Multi-link redundancy protection method for intelligent driving vehicles in extreme highway scenarios

Abstract

In the field of intelligent transportation and autonomous driving technology, the present application discloses a multi-link redundancy protection method for intelligent driving vehicles in extreme highway scenarios, including: obtaining on-board sensor data, roadside equipment data and cloud traffic data corresponding to a preset highway section, and constructing a global traffic situation map based on the on-board sensor data, the roadside equipment data and the cloud traffic data; evaluating the risk level of each highway section based on the global traffic situation map, and determining the takeover control strategy of the intelligent driving vehicles in each highway section based on the risk level; when the takeover control strategy is a formation coordination strategy, generating formation coordination instructions for the intelligent driving vehicles in the corresponding highway section, and sinking the formation coordination instructions to the roadside edge nodes; based on the multi-link redundant architecture, sending the formation coordination instructions to the intelligent driving vehicles.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation and autonomous driving technology, and in particular to a multi-link redundancy protection method for intelligent driving vehicles in extreme highway scenarios. Background Art

[0002] With the rapid development of intelligent transportation systems and autonomous driving technologies, highways, as crucial vehicles for intercity transportation, are placing higher demands on intelligent management and control, as well as the safe and efficient operation of autonomous vehicles. Especially in extreme or complex traffic scenarios such as road closures for construction, accident handling, and traffic congestion, remote control and coordinated dispatch of autonomous vehicles have become key issues in improving traffic efficiency and ensuring road safety. To this end, multi-source information fusion and control strategies that integrate onboard perception, roadside coordination, and cloud-based dispatching have become a growing research hotspot.

[0003] However, existing technologies still have numerous shortcomings when it comes to handling extreme highway scenarios. First, traditional single-vehicle intelligent perception methods are limited by line of sight and environmental interference, making them prone to blind spots in complex weather or emergencies. Second, some V2X (vehicle-to-everything)-based collaborative control methods rely on a single communication link, lacking redundancy mechanisms in the face of network fluctuations or outages, impacting the reliability of command transmission. Furthermore, most current takeover strategies employ a unified model, lacking the ability to dynamically adjust to different risk levels, making it difficult to meet the needs of tiered response in complex traffic situations. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides a multi-link redundancy protection method for intelligent driving vehicles in extreme scenarios on highways.

[0005] According to one aspect of the present application, a multi-link redundancy assurance method for intelligent vehicles in extreme highway scenarios is provided, comprising:

[0006] Obtaining vehicle-mounted sensor data, roadside equipment data, and cloud-based traffic data corresponding to a preset highway section, and constructing a global traffic situation map based on the vehicle-mounted sensor data, the roadside equipment data, and the cloud-based traffic data;

[0007] Assess the risk level of each highway section based on the global traffic situation map, and determine the takeover control strategy for intelligent driving vehicles within each highway section based on the risk level;

[0008] When the takeover control strategy is a platoon coordination strategy, generating platoon coordination instructions for intelligent driving vehicles in the corresponding highway section, and sinking the platoon coordination instructions to the roadside edge nodes;

[0009] Based on a multi-link redundant architecture, the formation coordination instruction is sent to the intelligent driving vehicle.

[0010] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the multi-link redundancy protection method for intelligent driving vehicles in extreme scenarios on highways is implemented.

[0011] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and runnable on the processor. When the processor executes the program, the multi-link redundancy protection method for intelligent driving vehicles in extreme highway scenarios is implemented.

[0012] By means of the above technical solution, the embodiment of the present application provides a multi-link redundancy guarantee method for intelligent vehicles in extreme highway scenarios. It constructs a global traffic situation map through multi-source data fusion, dynamically adjusts the takeover strategy based on risk classification, uses roadside edge computing to realize localized instruction processing, and adopts multi-link redundant communication to ensure reliable transmission of instructions. The beneficial effect is: transforming the traditional "passive response" into "active collaboration". Through global perception, hierarchical decision-making and redundant execution, it not only solves the perception and decision-making limitations of single-vehicle intelligence in complex scenarios, but also overcomes the reliability bottleneck of a single communication link, improves traffic control efficiency in extreme scenarios, reduces the risk of secondary accidents (reduces human error through formation collaboration), and enhances the system's fault tolerance for network anomalies and equipment failures, ultimately achieving a dual improvement in highway traffic efficiency and safety.

[0013] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0015] Figure 1 A flowchart of a multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios provided by an embodiment of the present application is shown;

[0016] Figure 2 A flowchart illustrating another method for ensuring multi-link redundancy for intelligent vehicles in extreme highway scenarios provided by an embodiment of the present application is shown;

[0017] Figure 3 A schematic diagram of the communication relationship between the cloud, roadside edge node and intelligent driving vehicle provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0019] In this embodiment, a multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios is provided, such as Figure 1 As shown, the method includes:

[0020] Step 101: Obtain vehicle-mounted sensor data, roadside equipment data, and cloud traffic data corresponding to a preset highway section, and construct a global traffic situation map based on the vehicle-mounted sensor data, the roadside equipment data, and the cloud traffic data.

[0021] Step 102: Based on the global traffic situation map, evaluate the risk level of each highway section, and determine the takeover control strategy of the intelligent driving vehicle in each highway section based on the risk level.

[0022] Step 103: When the takeover control strategy is a formation coordination strategy, generate a formation coordination instruction for the intelligent driving vehicles in the corresponding highway section, and sink the formation coordination instruction to the roadside edge node.

[0023] Step 104: Based on the multi-link redundant architecture, send the formation coordination instruction to the intelligent driving vehicle.

[0024] In the embodiments of this application, a global traffic situation map covering the entire highway section is constructed by integrating onboard sensors (such as cameras, radars, and lidars) of intelligent vehicles (unmanned autonomous vehicles), roadside equipment (such as roadside cameras and weather monitoring stations), and cloud-based traffic data (historical congestion patterns, construction information, and accident databases). For example, when visibility on a certain road section is reduced due to heavy rain, onboard sensors can capture real-time rainfall and road slipperiness data, roadside equipment can provide visibility monitoring values ​​for the area, and the cloud-based data can supplement historical accident rate data under similar weather conditions. These three are combined to form a comprehensive situation map that encompasses environmental risks, vehicle status, and road conditions. This overcomes the visual range and computing power limitations of traditional single-vehicle perception. By complementing multi-source data, blind spots (such as missing data in obstructed areas or in inclement weather) are eliminated, providing a global perspective for subsequent decision-making and avoiding misjudgments caused by local information.

[0025] Secondly, based on a global traffic situation map, the risk level of each highway section is assessed and corresponding takeover control strategies are formulated accordingly to flexibly respond to challenges under different road conditions. This ensures more cautious and effective control measures are taken in high-risk areas, improving the safety and adaptability of autonomous vehicles. This shift from "unified takeover" to "tiered response" avoids wasted resources (e.g., eliminating the need for excessive intervention in low-risk sections) while improving control accuracy in high-risk scenarios, balancing traffic efficiency and safety requirements.

[0026] Next, specifically for situations requiring platoon coordination, the present embodiment designs specific platoon coordination instructions and sends these instructions to roadside edge nodes for local processing by roadside edge computing nodes. Leveraging the advantages of edge computing, latency is reduced and response speed is improved, enabling intelligent vehicles to complete necessary adjustments and coordinated actions in a short period of time, enhancing the flexibility and coordination of the entire system.

[0027] Finally, a multi-link redundant architecture employs three parallel transmission links: 5G cellular network, C-V2X direct communication, and dedicated short-range communication (DSRC). Each link carries the same command but isolates interference through different frequency bands / protocols. For example, if the 5G signal is blocked in a tunnel, the system automatically switches to the C-V2X or DSRC link. If the latency of a particular link exceeds a threshold due to congestion, the system uses a multi-link voting mechanism to select the first arriving command for execution. This addresses the vulnerability of a single communication link, ensuring command availability through link redundancy and dynamic switching, and preventing the risk of vehicle loss of control due to communication interruptions.

[0028] By applying the technical solution of this embodiment, a global traffic situation map is constructed through multi-source data fusion, the takeover strategy is dynamically adjusted based on risk classification, roadside edge computing is used to realize localized instruction processing, and multi-link redundant communication is used to ensure reliable instruction transmission. The embodiment of this application transforms the traditional "passive response" into "active collaboration". Through global perception, hierarchical decision-making and redundant execution, it not only solves the perception and decision-making limitations of single-vehicle intelligence in complex scenarios, but also overcomes the reliability bottleneck of a single communication link, improves traffic control efficiency in extreme scenarios, reduces the risk of secondary accidents (reduces human error through platoon collaboration), and enhances the system's fault tolerance for network anomalies and equipment failures, ultimately achieving a dual improvement in highway traffic efficiency and safety.

[0029] Furthermore, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another multi-link redundancy protection method for intelligent driving vehicles in extreme scenarios on highways is provided, such as Figure 2 As shown, the method includes:

[0030] Step 201: Obtain vehicle-mounted sensor data, roadside equipment data, and cloud-based traffic data corresponding to a preset highway section; perform spatiotemporal alignment on the vehicle-mounted sensor data, the roadside equipment data, and the cloud-based traffic data; generate local dynamic obstacle information based on the spatiotemporally aligned vehicle-mounted sensor data, generate static obstacle information based on the spatiotemporally aligned roadside equipment data, and generate global traffic information based on the spatiotemporally aligned cloud-based traffic data; perform weighted fusion on the local dynamic obstacle information, the static obstacle information, and the global traffic information to obtain traffic fusion data; and generate a global traffic situation map based on the traffic fusion data, wherein the global traffic situation map includes the road network structure, traffic flow status, and obstacle distribution of each highway section.

[0031] In this embodiment, a timestamp synchronization algorithm (such as the NTP protocol) and coordinate conversion techniques (such as mapping vehicle GPS coordinates to a high-precision map coordinate system) are used to address temporal and spatial discrepancies between on-board sensor data, roadside equipment data, and cloud-based traffic data. This allows for spatiotemporal alignment of multi-source data, eliminating any temporal and spatial misalignment caused by acquisition frequency, transmission delay, or device location. For example, if an on-board radar detects an obstacle 50 meters ahead, while a roadside camera reports the same obstacle 60 meters away due to a different viewing angle, spatiotemporal alignment can unify both data sets to the same spatiotemporal reference through spatial coordinate conversion and temporal interpolation. Furthermore, based on the spatiotemporally aligned on-board sensor data, a target tracking algorithm (such as a Kalman filter) is used to identify moving obstacles (such as vehicles and pedestrians) and predict their motion trajectory over the next three seconds. This generates a dynamic obstacle layer containing position, velocity, and acceleration, thereby obtaining local dynamic obstacle information. Roadside equipment data is used to identify fixed road infrastructure (such as construction cones and guardrails) and unusual stationary objects (such as accident vehicles). Static obstacle information is then annotated with the high-precision map to identify the type and location of static obstacles. Leveraging cloud-based traffic data, including historical traffic flow data (e.g., average speed and volume), event information (e.g., construction plans), and weather forecasts, a lane-level traffic status map (e.g., congestion index and visibility level) covering the entire road section is generated, providing global traffic information. A dynamic weighting model is then designed to adjust fusion weights based on data source reliability (e.g., on-board sensors offer high real-time performance but are susceptible to obstruction, while roadside equipment has limited coverage but is highly stable) and scenario requirements (e.g., prioritizing cloud-based historical data during congestion, relying on on-board sensing during emergencies). For example, during heavy rain, the weight of roadside weather station data increases to 60%, while the weight of on-board camera data decreases to 30% due to rain interference. This fused data is then fed into a 3D mapping engine, where it is overlaid with road network structure (e.g., lane markings and ramp locations), traffic flow status (color gradients indicate congestion levels), and obstacle distribution (icons annotating type and distance) to generate a global traffic situation map that incorporates temporal and spatial continuity.

[0032] Step 202: For each highway section in the global traffic situation map, extract the current road closure length L of the highway section according to the road network structure of the highway section. now and the maximum road closure length L max , extract the current vehicle density ρ of the highway section according to the traffic flow state of the highway section now and the maximum vehicle density ρ max , extract the current number of accident points N of the highway section according to the obstacle distribution of the highway section now and the maximum possible number of accident points N max .

[0033] Step 203: Obtain the current visibility corresponding to each highway section According to the current road closure length, maximum road closure length, current vehicle density, maximum vehicle density, current number of accident points, maximum possible number of accident points, and current visibility of each highway section , determine the risk measurement RI of each highway section respectively; determine the risk level of each highway section according to the interval to which the risk measurement of each highway section belongs.

[0034] The risk measurement calculation formula is:

[0035] , Represent the first risk weight, second risk weight, third risk weight, and fourth risk weight respectively. Represents a natural constant.

[0036] In this embodiment, risk level assessment is based on multi-dimensional data from a global traffic situation map. The risk metrics for each road section are dynamically calculated using quantitative indicators. The specific steps are as follows: Road closure information for each road section is parsed from the road network structure of the global situation map. The current closure length (e.g., the length of lanes closed due to construction or accidents) and the maximum closure length for that section (e.g., the designed maximum allowable closure length) are extracted. The closure ratio (current closure length / maximum closure length) is calculated to reflect the degree of road capacity restriction. Current vehicle density data (the number of vehicles per unit length of road section) is parsed from the traffic flow state of the global traffic situation map. Combined with the historical maximum vehicle density (e.g., the vehicle density at the time of the most severe congestion on that section), a congestion index (current vehicle density / maximum vehicle density) is calculated to quantify traffic flow saturation. The current number of accident points (e.g., the locations of existing accidents or faulty vehicles) and the maximum number of possible accident points (e.g., potential risk points predicted based on historical accident rates) are analyzed from the obstacle distribution of the global situation map. The accident risk coefficient (current number of accident points / maximum number of possible accident points) is calculated to assess the probability of a secondary accident. The current visibility (such as the visible distance in haze or heavy rain) is obtained through roadside equipment or meteorological data as an environmental risk correction factor. Finally, the above parameters are normalized and integrated into a risk metric. For example, the formula: , Represent the first risk weight, second risk weight, third risk weight, and fourth risk weight respectively. Represents a natural constant. Furthermore, the risk of the road section is graded according to the risk measurement threshold (such as R<0.3 for low risk, 0.3≤R<0.6 for medium risk, and R≥0.6 for high risk) to provide a decision-making basis for the subsequent takeover strategy. The embodiment of the present application realizes the refined risk quantification of highway sections through multi-dimensional parameter fusion and weighting. Compared with the traditional evaluation method that relies on a single indicator (such as vehicle density only), this solution comprehensively considers road structure, traffic flow, obstacles and environmental factors, and avoids misjudgment caused by fluctuations in a single parameter. For example, when visibility is extremely low but vehicle density is acceptable, traditional methods may underestimate the risk, while this solution improves the risk measurement through the visibility correction factor to trigger a stricter control strategy.

[0037] Step 204: When the risk level is a low risk level, the takeover control strategy for the intelligent driving vehicles in the highway section is determined to be an autonomous planning strategy; when the risk level is a medium risk level, the takeover control strategy for the intelligent driving vehicles in the highway section is determined to be a platoon coordination strategy; when the risk level is a high risk level, the takeover control strategy for the intelligent driving vehicles in the highway section is determined to be a second-level takeover strategy.

[0038] Among them, the autonomous planning strategy instructs the intelligent driving vehicle to independently perform driving planning and send driving planning information to the remote safety officer terminal; the second-level takeover strategy instructs the intelligent driving vehicle to be immediately taken over by the remote safety officer terminal.

[0039] In an embodiment of the present application, in low-risk sections (such as normal visibility, vehicle density below the threshold, and no road closures), intelligent driving vehicles are allowed to make autonomous decisions based on on-board sensors and high-precision maps, including lane keeping, speed adjustment, and overtaking planning. At the same time, the vehicle needs to upload the planned trajectory (such as the driving path for the next 10 seconds) to the remote safety officer terminal in real time through the 5G network, and the safety officer can intervene and correct it at any time. In medium-risk sections (such as construction areas or minor accident areas), vehicles need to join the formation coordinated by the roadside edge nodes, maintaining a fixed spacing (such as 10 meters between vehicles) and a synchronized speed (such as 80km / h). The formation instructions are synchronized to all member vehicles to ensure the stability of the queue. In high-risk sections (such as visibility less than 50 meters or a dangerous goods leak is detected), the vehicle immediately triggers remote takeover, and the safety officer directly controls the steering wheel, throttle, and brakes. The takeover response time must be less than 1 second. At the same time, the vehicle automatically turns on the hazard lights and plays a takeover prompt tone. For example, when visibility plummets to 30 meters due to fog, vehicle sensors detect the risk and trigger a takeover within 0.5 seconds. The safety officer takes over the vehicle in the cloud cockpit, slows down to 20 km / h, and broadcasts the takeover status to surrounding vehicles through roadside equipment. Risk classification enables precise allocation of control resources, avoiding the efficiency losses of the traditional "one-size-fits-all" model. Autonomous planning in low-risk scenarios reduces the burden on remote safety officers while retaining a channel for manual intervention through real-time trajectory reporting, balancing automation and safety. In medium-risk scenarios, platooning collaboration utilizes swarm intelligence to replace individual vehicle decision-making, significantly reducing operational errors in complex environments. In high-risk scenarios, the second-by-second takeover shortens reaction times in extreme scenarios through a closed-loop "vehicle trigger-system response-manual control" process, improving accident avoidance capabilities, enhancing the system's adaptability to emergencies, and ensuring continuous traffic flow within safety boundaries.

[0040] Step 205: Determine a candidate leader vehicle based on the remote takeover capability of each intelligent driving vehicle in the corresponding highway section, determine a leader vehicle based on the driving parameters of each candidate leader vehicle, use the remaining intelligent driving vehicles in the corresponding highway section as following vehicles, number each following vehicle according to its position, and determine the preceding vehicle corresponding to each following vehicle based on the number of each following vehicle, wherein the preceding vehicle of the first following vehicle is the leader vehicle, and the preceding vehicles of the remaining following vehicles are the following vehicles with the previous number; the remote safety officer terminal generates a navigation control instruction for the leader vehicle as a formation coordination instruction for the leader vehicle, sends the formation coordination instruction of the leader vehicle to the leader vehicle and sinks it to the roadside edge node; the roadside edge node calculates the following control instruction of each following vehicle one by one based on the navigation control instruction and the number of each following vehicle, starting from the first following vehicle, to obtain the formation coordination instruction of each following vehicle.

[0041] In the embodiment of the present application, the implementation of the formation coordination strategy is achieved through dynamic election of the pilot vehicle, layered sinking of instructions and distributed computing. The specific process is as follows: First, candidate pilot vehicles are screened based on the vehicle's remote takeover capability (such as on-board computing power, communication delay, and historical takeover success rate), and then the optimal pilot vehicle is determined in combination with real-time driving parameters (such as vehicle position, speed stability, and distance to obstacles in front). For example, a smart driving vehicle that has direct communication capabilities with a remote safety officer terminal, has a good current communication status, and is located at the front of the fleet is given priority as the pilot vehicle. Then, the vehicles are numbered from back to front according to their positions (such as vehicles A, B, and C are numbered 1, 2, and 3, respectively). The preceding vehicle of the first following vehicle (numbered 1) is the pilot vehicle, and the subsequent vehicles are preceded by the previously numbered vehicle. This logic ensures that the formation forms a chain structure, which facilitates the transmission of instructions step by step. Figure 3Figure 2 shows the communication relationship between the cloud, roadside edge nodes, and intelligent driving vehicles. Furthermore, the remote safety officer terminal (cloud) generates instructions for the pilot vehicle (e.g., a target speed of 80 km / h) based on the global situation map. These instructions are sent simultaneously to the pilot vehicle and the roadside edge node. The roadside edge node stores the pilot instructions as the platooning baseline parameters and uses local caching to prevent instruction loss due to cloud outages. For example, in a tunnel scenario, even if the 5G signal is interrupted, the edge node can maintain the platooning for over 30 seconds based on the locally stored instructions. Subsequently, the roadside edge node uses a distributed computing framework to generate control instructions sequentially based on the number of following vehicles. For example, when the pilot vehicle accelerates, the edge node first calculates the speed and steering angle that must be matched by following vehicle number 1. It then derives instructions for following vehicle number 2 based on the real-time status of vehicle number 1, and so on. Furthermore, the following vehicle instructions can include compensation parameters (such as windage correction and predicted acceleration of the leading vehicle) to eliminate accumulated errors in chain transmission. For example, when the pilot vehicle brakes suddenly, the roadside edge node uses a prediction model to generate a deceleration instruction for the following vehicle 0.3 seconds in advance to avoid a rear-end collision. The embodiments of this application utilize a dynamic formation architecture in conjunction with edge computing to facilitate collaborative control capabilities in medium-risk scenarios. The dynamic election mechanism for the pilot vehicle avoids the risk of disintegration of a fixed formation in the event of a vehicle failure, ensuring that the formation is always led by the optimal vehicle. Layered command sinking and local caching free formation control from reliance on a single communication link, allowing local coordination to be maintained even in the event of a cloud outage. Furthermore, the chained preceding vehicle binding logic simplifies formation topology management and avoids command conflicts in complex scenarios. For example, when changing lanes in a construction zone, vehicles can quickly reorganize the formation based on the preceding vehicle number, reducing the need for manual intervention.

[0042] Step 206: Based on each communication link corresponding to the multi-link redundant architecture, a communication link score is performed for each following vehicle, and a target communication link for each following vehicle is determined based on the communication link with the highest communication link score corresponding to each following vehicle; data related to preset key parameters in the formation coordination instruction corresponding to each following vehicle is divided into a preset number of key parameter data blocks, and non-key parameters are packaged into other parameter data packets, and the key parameter data blocks and other parameter data packets are sent to each following vehicle based on the target communication link of each following vehicle; each following vehicle verifies the integrity of the received key parameter data blocks, and restores the formation coordination instruction of the following vehicle based on the complete data blocks and other parameter data packets, wherein, if the number of consecutively received incomplete data blocks reaches the emergency trigger number, the local degradation takeover strategy of the following vehicle is triggered, and the local degradation takeover strategy instructs the following vehicle to stop at the nearest possible stop position.

[0043] In this embodiment, a communication link score for each following vehicle is dynamically calculated based on the real-time communication performance between each communication link provided by the multi-link redundancy architecture and each following vehicle (e.g., 5G cellular network signal strength, C-V2X direct communication latency, and DSRC packet loss rate). For example, in a tunnel scenario, if a vehicle's 5G signal attenuates, the system automatically lowers its score, prioritizing the C-V2X link with greater interference immunity. Each following vehicle is then assigned the highest-scoring link as its target communication link. For example, the leading vehicle in a platoon may use C-V2X due to proximity to roadside equipment, while the trailing vehicle may switch to supplementary 5G coverage due to distance from the base station. During data transmission, key parameters in the platoon coordination command (e.g., target speed and steering angle) are divided into multiple data blocks, and redundant checksum information is added using forward error correction coding (e.g., Reed-Solomon code). Non-critical parameters (e.g., vehicle interior temperature and entertainment system status) are packaged into a single data packet. The following vehicle performs a cyclic redundancy check (CRC) on the received data block. If data corruption is detected, the following vehicle attempts to repair the data using the redundant checksum information. For example, if a data block loses 10% of its content due to interference, the system uses error-correcting codes to restore the complete data. If the data block cannot be repaired three times in a row (reaching the emergency trigger count), the system automatically triggers a local degradation strategy, gradually slowing down to the nearest emergency lane and stopping, activating the hazard lights to communicate with roadside equipment. This multi-link redundancy and data priority management improve the reliability and fault tolerance of command transmission, optimize network resource allocation, and enhance traffic efficiency and safety redundancy in risky highway scenarios while ensuring platoon stability.

[0044] In an embodiment of the present application, optionally, after triggering the local downgrade takeover strategy of the following vehicle, it also includes: the following vehicle sends a local downgrade takeover signal to the roadside edge node, the roadside edge node determines the leaving following vehicle based on the local downgrade takeover signal and updates the following vehicle, and redetermines the number of each following vehicle; the roadside edge node monitors the recovery of each communication link corresponding to the leaving following vehicle, and when the communication link score of any communication link of the leaving following vehicle is restored to the preset re-entry score, the roadside edge node performs following recovery on the leaving following vehicle, and redetermines the number of each following vehicle again.

[0045] In this embodiment, the subsequent process after triggering the local downgrade takeover strategy is implemented through dynamic platoon reorganization and link recovery monitoring. The specific process is as follows: After the following vehicle triggers the downgrade strategy, it can send a structured signal to the roadside edge node via V2X direct communication, including the vehicle ID, the reason for the downgrade (such as continuous data block loss), the current location, and the status (such as whether it has slowed down to a safe speed). After receiving the signal, the roadside edge node marks the vehicle as "leaving the platoon," indicating that it is a leaving following vehicle and removes it from the platoon list. For example, if vehicle number 3 leaves the platoon due to communication interruption, the edge node will move the numbers of vehicle number 4 and subsequent vehicles forward (from number 4 to 3, from number 5 to 4) and simultaneously update the platoon topology. The edge node recalculates the control parameters of each vehicle based on the new numbers (for example, adjusting the inter-vehicle distance from 15 meters to 12 meters to accommodate the shortened platoon length) and issues the update command via the target communication link. In addition, the roadside edge node continuously monitors the communication link scores of the vehicles leaving the team (such as 5G latency, C-V2X packet loss rate). When the score of any link exceeds the preset threshold (such as latency below 50ms), that is, when it recovers to the preset re-teaming score, the re-teaming process is triggered. The edge node sends a re-teaming instruction to the vehicle leaving the team, including the new number, target position and speed synchronization parameters. For example, after the original vehicle number 3 resumes communication, the edge node inserts it at the end of the current formation, adjusts the number to 5, and requires it to accelerate to 80km / h to match the speed of the team. The embodiment of the present application improves the system's adaptability to vehicle leaving and re-teaming through dynamic formation management and automatic recovery mechanisms. Degradation signaling and de-platooning processing ensure rapid platoon reorganization in the event of a vehicle failure, avoiding formation disintegration due to single-point failures (in traditional fixed platooning, a single vehicle leaving the platoon requires the entire platoon to stop). Dynamic numbering adjustments ensure the platoon maintains a compact structure, reducing control confusion caused by number gaps (such as a vehicle misidentifying the preceding vehicle's number and causing sudden braking). Link recovery monitoring and automatic re-platooning, through a closed "monitor-trigger-synchronization" loop, shorten the time it takes for vehicles to re-platoon after leaving, thereby improving platooning throughput. Furthermore, speed synchronization and position insertion during the re-platooning process avoid the risk of rear-end collisions, ensuring stable operation of the platoon despite dynamic changes.

[0046] In an embodiment of the present application, optionally, key parameter data blocks and other parameter data packets are sent to each following vehicle based on the target communication link of each following vehicle, including: if the communication link score of the target communication link is greater than a preset high-performance link score, key parameter data blocks and other parameter data packets are sent to each following vehicle based on the target communication link of each following vehicle; if the communication link score of the target communication link is less than or equal to the preset high-performance link score, key parameter databases are sent to each following vehicle based on the target communication link of each following vehicle, and other parameter data packets are sent to each following vehicle based on the communication link with the second highest communication link score corresponding to each following vehicle.

[0047] In an embodiment of the present application, optionally, after restoring the formation coordination instruction of the following vehicle based on the complete data block, the method further includes: each following vehicle performs a consistency calculation on the leading vehicle to determine a following difference between each following vehicle and the leading vehicle, and adjusts the following parameters of the formation coordination instruction of the following vehicle based on the following difference; wherein the consistency calculation formula is: ,in, represents the following difference, denote the expected formation spacing and the expected formation spacing change rate, respectively. They represent the actual distance between the i-th following vehicle and the preceding vehicle, and the rate of change of the actual distance between the i-th following vehicle and the preceding vehicle, respectively. They represent the spacing control gain and spacing change rate control gain respectively.

[0048] In this embodiment, after restoring the following vehicle's platooning coordination instructions based on a complete data block, the following vehicle achieves dynamic stability through consistency calculation and parameter adjustment mechanisms. The specific process is as follows: The following vehicle uses onboard sensors (such as radar and cameras) to collect the motion status of the leading vehicle in real time, including position (latitude and longitude coordinates), velocity (vector direction and magnitude), acceleration (longitudinal and lateral), and heading angle (vehicle direction of travel). Using a distributed consistency algorithm, the following vehicle calculates the following difference based on the state difference between the following vehicle and the leading vehicle. Based on the following difference calculation, the system dynamically adjusts the throttle position, brake pressure, and steering angle compensation value. Furthermore, after the following vehicle's parameters are adjusted, its state changes can be transmitted to subsequent vehicles via V2X communication, triggering cascaded adjustments. Furthermore, the roadside edge node can monitor the overall platoon parameters (such as average vehicle spacing and speed dispersion) in real time. When abnormal fluctuations are detected (such as three vehicles braking simultaneously), it sends a global speed adjustment command to the lead vehicle to restore platoon stability by adjusting the lead vehicle's driving parameters. This will improve the formation's ability to respond to emergencies (such as stability in sudden braking and lane changes), optimize the formation's traffic efficiency (reduce speed fluctuations caused by frequent adjustments), and enhance the system's adaptability to complex traffic situations (such as queue maintenance when detouring in construction areas).

[0049] The embodiment of the present application also provides a computer device, which can be specifically a personal computer, a server, a network device, etc. The computer device includes a bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in each method embodiment are implemented.

[0050] Those skilled in the art will understand that the structure of the above-mentioned computer device is only a partial structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components, or combine certain components, or have a different component arrangement.

[0051] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program thereon. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0052] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0053] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0054] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, and the like.

[0055] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0056] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios, characterized by: include: Obtaining vehicle-mounted sensor data, roadside equipment data, and cloud-based traffic data corresponding to a preset highway section, and constructing a global traffic situation map based on the vehicle-mounted sensor data, the roadside equipment data, and the cloud-based traffic data; Assessing the risk level of each highway section based on the global traffic situation map; When the risk level is low, determining that the takeover control strategy for the intelligent driving vehicle in the highway section is an autonomous planning strategy; wherein the autonomous planning strategy instructs the intelligent driving vehicle to autonomously plan driving and send driving planning information to the remote safety officer terminal; When the risk level is a high risk level, determining that the takeover control strategy for the intelligent driving vehicle in the highway section is a second-level takeover strategy; wherein the second-level takeover strategy indicates that the intelligent driving vehicle is immediately taken over by the remote safety officer terminal; When the risk level is a medium risk level, determining that the takeover control strategy for the intelligent driving vehicles in the highway section is a platooning coordination strategy; and, when the takeover control strategy is the platooning coordination strategy, generating a platooning coordination instruction for the intelligent driving vehicles in the corresponding highway section, and sinking the platooning coordination instruction to the roadside edge nodes; Based on a multi-link redundant architecture, the formation coordination instruction is sent to the intelligent driving vehicle.

2. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 1 is characterized in that: Constructing a global traffic situation map based on the vehicle-mounted sensor data, the roadside equipment data, and the cloud traffic data, including: performing spatiotemporal alignment on the vehicle-mounted sensor data, the roadside equipment data, and the cloud-based traffic data, generating local dynamic obstacle information based on the spatiotemporally aligned vehicle-mounted sensor data, generating static obstacle information based on the spatiotemporally aligned roadside equipment data, and generating global traffic information based on the spatiotemporally aligned cloud-based traffic data; performing weighted fusion on the local dynamic obstacle information, the static obstacle information, and the global traffic information to obtain traffic fusion data; A global traffic situation map is generated based on the traffic fusion data, wherein the global traffic situation map includes the road network structure, traffic flow status, and obstacle distribution of each highway section.

3. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 2 is characterized in that: Based on the global traffic situation map, the risk level of each highway section is assessed, including: For each highway section in the global traffic situation map, extract the current road closure length of the highway section based on the road network structure of the highway section and maximum road closure length , extract the current vehicle density of the highway section according to the traffic flow state of the highway section and maximum vehicle density , extract the current number of accident points on the highway section based on the obstacle distribution on the highway section and the maximum possible number of accident points ; Get the current visibility corresponding to each highway section ; According to the current road closure length, maximum road closure length, current vehicle density, maximum vehicle density, current number of accident points, maximum possible number of accident points, current visibility of each highway section , determine the risk metric RI of each highway section respectively, where the risk metric calculation formula is: , They represent the first risk weight, the second risk weight, the third risk weight, and the fourth risk weight respectively, and e represents a natural constant; The risk level of each highway section is determined according to the risk measurement interval of each highway section.

4. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 1 is characterized in that: When the takeover control strategy is a platoon coordination strategy, generating a platoon coordination instruction for intelligent driving vehicles in a corresponding highway section and sinking the platoon coordination instruction to a roadside edge node includes: Candidates for the pilot vehicle are determined based on the remote takeover capabilities of each intelligent driving vehicle within the corresponding highway section. The pilot vehicle is then determined based on the driving parameters of each candidate pilot vehicle. The remaining intelligent driving vehicles within the corresponding highway section are designated as follower vehicles. Each follower vehicle is numbered according to its position, and its corresponding preceding vehicle is determined based on its number. The preceding vehicle of the first following vehicle is designated as the pilot vehicle, and the preceding vehicles of the remaining following vehicles are designated as the following vehicles with the previous number. The remote safety officer terminal generates a navigation control instruction for the pilot vehicle as a formation coordination instruction for the pilot vehicle, sends the formation coordination instruction to the pilot vehicle, and sinks it to the roadside edge node; The roadside edge node calculates the following control instructions of each following vehicle one by one starting from the first following vehicle based on the pilot control instruction and the number of each following vehicle, so as to obtain the formation coordination instruction of each following vehicle.

5. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 4 is characterized in that: Based on the multi-link redundant architecture, the formation coordination instruction is sent to the intelligent driving vehicle, including: Based on the communication links corresponding to the multi-link redundant architecture, each following vehicle is scored for the communication link, and the target communication link of each following vehicle is determined based on the communication link with the highest communication link score corresponding to each following vehicle; Dividing data related to preset key parameters in the formation coordination instructions corresponding to each following vehicle into a preset number of key parameter data blocks and packaging non-key parameters into other parameter data packets, and sending the key parameter data blocks and other parameter data packets to each following vehicle based on the target communication link of each following vehicle; Each following vehicle verifies the integrity of the received key parameter data block and recovers the formation coordination instruction of the following vehicle based on the complete data block and other parameter data packets. If the number of consecutively received incomplete data blocks reaches the emergency trigger number, the local degradation takeover strategy of the following vehicle is triggered, and the local degradation takeover strategy instructs the following vehicle to stop at the nearest possible stop location.

6. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 5 is characterized in that: After triggering the local degradation takeover strategy of the following vehicle, the following also occurs: The following vehicle sends a local downgrade takeover signal to the roadside edge node. The roadside edge node determines the leaving following vehicle based on the local downgrade takeover signal, updates the following vehicles, and re-determines the number of each following vehicle. The roadside edge node monitors the recovery status of each communication link corresponding to the departing following vehicle, and when the communication link score of any communication link of the departing following vehicle is restored to the preset re-entry score, it recovers the departing following vehicle and re-determines the number of each following vehicle.

7. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 5 is characterized in that: Send key parameter data blocks and other parameter data packets to each following vehicle based on the target communication link of each following vehicle, including: If the communication link score of the target communication link is greater than the preset high-performance link score, sending key parameter data blocks and other parameter data packets to each following vehicle based on the target communication link of each following vehicle; If the communication link score of the target communication link is less than or equal to the preset high-performance link score, a key parameter database is sent to each following vehicle based on the target communication link of each following vehicle, and other parameter data packets are sent to each following vehicle based on the communication link with the second highest communication link score corresponding to each following vehicle.

8. The multi-link redundancy guarantee method for intelligent driving vehicles in extreme highway scenarios according to claim 5 is characterized in that: After restoring the formation coordination instruction of the following vehicle based on the complete data block, the method further includes: Each following vehicle performs consistency calculation on the leading vehicle to determine the following difference between each following vehicle and the leading vehicle, and adjusts the following parameters of the formation coordination instructions of the following vehicles based on the following difference. The consistency calculation formula is: ,in, represents the following difference, 、 denote the expected formation spacing and the expected formation spacing change rate, respectively. 、 They represent the actual distance between the i-th following vehicle and the preceding vehicle, and the rate of change of the actual distance between the i-th following vehicle and the preceding vehicle, respectively. 、 They represent the spacing control gain and spacing change rate control gain respectively.

Images