quantity to the first quantity is greater than a preset threshold for the proportion of traffic lights, the traffic light is identified as an abnormal traffic light, and the shared information including the abnormal traffic light is generated.

[0052] When the reported information includes location information, a third number of location information indicating that the first location is inaccurate is determined within the preset time range, wherein the first location is any location.

[0053] If the third quantity is greater than the preset location quantity threshold, the first location is determined as a location anomaly location; multiple location anomaly locations are clustered to obtain the unsafe area, and the shared information including the unsafe area is generated;

[0054] When the reported information includes environmental information, a fourth number of environmental information indicating that the second location does not meet the conditions for autonomous driving is determined within the preset time range, and / or a fifth number of environmental information indicating that the second location needs to be updated within the preset time range, wherein the second location is any location;

[0055] If the fourth quantity is greater than the preset first environmental quantity threshold, the second location is determined as an abnormal location for autonomous driving; multiple abnormal locations for autonomous driving are clustered to obtain the unsafe area, and the shared information including the unsafe area is generated;

[0056] If the fifth quantity is greater than the preset second environment quantity threshold, a local map corresponding to the second location is generated based on the environment information indicating the second location to be updated within the preset time range, and the shared information including the local map is generated.

[0057] Optionally, the reported information further includes traffic speed information; determining the shared information based on the reported information sent by multiple vehicles further includes:

[0058] If the reported information includes traffic flow speed information, the shared information including the traffic flow speed information is generated.

[0059] Optionally, determining the target shared information corresponding to the target vehicle from the shared information based on the target vehicle's driving information includes:

[0060] Based on the location information of the target vehicle included in the driving information, the target area where the target vehicle is located is determined, and the information in the shared information that matches the target area is used as the target shared information; or...

[0061] Based on the location information, driving path, and driving direction of the target vehicle included in the driving information, the remaining driving path of the target vehicle is determined, and the information in the shared information that matches the remaining driving path is used as the target shared information.

[0062] According to a third aspect of the present disclosure, a vehicle control device is provided, applied to a target vehicle, the device comprising: a control module and a processing module;

[0063] The processing module is used to collect target information, which includes at least one of traffic light information, location information, and environmental information.

[0064] The control module is used to send the target information to the cloud control platform, so that the cloud control platform can determine the shared information based on the target information and other information sent by other vehicles; the other vehicles are vehicles other than the target vehicle.

[0065] The control module is used to receive target shared information sent by the cloud control platform. The target shared information is part of the shared information and is determined by the cloud control platform based on the driving information of the target vehicle.

[0066] The processing module is configured to, if the target shared information includes information to be updated, update the positioning map stored in the target vehicle and / or the list of traffic lights to be detected according to the information to be updated; if the target shared information includes unsafe areas, control the target vehicle to drive according to the unsafe areas.

[0067] Optionally, the processing module may be multiple, including: a traffic light module, a positioning module, a sensing module, and a planning module;

[0068] The traffic light module is used to collect traffic light information; the positioning module is used to collect positioning information; and the sensing module is used to collect environmental information.

[0069] The positioning module is further configured to update the positioning map based on the local map included in the information to be updated;

[0070] The signal light module is also used to delete abnormal signal lights included in the information to be updated from the list of signal lights to be detected;

[0071] The planning module is used to adjust the driving path included in the driving information so that the adjusted driving path does not include the unsafe area.

[0072] Optionally, the target information further includes: traffic flow speed information;

[0073] The planning module is also used to collect the traffic flow speed information;

[0074] The planning module is further configured to adjust the driving path according to the traffic flow speed on the driving path if the target shared information includes the traffic flow speed on the driving path.

[0075] Optionally, the traffic light module includes:

[0076] The acquisition submodule is used to acquire multiple consecutive image frames corresponding to the target traffic light;

[0077] The identification submodule is used to identify the color of the target traffic light in each of the image frames;

[0078] A determination submodule is used to determine the state of the target traffic light based on the color of the target traffic light in multiple image frames;

[0079] A generation submodule is used to generate the traffic light information used to indicate the state of the target traffic light.

[0080] Optionally, the determining submodule is used for:

[0081] If the color of the target traffic light in multiple image frames satisfies the first constraint and the second constraint, but does not satisfy the third constraint, the state of the target traffic light is determined to be normal.

[0082] If the color of the target traffic light in multiple image frames satisfies the first constraint, the second constraint, and the third constraint, the state of the target traffic light is determined to be an abnormal state.

[0083] The first constraint is that the number of image frames is greater than a first threshold; the second constraint is that the number of image frames in which the target traffic light is a valid color is greater than a second threshold; and the third constraint is that the ratio of the number of image frames in which the target traffic light is black to the number of image frames in which the target traffic light is a valid color is greater than a third threshold. The valid colors include black, red, yellow, and green.

[0084] Optionally, the positioning module is used for:

[0085] The current location of the target vehicle is determined using multiple positioning methods;

[0086] If the locations corresponding to multiple positioning methods do not meet the first preset condition, the positioning information used to indicate that the current positioning of the target vehicle is inaccurate is generated;

[0087] The sensing module is used for:

[0088] The environmental status of the target vehicle's current environment is collected, and the environmental status includes at least one of the following: the strength of the positioning signal, the environmental image, and environmental parameters.

[0089] If the environmental state does not meet the second preset condition, the environmental information is generated.

[0090] The first preset condition includes at least one of the following:

[0091] The difference between the locations corresponding to multiple positioning methods is less than or equal to the fourth threshold;

[0092] The confidence level of the location corresponding to each positioning method is greater than the fifth threshold;

[0093] The second preset condition includes at least one of the following:

[0094] The strength of the positioning signal is greater than the sixth threshold;

[0095] The difference between the environmental image and the positioning map is less than or equal to the seventh threshold.

[0096] The environmental parameters are matched with preset autonomous driving environmental parameters.

[0097] According to a fourth aspect of the present disclosure, a vehicle control device is provided, applied to a cloud control platform, the device comprising:

[0098] A receiving module is used to receive reporting information sent by the control module of each of the multiple vehicles. The reporting information is collected by the processing module of the corresponding vehicle and includes at least one of traffic light information, location information, and environmental information.

[0099] The processing module is used to determine the shared information based on the reported information sent by the multiple vehicles;

[0100] The processing module is further configured to determine the target shared information corresponding to the target vehicle in the shared information based on the driving information of the target vehicle;

[0101] A sending module is used to send the target shared information to the control module of the target vehicle, so that when the target shared information includes information to be updated, the target vehicle updates the positioning map stored in the target vehicle and / or the list of traffic lights to be detected according to the information to be updated through the processing module of the target vehicle; when the target shared information includes unsafe areas, the target vehicle controls the driving of the target vehicle according to the unsafe areas through the processing module of the target vehicle. The target vehicle is any one of the multiple vehicles.

[0102] Optionally, the processing module includes:

[0103] The first processing submodule is used for:

[0104] When the reported information includes traffic light information, a first number of traffic light information indicating that any traffic light is in a normal state within a preset time range and a second number of traffic light information indicating that the traffic light is in an abnormal state within the preset time range are determined.

[0105] If the sum of the first quantity and the second quantity is greater than a preset threshold for the number of traffic lights, and the ratio of the second quantity to the first quantity is greater than a preset threshold for the proportion of traffic lights, the traffic light is identified as an abnormal traffic light, and the shared information including the abnormal traffic light is generated.

[0106] The second processing submodule is used for:

[0107] When the reported information includes location information, a third number of location information indicating that the first location is inaccurate is determined within the preset time range, wherein the first location is any location.

[0108] If the third quantity is greater than the preset location quantity threshold, the first location is determined as a location anomaly location; multiple location anomaly locations are clustered to obtain the unsafe area, and the shared information including the unsafe area is generated;

[0109] The third processing submodule is used for:

[0110] When the reported information includes environmental information, a fourth number of environmental information indicating that the second location does not meet the conditions for autonomous driving is determined within the preset time range, and / or a fifth number of environmental information indicating that the second location needs to be updated within the preset time range, wherein the second location is any location;

[0111] If the fourth quantity is greater than the preset first environmental quantity threshold, the second location is determined as an abnormal location for autonomous driving; multiple abnormal locations for autonomous driving are clustered to obtain the unsafe area, and the shared information including the unsafe area is generated;

[0112] If the fifth quantity is greater than the preset second environment quantity threshold, a local map corresponding to the second location is generated based on the environment information indicating the second location to be updated within the preset time range, and the shared information including the local map is generated.

[0113] Optionally, the reported information may also include vehicle speed information;

[0114] The processing module further includes:

[0115] The fourth processing submodule is used to generate the shared information including the traffic flow speed information when the reported information includes traffic flow speed information.

[0116] Optionally, the processing module is used to:

[0117] Based on the location information of the target vehicle included in the driving information, the target area where the target vehicle is located is determined, and the information in the shared information that matches the target area is used as the target shared information; or...

[0118] Based on the location information, driving path, and driving direction of the target vehicle included in the driving information, the remaining driving path of the target vehicle is determined, and the information in the shared information that matches the remaining driving path is used as the target shared information.

[0119] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0120] According to a sixth aspect of the present disclosure, an electronic device is provided, comprising:

[0121] A memory on which computer programs are stored;

[0122] A processor for executing the computer program in the memory to implement the steps of the method described in the first aspect.

[0123] According to a seventh aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the second aspect.

[0124] According to an eighth aspect of the present disclosure, an electronic device is provided, comprising:

[0125] A memory on which computer programs are stored;

[0126] A processor for executing the computer program in the memory to implement the steps of the method described in the second aspect.

[0127] Through the above technical solution, the target vehicle in this disclosure first collects target information, including at least one of traffic light information, positioning information, and environmental information, through a processing module, and then sends the target information to the cloud control platform through a control module. After receiving the target information and other information sent by other vehicles, the cloud control platform first determines the shared information, and then, based on the target vehicle's driving information, determines the target shared information corresponding to the target vehicle from the shared information and sends the target shared information to the target vehicle. The target vehicle receives the target shared information through the control module. If the target shared information includes information to be updated, the processing module updates the positioning map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes unsafe areas, the processing module controls the target vehicle's movement based on the unsafe areas. This disclosure utilizes the processing and storage resources of the cloud control platform to integrate the data reported by the modules of various vehicles, thereby achieving the sharing of multiple types of information between vehicles at the module level, improving the processing efficiency and accuracy of vehicle control.

[0128] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0129] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0130] Figure 1 This is a structural diagram of an autonomous driving system according to an exemplary embodiment;

[0131] Figure 2 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment;

[0132] Figure 3 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment;

[0133] Figure 4 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment;

[0134] Figure 5 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment;

[0135] Figure 6 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment;

[0136] Figure 7 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment;

[0137] Figure 8 This is a block diagram illustrating a vehicle control device according to an exemplary embodiment;

[0138] Figure 9 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment;

[0139] Figure 10 This is a block diagram illustrating a vehicle control device according to an exemplary embodiment;

[0140] Figure 11 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment;

[0141] Figure 12 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment;

[0142] Figure 13 This is a block diagram illustrating an electronic device according to an exemplary embodiment;

[0143] Figure 14 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0144] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0145] Before introducing the vehicle control method, device, storage medium, and electronic device provided in this disclosure, the application scenarios involved in the various embodiments of this disclosure are first described. The various embodiments provided in this disclosure can be an autonomous driving system, applicable to various autonomous driving scenarios, such as autonomous vehicle driving, unmanned vehicle delivery, etc. The structure of the autonomous driving system is as follows... Figure 1As shown, this includes a cloud control platform and multiple vehicles within the platform's jurisdiction. Any one of these vehicles can be considered the target vehicle mentioned later, and one or more other vehicles can be considered as other vehicles mentioned later. The target vehicle and other vehicles are equivalent and can be converted into each other. Each vehicle is equipped with a control module and a processing module. Data transmission between the control module and the processing module can be achieved via wired or wireless connection. There can be one or more processing modules. Data transmission between the control module and the cloud control platform can be achieved via various wireless communication protocols, including but not limited to: 5G (the 5th Generation mobile communication technology), 4G (the 4th Generation mobile communication technology), and WLAN (Wireless Local Area Networks).

[0146] Figure 2 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 2 As shown, this method is applied to a target vehicle, which includes a control module and a processing module. The method includes the following steps:

[0147] Step 101: Collect target information through the processing module. The target information includes at least one of the following: traffic light information, location information, and environmental information.

[0148] Step 102: The target information is sent to the cloud control platform via the control module, so that the cloud control platform can determine the shared information based on the target information and other information sent by other vehicles. Other vehicles are vehicles other than the target vehicle.

[0149] For example, the target vehicle's processing module can collect target information in real time, including at least one of traffic light information, location information, and environmental information. Traffic light information indicates the status of traffic lights the target vehicle passes during its journey, which can be normal, abnormal (e.g., a malfunctioning traffic light, a power outage), or unknown. For instance, traffic light information may include the numbers of one or more traffic lights and the corresponding status for each number. Location information indicates that the target vehicle's location is inaccurate during its journey. For instance, location information may include the coordinates of the inaccurate location (which can be understood as coarse-grained location coordinates determined by the GPS device on the target vehicle, and can be latitude and longitude, or three-dimensional coordinates). Environmental information indicates that the target vehicle's location does not meet the conditions for autonomous driving during its journey. For instance, environmental information may include the coordinates of the location that does not meet the conditions for autonomous driving (which can be latitude and longitude, or three-dimensional coordinates). Environmental information can also be used to indicate significant differences between environmental images captured during the target vehicle's journey and locally stored location maps (e.g., the environmental images include newly appearing obstacles such as embankments, roadblocks, or barriers). For example, environmental information can include environmental images that differ significantly from locally stored location maps, and the latitude and longitude, or three-dimensional coordinates, of the location where the environmental images were captured. Autonomous driving conditions could include, for example, insufficient strength of positioning signals (e.g., GPS signals) or mismatch between environmental parameters and autonomous driving environmental parameters. Specifically, the target vehicle can be equipped with multiple processing modules, each dedicated to collecting traffic light information, location information, and environmental information, or a single processing module can be used to collect traffic light information, location information, and environmental information uniformly.

[0150] After obtaining the target information, the processing module can send it to the control module, which then sends it to the cloud control platform. Upon receiving the target information and other information from other vehicles, the cloud control platform integrates and processes this information (e.g., statistics, filtering, deduplication) to obtain shared information. This shared information can be understood as integrating all information reported by all vehicles (including target information and other information), reflecting abnormal situations within the cloud control platform's jurisdiction. These abnormal situations may include: certain traffic lights being in an abnormal state, inaccurate positioning in certain areas, areas unsuitable for autonomous driving, and location maps requiring updates in certain locations. The other information can be understood as information collected in real-time by processing modules on other vehicles, and may include at least one of the following: traffic light information, positioning information, and environmental information for other vehicles. These other vehicles can be one or multiple vehicles.

[0151] Step 103: Receive target shared information sent by the cloud control platform through the control module. The target shared information is shared information and is determined by the cloud control platform based on the target vehicle's driving information.

[0152] For example, after determining the shared information, the cloud control platform can filter out the shared information that each vehicle needs to pay attention to based on its driving information and send it to the corresponding vehicle. Driving information may include, for example, the vehicle's location, driving path, driving direction, and also its speed and acceleration. For a target vehicle, the cloud control platform can filter out target shared information from the shared information based on the target vehicle's driving information. Target shared information is part or all of the shared information. Target shared information can be understood as abnormal situations within the cloud control platform's jurisdiction that the target vehicle needs to pay attention to. For example, the cloud control platform can send abnormal situations along the target vehicle's driving path as target shared information to the target vehicle. The cloud control platform can also divide its jurisdiction into multiple areas, determine the target area where the target vehicle is currently located based on its location information, and then send the abnormal situations within that target area as target shared information to the target vehicle.

[0153] The target shared information may include: information to be updated, and / or unsafe areas. The information to be updated may be a local map of a location (e.g., an environmental image) to instruct the target vehicle to update the location map for that location. It may also be the number of a traffic light in an abnormal state, indicating that the target vehicle should no longer detect that traffic light. The unsafe area may be a range of locations indicating that autonomous driving is not suitable for the target vehicle within that range. The target vehicle's control module receives the target shared information sent by the cloud control platform and forwards it to the processing module.

[0154] Step 104: If the target shared information includes information to be updated, the processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated.

[0155] Step 105: If the target shared information includes an unsafe area, the processing module controls the target vehicle's movement based on the unsafe area.

[0156] For example, when the target shared information includes information to be updated, the processing module can update the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the content of the information to be updated. For instance, if the information to be updated is a local map of a certain location, the processing module can update the image of that location in the locally stored location map based on that local map. In this way, the location map stored locally by the target vehicle can reflect the road conditions at that location in real time, improving the accuracy of the location map and thus improving the accuracy of vehicle control. As another example, if the information to be updated is the number of a traffic light in an abnormal state, the processing module can update the list of traffic lights to be detected locally based on that number. This can be understood as the list of traffic lights to be detected recording the numbers of multiple traffic lights that the target vehicle needs to detect during its journey along the corresponding driving path. If the information to be updated indicates that the traffic light indicated by that number is in an abnormal state, then the traffic light indicated by that number can be deleted from the list of traffic lights to be detected. Consequently, when the target vehicle passes by the traffic light indicated by that number, it does not need to identify it again, saving the target vehicle's processing resources and improving the processing efficiency of vehicle control.

[0157] When the shared target information includes unsafe areas, the processing module can control the target vehicle's movement based on these areas. For example, the pre-planned driving path can be adjusted to avoid unsafe areas. In scenarios where the target vehicle is an autonomous vehicle, a connection can be established with a remote HMI (Human Machine Interface) platform when the target vehicle is about to enter an unsafe area, requesting the HMI platform to take over. In scenarios where a driver is present in the target vehicle, a warning message can be issued when the target vehicle is about to enter an unsafe area, prompting the driver to take over. This allows the target vehicle to plan ahead for unsafe areas, preventing loss of control and improving the accuracy of vehicle control. Therefore, the target vehicle can be controlled using shared target information obtained from the cloud control platform, significantly conserving its computing and processing resources.

[0158] In summary, in this disclosure, the target vehicle first collects target information, including at least one of traffic light information, location information, and environmental information, through a processing module, and then sends the target information to the cloud control platform through a control module. After receiving the target information and other information sent by other vehicles, the cloud control platform first determines the shared information, and then, based on the target vehicle's driving information, determines the target shared information corresponding to the target vehicle from the shared information and sends the target shared information to the target vehicle. The target vehicle receives the target shared information through the control module. If the target shared information includes information to be updated, the processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes unsafe areas, the processing module controls the target vehicle's movement based on the unsafe areas. This disclosure utilizes the processing and storage resources of the cloud control platform to integrate the data reported by the modules of various vehicles, thereby achieving the sharing of multiple types of information between vehicles at the module level, improving the processing efficiency and accuracy of vehicle control.

[0159] Figure 3 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 3 As shown, the target vehicle can be equipped with multiple processing modules, such as a traffic light module, a positioning module, a perception module, and a planning module.

[0160] Step 101 can be achieved through the following steps:

[0161] Step 1011: Collect traffic light information through the traffic light module.

[0162] Step 1012: Collect location information through the positioning module.

[0163] Step 1013: Collect environmental information through the sensing module.

[0164] For example, the traffic light module can collect traffic light information, the positioning module can collect positioning information, and the sensing module can collect environmental information. Correspondingly, the control module can obtain traffic light information, positioning information, and environmental information from the traffic light module, positioning module, and sensing module respectively, and send them to the cloud control platform.

[0165] Step 104 can be implemented in the following ways:

[0166] Step 1041: Update the positioning map using the positioning module based on the local map included in the information to be updated.

[0167] Step 1042: Using the traffic light module, delete the abnormal traffic lights included in the information to be updated from the list of traffic lights to be detected.

[0168] Step 105 can be implemented as follows:

[0169] The planning module adjusts the driving routes included in the driving information to ensure that the adjusted driving routes do not include unsafe areas.

[0170] For example, in a scenario where multiple processing modules are installed on the target vehicle, the control module can first parse the target shared information. Then, depending on whether the parsed target shared information includes a local map, abnormal traffic lights, or unsafe areas, it can be assigned to different processing modules for processing. Specifically, after receiving the target shared information, the control module can pre-parse it. If the target shared information includes a local map, the control module can send the local map to the positioning module. The positioning module then updates the positioning map based on the local map included in the information to be updated. Specifically, the local map can also carry corresponding coordinates, and the positioning module can use the local map to replace the image at the corresponding coordinates in the positioning map. If the target shared information includes abnormal traffic lights, the control module can send the abnormal traffic lights to the traffic light module, which then removes the abnormal traffic lights from the list of traffic lights to be detected. If the target shared information includes unsafe areas, the control module can send the unsafe areas to the planning module, which then adjusts the driving path to ensure that the adjusted path does not include the unsafe areas.

[0171] Figure 4 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 4 As shown, the target information also includes: traffic speed information.

[0172] Step 101 may also include:

[0173] Step 1014: Collect traffic flow speed information through the planning module.

[0174] Accordingly, the method also includes:

[0175] Step 106: If the target shared information includes the traffic speed on the driving path, the driving path is adjusted by the planning module according to the traffic speed on the driving path.

[0176] For example, the planning module can also collect traffic flow speed information. This information indicates the speed of traffic at various points along the target vehicle's route. For instance, traffic flow speed information may include the numbers of one or more intersections (or roads, areas, etc.) and the corresponding traffic flow speed for each intersection. Correspondingly, the control module sends the target information, including traffic flow speed data, to the cloud control platform. The cloud control platform then integrates all traffic flow speed information reported by all vehicles to obtain shared information. This shared information reflects the traffic flow speed at various points within the cloud control platform's jurisdiction. Next, the cloud control platform, based on the target vehicle's driving information, filters the shared information from the shared information to include the traffic flow speed along the target vehicle's path and sends this information to the target vehicle's control module. The control module then sends the traffic flow speed along the target vehicle's path to the planning module, which adjusts the driving path accordingly. For example, the planning module can adjust the driving path to avoid intersections with slow traffic or to select intersections with faster traffic as much as possible.

[0177] In a specific application scenario, step 1011 can be achieved through the following steps:

[0178] Step A: Acquire multiple consecutive image frames corresponding to the target traffic light.

[0179] Step B: Identify the color of the target traffic light in each image frame.

[0180] Step C: Determine the state of the target traffic light based on the color of the target traffic light in multiple image frames.

[0181] Step D: Generate signal light information to indicate the status of the target signal light.

[0182] For example, when collecting traffic light information, the traffic light module first captures multiple consecutive image frames corresponding to the target traffic light, with each image frame containing the target traffic light. The target traffic light can be understood as the traffic light the target vehicle is about to reach from the list of traffic lights to be detected; for example, the target traffic light can be determined based on the driving path and direction. Next, the color of the target traffic light in each image frame is identified, and the state of the target traffic light is determined based on the colors of the target traffic light in multiple image frames. Finally, traffic light information is generated to indicate the state of the target traffic light; for example, the traffic light information may include the target traffic light's number and its state.

[0183] Specifically, step C may include:

[0184] If the color of the target traffic light in multiple image frames satisfies the first and second constraints, but does not satisfy the third constraint, the state of the target traffic light is determined to be normal.

[0185] If the color of the target traffic light in multiple image frames satisfies the first constraint, the second constraint, and the third constraint, the state of the target traffic light is determined to be an abnormal state.

[0186] The first constraint is that the number of image frames is greater than a first threshold; the second constraint is that the number of image frames whose target traffic light color is a valid color is greater than a second threshold; and the third constraint is that the ratio of the number of image frames whose target traffic light color is black to the number of image frames whose target traffic light color is a valid color is greater than a third threshold. Valid colors include black, red, yellow, and green.

[0187] For example, the number of image frames can be counted, denoted as N. 总 The number of image frames in which the target traffic light is identified as red is denoted as N. 红 The number of image frames in which the target traffic light is identified as yellow is denoted as N. 黄 The number of image frames in which the target traffic light is identified as green is denoted as N. 绿 The number of image frames in which the target traffic light is identified as black is denoted as N. 黑 The number of image frames that misidentify the color of the target traffic light, denoted as N. 未知 Among them, the image frame in which the target traffic light is incorrectly identified may be one that does not include the target traffic light (e.g., the target traffic light is obscured by an obstacle), or one that identifies a color other than black, red, yellow, and green (e.g., it is identified as white due to reflection or other reasons).

[0188] Then, the state of the target traffic light can be determined based on the first, second, and third constraints. The first constraint can be represented as: N 总 The first threshold (e.g., 1000 frames) and the second constraint can be expressed as: N 红 +N 黄 +N 绿 +N 黑 The second threshold (e.g., 800 frames) and the third constraint can be expressed as N. 黑 / (N 红 +N 黄 +N 绿 +N 黑The third threshold (e.g., 0.85) is defined as follows: Satisfying the first constraint means the traffic light module has acquired a sufficient number of image frames. Satisfying the second constraint means that the traffic light module has acquired a sufficient number of valid image frames (i.e., the identified color is a valid color). Satisfying the third constraint means that the target traffic light color identified by the majority of image frames acquired by the traffic light module is black. Therefore, if the target traffic light color in multiple image frames satisfies the first and second constraints but not the third constraint, the target traffic light's state can be determined to be normal. If the target traffic light color in multiple image frames satisfies the first, second, and third constraints, the target traffic light's state is determined to be abnormal. Furthermore, if the target traffic light color in multiple image frames does not satisfy the first constraint and / or the second constraint, the target traffic light's state is determined to be unknown.

[0189] In another specific application scenario, step 1012 can be implemented in the following ways:

[0190] Step E: Determine the current location of the target vehicle using multiple positioning methods.

[0191] Step F: If the locations corresponding to multiple positioning methods do not meet the first preset condition, generate positioning information to indicate that the current positioning of the target vehicle is inaccurate.

[0192] For example, when collecting positioning information, the positioning module can first determine the current position of the target vehicle using multiple positioning methods. These multiple positioning methods may include, for example, satellite positioning (e.g., GPS positioning), IMU (Inertial Measurement Unit) positioning, feature point matching positioning, and other positioning methods known to those skilled in the art; this disclosure does not specifically limit these methods. If the positioning is accurate, the positioning module will perform Kalman filtering on the positions corresponding to the multiple positioning methods to obtain a high-precision position. If any of the multiple positioning methods is inaccurate, the Kalman filtering result may have a large error, leading to inaccurate positioning. Therefore, it can be determined whether the positions corresponding to the multiple positioning methods meet a first preset condition to determine whether the current positioning of the target vehicle is accurate.

[0193] The first preset condition includes at least one of the following: the difference between the locations corresponding to multiple positioning methods is less than or equal to a fourth threshold; and the confidence level of the location corresponding to each positioning method is greater than a fifth threshold.

[0194] Specifically, if the difference between the locations corresponding to multiple positioning methods is less than or equal to the fourth threshold, it can be understood that the locations corresponding to the multiple positioning methods are close. If the confidence level of the location corresponding to each positioning method is greater than the fifth threshold, it can be understood that the location corresponding to each positioning method is valid. Taking feature point matching positioning as an example, if the target vehicle's current location is blocked by a barrier, preventing the positioning module from acquiring valid feature points, then feature point matching positioning cannot output the corresponding location. In this case, the confidence level of feature point matching positioning is very low, meaning it is invalid. If the locations corresponding to multiple positioning methods do not meet the first preset condition, then it can be determined that the target vehicle's current positioning is inaccurate, and the positioning module can generate positioning information to indicate that the target vehicle's current positioning is inaccurate. If the locations corresponding to multiple positioning methods meet the first preset condition, then it can be determined that the target vehicle's current positioning is accurate, and the positioning module does not need to generate positioning information.

[0195] Step 1013 can be implemented in the following ways:

[0196] Step G: Collect the environmental status of the target vehicle's current environment. The environmental status includes at least one of the following: the strength of the positioning signal, environmental image, and environmental parameters.

[0197] Step H: If the environmental state does not meet the second preset condition, generate environmental information.

[0198] For example, when collecting environmental information, the perception module can collect the current environmental state of the target vehicle. The environmental state can include the strength of the positioning signal, environmental images, and environmental parameters (such as weather, humidity, water depth, slope, road type, etc.). Then, by judging whether the environmental state meets the second preset condition, it can determine whether the current environment of the target vehicle meets the conditions for autonomous driving or whether the positioning map needs to be updated.

[0199] The second preset condition includes at least one of the following:

[0200] The strength of the positioning signal is greater than the sixth threshold. The difference between the environmental image and the positioning map is less than or equal to the seventh threshold. The environmental parameters match the preset autonomous driving environmental parameters.

[0201] Specifically, if the strength of the positioning signal is greater than the sixth threshold, it can be understood that the signal strength is sufficient for the positioning module to perform positioning. If the difference between the environmental image and the positioning map is less than or equal to the seventh threshold, it can be understood that the difference between the environmental image and the locally stored positioning map is small. The environmental parameters match the preset autonomous driving environmental parameters, which may include, for example, a list of weather conditions suitable for autonomous driving, a list of road surface types, and suitable humidity, water depth, and slope ranges. If the environmental state does not meet the second preset condition, the perception module can generate environmental information; if the environmental state meets the second preset condition, the perception module does not need to generate environmental information.

[0202] In summary, in this disclosure, the target vehicle first collects target information, including at least one of traffic light information, location information, and environmental information, through a processing module, and then sends the target information to the cloud control platform through a control module. After receiving the target information and other information sent by other vehicles, the cloud control platform first determines the shared information, and then, based on the target vehicle's driving information, determines the target shared information corresponding to the target vehicle from the shared information and sends the target shared information to the target vehicle. The target vehicle receives the target shared information through the control module. If the target shared information includes information to be updated, the processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes unsafe areas, the processing module controls the target vehicle's movement based on the unsafe areas. This disclosure utilizes the processing and storage resources of the cloud control platform to integrate the data reported by the modules of various vehicles, thereby achieving the sharing of multiple types of information between vehicles at the module level, improving the processing efficiency and accuracy of vehicle control.

[0203] Figure 5 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 5 As shown, this method is applied to a cloud control platform and includes the following steps:

[0204] Step 201: Receive the reported information sent by the control module of each of the multiple vehicles. The reported information is collected by the processing module of the corresponding vehicle and includes at least one of the following: traffic light information, location information, and environmental information.

[0205] Step 202: Determine the shared information based on the reported information sent by multiple vehicles.

[0206] For example, after receiving the reported information (including the target information and other information mentioned above) from the control module of each vehicle, the cloud control platform will integrate and process the reported information (e.g., statistics, filtering, deduplication, etc.) to obtain shared information. The shared information can be understood as the integrated reported information of all vehicles, which can reflect the abnormal situation within the jurisdiction of the cloud control platform. Abnormal situations may include: the status of some traffic lights is abnormal, the positioning of some areas is inaccurate, some areas are not suitable for autonomous driving, and the positioning map of some locations needs to be updated.

[0207] Step 203: Based on the driving information of the target vehicle, determine the target shared information corresponding to the target vehicle in the shared information.

[0208] Step 204: Send the target shared information to the control module of the target vehicle so that, if the target shared information includes information to be updated, the target vehicle's processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected according to the information to be updated; if the target shared information includes unsafe areas, the target vehicle's processing module controls the target vehicle to drive according to the unsafe areas. The target vehicle can be any of multiple vehicles.

[0209] For example, after determining the shared information, the cloud control platform can filter out the shared information that each vehicle needs to pay attention to based on its driving information and send it to the corresponding vehicle. Driving information may include, for example, the vehicle's location, driving path, driving direction, and also its speed and acceleration. For a target vehicle, the cloud control platform can filter out target shared information from the shared information based on the target vehicle's driving information. Target shared information is part or all of the shared information. Target shared information can be understood as abnormal situations within the cloud control platform's jurisdiction that the target vehicle needs to pay attention to. For example, the cloud control platform can send abnormal situations along the target vehicle's driving path as target shared information to the target vehicle. The cloud control platform can also divide its jurisdiction into multiple areas, determine the target area where the target vehicle is currently located based on its location information, and then send the abnormal situations within that target area as target shared information to the target vehicle's control module.

[0210] The target shared information may include: information to be updated, and / or unsafe areas. The information to be updated may be a local map of a location (e.g., an environmental image) to instruct the target vehicle to update the location map for that location. It may also be the number of a traffic light in an abnormal state, indicating that the target vehicle should no longer detect that traffic light. The unsafe area may be a range of locations indicating that autonomous driving is not suitable for the target vehicle within that range. The target vehicle's control module receives the target shared information sent by the cloud control platform and then forwards it to the processing module.

[0211] When the shared target information includes information to be updated, the target vehicle's processing module can update the location map stored within the target vehicle and / or the list of traffic lights to be detected based on the content of the information to be updated. For example, if the information to be updated is a local map of a certain location, the processing module can update the image of that location in the locally stored location map based on that local map. As another example, if the information to be updated is the number of a traffic light in an abnormal state, the processing module can update the locally stored list of traffic lights to be detected based on that number. When the shared target information includes unsafe areas, the processing module can control the target vehicle's movement based on the unsafe areas. For example, the pre-planned driving path for the target vehicle can be adjusted so that the adjusted path avoids unsafe areas.

[0212] Figure 6 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 6 As shown, step 202 can be achieved through the following steps:

[0213] Step 2021: If the reported information includes traffic light information, determine a first number of traffic light information indicating that any traffic light is in a normal state within a preset time range, and a second number of traffic light information indicating that the traffic light is in an abnormal state within a preset time range.

[0214] Step 2022: If the sum of the first quantity and the second quantity is greater than the preset signal light quantity threshold, and the ratio of the second quantity to the first quantity is greater than the preset signal light ratio threshold, the signal light is identified as an abnormal signal light, and shared information including the abnormal signal light is generated.

[0215] For example, the cloud control platform can first extract traffic light information from reports sent by multiple vehicles, and then determine a first number of traffic light messages indicating that any traffic light is in a normal state within a preset time range, and a second number of traffic light messages indicating that the traffic light is in an abnormal state within the preset time range. The preset time range can be a period of time before the current moment (e.g., 2 hours), thereby ensuring the timeliness of the reported information. In other words, based on the reports sent by multiple vehicles, the cloud control platform determines that the first number of vehicles' reports indicate that the traffic light is in a normal state, and the second number of vehicles' reports indicate that the traffic light is in an abnormal state.

[0216] The status of the traffic light can then be determined based on the first and second quantities. Specifically, if the sum of the first and second quantities is greater than a preset traffic light quantity threshold (e.g., 20), and the ratio of the second quantity to the first quantity is greater than a preset traffic light ratio threshold (e.g., 2.5), then the traffic light can be identified as an abnormal traffic light. Correspondingly, shared information including the abnormal traffic light can be generated; for example, the shared information may include the abnormal traffic light's number. Further, if the sum of the first and second quantities is greater than the traffic light quantity threshold, and the ratio of the first and second quantities is greater than the traffic light ratio threshold, then the traffic light can be identified as a normal traffic light. If the sum of the first and second quantities is less than or equal to the traffic light quantity threshold, then no judgment is made on the traffic light. If neither the first nor the second quantity falls under any of the above conditions, then the traffic light can be identified as an unknown traffic light.

[0217] Step 2023: If the reported information includes location information, determine a third number of location information indicating that the first location is inaccurate within a preset time range, where the first location is any location.

[0218] Step 2024: If the third quantity is greater than the preset location quantity threshold, the first location is determined as an abnormal location. Multiple abnormal locations are clustered to obtain unsafe areas, and shared information including unsafe areas is generated.

[0219] For example, the cloud control platform can first extract location information from the reported information sent by multiple vehicles, and then determine a third number of location messages indicating that the first location is inaccurate within a preset time range. This can be understood as the cloud control platform determining, based on the reported information sent by multiple vehicles, the third number of vehicles whose reported information indicates that the first location is inaccurate.

[0220] Therefore, the third quantity can be used to determine whether the first location is an abnormal location. Specifically, if the third quantity is greater than a preset location quantity threshold (e.g., 30), the first location can be determined to be an abnormal location; if the third quantity is less than or equal to the location quantity threshold, the first location can be determined to be a normal location. The cloud control platform can cluster the obtained multiple abnormal locations to obtain one or more clusters, and determine unsafe areas based on each cluster. Correspondingly, shared information including unsafe areas can be generated; for example, the shared information may include the coordinate range of the unsafe areas.

[0221] Specifically, the method for determining insecure regions based on each cluster can be as follows:

[0222] First, determine the number of abnormal locations included in each cluster. If the number of abnormal locations exceeds a preset threshold (e.g., 25), then that cluster is designated as the target cluster. This can be understood as the abnormal locations in the target cluster being more clustered, while the abnormal locations in the non-target clusters are more dispersed. Then, generate connected regions based on the abnormal locations in the target clusters, and designate these connected regions as unsafe regions.

[0223] Step 2025: If the reported information includes environmental information, determine a fourth number of environmental information indicating that the second position does not meet the conditions for autonomous driving within a preset time range, and / or a fifth number of environmental information indicating that the second position needs to be updated within a preset time range, wherein the second position is any position.

[0224] Step 2026: If the fourth quantity is greater than the preset first environmental quantity threshold, the second location is determined as an abnormal autonomous driving location. Multiple abnormal autonomous driving locations are clustered to obtain unsafe areas, and shared information including these unsafe areas is generated.

[0225] Step 2027: If the fifth quantity is greater than the preset second environment quantity threshold, generate a local map corresponding to the second location based on the environment information indicating the second location to be updated within the preset time range, and generate shared information including the local map.

[0226] For example, the cloud control platform can first extract environmental information from the reported information sent by multiple vehicles, and then determine a fourth number of environmental information indicating that the second location does not meet the conditions for autonomous driving within a preset time range, and / or a fifth number of environmental information indicating that the second location needs to be updated within a preset time range. This can be understood as the cloud control platform determining, based on the reported information sent by multiple vehicles, that the fourth number of vehicles' reports indicate that the second location does not meet the conditions for autonomous driving, and the fifth number of vehicles' reports indicate that the second location needs to be updated. Here, "the second location does not meet the conditions for autonomous driving" can be understood as: the strength of the positioning signal at the second location is too low, and / or the environmental parameters at the second location do not match the preset autonomous driving environmental parameters. "The second location needs to be updated" can be understood as: the difference between the environmental image collected at the second location and the positioning map stored locally by the vehicle is too large.

[0227] Therefore, the fourth quantity can be used to determine whether the second location is an abnormal autonomous driving location. Specifically, if the fourth quantity is greater than a preset first environmental quantity threshold (e.g., 15), the second location can be determined to be an abnormal autonomous driving location; if the fourth quantity is less than or equal to the first environmental quantity threshold, the second location can be determined to be a normal autonomous driving location. The cloud control platform can cluster the obtained multiple abnormal autonomous driving locations to obtain one or more clusters, and determine unsafe areas based on each cluster. Correspondingly, shared information including unsafe areas can be generated; for example, the shared information may include the coordinate range of the unsafe areas. The method of determining unsafe areas based on each cluster is the same as in step 2024, and will not be repeated here.

[0228] Similarly, the fifth quantity can be used to determine whether the second location in the vehicle's locally stored positioning map needs to be updated. Specifically, if the fifth quantity is greater than a preset second environment quantity threshold (e.g., it could be 9), then it can be determined that the second location needs to be updated (e.g., there is a static obstacle). Then, based on the environmental information indicating that the second location needs updating within a preset time range, a local map corresponding to the second location can be generated, along with shared information including the local map. Specifically, since the environmental information reported by multiple vehicles can include images of the second location from multiple angles, the cloud control platform can integrate the environmental information reported by multiple vehicles to obtain a local map that reflects complete information about the second location (e.g., the coordinates, shape, and size of obstacles).

[0229] Figure 7 This is a flowchart illustrating another vehicle control method according to an exemplary embodiment, such as... Figure 7 As shown, the reported information also includes traffic speed information. Step 202 may also include:

[0230] Step 2028: If the reported information includes traffic speed information, generate shared information that includes traffic speed information.

[0231] For example, the cloud control platform can first extract traffic flow speed information from the reported information sent by multiple vehicles, and then perform statistical analysis, filtering, and deduplication on the traffic flow speed information. For instance, it can determine the traffic flow speed at any intersection (or road, area, etc.) reported by multiple vehicles within a preset time range. Then, it can cluster the multiple traffic flow speeds at that intersection to remove discrete speeds. Finally, the average (or maximum) value of the traffic flow speeds included in the clusters can be used as the traffic flow speed for that intersection (or road, area, etc.). The traffic flow speed information included in the generated shared information can be understood as including the traffic flow speeds at various locations within the jurisdiction of the cloud control platform.

[0232] It should be noted that after determining the information to be shared, the cloud control platform can also send the shared information to the traffic management platform to achieve information sharing between the two platforms. For example, the cloud control platform can send the number of the abnormal traffic light to the traffic management platform to notify relevant personnel to check and maintain the abnormal traffic light.

[0233] In one application scenario, step 203 can be implemented in the following two ways:

[0234] Method 1: Based on the location information of the target vehicle included in the driving information, determine the target area where the target vehicle is located, and use the information in the shared information that matches the target area as the target shared information.

[0235] Method 2: Based on the target vehicle's location information, driving path, and driving direction included in the driving information, determine the target vehicle's remaining driving path, and use the information in the shared information that matches the remaining driving path as the target shared information.

[0236] For example, a cloud control platform can divide its jurisdiction into multiple areas. Then, based on the target vehicle's location information included in the driving information, it determines the target area where the target vehicle is located. Finally, it uses information from the shared information that matches the target area as the target shared information. This information matching the target area can be understood as information reflecting abnormal situations within the target area, such as abnormal traffic lights, unsafe areas, or partial maps within the target area. In another implementation, the cloud control platform can first determine the target vehicle's remaining driving path based on its location, driving route, and driving direction included in the driving information. The remaining driving path is a part of the driving path, which can be understood as the portion of the driving path that the target vehicle has not yet traversed. Then, the cloud control platform can use information from the shared information that matches the remaining driving path as the target shared information. This information matching the remaining driving path can be understood as information reflecting abnormal situations within the remaining driving path, such as abnormal traffic lights along the remaining driving path, unsafe areas traversed by the remaining driving path, or partial maps along the remaining driving path. In this way, the cloud control platform can filter out the shared information that each vehicle needs to pay attention to from the shared information according to the specific needs of each vehicle, and send it to the corresponding vehicle, thereby improving the effectiveness and efficiency of data transmission.

[0237] In summary, in this disclosure, the target vehicle first collects target information, including at least one of traffic light information, location information, and environmental information, through a processing module, and then sends the target information to the cloud control platform through a control module. After receiving the target information and other information sent by other vehicles, the cloud control platform first determines the shared information, and then, based on the target vehicle's driving information, determines the target shared information corresponding to the target vehicle from the shared information and sends the target shared information to the target vehicle. The target vehicle receives the target shared information through the control module. If the target shared information includes information to be updated, the processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes unsafe areas, the processing module controls the target vehicle's movement based on the unsafe areas. This disclosure utilizes the processing and storage resources of the cloud control platform to integrate the data reported by the modules of various vehicles, thereby achieving the sharing of multiple types of information between vehicles at the module level, improving the processing efficiency and accuracy of vehicle control.

[0238] Figure 8 This is a block diagram illustrating a vehicle control device according to an exemplary embodiment, such as... Figure 8 As shown, the device 300 is applied to the target vehicle, and the device 300 includes a control module 301 and a processing module 302.

[0239] The processing module 302 is used to collect target information, which includes at least one of the following: traffic light information, location information, and environmental information.

[0240] The control module 301 is used to send target information to the cloud control platform, so that the cloud control platform can determine the shared information based on the target information and other information sent by other vehicles. Other vehicles are vehicles other than the target vehicle.

[0241] The control module 301 is used to receive target shared information sent by the cloud control platform. The target shared information is shared information and is determined by the cloud control platform based on the driving information of the target vehicle.

[0242] The processing module 302 is used to update the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated if the target shared information includes information to be updated. If the target shared information includes unsafe areas, the module controls the target vehicle's movement based on the unsafe areas.

[0243] Figure 9 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment, such as... Figure 9 As shown, there are multiple processing modules 302, including: traffic light module 3021, positioning module 3022, sensing module 3023 and planning module 3024.

[0244] Traffic light module 3021 is used to collect traffic light information. Positioning module 3022 is used to collect positioning information. Sensing module 3023 is used to collect environmental information.

[0245] The positioning module 3021 is also used to update the positioning map based on the local map included in the information to be updated.

[0246] The traffic light module 3022 is also used to delete abnormal traffic lights included in the information to be updated from the list of traffic lights to be detected.

[0247] The planning module 3024 is used to adjust the driving path included in the driving information so that the adjusted driving path does not include unsafe areas.

[0248] In one application scenario, the target information also includes: traffic speed information.

[0249] The planning module 3024 is also used to collect traffic speed information.

[0250] The planning module 3024 is also used to adjust the driving path based on the traffic flow speed along the driving path if the target shared information includes the traffic flow speed along the driving path.

[0251] In another application scenario, the traffic light module 3021 includes:

[0252] The acquisition submodule is used to acquire multiple consecutive image frames corresponding to the target traffic light.

[0253] The recognition submodule is used to identify the color of the target traffic light in each image frame.

[0254] The determination submodule is used to determine the state of the target traffic light based on the color of the target traffic light in multiple image frames.

[0255] The generation submodule is used to generate signal light information to indicate the status of the target signal light.

[0256] In another application scenario, determining the submodule can be used for:

[0257] If the color of the target traffic light in multiple image frames satisfies the first and second constraints, but does not satisfy the third constraint, the state of the target traffic light is determined to be normal.

[0258] If the color of the target traffic light in multiple image frames satisfies the first constraint, the second constraint, and the third constraint, the state of the target traffic light is determined to be an abnormal state.

[0259] The first constraint is that the number of image frames is greater than a first threshold. The second constraint is that the number of image frames whose target traffic light color is a valid color is greater than a second threshold. The third constraint is that the ratio of the number of image frames whose target traffic light color is black to the number of image frames whose target traffic light color is a valid color is greater than a third threshold. Valid colors include black, red, yellow, and green.

[0260] In another application scenario, the positioning module 3022 is used for:

[0261] The current location of the target vehicle is determined using multiple positioning methods.

[0262] If the locations corresponding to multiple positioning methods do not meet the first preset condition, positioning information is generated to indicate that the current positioning of the target vehicle is inaccurate.

[0263] The sensing module 3023 is used for:

[0264] Collect the environmental status of the target vehicle's current environment. The environmental status includes at least one of the following: the strength of the positioning signal, environmental image, and environmental parameters.

[0265] If the environmental conditions do not meet the second preset condition, environmental information is generated.

[0266] The first preset condition includes at least one of the following:

[0267] The difference between the locations corresponding to the various positioning methods is less than or equal to the fourth threshold.

[0268] The confidence level of the location corresponding to each positioning method is greater than the fifth threshold.

[0269] The second preset condition includes at least one of the following:

[0270] The strength of the positioning signal is greater than the sixth threshold.

[0271] The difference between the environmental image and the location map is less than or equal to the seventh threshold.

[0272] The environmental parameters match the preset autonomous driving environmental parameters.

[0273] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0274] In summary, in this disclosure, the target vehicle first collects target information, including at least one of traffic light information, location information, and environmental information, through a processing module, and then sends the target information to the cloud control platform through a control module. After receiving the target information and other information sent by other vehicles, the cloud control platform first determines the shared information, and then, based on the target vehicle's driving information, determines the target shared information corresponding to the target vehicle from the shared information and sends the target shared information to the target vehicle. The target vehicle receives the target shared information through the control module. If the target shared information includes information to be updated, the processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes unsafe areas, the processing module controls the target vehicle's movement based on the unsafe areas. This disclosure utilizes the processing and storage resources of the cloud control platform to integrate the data reported by the modules of various vehicles, thereby achieving the sharing of multiple types of information between vehicles at the module level, improving the processing efficiency and accuracy of vehicle control.

[0275] Figure 10 This is a block diagram illustrating a vehicle control device according to an exemplary embodiment, such as... Figure 10 As shown, the device 400 is applied to a cloud control platform and includes: a receiving module 401, a processing module 402, and a sending module 403.

[0276] The receiving module 401 is used to receive the reported information sent by the control module of each of the multiple vehicles. The reported information is collected by the processing module of the corresponding vehicle and includes at least one of traffic light information, location information, and environmental information.

[0277] The processing module 402 is used to determine the shared information based on the reported information sent by multiple vehicles.

[0278] The processing module 402 is also used to determine the target shared information corresponding to the target vehicle in the shared information based on the driving information of the target vehicle.

[0279] The sending module 403 is used to send target shared information to the control module of the target vehicle, so that if the target shared information includes information to be updated, the target vehicle's processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected according to the information to be updated. If the target shared information includes unsafe areas, the target vehicle's processing module controls the target vehicle to drive according to the unsafe areas. The target vehicle can be any one of multiple vehicles.

[0280] Figure 11 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment, such as... Figure 11 As shown, the processing module 402 may include:

[0281] The first processing submodule 4021 is used for:

[0282] When the reported information includes traffic light information, a first number of traffic light information indicating that any traffic light is in a normal state within a preset time range and a second number of traffic light information indicating that the traffic light is in an abnormal state within the preset time range are determined.

[0283] If the sum of the first quantity and the second quantity is greater than the preset threshold for the number of traffic lights, and the ratio of the second quantity to the first quantity is greater than the preset threshold for the proportion of traffic lights, the traffic light is identified as an abnormal traffic light, and shared information including the abnormal traffic light is generated.

[0284] The second processing submodule 4022 is used for:

[0285] When the reported information includes location information, a third number of location information indicating that the first location is inaccurate is determined within a preset time range, where the first location is any location.

[0286] If the third number exceeds a preset location number threshold, the first location is identified as an abnormal location. Multiple abnormal locations are clustered to obtain unsafe regions, and shared information including these unsafe regions is generated.

[0287] The third processing submodule 4023 is used for:

[0288] When the reported information includes environmental information, a fourth number of environmental information indicating that the second location does not meet the conditions for autonomous driving is determined within a preset time range, and / or a fifth number of environmental information indicating that the second location needs to be updated is determined within a preset time range, wherein the second location is any location.

[0289] If the fourth number exceeds a preset first environmental quantity threshold, the second location is identified as an abnormal autonomous driving location. Multiple abnormal autonomous driving locations are clustered to identify unsafe areas, and shared information including these unsafe areas is generated.

[0290] If the fifth quantity is greater than the preset second environment quantity threshold, a local map corresponding to the second location is generated based on the environment information indicating the second location to be updated within the preset time range, and shared information including the local map is generated.

[0291] Figure 12 This is a block diagram illustrating another vehicle control device according to an exemplary embodiment, such as... Figure 12 As shown, the reported information also includes traffic speed information.

[0292] Processing module 402 also includes:

[0293] The fourth processing submodule 4024 is used to generate shared information including traffic flow speed information when the reported information includes traffic flow speed information.

[0294] In one application scenario, processing module 402 is used for:

[0295] Based on the target vehicle's location information included in the driving information, determine the target area where the target vehicle is located, and use the information in the shared information that matches the target area as the target shared information. Alternatively,

[0296] Based on the target vehicle's location information, driving path, and driving direction included in the driving information, the remaining driving path of the target vehicle is determined, and the information in the shared information that matches the remaining driving path is used as the target shared information.

[0297] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0298] In summary, in this disclosure, the target vehicle first collects target information, including at least one of traffic light information, location information, and environmental information, through a processing module, and then sends the target information to the cloud control platform through a control module. After receiving the target information and other information sent by other vehicles, the cloud control platform first determines the shared information, and then, based on the target vehicle's driving information, determines the target shared information corresponding to the target vehicle from the shared information and sends the target shared information to the target vehicle. The target vehicle receives the target shared information through the control module. If the target shared information includes information to be updated, the processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes unsafe areas, the processing module controls the target vehicle's movement based on the unsafe areas. This disclosure utilizes the processing and storage resources of the cloud control platform to integrate the data reported by the modules of various vehicles, thereby achieving the sharing of multiple types of information between vehicles at the module level, improving the processing efficiency and accuracy of vehicle control.

[0299] Figure 13 This is a block diagram illustrating an electronic device 500 according to an exemplary embodiment. For example... Figure 13 As shown, the electronic device 500 may include a processor 501 and a memory 502. The electronic device 500 may also include one or more of a multimedia component 503, an input / output (I / O) interface 504, and a communication component 505.

[0300] The processor 501 controls the overall operation of the electronic device 500 to complete all or part of the steps in the vehicle control method applied to the target vehicle. The memory 502 stores various types of data to support the operation of the electronic device 500. This data may include, for example, instructions for any application or method operating on the electronic device 500, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 503 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 502 or transmitted via communication component 505. The audio component also includes at least one speaker for outputting audio signals. I / O interface 504 provides an interface between processor 501 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 505 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0301] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the vehicle control method applied to the target vehicle described above.

[0302] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the vehicle control method applied to the target vehicle described above. For example, the computer-readable storage medium may be the memory 502 including the program instructions described above, which may be executed by the processor 501 of the electronic device 500 to complete the vehicle control method applied to the target vehicle described above.

[0303] Figure 14 This is a block diagram illustrating an electronic device 600 according to an exemplary embodiment. For example, the electronic device 600 may be provided as a server. (Refer to...) Figure 14 The electronic device 600 includes a processor 622, which may be one or more, and a memory 632 for storing computer programs executable by the processor 622. The computer programs stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 622 may be configured to execute the computer program to perform the aforementioned vehicle control method applied to the cloud control platform.

[0304] Additionally, the electronic device 600 may also include a power supply component 626 and a communication component 650. The power supply component 626 can be configured to perform power management of the electronic device 600, and the communication component 650 can be configured to enable communication of the electronic device 600, such as wired or wireless communication. Furthermore, the electronic device 600 may also include an input / output (I / O) interface 658. The electronic device 600 can operate on an operating system, such as Windows Server, stored in memory 632. TM Mac OSX TM Unix TM Linux TM etc.

[0305] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the vehicle control method applied to the cloud control platform described above. For example, the computer-readable storage medium may be the memory 632 including program instructions, which may be executed by the processor 622 of the electronic device 600 to complete the vehicle control method applied to the cloud control platform described above.

[0306] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the control method of the vehicle described above when executed by the programmable device.

[0307] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, those skilled in the art can easily conceive of other embodiments of this disclosure after considering the specification and practicing this disclosure, and all of them fall within the protection scope of this disclosure.

[0308] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. Furthermore, different embodiments of this disclosure can also be combined arbitrarily, as long as they do not violate the spirit of this disclosure, and should also be considered as part of this disclosure. This disclosure is not limited to the precise structures described above; the scope of this disclosure is limited only by the appended claims.

Claims

1. A control method of a vehicle, characterized by, Applied to a target vehicle, the target vehicle including a control module and a processing module, the method includes: The processing module collects target information, which includes at least one of traffic light information, location information, and environmental information. The control module sends the target information to the cloud control platform, so that the cloud control platform can determine the shared information based on the target information and other information sent by other vehicles; the other vehicles are vehicles other than the target vehicle. The control module receives target shared information sent by the cloud control platform. This target shared information is part of the shared information and is determined by the cloud control platform based on the driving information of the target vehicle. If the target shared information includes information to be updated, the processing module updates the positioning map stored in the target vehicle and / or the list of traffic lights to be detected based on the information to be updated. If the target shared information includes an unsafe area, the processing module controls the target vehicle to drive based on the unsafe area. The target shared information is determined by the cloud control platform in the following manner: Based on the location information of the target vehicle included in the driving information, the target area where the target vehicle is located is determined, and the information in the shared information that matches the target area is used as the target shared information; or... Based on the location information, driving path, and driving direction of the target vehicle included in the driving information, the remaining driving path of the target vehicle is determined, and the information in the shared information that matches the remaining driving path is used as the target shared information.

2. The method according to claim 1, characterized in that, The processing modules are multiple, including: a traffic light module, a positioning module, a sensing module, and a planning module; The process of collecting target information through the processing module includes: The traffic light module collects traffic light information, the positioning module collects positioning information, and the sensing module collects environmental information. The step of updating the location map stored in the target vehicle and / or the list of traffic lights to be detected by the processing module according to the information to be updated includes: The positioning module updates the positioning map based on the local map included in the information to be updated, and the traffic light module deletes abnormal traffic lights included in the information to be updated from the list of traffic lights to be detected. The step of controlling the target vehicle's movement based on the unsafe area via the processing module includes: The planning module adjusts the driving path included in the driving information so that the adjusted driving path does not include the unsafe area.

3. The method according to claim 2, characterized in that, The target information also includes: traffic flow speed information; The process of collecting target information through the processing module also includes: The traffic flow speed information is collected through the planning module. The method further includes: If the target shared information includes the traffic flow speed on the driving path, the planning module adjusts the driving path according to the traffic flow speed on the driving path.

4. The method according to claim 2, characterized in that, The step of collecting traffic light information through the traffic light module includes: Acquire multiple consecutive image frames corresponding to the target traffic light; Identify the color of the target traffic light in each of the image frames; The state of the target traffic light is determined based on the color of the target traffic light in multiple image frames; Generate the traffic light information used to indicate the status of the target traffic light.

5. The method according to claim 4, characterized in that, Determining the state of the target traffic light based on the color of the target traffic light in multiple image frames includes: If the color of the target traffic light in multiple image frames satisfies the first constraint and the second constraint, but does not satisfy the third constraint, the state of the target traffic light is determined to be normal. If the color of the target traffic light in multiple image frames satisfies the first constraint, the second constraint, and the third constraint, the state of the target traffic light is determined to be an abnormal state. The first constraint is that the number of image frames is greater than a first threshold; the second constraint is that the number of image frames in which the target traffic light is a valid color is greater than a second threshold; and the third constraint is that the ratio of the number of image frames in which the target traffic light is black to the number of image frames in which the target traffic light is a valid color is greater than a third threshold. The valid colors include black, red, yellow, and green.

6. The method according to claim 2, characterized in that, The step of collecting the location information through the positioning module includes: The current location of the target vehicle is determined using multiple positioning methods; If the locations corresponding to multiple positioning methods do not meet the first preset condition, the positioning information used to indicate that the current positioning of the target vehicle is inaccurate is generated; The process of collecting environmental information through the sensing module includes: The environmental status of the target vehicle's current environment is collected, and the environmental status includes at least one of the following: the strength of the positioning signal, the environmental image, and environmental parameters. If the environmental state does not meet the second preset condition, the environmental information is generated. The first preset condition includes at least one of the following: The difference between the locations corresponding to multiple positioning methods is less than or equal to the fourth threshold; The confidence level of the location corresponding to each positioning method is greater than the fifth threshold; The second preset condition includes at least one of the following: The strength of the positioning signal is greater than the sixth threshold; The difference between the environmental image and the positioning map is less than or equal to the seventh threshold. The environmental parameters are matched with preset autonomous driving environmental parameters.

7. A method for controlling a vehicle, characterized in that, Applied to a cloud control platform, the method includes: The system receives reporting information from the control module of each of multiple vehicles. The reporting information is collected by the processing module of the corresponding vehicle and includes at least one of traffic light information, location information, and environmental information. Based on the reported information sent by multiple vehicles, the shared information is determined; Based on the driving information of the target vehicle, determine the target shared information corresponding to the target vehicle in the shared information; The target shared information is sent to the control module of the target vehicle, so that if the target shared information includes information to be updated, the target vehicle's processing module updates the location map stored in the target vehicle and / or the list of traffic lights to be detected according to the information to be updated. If the target shared information includes unsafe areas, the target vehicle's processing module controls the target vehicle to drive according to the unsafe areas. The target vehicle is any one of the multiple vehicles. Wherein, determining the target shared information corresponding to the target vehicle from the shared information based on the target vehicle's driving information includes: Based on the location information of the target vehicle included in the driving information, the target area where the target vehicle is located is determined, and the information in the shared information that matches the target area is used as the target shared information; or... Based on the location information, driving path, and driving direction of the target vehicle included in the driving information, the remaining driving path of the target vehicle is determined, and the information in the shared information that matches the remaining driving path is used as the target shared information.

8. The method according to claim 7, characterized in that, The step of determining shared information based on the reported information sent by multiple vehicles includes: When the reported information includes traffic light information, a first number of traffic light information indicating that any traffic light is in a normal state within a preset time range and a second number of traffic light information indicating that the traffic light is in an abnormal state within the preset time range are determined. If the sum of the first quantity and the second quantity is greater than a preset threshold for the number of traffic lights, and the ratio of the second quantity to the first quantity is greater than a preset threshold for the proportion of traffic lights, the traffic light is identified as an abnormal traffic light, and the shared information including the abnormal traffic light is generated. When the reported information includes location information, a third number of location information indicating that the first location is inaccurate is determined within the preset time range, wherein the first location is any location. If the third quantity is greater than the preset location quantity threshold, the first location is determined as a location anomaly location; multiple location anomaly locations are clustered to obtain the unsafe area, and the shared information including the unsafe area is generated; When the reported information includes environmental information, a fourth number of environmental information indicating that the second location does not meet the conditions for autonomous driving is determined within the preset time range, and / or a fifth number of environmental information indicating that the second location needs to be updated within the preset time range, wherein the second location is any location; If the fourth quantity is greater than the preset first environmental quantity threshold, the second location is determined as an abnormal location for autonomous driving; multiple abnormal locations for autonomous driving are clustered to obtain the unsafe area, and the shared information including the unsafe area is generated; If the fifth quantity is greater than the preset second environment quantity threshold, a local map corresponding to the second location is generated based on the environment information indicating the second location to be updated within the preset time range, and the shared information including the local map is generated.

9. The method according to claim 8, characterized in that, The reported information also includes traffic speed information; the step of determining shared information based on the reported information sent by multiple vehicles further includes: If the reported information includes traffic flow speed information, the shared information including the traffic flow speed information is generated.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method as described in any one of claims 1-6 or 7-9.

11. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-6 or 7-9.

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