Adaptive control method and system for vehicle-mounted navigation broadcasting

Abstract

The invention relates to the technical field related to vehicle navigation, and discloses an adaptive control method and system for vehicle-mounted navigation broadcast, which are suitable for urban roads, especially for vehicle navigation broadcast control in time periods with more traffic flows in morning and evening rush hours. According to the method, more appropriate lane changing sections are divided in advance before approaching the intersection, a user is guided to complete the lane changing action in advance, the situation that after approaching the intersection, the traffic flow density is increased, the vehicle speed is reduced, and then lane changing is difficult can be effectively avoided, meanwhile, the interference situation between vehicles needing crossing lane changing at the intersection is reduced, and the passing smoothness degree of the road is optimized.

Description

Technical Field

[0001] This invention relates to the field of vehicle navigation technology, specifically an adaptive control method and system for in-vehicle navigation broadcasting. Background Technology

[0002] In-vehicle navigation is a comprehensive user terminal that integrates satellite positioning systems, embedded devices, and Internet technology. Through the coordinated operation of multiple terminals, it provides users with comprehensive map services and vehicle navigation services. Compared with traditional paper map navigation, users can obtain accurate and real-time location and route information without having to repeatedly confirm. It is one of the necessary terminal technologies for the rapid development of urban road networks.

[0003] However, existing navigation systems still suffer from technological lag. As the number of vehicles in cities gradually increases, traffic density on roads also grows rapidly. In current scenarios, when traffic density is high, it is often difficult to change lanes before entering the corresponding lane at an intersection. High-volume and high-density traffic slows down as it approaches an intersection, and the distance between vehicles also decreases. At this point, it becomes difficult to complete a lane change. Furthermore, drivers have to slow down to complete the lane change, and vehicles behind in the target lane also need to control their speed to avoid it. This leads to a further reduction in the speed of traffic in multiple lanes, greatly reducing the overall traffic efficiency of the road and exacerbating traffic congestion. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive control method and system for vehicle navigation broadcasting, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An adaptive control method for vehicle navigation announcements, comprising: Real-time synchronization of vehicle navigation information is used to update adaptive navigation parameters, which include the vehicle's current location, current planned route, and real-time traffic data corresponding to the current planned route. The current planned path is determined based on the vehicle's current position, the next lane change intersection on the driving path is obtained, and the lane change requirement is calculated based on the target lane of the lane change intersection. The lane change requirement is used to characterize the number of lane changes and the direction required to enter the target lane. The system obtains the traffic congestion situation between lane change intersections and the current location of vehicles by real-time traffic data, and sets congestion levels for each part of the road segment based on preset criteria. The congestion levels are used to characterize traffic flow density and vehicle speed. Based on the current road congestion level, navigation pre-guidance for the next intersection is generated, and corresponding voice broadcast is generated and output. The advance generation time and distance of the navigation pre-guidance are positively correlated with the degree of road congestion.

[0006] As a further aspect of the present invention, it also includes road segmentation based on congestion levels, specifically including: Difficult lane change sections are used to characterize road sections with high congestion levels adjacent to lane change intersections. In difficult lane change sections, the vehicle flow speed is low and the vehicle density is high. Difficult lane change sections are non-guided sections, and vehicles are not recommended to perform lane change actions in non-guided sections. Restricted lane change section is used to characterize a road section with a medium level of congestion that is closest to a lane change intersection. The restricted lane change section is adjacent to the difficult lane change section or the restricted lane change section. The restricted lane change section is a supplementary guidance section. Within the supplementary guidance section, vehicles need to complete the lane change action quickly. Recommended lane change sections are road sections with the lowest congestion levels between lane change intersections and the current position of vehicles. These recommended lane change sections are optimal guidance sections, within which vehicles are recommended to complete the lane change maneuvers required for the lane change intersections in advance.

[0007] As a further aspect of the present invention: the step of generating navigation pre-guidance for the next intersection based on the current road congestion level, and generating and outputting corresponding voice broadcasts includes: Obtain the congestion level distribution between lane change intersections and the current vehicle location, and divide road segments based on the congestion level; The adjacent road sections at the road intersection are judged sequentially to determine and mark the restricted lane-changing area and the recommended lane-changing area that are closest to the road intersection. The vehicle's current position is synchronized in real time. When the vehicle enters the marked recommended lane change area and restricted lane change area, lane change guidance is generated and output through voice broadcast. The lane change guidance frequency in the restricted lane change area is higher than that in the recommended lane change area. Lane determination is performed based on the vehicle's current position. If the vehicle has already entered the target lane, lane change guidance will not be updated.

[0008] As a further aspect of the present invention, it also includes the following steps: Based on the cloud-based navigation service, the vehicle status of navigation vehicles at multiple intersections of the road network is synchronized, and the vehicle status is used to represent vehicle speed information. The system statistically evaluates the traffic density and vehicle speed information at the corresponding road intersections. If no navigation vehicle information is available for synchronization at the current intersection, a cross-platform synchronization command is generated. In response to the cross-platform synchronization command, the system accesses the traffic management center via the cloud, assesses the traffic density and vehicle speed of the corresponding road based on road monitoring images, and synchronizes them to the navigation service cloud.

[0009] As a further aspect of the present invention, it also includes the following steps: Historical traffic data of the current road intersection is obtained, and a traffic flow change model of the road intersection is obtained by fitting the historical traffic data. The traffic flow change model is used to characterize the traffic flow change based on the current traffic flow distribution within a certain time period. The traffic flow change is associated with traffic signals. The current traffic flow distribution at the intersection is obtained and fitted using a traffic flow change model to obtain the traffic flow changes within a specified time period. These traffic flow changes are used to determine the dynamic congestion of vehicles passing through the intersection.

[0010] This invention aims to provide an adaptive control system for in-vehicle navigation announcements, comprising: The information synchronization module is used to synchronize vehicle navigation information in real time to update adaptive navigation parameters through vehicle navigation information. The adaptive navigation parameters include the vehicle's current location, the current planned route, and real-time traffic data corresponding to the current planned route. The navigation planning module is used to determine the current planned path based on the vehicle's current position, obtain the next lane change intersection on the driving path, and calculate the lane change requirement based on the target lane of the lane change intersection. The lane change requirement is used to characterize the number of lane changes and the direction required to enter the target lane. The congestion determination module is used to obtain the vehicle congestion situation between the lane change intersection and the current position of the vehicle through real-time traffic data, and to set the congestion level for each part of the road segment based on the preset delineation standard. The congestion level is used to characterize the traffic flow density and vehicle speed. The traffic guidance module is used to generate navigation pre-guidance for the next intersection based on the current road congestion level, and generate and output corresponding voice broadcasts. The advance generation time and distance of the navigation pre-guidance are positively correlated with the degree of road congestion.

[0011] As a further aspect of the present invention, it also includes road segmentation based on congestion levels, specifically including: Difficult lane change sections are used to characterize road sections with high congestion levels adjacent to lane change intersections. In difficult lane change sections, the vehicle flow speed is low and the vehicle density is high. Difficult lane change sections are non-guided sections, and vehicles are not recommended to perform lane change actions in non-guided sections. Restricted lane change section is used to characterize a road section with a medium level of congestion that is closest to a lane change intersection. The restricted lane change section is adjacent to the difficult lane change section or the restricted lane change section. The restricted lane change section is a supplementary guidance section. Within the supplementary guidance section, vehicles need to complete the lane change action quickly. Recommended lane change sections are road sections with the lowest congestion levels between lane change intersections and the current position of vehicles. These recommended lane change sections are optimal guidance sections, within which vehicles are recommended to complete the lane change maneuvers required for the lane change intersections in advance.

[0012] As a further embodiment of the present invention: the traffic guidance module includes: The road segmentation unit is used to obtain the congestion level distribution between lane change intersections and the current location of vehicles, and to divide road segments based on the congestion level; The judgment and marking unit is used to sequentially judge adjacent road segments at the road intersection, determine the restricted lane-changing area and the recommended lane-changing area closest to the road intersection, and mark them. The real-time broadcasting unit is used to synchronize the vehicle's current position in real time. When the vehicle enters the marked recommended lane-changing area and restricted lane-changing area, it generates lane-changing guidance and outputs it through voice broadcast. The lane-changing guidance frequency in the restricted lane-changing area is higher than that in the recommended lane-changing area. The action judgment unit is used to determine the lane based on the vehicle's current position. If the vehicle has already entered the target lane, the lane change guidance will not be updated.

[0013] As a further aspect of the present invention, it also includes: The navigation synchronization unit is used to synchronize the vehicle status of navigation vehicles at multiple intersections of the road network based on the navigation service cloud. The vehicle status is used to represent vehicle speed information. The information evaluation unit is used to statistically evaluate the traffic density and vehicle speed information of the corresponding road intersection. If there is no available navigation vehicle information to be synchronized at the current intersection, a cross-platform synchronization command is generated. The auxiliary synchronization unit is used to respond to the cross-platform synchronization command, access the traffic management center through the cloud, assess the traffic flow density and vehicle speed of the corresponding road based on road monitoring images, and synchronize them to the navigation service cloud.

[0014] As a further aspect of the present invention, it also includes: The intersection fitting unit is used to acquire historical traffic data of the current road intersection, and to fit the historical traffic data to obtain the traffic flow change model of the road intersection. The traffic flow change model is used to characterize the traffic flow change based on the current traffic flow distribution within a certain time period. The traffic flow change is associated with traffic signals. The intersection prediction unit is used to obtain the current traffic flow distribution at the road intersection and fit it with a traffic flow change model to obtain the traffic flow change within a specified time in the future. The traffic flow change is used to determine the dynamic congestion of vehicles passing through the intersection.

[0015] Compared with the prior art, the beneficial effects of the present invention are: it is applicable to urban roads, especially vehicle navigation broadcast control during peak traffic periods. Based on the road planning of existing navigation schemes, it judges the degree of road congestion and pre-divides more suitable lane-changing segments before approaching intersections, guiding users to complete lane-changing actions in advance. This can effectively avoid the situation where lane-changing becomes difficult after the traffic density increases and the vehicle speed decreases near the intersection, while reducing interference between vehicles with lane-changing needs at intersections and optimizing the smoothness of road traffic. Attached Figure Description

[0016] Figure 1 This is a flowchart of an adaptive control method for in-vehicle navigation broadcasting.

[0017] Figure 2 This is a flowchart illustrating the generation of voice broadcast in an adaptive control method for in-vehicle navigation broadcast.

[0018] Figure 3 This is a diagram of an adaptive control system for in-vehicle navigation broadcasting. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0021] like Figure 1 The aforementioned adaptive control method for vehicle navigation broadcasting, provided in one embodiment of the present invention, includes the following steps: S10, Real-time synchronization of vehicle navigation information to update adaptive navigation parameters through vehicle navigation information, wherein the adaptive navigation parameters include the vehicle's current location, the current planned route, and real-time traffic data corresponding to the current planned route; S20: Based on the vehicle's current position, determine the current planned path, obtain the next lane change intersection on the driving path, and calculate the lane change requirement based on the target lane of the lane change intersection. The lane change requirement is used to characterize the number of lane changes and direction required to enter the target lane. S30: The vehicle congestion situation between the lane change intersection and the current position of the vehicle is obtained through real-time traffic data, and the congestion level is set for each part of the road segment based on the preset delineation standard. The congestion level is used to characterize the traffic flow density and vehicle speed. S40, based on the current road congestion level, generate navigation pre-guidance for the next intersection, generate corresponding voice broadcast and output it. The advance generation distance and time of the navigation pre-guidance are positively correlated with the degree of road congestion.

[0022] This embodiment presents an adaptive control method for in-vehicle navigation broadcasting, applicable to urban roads, especially during peak traffic hours. Based on existing navigation schemes' road planning, it assesses road congestion levels to pre-define more suitable lane-changing segments before approaching intersections, guiding users to complete lane-changing actions in advance. This effectively avoids difficulties in lane changing due to increased traffic density and reduced vehicle speed near intersections, while also reducing interference between vehicles requesting lane changes at intersections, thus optimizing traffic flow. In daily traffic, when road traffic density is high (e.g., on arterial roads with multiple intersections, or during peak hours), this method is particularly effective. This often leads to difficulties in changing lanes before entering the corresponding lane at an intersection. High-volume, high-density traffic slows down as it approaches the intersection, and the distance between vehicles also decreases, making lane changes difficult. Drivers then have to slow down further to complete the lane change, and vehicles behind in the target lane also need to control their speed to avoid it. This results in a further reduction in traffic speed across multiple lanes, significantly lowering overall road efficiency and exacerbating congestion. Therefore, this application proposes an adaptive control method for navigation broadcasts. Through guidance and coordination from the navigation system, this method reduces the occurrence of such situations, thereby optimizing road efficiency. Specifically, steps S10 and S20... The steps performed in step S20 are consistent with common navigation methods in current technology. A navigation route is established by acquiring the driver's destination and current location information. Lane-level navigation determines the vehicle's current lane and the lane change required to reach the next intersection. After this step, in existing navigation methods, lane change information is typically provided visually and audibly at a certain distance before the intersection. In the actual navigation map, multiple lanes are passable. Approaching the intersection, the lanes narrow to the only lane necessary to pass through. Audio navigation is also broadcast at multiple set distance nodes. This results in a shorter available lane-changing distance after the vehicle reaches the intersection. In cases where a significant reduction in speed is necessary to complete a lane change, this embodiment includes a step in step S30 to assess lane congestion. When a vehicle enters a road and determines the lane requirement at the next intersection, the congestion level of the current road is assessed. Then, in step S40, navigation guidance is provided based on the road's congestion level, with the distance to the intersection in advance (and the arrival time at the intersection in advance, with time being a secondary factor and the primary objective being to obtain more normal lane-changing distance). This guides the driver to complete the lane change before the vehicle enters a congested, low-speed section, effectively preventing lane changes caused by slowing down and reducing road congestion.

[0023] As another preferred embodiment of the present invention, it further includes road segmentation based on congestion level, specifically including: Difficult lane change sections are used to characterize road sections with high congestion levels adjacent to lane change intersections. In difficult lane change sections, the vehicle flow speed is low and the vehicle density is high. Difficult lane change sections are non-guided sections, and vehicles are not recommended to perform lane change actions in non-guided sections. Restricted lane change section is used to characterize a road section with a medium level of congestion that is closest to a lane change intersection. The restricted lane change section is adjacent to the difficult lane change section or the restricted lane change section. The restricted lane change section is a supplementary guidance section. Within the supplementary guidance section, vehicles need to complete the lane change action quickly. Recommended lane change sections are road sections with the lowest congestion levels between lane change intersections and the current position of vehicles. These recommended lane change sections are optimal guidance sections, within which vehicles are recommended to complete the lane change maneuvers required for the lane change intersections in advance.

[0024] This embodiment supplements the specific division of road segments based on road congestion levels. In difficult lane-changing sections, traffic speed is already low, vehicle density is high, and the interval between vehicles is small. Vehicles can only complete lane changes by having adjacent vehicles slow down and give way. However, the slowing down of adjacent vehicles leads to congestion behind them in the adjacent lane. Furthermore, the randomness of adjacent vehicles slowing down and giving way results in the current vehicle maintaining a low or even semi-stationary speed, causing congestion behind it. Therefore, lane changes are no longer recommended in difficult lane-changing sections. In such cases, the navigation system can automatically re-evaluate vehicle lane changes. The navigation route is planned to obtain a more efficient navigation solution for the overall traffic. In restricted lane-change areas, vehicles on this section of road are already gradually slowing down (which can be judged based on the road speed limit) and are accumulating. In this case, the navigation should guide vehicles to quickly find opportunities to change lanes. At this time, lane changing has already affected the traffic efficiency, but there will not be serious slowdowns or traffic jams. Recommended lane-change sections are the normal traffic sections before difficult lane-change areas and restricted lane-change areas. Vehicles on this section are driving normally within the speed limit, and lane changing and safe following distance control have no impact on vehicles behind.

[0025] like Figure 2 As shown, in another preferred embodiment of the present invention, the steps of generating navigation pre-guidance for the next intersection based on the current road congestion level, and generating and outputting corresponding voice broadcasts, include: S41, obtain the congestion level distribution between lane change intersections and the current vehicle position, and divide the road segments based on the congestion level; S42, sequentially judge the adjacent road sections at the road intersection, determine the restricted lane-changing area and the recommended lane-changing area that are closest to the road intersection, and mark them; S43, Real-time synchronization of the vehicle's current position. When the vehicle enters the marked recommended lane-changing area and restricted lane-changing area, lane-changing guidance is generated and output through voice broadcast. The lane-changing guidance frequency in the restricted lane-changing area is higher than that in the recommended lane-changing area. S44 determines the lane based on the vehicle's current position. If the vehicle has already entered the target lane, it will no longer update the lane change guidance output.

[0026] In this embodiment, step S40 is described. The main process involves dividing the road segments and, after the various lane-changing segments are divided, backtracking and marking the segments with the road intersection as the origin. The reason for using backtracking and marking is that, when the current road length is long, there may be multiple restricted or recommended lane-changing areas. By using backtracking and marking, the lane-changing segment of the vehicle can be closer to the road intersection, giving the vehicle more free lane driving distance. Moreover, completing the lane change too early may still require the vehicle to re-enter other lanes for overtaking or other actions in subsequent driving, requiring the navigation system to repeat the judgment and guidance process. Therefore, by selecting the recommended and restricted lane-changing areas closest to the road intersection as the lane-changing broadcast guidance trigger areas, a smoother lane-changing interaction experience can be achieved.

[0027] As another preferred embodiment of the present invention, the method further includes the following steps: Based on the cloud-based navigation service, the vehicle status of navigation vehicles at multiple intersections of the road network is synchronized, and the vehicle status is used to represent vehicle speed information. The system statistically evaluates the traffic density and vehicle speed information at the corresponding road intersections. If no navigation vehicle information is available for synchronization at the current intersection, a cross-platform synchronization command is generated. In response to the cross-platform synchronization command, the system accesses the traffic management center via the cloud, assesses the traffic density and vehicle speed of the corresponding road based on road monitoring images, and synchronizes them to the navigation service cloud.

[0028] This embodiment supplements the method for obtaining road vehicle status. The first method, which synchronizes through navigation services, is a common technique used by navigation systems to obtain road congestion status in the prior art. This involves comprehensively judging the speed of vehicles using navigation services and the density of service users on the road segment. For example, if ten vehicles in the area are using navigation services and their speeds are all decreasing and showing a certain consistency, it indicates that the current road segment may be congested. However, in some fixed roads or cities with few outside vehicles, most local vehicles may not use navigation services, making it difficult for navigation services to obtain road congestion information. When other vehicles pass through the road segment, the technical solution provided in the embodiment cannot be provided. Therefore, this embodiment additionally supplements the method of synchronizing traffic flow information obtained through traffic management systems to compensate for the part where the navigation system cannot complete the synchronization of traffic status information.

[0029] As another preferred embodiment of the present invention, the method further includes the following steps: Historical traffic data of the current road intersection is obtained, and a traffic flow change model of the road intersection is obtained by fitting the historical traffic data. The traffic flow change model is used to characterize the traffic flow change based on the current traffic flow distribution within a certain time period. The traffic flow change is associated with traffic signals. The current traffic flow distribution at the intersection is obtained and fitted using a traffic flow change model to obtain the traffic flow changes within a specified time period. These traffic flow changes are used to determine the dynamic congestion of vehicles passing through the intersection.

[0030] In this embodiment, the level of congestion on a road changes constantly with the real-time traffic flow and traffic light changes. Therefore, using the current traffic conditions for road segment division and adaptive guidance may not be accurate. For example, the current state at an intersection may be a very smooth restricted lane-changing area or a recommended lane-changing area because a green light has just been passed. However, there may be a large number of vehicles about to enter the intersection at the current location. Therefore, when the vehicles actually arrive at the intersection, it may be a difficult lane-changing area. Thus, using the current traffic conditions for judgment is inaccurate. Therefore, in this embodiment, a traffic model for the corresponding intersection can be built by simulating historical data. The current state can be simulated based on the model to obtain accurate traffic changes during the process of vehicles entering the intersection, thereby achieving effective and reliable congestion judgment and dynamic guidance.

[0031] like Figure 3 As shown, the present invention also provides an adaptive control system for vehicle navigation broadcasting, comprising: The information synchronization module 100 is used to synchronize vehicle navigation information in real time to update adaptive navigation parameters through vehicle navigation information. The adaptive navigation parameters include the vehicle's current location, the current planned route, and real-time traffic data corresponding to the current planned route. The navigation planning module 200 is used to determine the current planned path based on the current position of the vehicle, obtain the next lane change intersection on the driving path, and calculate the lane change requirement based on the target lane of the lane change intersection. The lane change requirement is used to characterize the number of lane changes and the direction required to enter the target lane. The congestion determination module 300 is used to obtain the vehicle congestion situation between the lane change intersection and the current position of the vehicle through real-time traffic data, and to set the congestion level for each part of the road segment based on the preset delineation standard. The congestion level is used to characterize the traffic flow density and vehicle speed. The traffic guidance module 400 is used to generate navigation pre-guidance for the next intersection according to the current road congestion level, generate corresponding voice broadcast and output it. The advance generation time and distance of the navigation pre-guidance are positively correlated with the degree of road congestion.

[0032] As another preferred embodiment of the present invention, it further includes road segmentation based on congestion level, specifically including: Difficult lane change sections are used to characterize road sections with high congestion levels adjacent to lane change intersections. In difficult lane change sections, the vehicle flow speed is low and the vehicle density is high. Difficult lane change sections are non-guided sections, and vehicles are not recommended to perform lane change actions in non-guided sections. Restricted lane change section is used to characterize a road section with a medium level of congestion that is closest to a lane change intersection. The restricted lane change section is adjacent to the difficult lane change section or the restricted lane change section. The restricted lane change section is a supplementary guidance section. Within the supplementary guidance section, vehicles need to complete the lane change action quickly. Recommended lane change sections are road sections with the lowest congestion levels between lane change intersections and the current position of vehicles. These recommended lane change sections are optimal guidance sections, within which vehicles are recommended to complete the lane change maneuvers required for the lane change intersections in advance.

[0033] In another preferred embodiment of the present invention, the traffic guidance module 400 includes: The road segmentation unit is used to obtain the congestion level distribution between lane change intersections and the current location of vehicles, and to divide road segments based on the congestion level; The judgment and marking unit is used to sequentially judge adjacent road segments at the road intersection, determine the restricted lane-changing area and the recommended lane-changing area closest to the road intersection, and mark them. The real-time broadcasting unit is used to synchronize the vehicle's current position in real time. When the vehicle enters the marked recommended lane-changing area and restricted lane-changing area, it generates lane-changing guidance and outputs it through voice broadcast. The lane-changing guidance frequency in the restricted lane-changing area is higher than that in the recommended lane-changing area. The action judgment unit is used to determine the lane based on the vehicle's current position. If the vehicle has already entered the target lane, the lane change guidance will not be updated.

[0034] As another preferred embodiment of the present invention, it further includes: The navigation synchronization unit is used to synchronize the vehicle status of navigation vehicles at multiple intersections of the road network based on the navigation service cloud. The vehicle status is used to represent vehicle speed information. The information evaluation unit is used to statistically evaluate the traffic density and vehicle speed information of the corresponding road intersection. If there is no available navigation vehicle information to be synchronized at the current intersection, a cross-platform synchronization command is generated. The auxiliary synchronization unit is used to respond to the cross-platform synchronization command, access the traffic management center through the cloud, assess the traffic flow density and vehicle speed of the corresponding road based on road monitoring images, and synchronize them to the navigation service cloud.

[0035] As another preferred embodiment of the present invention, it further includes: The intersection fitting unit is used to acquire historical traffic data of the current road intersection, and to fit the historical traffic data to obtain the traffic flow change model of the road intersection. The traffic flow change model is used to characterize the traffic flow change based on the current traffic flow distribution within a certain time period. The traffic flow change is associated with traffic signals. The intersection prediction unit is used to obtain the current traffic flow distribution at the road intersection and fit it with a traffic flow change model to obtain the traffic flow change within a specified time in the future. The traffic flow change is used to determine the dynamic congestion of vehicles passing through the intersection.

[0036] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0037] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0038] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An adaptive control method for vehicle navigation broadcasting, characterized in that, Include: Real-time synchronization of vehicle navigation information is used to update adaptive navigation parameters, which include the vehicle's current location, current planned route, and real-time traffic data corresponding to the current planned route. The current planned path is determined based on the vehicle's current position, the next lane change intersection on the driving path is obtained, and the lane change requirement is calculated based on the target lane of the lane change intersection. The lane change requirement is used to characterize the number of lane changes and the direction required to enter the target lane. The system obtains the traffic congestion situation between lane change intersections and the current location of vehicles by real-time traffic data, and sets congestion levels for each part of the road segment based on preset criteria. The congestion levels are used to characterize traffic flow density and vehicle speed. Based on the current road congestion level, navigation pre-guidance for the next intersection is generated, and corresponding voice broadcast is generated and output. The advance generation time and distance of the navigation pre-guidance are positively correlated with the degree of road congestion.

2. The adaptive control method for vehicle navigation broadcasting according to claim 1, characterized in that, It also includes road segmentation based on congestion levels, specifically including: Difficult lane change sections are used to characterize road sections with high congestion levels adjacent to lane change intersections. In difficult lane change sections, the vehicle flow speed is low and the vehicle density is high. Difficult lane change sections are non-guided sections, and vehicles are not recommended to perform lane change actions in non-guided sections. Restricted lane change section is used to characterize a road section with a medium level of congestion that is closest to a lane change intersection. The restricted lane change section is adjacent to the difficult lane change section or the restricted lane change section. The restricted lane change section is a supplementary guidance section. Within the supplementary guidance section, vehicles need to complete the lane change action quickly. Recommended lane change sections are road sections with the lowest congestion levels between lane change intersections and the current position of vehicles. These recommended lane change sections are optimal guidance sections, within which vehicles are recommended to complete the lane change maneuvers required for the lane change intersections in advance.

3. The adaptive control method for vehicle navigation broadcasting according to claim 2, characterized in that, The steps of generating navigation pre-guidance for the next intersection based on the current road congestion level, and generating and outputting corresponding voice broadcasts include: Obtain the congestion level distribution between lane change intersections and the current vehicle location, and divide road segments based on the congestion level; The adjacent road sections at the road intersection are judged sequentially to determine and mark the restricted lane-changing area and the recommended lane-changing area that are closest to the road intersection. The vehicle's current position is synchronized in real time. When the vehicle enters the marked recommended lane change area and restricted lane change area, lane change guidance is generated and output through voice broadcast. The lane change guidance frequency in the restricted lane change area is higher than that in the recommended lane change area. Lane determination is performed based on the vehicle's current position. If the vehicle has already entered the target lane, lane change guidance will not be updated.

4. The adaptive control method for vehicle navigation broadcasting according to claim 3, characterized in that, It also includes the following steps: Based on the cloud-based navigation service, the vehicle status of navigation vehicles at multiple intersections of the road network is synchronized, and the vehicle status is used to represent vehicle speed information. The system statistically evaluates the traffic density and vehicle speed information at the corresponding road intersections. If no navigation vehicle information is available for synchronization at the current intersection, a cross-platform synchronization command is generated. In response to the cross-platform synchronization command, the system accesses the traffic management center via the cloud, assesses the traffic density and vehicle speed of the corresponding road based on road monitoring images, and synchronizes them to the navigation service cloud.

5. The adaptive control method for vehicle navigation broadcasting according to claim 4, characterized in that, It also includes the following steps: Historical traffic data of the current road intersection is obtained, and a traffic flow change model of the road intersection is obtained by fitting the historical traffic data. The traffic flow change model is used to characterize the traffic flow change based on the current traffic flow distribution within a certain time period. The traffic flow change is associated with traffic signals. The current traffic flow distribution at the intersection is obtained and fitted using a traffic flow change model to obtain the traffic flow changes within a specified time period. These traffic flow changes are used to determine the dynamic congestion of vehicles passing through the intersection.

6. An adaptive control system for vehicle navigation broadcasting, characterized in that, Include: The information synchronization module is used to synchronize vehicle navigation information in real time to update adaptive navigation parameters through vehicle navigation information. The adaptive navigation parameters include the vehicle's current location, the current planned route, and real-time traffic data corresponding to the current planned route. The navigation planning module is used to determine the current planned path based on the vehicle's current position, obtain the next lane change intersection on the driving path, and calculate the lane change requirement based on the target lane of the lane change intersection. The lane change requirement is used to characterize the number of lane changes and the direction required to enter the target lane. The congestion determination module is used to obtain the vehicle congestion situation between the lane change intersection and the current position of the vehicle through real-time traffic data, and to set the congestion level for each part of the road segment based on the preset delineation standard. The congestion level is used to characterize the traffic flow density and vehicle speed. The traffic guidance module is used to generate navigation pre-guidance for the next intersection based on the current road congestion level, and generate and output corresponding voice broadcasts. The advance generation time and distance of the navigation pre-guidance are positively correlated with the degree of road congestion.

7. An adaptive control system for vehicle navigation broadcasting according to claim 6, characterized in that, It also includes road segmentation based on congestion levels, specifically including: Difficult lane change sections are used to characterize road sections with high congestion levels adjacent to lane change intersections. In difficult lane change sections, the vehicle flow speed is low and the vehicle density is high. Difficult lane change sections are non-guided sections, and vehicles are not recommended to perform lane change actions in non-guided sections. Restricted lane change section is used to characterize a road section with a medium level of congestion that is closest to a lane change intersection. The restricted lane change section is adjacent to the difficult lane change section or the restricted lane change section. The restricted lane change section is a supplementary guidance section. Within the supplementary guidance section, vehicles need to complete the lane change action quickly. Recommended lane change sections are road sections with the lowest congestion levels between lane change intersections and the current position of vehicles. These recommended lane change sections are optimal guidance sections, within which vehicles are recommended to complete the lane change maneuvers required for the lane change intersections in advance.

8. An adaptive control system for vehicle navigation broadcasting according to claim 7, characterized in that, The traffic guidance module includes: The road segmentation unit is used to obtain the congestion level distribution between lane change intersections and the current location of vehicles, and to divide road segments based on the congestion level; The judgment and marking unit is used to sequentially judge adjacent road segments at the road intersection, determine the restricted lane-changing area and the recommended lane-changing area closest to the road intersection, and mark them. The real-time broadcasting unit is used to synchronize the vehicle's current position in real time. When the vehicle enters the marked recommended lane-changing area and restricted lane-changing area, it generates lane-changing guidance and outputs it through voice broadcast. The lane-changing guidance frequency in the restricted lane-changing area is higher than that in the recommended lane-changing area. The action judgment unit is used to determine the lane based on the vehicle's current position. If the vehicle has already entered the target lane, the lane change guidance will not be updated.

9. An adaptive control system for vehicle navigation broadcasting according to claim 8, characterized in that, Also includes: The navigation synchronization unit is used to synchronize the vehicle status of navigation vehicles at multiple intersections of the road network based on the navigation service cloud. The vehicle status is used to represent vehicle speed information. The information evaluation unit is used to statistically evaluate the traffic density and vehicle speed information of the corresponding road intersection. If there is no available navigation vehicle information to be synchronized at the current intersection, a cross-platform synchronization command is generated. The auxiliary synchronization unit is used to respond to the cross-platform synchronization command, access the traffic management center through the cloud, assess the traffic flow density and vehicle speed of the corresponding road based on road monitoring images, and synchronize them to the navigation service cloud.

10. An adaptive control system for vehicle navigation broadcasting according to claim 9, characterized in that, Also includes: The intersection fitting unit is used to acquire historical traffic data of the current road intersection, and to fit the historical traffic data to obtain the traffic flow change model of the road intersection. The traffic flow change model is used to characterize the traffic flow change based on the current traffic flow distribution within a certain time period. The traffic flow change is associated with traffic signals. The intersection prediction unit is used to obtain the current traffic flow distribution at the road intersection and fit it with a traffic flow change model to obtain the traffic flow change within a specified time in the future. The traffic flow change is used to determine the dynamic congestion of vehicles passing through the intersection.

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