Navigation method, device and equipment for rescue scene and computer program product

Abstract

The invention discloses a navigation method, device and equipment for a rescue scene and a computer program product. The method comprises the following steps: acquiring an initial path planning result and multi-dimensional path optimization information of a rescue vehicle; the initial path planning result is optimized according to the multi-dimensional path optimization information, and driving of the rescue vehicle is controlled according to the optimized path; real-time traffic information is obtained through V2X communication, so that auxiliary reminding information is generated, and whole-road-section auxiliary reminding is carried out; and when the navigation auxiliary condition is met, acquiring navigation auxiliary information through the intelligent wearable device so as to carry out auxiliary navigation on a special road section. According to the invention, path optimization is carried out by using the multi-dimensional path optimization information, and a more reasonable and efficient driving path is planned for the rescue vehicle. And external information is acquired by means of V2X communication, and all-road-section auxiliary reminding is carried out, so that the driving safety and the passing efficiency are improved. And navigation auxiliary information is provided through the intelligent wearable device in a special road section, so that the rescue capability in a complex scene is further improved.

Description

Technical Field

[0001] This application relates to the field of intelligent rescue technology, and in particular to a navigation method, device and equipment, and computer program product for rescue scenarios. Background Technology

[0002] With the widespread adoption of navigation systems and the application of large-scale model algorithms, navigation in the passenger vehicle market has become increasingly intelligent, with features such as route planning, traffic light countdowns, lane-level warnings, and AR augmented reality widely used. Leveraging a massive user base, current passenger vehicle navigation systems have fully entered a state of intelligence and widespread adoption.

[0003] Currently, most ambulance rescue systems on the market still rely on remote dispatch and vehicle-to-infrastructure (V2I) coordination—assistance mechanisms controlled by human intervention. Drivers generally depend on mobile navigation and human coordination for assistance. In relatively complex scenarios such as wrong-way roads, traffic light intersections, and internal alleys, the system relies heavily on the driver's experience and judgment for quick passage. This model does not effectively utilize the advantages of current passenger vehicle navigation development and the big data available in the passenger vehicle market, and its rescue efficiency has not reached the levels expected in today's technologically advanced world. Summary of the Invention

[0004] This application provides a navigation method, device, equipment, and computer program product for rescue scenarios to improve the rescue efficiency of rescue vehicles.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a navigation method for a rescue scenario, the navigation method for a rescue scenario including:

[0007] The initial route planning results and multi-dimensional route optimization information of the rescue vehicle are obtained, including road segment information, traffic congestion information and traffic light information.

[0008] The initial path planning result is optimized based on the multi-dimensional path optimization information to obtain an optimized path planning result, and the driving of the rescue vehicle is controlled based on the optimized path planning result.

[0009] The system utilizes V2X communication to acquire real-time traffic information during the rescue vehicle's journey and generates auxiliary reminder information based on the real-time traffic information, thereby providing auxiliary reminders for the entire road segment.

[0010] When the rescue vehicle meets the navigation assistance conditions for the special road section, it obtains navigation assistance information for the special road section through a smart wearable device and uses the navigation assistance information to provide assisted navigation for the special road section.

[0011] Secondly, embodiments of this application also provide a navigation device for rescue scenarios, the navigation device for rescue scenarios comprising:

[0012] The acquisition unit is used to acquire the initial route planning results and multi-dimensional route optimization information of the rescue vehicle. The multi-dimensional route optimization information includes road segment information, traffic congestion information, and traffic light information.

[0013] The path optimization unit is used to optimize the initial path planning result based on the multi-dimensional path optimization information to obtain the optimized path planning result, and to control the driving of the rescue vehicle based on the optimized path planning result.

[0014] The auxiliary reminder unit is used to obtain real-time traffic information during the driving process of the rescue vehicle using V2X communication, and to generate auxiliary reminder information based on the real-time traffic information, so as to provide auxiliary reminders for the entire road segment through the auxiliary reminder information;

[0015] The navigation assistance unit is used to obtain navigation assistance information for special road sections through a smart wearable device when the rescue vehicle meets the navigation assistance conditions for special road sections, and to perform assisted navigation for special road sections through the navigation assistance information.

[0016] Thirdly, embodiments of this application also provide an apparatus, comprising:

[0017] A processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform a navigation method for any of the aforementioned rescue scenarios.

[0018] Fourthly, embodiments of this application also provide a computer program product, including a computer program / instructions, which, when executed by a processor, implements a navigation method for any of the aforementioned rescue scenarios.

[0019] The above-mentioned at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: The navigation method for rescue scenarios in the embodiments of this application first obtains the initial path planning result and multi-dimensional path optimization information of the rescue vehicle, wherein the multi-dimensional path optimization information includes road segment information, traffic congestion information, and traffic light information; then, the initial path planning result is optimized according to the multi-dimensional path optimization information to obtain an optimized path planning result, and the driving of the rescue vehicle is controlled according to the optimized path planning result; then, real-time traffic information during the driving process of the rescue vehicle is obtained using V2X communication, and auxiliary reminder information is generated according to the real-time traffic information to provide auxiliary reminders for the entire road segment; finally, when the rescue vehicle meets the navigation assistance conditions for special road segments, navigation assistance information for special road segments is obtained through a smart wearable device, and assisted navigation for special road segments is performed through the navigation assistance information. The navigation method for rescue scenarios in the embodiments of this application, by optimizing the initial path by obtaining multi-dimensional path optimization information, can plan a more reasonable and efficient driving path for the rescue vehicle, effectively avoiding congested road segments and reducing driving time. By utilizing V2X communication to acquire real-time traffic information and provide roadside assistance alerts, relevant vehicles can avoid obstacles in a timely manner, improving driving safety and traffic efficiency. In special road sections, smart wearable devices provide navigation assistance information, further enhancing the ability of rescue vehicles to navigate complex road conditions, thereby improving rescue efficiency. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a flowchart illustrating a navigation method for a rescue scenario in an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of the path planning and control process for a rescue scenario in an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of a full-segment auxiliary reminder process based on V2X communication in an embodiment of this application;

[0024] Figure 4 This is a schematic diagram illustrating the judgment process for a bypass scenario in an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of a driving navigation assistance process in an embodiment of this application;

[0026] Figure 6This is a schematic diagram of an off-vehicle navigation assistance process in an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of the structure of a navigation device for a rescue scenario according to an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of a device according to an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0031] The technical terms used in this application mainly include:

[0032] (1)LSTM: Long Short-Term Memor, a type of time-recurrent neural network.

[0033] (2) V2X: Vehicle to everything, the exchange of information between the vehicle and the outside world.

[0034] (3) AR navigation: An innovative navigation mode realized by augmented reality technology, which integrates map data, real-world images captured by mobile terminal cameras and virtual guidance models.

[0035] (4) Genetic algorithm: Intelligent optimization algorithm constructed by simulating biological evolution mechanism.

[0036] (5) OBU: On-Board Unit, a type of vehicle-mounted equipment.

[0037] (6) RSU: Road Side Unit. The roadside unit is a key device for realizing intelligent road and vehicle-road coordination. It is set on the roadside and communicates and exchanges data with nearby passing vehicles in both directions. It is an important device in the intelligent transportation system.

[0038] (7) A* search algorithm: an algorithm for finding the path with the lowest cost in a graph plane with multiple nodes.

[0039] (8) Dijkstra's algorithm: a shortest path algorithm from one vertex to all other vertices, which solves the shortest path problem in a weighted graph.

[0040] This application provides a navigation method for rescue scenarios, such as... Figure 1 The diagram shows a flowchart of a navigation method for a rescue scenario according to an embodiment of this application. The navigation method for the rescue scenario includes the following steps S110 to S140:

[0041] Step S110: Obtain the initial route planning results and multi-dimensional route optimization information for the rescue vehicle. The multi-dimensional route optimization information includes road segment information, traffic congestion information, and traffic light information.

[0042] The rescue vehicle in this embodiment is pre-equipped with an OBU (On-Board Unit), enabling real-time V2X signal interaction with external RSU (Rescue Unit) and other devices. The rescue vehicle is equipped with an in-vehicle navigation system configured in rescue mode, dynamically adjusting routes and sending alerts via V2X signals. Furthermore, it is equipped with smart wearable devices such as AR glasses for the driver to wear, enabling AR real-scene fusion navigation alerts.

[0043] First, based on conventional path planning algorithms such as A* search and Dijkstra's algorithm, and combined with the departure and destination information of the rescue vehicle, an initial path plan is generated. This process mainly considers basic road topology, distance, and other factors, providing a basic path framework for subsequent optimization.

[0044] Then, multi-dimensional path optimization information is collected through various data sources. Road segment information includes segment length, connection information, road classification, travel time, and traffic restrictions. This information can be obtained from urban geographic information systems or dedicated road databases. Traffic congestion information can include current congestion and predicted congestion. Predicted congestion can be obtained using real-time data uploaded from traffic monitoring cameras, vehicle sensors, and mobile devices, and predicted using models deployed in the cloud or locally. Traffic light information is obtained through data interaction with the traffic signal control system, acquiring detailed information such as the phase and duration of traffic lights at each intersection. Traffic light information can also be predicted using models deployed in the cloud.

[0045] Step S120: Optimize the initial path planning result based on the multi-dimensional path optimization information to obtain the optimized path planning result, and control the driving of the rescue vehicle based on the optimized path planning result.

[0046] The initial route planning results are comprehensively evaluated and adjusted based on the acquired multi-dimensional route optimization information. For example, if severe congestion occurs on a certain road segment, the route will be replanned to bypass the congested area. The optimized route planning results are transmitted to the navigation control system of the rescue vehicle. This system controls the vehicle's steering, acceleration, and deceleration based on the route information to ensure that the vehicle travels along the optimized route. Simultaneously, the navigation interface displays route information in real time, providing intuitive guidance to the driver.

[0047] Combination Figure 2 This application provides a schematic diagram of the route planning and control process for a rescue scenario in an embodiment of the present application. Unlike the route planning logic for passenger vehicles, route planning in rescue scenarios needs to prioritize time, quickly picking up the patient and transporting them to the hospital. Therefore, route planning needs to consider more scenarios and minimize traffic light avoidance, while also considering both remotely controllable and non-remotely controllable traffic lights. During route planning, all traffic lights can be optimized based on data extrapolated from a large-scale passenger vehicle model. After the route is generated, the controllable traffic lights are coordinated through a traffic cloud platform to facilitate the ambulance's rapid passage.

[0048] Step S130: Use V2X communication to obtain real-time traffic information during the driving process of the rescue vehicle, and generate auxiliary reminder information based on the real-time traffic information to provide auxiliary reminders for the entire road segment.

[0049] Combination Figure 3 This application provides a schematic diagram of a full-segment auxiliary alert process based on V2X communication in an embodiment of this application. After route planning is completed, during the real-time driving of the rescue vehicle, considering changes in actual road conditions, it is necessary to utilize V2X communication capabilities to summarize information such as actual route and lane conditions to assist driving. For example, based on V2X communication technology, the rescue vehicle can interact with other surrounding vehicles (V2V) and traffic infrastructure (V2I, such as traffic lights, roadside units, etc.) in real time. In this way, real-time traffic information during the rescue vehicle's driving process can be obtained, including the speed of vehicles ahead, distance, and whether there are any sudden accidents.

[0050] Based on real-time traffic information, intelligent algorithms are used for analysis and judgment. For example, when it is detected that a vehicle ahead can be overtaken, corresponding auxiliary reminder information can be generated to remind the vehicle ahead to give way in advance. By acquiring and analyzing real-time traffic information across the entire road segment through V2X communication, real-time auxiliary reminders can be achieved across the entire road segment, helping drivers to respond promptly to various traffic situations.

[0051] Step S140: When the rescue vehicle meets the navigation assistance conditions for the special road section, it obtains the navigation assistance information for the special road section through a smart wearable device and performs assisted navigation for the special road section through the navigation assistance information.

[0052] This application embodiment can also pre-set a series of navigation assistance conditions for special road sections, such as entering multi-way intersections, small roads, and complex overpasses. The rescue vehicle's positioning system (such as GPS, Beidou, etc.) and sensors (such as cameras, radar, etc.) monitor the vehicle's position and surrounding environment in real time to determine whether the navigation assistance conditions for special road sections are met.

[0053] When a rescue vehicle meets the navigation assistance requirements for a special road section, it obtains navigation assistance information for that section through smart wearable devices (such as AR glasses). This information can be overlaid on the driver's field of vision in augmented reality form, displaying detailed routes, turning prompts, and obstacle locations for the special road section. The driver can visually see this information through AR glasses, enabling them to navigate the special road section more accurately and safely, thus achieving precise assisted navigation for that section.

[0054] From a practical perspective, it is easy to get lost in scenarios with multiple intersections and complex road conditions. Currently, there are more elderly people living in older residential areas, and the road conditions in older residential areas are relatively complex. Therefore, the embodiments of this application, in conjunction with AR glasses, realize real-scene fusion, which can combine real-scene navigation with the internal roads after leaving the vehicle during driving, so as to avoid getting lost.

[0055] The navigation method for rescue scenarios in this application optimizes the initial path by acquiring multi-dimensional path optimization information, enabling the rescue vehicle to plan a more reasonable and efficient driving route, effectively avoiding congested road sections and reducing travel time. Utilizing V2X communication to obtain real-time traffic information and provide full-segment auxiliary alerts allows relevant vehicles to avoid obstacles in a timely manner, improving vehicle driving safety and traffic efficiency. In special road sections, navigation assistance information is provided through smart wearable devices, further enhancing the rescue vehicle's ability to navigate complex road conditions, thereby improving the rescue efficiency of the rescue vehicle.

[0056] In some embodiments of this application, optimizing the initial path planning result based on the multi-dimensional path optimization information to obtain an optimized path planning result includes: obtaining traffic congestion information for the entire road segment in the initial path planning result based on the initial path planning result and the multi-dimensional path optimization information; generating an avoidance road segment set based on the traffic congestion information for the entire road segment in the initial path planning result; generating multiple new paths based on the initial path planning result and the avoidance road segment set; and determining the optimized path planning result based on the multiple new paths.

[0057] This application's embodiments first employ a conventional path planning algorithm, such as the A* algorithm, for initial path planning. The A* algorithm, as an extension of Dijkstra's algorithm, introduces a heuristic function to optimize the search direction, comprehensively considering both the actual cost from the starting point to the current node and the estimated cost from the current node to the target node, thereby quickly generating an initial path planning result. Based on this, multi-dimensional path optimization information is obtained to prepare for subsequent path optimization.

[0058] This application's embodiments, based on conventional path algorithms, integrate real-time traffic data and traffic light information, and adopt a multi-objective optimization framework, divided into three layers: data layer, analysis layer, and decision layer.

[0059] (1) Data Layer: This layer integrates multiple data sources. On one hand, it obtains real-time location information of rescue vehicles through GPS data, providing basic positioning support for route planning. On the other hand, it reads congestion information predicted by models trained on passenger vehicles from the cloud. This information, after analysis and learning from a large amount of passenger vehicle driving data, can accurately reflect the congestion situation of different road sections. At the same time, the data layer also integrates traffic light prediction information to understand the changing patterns of traffic lights at various intersections in advance.

[0060] (2) Analysis Layer: This layer performs in-depth processing of the data integrated from the data layer. First, it processes traffic flow data, analyzing vehicle speed, traffic volume, and other information to monitor road traffic conditions in real time. It then uses relevant algorithms to predict traffic light status, combining historical data and real-time monitoring information to accurately determine the phase and duration of traffic lights at each intersection. Simultaneously, it identifies congested areas, determining which road segments are congested and the degree of congestion based on traffic flow data and preset congestion criteria.

[0061] (3) Decision-making level: The optimal path is calculated using intelligent algorithms. A multi-objective optimization framework is adopted, considering multiple dimensions such as time, comfort, and energy consumption. The following new evaluation function is introduced:

[0062] f(n)=α·g(n)+β·h(n)+γ·P(n),

[0063] Where g(n) represents the actual driving distance, h(n) is a heuristic estimate (such as straight-line distance), P(n) is the congestion probability (predicted by an LSTM model deployed in the cloud), and α, β, and γ are weight coefficients that can be determined by optimization using a genetic algorithm.

[0064] After the traditional route calculation is completed, the current congestion information and predicted congestion information of all road segments along the entire route are read. Based on the congestion information, a set of avoidance road segments is generated. Within the currently calculated route, multiple new routes are generated by combining the avoidance road segment set. These new routes attempt to find better driving routes by bypassing congested or high-congestion-probability road segments.

[0065] Among the multiple new paths generated, the final optimized path is selected by comprehensively considering multiple dimensions such as time, comfort, and energy consumption. For example, the route with the shortest time is selected, and then the route is provided to the vehicle system for guidance and route calculation, so that the rescue vehicle can travel along the optimized path.

[0066] This application's embodiments innovatively optimize conventional path planning algorithms. By integrating real-time traffic data and traffic light information, and employing a multi-objective optimization framework, it comprehensively considers multiple dimensions such as time, comfort, and energy consumption. Utilizing a layered processing mechanism of data, analysis, and decision layers, it accurately acquires and processes traffic information, and precisely predicts congestion and traffic light status. By introducing a new evaluation function and a set of avoidance road segments to generate multiple new paths, and selecting the optimal path, it effectively avoids congested road segments, reduces the travel time of rescue vehicles, improves rescue efficiency, enhances driving comfort, and reduces energy consumption, providing more reliable and efficient navigation support for rescue operations.

[0067] In some embodiments of this application, the traffic congestion information includes traffic congestion probability and traffic congestion coefficient. The step of generating an avoidance road segment set based on the traffic congestion information of the entire road segment in the initial path planning result includes: if the traffic congestion probability of a road segment is greater than a preset congestion probability threshold, then the road segment is added to the avoidance road segment set as an avoidance road segment; if the traffic congestion coefficient of a road segment is greater than a first preset congestion coefficient threshold, then the road segment is added to the avoidance road segment set as an avoidance road segment.

[0068] In this embodiment, traffic congestion information mainly includes traffic congestion probability and traffic congestion coefficient. Traffic congestion probability reflects the likelihood of congestion on a road segment, and is usually calculated through analysis of a large amount of historical traffic data and real-time monitoring data. Traffic congestion coefficient is a quantitative value that comprehensively measures the degree of congestion on a road segment. Its value range and level classification have clear standards. For example, the Congestion Index (TPI) ranges from 0 to 10 and is further subdivided into five levels: smooth (0-2), basically smooth (2-4), lightly congested (4-6), moderately congested (6-8), and severely congested (8-10).

[0069] First, obtain the traffic congestion probability information for the entire road segment from the initial route planning results. Set a preset congestion probability threshold, such as 50%. When reading each link (segment) on the current path, if the traffic congestion probability P(n) of a certain segment is found to be >50%, it is determined that the segment is likely to be congested. In order to ensure the driving efficiency and safety of rescue vehicles, the segment is marked as an avoidance segment and added to the avoidance segment set.

[0070] In addition to considering the probability of traffic congestion, the congestion index (TPI) of the corresponding intersection is also read. A first preset congestion index threshold is set, such as 6, which corresponds to the starting value of moderate congestion. When the TPI of a traffic light intersection is detected to be greater than 6, it indicates that the intersection is in a state of moderate or severe congestion. Vehicle passage at this intersection may be significantly affected, leading to increased travel time or even stagnation. Therefore, the road segment at this intersection is also set as an avoidance segment and included in the avoidance segment set.

[0071] By assessing both the probability and coefficient of traffic congestion across the entire road segment, sections that may affect the movement of rescue vehicles can be comprehensively and accurately identified. After determining all road segments and intersections, all segments marked as avoidance routes are compiled to form a final set of avoidance routes, providing a foundation for subsequent route optimization.

[0072] By comprehensively considering information from both traffic congestion probability and traffic congestion coefficient to generate a set of avoidance routes, this approach can more accurately identify road segments that may affect rescue vehicles during actual driving. Compared to methods that rely solely on distance or simple traffic conditions, this method can proactively avoid road segments and intersections with high congestion probability and severe congestion, effectively reducing the likelihood of rescue vehicles encountering congestion during their journey. This not only significantly shortens the travel time of rescue vehicles and improves rescue efficiency but also reduces the risk of rescue delays caused by prolonged congestion, buying valuable time for emergency rescue operations and thus significantly enhancing the practicality and reliability of the navigation method in the entire rescue scenario.

[0073] In some embodiments of this application, generating multiple new paths based on the initial path planning result and the set of avoidance segments includes: calculating the arc length corresponding to each avoidance segment in the set of avoidance segments; generating multiple new paths based on the initial path planning result and the arc length corresponding to each avoidance segment; determining the optimized path planning result based on the multiple new paths includes: calculating the time weight of each new path; and determining the optimized path planning result based on the time weight of each new path.

[0074] In the process of generating multiple new paths based on the initial path planning results and the set of avoidance road segments, the arc length of each avoidance road segment in the avoidance road segment set is first calculated. This is because when avoiding avoidance road segments, vehicles often need to change their driving direction to bypass these congested or unsuitable road segments, and this turning trajectory can usually be approximated by an arc. Through specific geometric calculation methods, combined with parameters such as the vehicle's turning radius (these parameters can be preset based on vehicle model and performance or dynamically adjusted according to actual conditions), the arc length corresponding to each avoidance road segment can be accurately calculated.

[0075] After obtaining the arc lengths corresponding to each avoidance segment, multiple new paths are generated by combining them with the initial path planning results. The initial path planning results include basic route information from the starting point to the target point, such as the length of straight segments. The new paths are generated by replacing the straight sections that originally passed through the avoidance segments with arc paths that bypass the avoidance segments, while retaining the straight sections of other non-avoidance segments. That is, new path = ∑(straight segment length) + ∑(arc length). In this way, by comprehensively considering the situation of all avoidance segments, multiple different new routes are generated, all of which successfully avoid the avoidance segments in the initial path.

[0076] When determining the optimized route planning results, it is necessary to calculate the time weight of each new path. The calculation of time weight comprehensively considers various factors affecting travel time, including the length of each segment (straight and circular segments) in the new path, and the average travel speed of different segments (which can be determined based on historical traffic data, real-time traffic information, and segment type, etc.). For example, for each segment, the time required to traverse it can be estimated based on its length and average travel speed. The total travel time of the new path is obtained by summing the times of all segments. Then, based on the total travel time and other factors that may affect time efficiency (such as traffic light waiting time, which can be estimated using traffic light prediction information and vehicle arrival time), a comprehensive calculation is performed to obtain the time weight of each new path.

[0077] After calculating the time weight of each new path, the optimized path planning result is determined based on the time weight. For example, the route with the highest time weight (i.e., the shortest travel time or the highest overall time efficiency) can be selected from multiple new paths and provided to the vehicle system for guidance and route calculation, controlling the vehicle to travel along the optimized path.

[0078] This application's embodiments generate new paths by calculating the arc length of the bypass section, which can more accurately simulate the actual driving trajectory of vehicles bypassing congested sections, making the new path planning more in line with reality. Simultaneously, by comprehensively considering multiple factors to calculate the time weight of the new path and selecting the route with the highest time weight as the optimized path, the travel time of rescue vehicles is effectively reduced, improving rescue efficiency.

[0079] In some embodiments of this application, the V2X communication includes at least one of V2V communication, V2N communication, V2I communication, and V2P communication. The step of using V2X communication to obtain real-time traffic information during the rescue vehicle's journey and generating auxiliary alert information based on the real-time traffic information includes: using the V2V communication to read information about vehicles ahead of the rescue vehicle during its journey, and generating a first avoidance alert based on the vehicle ahead information and sending it to the cloud; and / or, using the V2N communication to read congestion update information during the rescue vehicle's journey, and triggering route avoidance calculation based on the congestion update information if the congestion update information affects the optimized route planning result; and / or, using the V2I communication to read traffic information on special road segments during the rescue vehicle's journey, and generating a second avoidance alert based on the special road segment traffic information and sending it to the cloud; and / or, using the V2P communication to read pedestrian information ahead of the rescue vehicle during its journey, and issuing a third avoidance alert based on the pedestrian information if the pedestrian information ahead affects the optimized route planning result.

[0080] The on-board unit (OBU) of the rescue vehicle utilizes V2V (Vehicle-to-Vehicle) communication technology to read information about vehicles ahead as the rescue vehicle travels. This information includes the distance, speed, and lane position of the vehicles ahead. Based on this information, it determines the distance range and the feasibility of overtaking. If overtaking is necessary and conditions permit, or if there are other situations requiring the vehicles ahead to yield, it generates a first yielding warning and sends it to the cloud. The cloud then relays the yielding information to the vehicles ahead, enabling coordinated yielding between vehicles.

[0081] Through V2N (Vehicle-to-Network) communication, the OBU unit of the rescue vehicle can read congestion update information during the rescue vehicle's journey. When it receives real-time information from the network regarding a sudden traffic accident or other cause of congestion on a certain road segment, it determines whether this congestion update information affects the previously optimized route planning results. If it does, it will trigger route avoidance calculation based on the congestion update information, replanning the driving route to avoid congested sections and ensure that the rescue vehicle can travel efficiently.

[0082] Using V2I (Vehicle-to-Infrastructure) communication, the OBU unit of the rescue vehicle reads traffic information on special road sections during the rescue vehicle's journey, such as traffic flow conditions in tunnels and ETC (Electronic Toll Collection) areas. Based on this traffic information, a second yielding alert is generated and sent to the cloud. The cloud then sends the corresponding yielding alert to the relevant vehicles, reminding them to pay attention to the driving conditions on the special road sections and to yield if necessary.

[0083] Using V2P (Vehicle-to-Pedestrian) communication, the OBU unit of the rescue vehicle reads pedestrian information ahead of the vehicle during its journey. If the pedestrian information indicates that the presence or movement of a pedestrian will affect the optimized path planning results, such as a pedestrian suddenly crossing the road, a third avoidance warning will be issued based on the pedestrian information. This warning will be issued promptly through means such as honking the horn or alarm to alert the pedestrian, ensuring pedestrian safety while ensuring that the rescue vehicle's movement is not significantly affected.

[0084] This application fully utilizes V2V, V2N, V2I, and V2P communication methods in V2X communication to comprehensively acquire various real-time traffic information during the rescue vehicle's journey. Through information processing based on different communication methods and corresponding alerts and route planning measures, effective coordination between vehicles, between vehicles and networks, between vehicles and infrastructure, and between vehicles and pedestrians is achieved. This not only helps rescue vehicles obtain road conditions ahead in a timely manner, rationally plan their routes, and avoid congested and dangerous sections, but also provides advance warnings to other vehicles and pedestrians to give way, significantly improving the driving efficiency and safety of rescue vehicles in complex traffic environments. It provides strong technical support for emergency rescue work, minimizes rescue time, and increases the success rate of rescue operations.

[0085] In some embodiments of this application, the step of acquiring real-time traffic information during the movement of the rescue vehicle using V2X communication and generating auxiliary reminder information based on the real-time traffic information includes: determining whether a lane-borrowing scenario judgment condition is triggered based on the current location information of the rescue vehicle; if the lane-borrowing scenario judgment condition is triggered, reading multi-dimensional lane-borrowing judgment information, which includes attribute information of the oncoming road segment, the congestion coefficients of the current lane where the rescue vehicle is located and the oncoming lane, and the distance between the rescue vehicle and the oncoming vehicle; generating lane-borrowing reminder information when the attribute information of the oncoming road segment does not contain physical isolation information, the congestion coefficient of the current lane where the rescue vehicle is located is greater than a second preset congestion coefficient threshold, the congestion coefficient of the oncoming lane is less than a third preset congestion coefficient threshold, and the distance between the rescue vehicle and the oncoming vehicle is greater than a preset safe distance threshold.

[0086] Combination Figure 4This application provides a schematic diagram of the judgment process for a lane-borrowing scenario in an embodiment of the present application. First, the current location information of the rescue vehicle is used to determine whether the lane-borrowing scenario judgment condition has been triggered. For example, this judgment process is triggered when the rescue vehicle approaches the entrance of the rescue community and must turn around to enter, or when there is a sudden congestion in the current driving lane. Through accurate judgment of location and scenario information, subsequent operations are only performed when lane-borrowing is truly necessary, avoiding unnecessary reminders and interference.

[0087] After triggering the lane-sharing scenario, multi-dimensional lane-sharing judgment information is read. This includes the attribute information of the oncoming road segment, which can be determined by reading the oncoming road segment (link) attribute in the map data to see if it contains physical isolation information. The presence or absence of physical isolation directly affects the feasibility and safety of lane-sharing; the lane congestion coefficient, which can be read separately for the current lane where the rescue vehicle is located and the oncoming lane. The congestion coefficient reflects the traffic busyness of the lane and is an important basis for judging whether lane-sharing is reasonable; and the distance between vehicles, which can be read using V2X communication to ensure that there is a sufficient safe distance during lane-sharing to avoid collisions.

[0088] A comprehensive judgment is made based on the multi-dimensional information retrieved regarding lane-borrowing. A lane-borrowing reminder is generated when the attribute information of the oncoming road segment does not include physical isolation information, the congestion coefficient of the current lane where the rescue vehicle is located is greater than the second preset congestion coefficient threshold (e.g., greater than 6), the congestion coefficient of the oncoming lane is less than the third preset congestion coefficient threshold (e.g., less than 4), and the distance between the rescue vehicle and the oncoming vehicle is greater than the preset safe distance threshold (e.g., greater than 80 meters). This series of conditions comprehensively considers the safety, necessity, and feasibility of lane-borrowing; a lane-borrowing reminder is only issued when all conditions are met, ensuring the safety and efficiency of lane-borrowing.

[0089] By combining the location information of rescue vehicles with multi-dimensional detour assessment information, accurate judgment of detour feasibility is achieved in emergency scenarios. Under the premise of meeting safety conditions, detour reminders are generated in a timely manner to alert relevant vehicles to give way. This not only effectively solves the passage difficulties of rescue vehicles in specific scenarios and improves their driving efficiency, but also ensures the safety of the detour process, avoiding traffic accidents caused by detours. It provides more flexible and efficient navigation support for emergency rescue work, maximizing the timeliness and smoothness of rescue operations.

[0090] In some embodiments of this application, the step of obtaining navigation assistance information for a special road segment through a smart wearable device and performing assisted navigation on the special road segment using the navigation assistance information when the rescue vehicle meets the navigation assistance conditions for that special road segment includes: determining whether the rescue vehicle meets the driving navigation assistance conditions or the exit navigation assistance conditions for the special road segment; if the rescue vehicle meets the driving navigation assistance conditions for the special road segment, obtaining the navigation assistance information for the special road segment through the smart wearable device, and fusing the navigation assistance information with the optimized path planning result to obtain first guidance information and displaying it through the smart wearable device; if the rescue vehicle meets the exit navigation assistance conditions for the special road segment, obtaining the navigation assistance information for the special road segment through the smart wearable device, obtaining second guidance information based on the navigation assistance information and displaying it through the smart wearable device.

[0091] During the real-time movement of the rescue vehicle, it can also be determined whether the vehicle meets the conditions for driving navigation assistance or off-vehicle navigation assistance for special road sections. This determination process is based on multiple factors, including the rescue vehicle's current location, driving status, and distance from the special road section. For example, when the rescue vehicle approaches complex scenarios such as multi-way intersections or small roads during the rescue, the driving navigation assistance conditions are considered to be triggered; while when the rescue vehicle is relatively close to the rescue destination (e.g., within 1 kilometer) and enters a residential area or other scenario where driving is not possible but accurate navigation is required, the off-vehicle navigation assistance conditions are considered to be met.

[0092] Combination Figure 5 This application provides a schematic diagram of a driving navigation assistance process according to an embodiment. If the rescue vehicle meets the driving navigation assistance conditions for a special road section, it can connect to the vehicle's infotainment system via a smart wearable device (such as AR glasses), activate the AR assistance function, use the camera on the AR glasses for real-scene recognition, and simultaneously receive route planning information and guidance information sent by the vehicle's navigation system. This guidance information is fused with the video signal to obtain the first guidance information, which is then displayed to the driver through the smart wearable device to help them distinguish the route and achieve assisted driving.

[0093] Combination Figure 6 This application provides a schematic diagram of an off-vehicle navigation assistance process according to an embodiment. If the rescue vehicle meets the off-vehicle navigation assistance conditions for a special road section, navigation assistance information for that special road section is also obtained through a smart wearable device. At this time, second guidance information is obtained based on this navigation assistance information and displayed through the smart wearable device. For example, when entering a residential area, after the rescue personnel turn off the engine and get out of the vehicle or click the off-vehicle navigation button on the vehicle's infotainment system, the AR glasses can perform last-mile route calculation and activate AR guidance, enabling the rescue personnel to accurately find the correct route and carry out rescue work in a timely manner.

[0094] By collaborating with smart wearable devices and the vehicle's infotainment system, the system accurately assesses and provides corresponding navigation assistance information based on the different driving scenarios and needs of rescue vehicles. In driving scenarios, it effectively helps drivers cope with complex road conditions, improving driving accuracy and safety. In off-vehicle scenarios, it provides precise short-range navigation for rescue personnel, ensuring they can quickly and accurately reach their destination. The overall solution enhances the navigation capabilities of rescue vehicles on special road sections, shortens rescue time, improves rescue efficiency, and provides strong technical support for emergency rescue operations.

[0095] In summary, the key points and main technical effects of this application include:

[0096] (1) This application improves the traditional route planning method by combining congestion information and traffic light information control.

[0097] (2) This application introduces the practical application of V2X communication, combined with external information, to assist the rescue system in operating patients efficiently.

[0098] (3) This application utilizes real-time data and actual route data to read multi-lane information, which increases the feasibility of ambulances using other lanes in special scenarios.

[0099] (4) This application realizes the interaction between the rescue and passenger vehicle system. In emergency scenarios, it can send early warning signals to passenger vehicles on the same route to enhance rescue efficiency.

[0100] (5) This application combines smart wearable devices for assisted navigation, which helps rescuers accurately identify roads and improves rescue efficiency in navigation at complex intersections and internal roads.

[0101] This application also provides a navigation device 700 for rescue scenarios, such as... Figure 7 As shown, a schematic diagram of a navigation device for a rescue scenario is provided in an embodiment of this application. The navigation device 700 for the rescue scenario includes: an acquisition unit 710, a path optimization unit 720, an auxiliary reminder unit 730, and a navigation assistance unit 740, wherein:

[0102] The acquisition unit 710 is used to acquire the initial route planning results and multi-dimensional route optimization information of the rescue vehicle. The multi-dimensional route optimization information includes road segment information, traffic congestion information and traffic light information.

[0103] The path optimization unit 720 is used to optimize the initial path planning result based on the multi-dimensional path optimization information to obtain the optimized path planning result, and to control the driving of the rescue vehicle based on the optimized path planning result.

[0104] The auxiliary reminder unit 730 is used to obtain real-time traffic information during the driving process of the rescue vehicle using V2X communication, and to generate auxiliary reminder information based on the real-time traffic information, so as to provide auxiliary reminders for the entire road segment through the auxiliary reminder information;

[0105] The navigation assistance unit 740 is used to obtain navigation assistance information for special road sections through a smart wearable device when the rescue vehicle meets the navigation assistance conditions for special road sections, and to perform assisted navigation for special road sections through the navigation assistance information.

[0106] In some embodiments of this application, the path optimization unit 720 is specifically used to: obtain traffic congestion information of the entire road segment in the initial path planning result based on the initial path planning result and multi-dimensional path optimization information; generate an avoidance road segment set based on the traffic congestion information of the entire road segment in the initial path planning result; generate multiple new paths based on the initial path planning result and the avoidance road segment set; and determine the optimized path planning result based on the multiple new paths.

[0107] In some embodiments of this application, the traffic congestion information includes traffic congestion probability and traffic congestion coefficient. The route optimization unit 720 is specifically used to: if the traffic congestion probability of a road segment is greater than a preset congestion probability threshold, then add the road segment as an avoidance road segment to the avoidance road segment set; if the traffic congestion coefficient of a road segment is greater than a first preset congestion coefficient threshold, then add the road segment as an avoidance road segment to the avoidance road segment set.

[0108] In some embodiments of this application, the path optimization unit 720 is specifically used to: calculate the arc length corresponding to each avoidance segment based on each avoidance segment in the avoidance segment set; generate multiple new paths based on the initial path planning result and the arc length corresponding to each avoidance segment; the step of determining the optimized path planning result based on the multiple new paths includes: calculating the time weight of each new path; and determining the optimized path planning result based on the time weight of each new path.

[0109] In some embodiments of this application, the V2X communication includes at least one of V2V communication, V2N communication, V2I communication, and V2P communication. The auxiliary reminder unit 730 is specifically used for: using the V2V communication to read information about vehicles ahead during the rescue vehicle's journey, and generating a first avoidance reminder message based on the information about vehicles ahead and sending it to the cloud; and / or using the V2N communication to read congestion update information during the rescue vehicle's journey, and triggering route avoidance calculation based on the congestion update information if the congestion update information affects the optimized route planning result; and / or using the V2I communication to read traffic information about special road sections during the rescue vehicle's journey, and generating a second avoidance reminder message based on the traffic information about special road sections and sending it to the cloud; and / or using the V2P communication to read information about pedestrians ahead during the rescue vehicle's journey, and issuing a third avoidance reminder message based on the information about pedestrians ahead if the pedestrian information affects the optimized route planning result.

[0110] In some embodiments of this application, the auxiliary reminder unit 730 is specifically used to: determine whether a detour scenario judgment condition is triggered based on the current location information of the rescue vehicle; if the detour scenario judgment condition is triggered, read multi-dimensional detour judgment information, the detour judgment information including the attribute information of the opposite road segment, the congestion coefficient of the current lane where the rescue vehicle is located and the opposite lane, and the distance between the rescue vehicle and the opposite vehicle; and generate detour reminder information if the attribute information of the opposite road segment does not contain physical isolation information, the congestion coefficient of the current lane where the rescue vehicle is located is greater than a second preset congestion coefficient threshold, the congestion coefficient of the opposite lane is less than a third preset congestion coefficient threshold, and the distance between the rescue vehicle and the opposite vehicle is greater than a preset safe distance threshold.

[0111] In some embodiments of this application, the navigation assistance unit 740 is specifically used for: determining whether the rescue vehicle meets the driving navigation assistance conditions or the exit navigation assistance conditions for the special road section; if the rescue vehicle meets the driving navigation assistance conditions for the special road section, then obtaining navigation assistance information for the special road section through a smart wearable device, and fusing the navigation assistance information with the optimized path planning result to obtain first guidance information and displaying it through the smart wearable device; if the rescue vehicle meets the exit navigation assistance conditions for the special road section, then obtaining navigation assistance information for the special road section through a smart wearable device, and obtaining second guidance information based on the navigation assistance information and displaying it through the smart wearable device.

[0112] It is understood that the navigation device for the above-mentioned rescue scenario can realize each step of the navigation method for the rescue scenario provided in the foregoing embodiments. The relevant explanations of the navigation method for the rescue scenario are applicable to the navigation device for the rescue scenario, and will not be repeated here.

[0113] Figure 8 This is a schematic diagram of the structure of a device according to an embodiment of this application. For example... Figure 8 As shown, the device includes one or more processors (or processing units), and may also include one or more memories coupled to the processors, and may also include a communication module coupled to the processors.

[0114] A communication module can be used to communicate with other devices or apparatuses, such as sending or receiving data and / or signals. A communication module may have at least one communication module for communication. A communication module may include any interface necessary for communicating with other devices. Exemplarily, a communication module may be a transceiver, circuit, bus, module, or other type of communication module.

[0115] The processor may include, but is not limited to, one or more of the following: a general-purpose computer, a special-purpose computer, a microcontroller, a digital signal processor (DSP), or a controller-based multi-core controller architecture. The device may have multiple processors, such as application-specific integrated circuit (ASIC) chips, which are time-dependent on a clock synchronized with the main processor.

[0116] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), or other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM), or other volatile memories that do not persist during the duration of a power outage.

[0117] A computer program consists of computer-executable instructions that are executed by an associated processor. Programs can be stored in ROM. A processor can perform any appropriate action and processing by loading the program into RAM.

[0118] Possible implementations of this application can be achieved through a program, enabling the communication device to execute any of the processes discussed in the foregoing embodiments. Possible implementations of this application can also be achieved through hardware or a combination of software and hardware.

[0119] In some implementations, the program may be tangibly contained in a computer-readable storage medium, which may include in a device (such as in memory) or other storage device accessible by the device. The program may be loaded from the computer-readable storage medium into RAM for execution. The computer-readable storage medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc.

[0120] This application also provides a computer-readable storage medium storing computer instructions or program code thereon, which, when executed by a processor, causes the processor to perform the methods and functions involved in any of the above embodiments. A computer-readable medium can be any tangible medium that contains or stores a program for or relating to an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. More detailed examples of computer-readable storage media include electrical connections with one or more wires, magnetic media (e.g., disks, floppy disks, hard disks, magnetic tapes, magnetic storage devices), optical media (e.g., optical storage devices, DVDs), semiconductor media (e.g., solid-state drives), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), or any suitable combination thereof.

[0121] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. Embodiments of this application also provide at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. This computer program product includes one or more computer-executable instructions, such as instructions included in a program module, which execute in a device on a target's real or virtual processor to perform the processes, methods, and functions involved in any of the above embodiments. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0122] This application also proposes a computer program product, including a computer program or instructions that, when run on a computer, cause the computer to perform the processes, methods, and functions described in the above embodiments. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided as needed. The machine-executable instructions for the program modules can be executed locally or in a distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0123] Generally, the various embodiments of this application can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0124] It should be noted that although embodiments of this application have been described above with reference to the accompanying drawings, these embodiments are not independent of each other, and they can be combined to obtain other embodiments. The methods, situations, categories, and classifications of embodiments in this application are only for the convenience of description and should not constitute a special limitation. Various methods, categories, situations, and features in embodiments can be combined with each other if logically consistent. The various embodiments of this application can be arbitrarily combined to achieve different technical effects. The embodiments of this application will not list various combinations.

[0125] Furthermore, although the operation of the methods of this disclosure is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps. It should also be noted that the features and functions of two or more devices according to this disclosure may be embodied in one device. Conversely, the features and functions of one device described above may be further divided and embodied by multiple devices.

[0126] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A navigation method for a rescue scenario, characterized in that, The navigation methods for the rescue scenario include: The initial route planning results and multi-dimensional route optimization information of the rescue vehicle are obtained, including road segment information, traffic congestion information and traffic light information. The initial path planning result is optimized based on the multi-dimensional path optimization information to obtain an optimized path planning result, and the driving of the rescue vehicle is controlled based on the optimized path planning result. The system utilizes V2X communication to acquire real-time traffic information during the rescue vehicle's journey and generates auxiliary reminder information based on the real-time traffic information, thereby providing auxiliary reminders for the entire road segment. When the rescue vehicle meets the navigation assistance conditions for the special road section, it obtains navigation assistance information for the special road section through a smart wearable device and uses the navigation assistance information to provide assisted navigation for the special road section.

2. The navigation method for a rescue scenario according to claim 1, characterized in that, The step of optimizing the initial path planning result based on the multi-dimensional path optimization information to obtain the optimized path planning result includes: Based on the initial path planning results and multi-dimensional path optimization information, obtain the traffic congestion information for the entire road segment from the initial path planning results; A set of alternative routes is generated based on the traffic congestion information of the entire road segment in the initial route planning results; Multiple new paths are generated based on the initial path planning results and the set of avoidance routes; The optimized path planning result is determined based on multiple new paths.

3. The navigation method for a rescue scenario according to claim 2, characterized in that, The traffic congestion information includes traffic congestion probability and traffic congestion coefficient. Generating the avoidance route set based on the traffic congestion information of the entire road segment in the initial path planning result includes: If the traffic congestion probability of a road segment is greater than a preset congestion probability threshold, then the road segment is added to the avoidance road segment set as an avoidance road segment. If the traffic congestion coefficient of a road segment is greater than the first preset congestion coefficient threshold, then the road segment is added to the avoidance road segment set as an avoidance road segment.

4. The navigation method for a rescue scenario according to claim 2, characterized in that, The step of generating multiple new paths based on the initial path planning result and the set of avoidance road segments includes: Calculate the arc length corresponding to each avoidance segment based on each avoidance segment in the avoidance segment set; Multiple new paths are generated based on the initial path planning results and the arc lengths corresponding to each avoidance segment; The process of determining the optimized path planning result based on multiple new paths includes: Calculate the time weight of each new path; The optimized path planning result is determined based on the time weight of each new path.

5. The navigation method for a rescue scenario according to claim 1, characterized in that, The V2X communication includes at least one of V2V communication, V2N communication, V2I communication, and V2P communication. The step of using V2X communication to obtain real-time traffic information during the rescue vehicle's journey, and generating auxiliary reminder information based on the real-time traffic information, includes: The V2V communication is used to read information about vehicles ahead of the rescue vehicle during its journey, and a first avoidance warning message is generated based on the information and sent to the cloud; and / or, The V2N communication is used to read congestion update information during the rescue vehicle's journey, and if the congestion update information affects the optimized route planning result, route avoidance calculation is triggered based on the congestion update information; and / or, The V2I communication is used to read traffic information on special road sections during the rescue vehicle's journey, and a second avoidance warning message is generated based on the traffic information on the special road sections and sent to the cloud; and / or, The V2P communication is used to read pedestrian information ahead of the rescue vehicle during its journey, and if the pedestrian information ahead affects the optimized path planning result, a third avoidance warning message is issued based on the pedestrian information ahead.

6. The navigation method for a rescue scenario according to claim 1, characterized in that, The step of acquiring real-time traffic information during the rescue vehicle's journey using V2X communication and generating auxiliary reminder information based on the real-time traffic information includes: Determine whether the detour scenario judgment condition is triggered based on the current location information of the rescue vehicle; When the conditions for triggering the lane-borrowing scenario are met, multi-dimensional lane-borrowing judgment information is read. The lane-borrowing judgment information includes the attribute information of the opposite road segment, the congestion coefficient of the current lane where the rescue vehicle is located and the opposite lane, and the distance between the rescue vehicle and the opposite vehicle. If the attribute information of the opposite road segment does not contain physical isolation information, the congestion coefficient of the current lane where the rescue vehicle is located is greater than the second preset congestion coefficient threshold, the congestion coefficient of the opposite lane is less than the third preset congestion coefficient threshold, and the distance between the rescue vehicle and the opposite vehicle is greater than the preset safe distance threshold, a lane-borrowing reminder message is generated.

7. The navigation method for a rescue scenario according to claim 1, characterized in that, When the rescue vehicle meets the navigation assistance conditions for the special road section, the step of obtaining navigation assistance information for the special road section through a smart wearable device and using the navigation assistance information to provide assisted navigation for the special road section includes: Determine whether the rescue vehicle meets the driving navigation assistance conditions or off-vehicle navigation assistance conditions for the special road section; If the rescue vehicle meets the driving navigation assistance conditions for the special road section, then the navigation assistance information for the special road section is obtained through the smart wearable device, and the navigation assistance information is fused with the optimized path planning result to obtain the first guidance information and displayed through the smart wearable device. If the rescue vehicle meets the conditions for off-vehicle navigation assistance for the special road section, it obtains navigation assistance information for the special road section through a smart wearable device, and obtains second guidance information based on the navigation assistance information and displays it through the smart wearable device.

8. A navigation device for a rescue scenario, characterized in that, The navigation device for the rescue scenario includes: The acquisition unit is used to acquire the initial route planning results and multi-dimensional route optimization information of the rescue vehicle. The multi-dimensional route optimization information includes road segment information, traffic congestion information, and traffic light information. The path optimization unit is used to optimize the initial path planning result based on the multi-dimensional path optimization information to obtain the optimized path planning result, and to control the driving of the rescue vehicle based on the optimized path planning result. The auxiliary reminder unit is used to obtain real-time traffic information during the driving process of the rescue vehicle using V2X communication, and to generate auxiliary reminder information based on the real-time traffic information, so as to provide auxiliary reminders for the entire road segment through the auxiliary reminder information; The navigation assistance unit is used to obtain navigation assistance information for special road sections through a smart wearable device when the rescue vehicle meets the navigation assistance conditions for special road sections, and to perform assisted navigation for special road sections through the navigation assistance information.

9. An apparatus comprising: processor; And a memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the navigation method for any of the rescue scenarios described in claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the navigation method for any of the rescue scenarios described in claims 1 to 7.

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