Time-sensitive network routing planning method and system for intelligent connected vehicles

Abstract

This application discloses a time-sensitive network routing planning method and system for intelligent connected vehicles, relating to the field of in-vehicle communication technology. The method includes obtaining a domain-centralized electronic and electrical architecture network topology model of the intelligent connected vehicle, constructing a weighted directed graph, calculating link path reliability probabilities based on a link evaluation model, and marking information flow priorities using an Ethernet frame marking method; generating a primary path routing table using the NSGA2 algorithm; and determining a redundant path routing table using an improved Dijkstra algorithm based on the weighted directed graph and reliability probabilities. Based on this application's planning method, real-time communication within the vehicle and reliable data transmission can be achieved.

Description

Technical Field

[0001] The present application relates to the field of vehicle-mounted communication technology, and in particular to a time-sensitive network routing planning method and system for intelligent connected vehicles. Background Art

[0002] In modern vehicle development, the complexity of electronic and electrical (E / E) systems is increasing, especially for advanced driver assistance systems (ADAS) and fully autonomous driving technologies. This increasing complexity continues to drive the development of domain-centralized E / E architectures, a key aspect of which is the real-time and accuracy of in-vehicle communication networks. Domain-centralized architectures integrate sensors, actuators, and control units. To ensure timely information delivery and secure and smooth vehicle operation, the reliance on time-sensitive networking (TSN) has become increasingly important. These demands have made managing data flows within vehicle networks and ensuring low-latency and high-reliability communication a significant challenge. The real-time and accuracy of in-vehicle communication networks are key factors, further strengthening the reliance on time-sensitive networking (TSN). Managing data flows within vehicle networks and ensuring low-latency and high-reliability communication has become a significant challenge. Among the many TSN standards, IEEE 802.1CB is specifically designed to implement network redundant transmission. Improving data stream reliability through frame duplication and elimination mechanisms enables seamless communication with virtually no packet loss, which is critical for performance-demanding automotive applications.

[0003] However, although the IEEE802.1CB protocol provides powerful tools, its implementation requires appropriate routing planning. The current TSN standard does not specify a routing planning method, and there is still a gap in the research on applying routing planning to the IEEE802.1CB protocol. Summary of the Invention

[0004] The purpose of this application is to provide a high-efficiency, high-reliability time-sensitive network routing planning method and system for intelligent connected vehicles.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a time-sensitive network routing planning method for intelligent connected vehicles, the time-sensitive network routing planning method comprising:

[0007] Obtain a domain-centralized electronic and electrical architecture network topology model of an intelligent connected vehicle; the domain-centralized electronic and electrical architecture network topology model is composed of lidar, millimeter-wave radar, camera, domain controller and functional domain controller as nodes.

[0008] Based on the domain-centralized electronic and electrical architecture network topology model, a weighted directed graph of the intelligent connected vehicle is constructed; the weighted directed graph includes each node in the domain-centralized electronic and electrical architecture network topology model and the links between each node.

[0009] According to the links between the nodes in the weighted directed graph, the reliability probability of each link path is calculated based on a link evaluation model; the link evaluation model is used to calculate the reliability probability of each link based on the average repair time of the domain controller, sensor or link.

[0010] According to the information flow in each link and the reliability probability of each link, the information flow in each link is marked with priority based on the Ethernet frame marking method of the virtual local area network; the Ethernet frame marking method of the virtual local area network is marked based on the IEEE802.1Q standard.

[0011] Based on the information flow in each link after priority marking, the NSGA2 algorithm is used to generate a main path routing table; the main path routing table is used to provide routing selection with optimal load balancing and end-to-end delay adaptability based on specific network performance requirements.

[0012] Based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link, an improved Dijkstra algorithm is used to determine a redundant path routing table; the redundant path routing table is used to record the path reliability probability of each information flow.

[0013] Optionally, the formula expression of the weighted directed graph of the intelligent connected vehicle is specifically:

[0014]

[0015] In the formula, V represents the set of nodes, E represents the set of links, and e ij Represents the slave node s i To node s j links, n represents the number of nodes, s n Represents node s n .

[0016] Optionally, the link evaluation model is expressed as follows:

[0017]

[0018] Where A is the reliability probability, MTTF is the mean time to failure, and MTTR is the mean time to repair based on the domain controller, sensor, or link.

[0019] Optionally, before generating a primary path routing table based on the priority-tagged information flows on each link using the NSGA2 algorithm, the following steps may be performed:

[0020] Based on the information flow in each link after priority marking, load balancing and end-to-end delay adaptability are selected as evaluation indicators of network performance; the network performance is a time-sensitive network for intelligent connected vehicles.

[0021] A dual-objective optimization function is determined based on the load balancing degree and the end-to-end delay adaptability.

[0022] Optionally, the calculation formula for the load balancing degree is:

[0023]

[0024] Where, L i Indicates the load utilization on link i corresponding to the specified port, is the average load utilization of all designated ports, N is the number of designated ports, L b The load balancing degree.

[0025] Optionally, the calculation formula for the end-to-end delay adaptability is:

[0026]

[0027] Where: λ1 and λ2 are weighting coefficients, Representative flow F i transmission delay; Representative flow F i Propagation delay at the nth port; Delegates processing delay; represents the queue delay; T i others Represents other delays, k i is the information flow F i The number of hops from the sending port to the receiving port.

[0028] Optionally, based on the priority-tagged information flows on each link, the NSGA2 algorithm is used to generate a primary path routing table, specifically including:

[0029] Based on the information flow in each link after priority marking, a certain number of routing tables are randomly generated as an initial population; each routing table contains the decisions of all paths in the network.

[0030] For each routing table, the load balancing degree and end-to-end delay of the routing table are calculated.

[0031] According to the load balancing degree and end-to-end delay of each routing table, the routing tables are divided into several levels of frontiers based on non-dominance relationships; the routing tables in each level do not dominate each other.

[0032] The current population is iteratively optimized using selection, crossover, and mutation methods, and the newly generated routing table replaces part or all of the old routing table in the population to obtain a new population until the stopping condition is met.

[0033] After the iteration, all non-dominated solutions are extracted from the final population to form the Pareto front.

[0034] Based on specific requirements, one or more routing tables are selected in the Pareto frontier as the main path routing tables.

[0035] Optionally, based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link, an improved Dijkstra algorithm is used to determine a redundant path routing table; the redundant path routing table is used to record the path reliability probability of each information flow, specifically including:

[0036] The weighted directed graph of the intelligent connected vehicle is weighted to obtain a weighted directed graph; the weighted directed graph contains a weight function; the weight function is a function that maps each link in the link set to a non-negative real number to represent the unreliability weight of the link.

[0037] Based on the weighted directed graph, the unreliability probability of each link path is calculated.

[0038] For any link path, the improved Dijkstra algorithm is used to update the link path to obtain a path with the minimum unreliability.

[0039] Based on the path with the least unreliability, an antilogarithmic transformation is used to obtain the reliability probability of the path.

[0040] Record the path reliability probabilities under different links and generate a redundant path routing table.

[0041] Optionally, the formula expression of the weighted directed graph after weighting is:

[0042]

[0043] Where, W(e ij ) is to connect each link e in the link set E ijA function that maps to a non-negative real number to represent the unreliability weight of the link; A(e ij ) represents link e ij The reliability probability, A(e ij )∈(0,1].

[0044] In a second aspect, the present application provides a time-sensitive network routing planning system for intelligent connected vehicles, including:

[0045] The model acquisition module is used to obtain the domain-centralized electronic and electrical architecture network topology model of the intelligent connected vehicle; the domain-centralized electronic and electrical architecture network topology model is composed of lidar, millimeter-wave radar, camera, domain controller and functional domain controller as nodes.

[0046] A construction module is used to construct a weighted directed graph of the intelligent connected vehicle based on the domain-centralized electronic and electrical architecture network topology model; the weighted directed graph includes each node in the domain-centralized electronic and electrical architecture network topology model and the links between each node.

[0047] A probability calculation module is used to calculate the reliability probability of each link path based on the links between the nodes in the weighted directed graph and based on a link evaluation model; the link evaluation model is used to calculate the reliability probability of each link based on the average repair time of the domain controller, sensor or link.

[0048] The marking module is used to mark the priority of the information flow in each link according to the information flow in each link and the reliability probability of each link based on the Ethernet frame marking method of the virtual local area network; the Ethernet frame marking method of the virtual local area network is based on the IEEE802.1Q standard for marking.

[0049] The main path routing table module is used to generate a main path routing table based on the information flow in each link after priority marking using the NSGA2 algorithm; the main path routing table is used to provide routing selection with optimal load balancing and end-to-end delay adaptability based on specific network performance requirements.

[0050] The redundant path routing table module is used to determine a redundant path routing table based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link using an improved Dijkstra algorithm; the redundant path routing table is used to record the path reliability probability of each information flow.

[0051] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0052] This application provides a time-sensitive network routing planning method and system for intelligent connected vehicles. The method includes: obtaining a domain-centralized electronic and electrical architecture network topology model of the intelligent connected vehicle, constructing a weighted directed graph, calculating link path reliability probabilities based on a link assessment model, and marking information flow priorities using an Ethernet frame marking method; generating a primary path routing table using the NSGA2 algorithm; and determining a redundant path routing table using an improved Dijkstra algorithm based on the weighted directed graph and reliability probabilities. This method can adapt to instantaneous changes in network traffic demand without sacrificing data transmission reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A flowchart of a time-sensitive network routing planning method for intelligent connected vehicles provided in Example 1 of the present application.

[0055] Figure 2 A schematic diagram of a domain-centralized electronic and electrical architecture provided in Example 1 of the present application.

[0056] Figure 3 A Pareto front solution result diagram provided in Example 1 of the present application.

[0057] Figure 4 A flowchart for generating a redundant path routing table using an improved Dijkstra algorithm is provided in Example 1 of the present application.

[0058] Figure 5 This is a single-transmit and single-receive model diagram corresponding to a simple driving scenario provided in Example 1 of the present application.

[0059] Figure 6 A schematic diagram of the structure of a time-sensitive network routing planning system for intelligent connected vehicles provided in Example 2 of the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0062] Example 1

[0063] like Figure 1 As shown, this embodiment provides a time-sensitive network routing planning method for intelligent connected vehicles, and the time-sensitive network routing planning method includes:

[0064] Step 101: Obtain a domain-centralized electrical and electronic architecture network topology model of an intelligent connected vehicle; the domain-centralized electrical and electronic architecture network topology model is composed of a lidar, a millimeter-wave radar, a camera, a domain controller, and a functional domain controller as nodes.

[0065] Step 102: Based on the domain-centralized electronic and electrical architecture network topology model, construct a weighted directed graph of the intelligent connected vehicle; the weighted directed graph includes each node in the domain-centralized electronic and electrical architecture network topology model and the links between each node.

[0066] Step 103: Based on the links between the nodes in the weighted directed graph, the reliability probability of each link path is calculated based on a link evaluation model; the link evaluation model is used to calculate the reliability probability of each link based on the average repair time of the domain controller, sensor or link.

[0067] Step 104: Priority marking is performed on the information flows in each link according to the information flows in each link and the reliability probability of each link based on the Ethernet frame marking method for virtual local area networks; the Ethernet frame marking method for virtual local area networks is based on the IEEE802.1Q standard.

[0068] Step 105: Based on the priority-tagged information flows in each link, a primary path routing table is generated using the NSGA2 algorithm. The primary path routing table is used to provide routing selection with optimal load balancing and end-to-end delay adaptability based on specific network performance requirements.

[0069] Step 106: Based on the weighted directed graph of the intelligent connected vehicles and the reliability probability of each link, an improved Dijkstra algorithm is used to determine a redundant path routing table; the redundant path routing table is used to record the path reliability probability of each information flow.

[0070] In some implementations of this embodiment, when executing step 101, the following steps may be specifically included:

[0071] Get Figure 2 The domain-centralized electrical and electronic architecture model shown.

[0072] Specifically, the domain-centralized electrical and electronic architecture model uses Lidar, millimeter-wave radar, camera, domain controller (VIU), and functional domain controller as nodes. Functional domain controllers include CDC (Smart Cockpit Domain Controller), MDC (Maintenance Domain Controller), and VDC (Vehicle Domain Controller).

[0073] When executing step 102, the specific steps may be as follows:

[0074] The network topology is abstracted into a directed graph G, as shown in the following formula:

[0075]

[0076] In the formula, V represents the set of nodes, E represents the set of links, and e ij Represents the slave node s i To node s j links, n represents the number of nodes.

[0077] When executing step 103, the specific steps may be as follows:

[0078] During actual operation, a vehicle's communication links may be interrupted or fail due to factors such as line wear and aging. Sensors or domain controllers may also malfunction due to microcircuit damage, memory errors, and other reasons, resulting in data packet loss or increased transmission delays in the vehicle network. Based on these sudden failure phenomena in real vehicles, for safety reasons, a reliability probabilistic assessment of the vehicle network should be conducted to facilitate subsequent quantitative analysis of the vehicle network's stability.

[0079] The reliability (A) of the system is estimated based on the mean time to failure (MTTF) and the mean time to repair (MTTR). The formula is as follows:

[0080]

[0081] Where A is the reliability probability, MTTF is the mean time to failure, and MTTR is the mean time to repair based on the domain controller, sensor, or link.

[0082] Substituting the MTTR of the domain controller, sensor, and link into the formula respectively, the reliability probability of each node and link can be obtained.

[0083] Before executing step 104, the method further includes:

[0084] Establish an information flow model based on QoS classification, including:

[0085] Nodes in the network (domain controllers and sensors) send information flows, and the parameters of the information flows are mathematically represented using six-dimensional tuples. The formula is as follows:

[0086] F k =(n k,s ,n k,d ,p k ,t k ,d k ,ζ k ,a k ).

[0087] Where: n k,s ∈V is the sending node; n k,d ∈V is the receiving node; p k and t k are the single frame length and period of the information flow respectively; d k The deadline for sending information flow; k is the priority of the information flow; a k is the expected reliability probability of the information flow.

[0088] When executing step 104, the specific steps may be as follows:

[0089] According to the IEEE802.1Q standard, a virtual local area network (VLAN)-based Ethernet frame tagging method is proposed. A 3-bit priority code point (PCP) field is designed to enable the network to distinguish information flows with eight different priority levels, ranging from 0 to 7, with priority increasing step by step to support different levels of quality of service (QoS).

[0090] The clock synchronization flow has a priority of 7, the control data flow has priorities of 5 and 6, the media flow has priorities of 2 to 4, and the best-effort flow has a priority of 0.

[0091] Before executing step 105, the method further includes:

[0092] Based on the information flow in each link after priority marking, load balancing and end-to-end delay adaptability are selected as evaluation indicators of network performance; the network performance is a time-sensitive network for intelligent connected vehicles.

[0093] A dual-objective optimization function is determined based on the load balancing degree and the end-to-end delay adaptability.

[0094] Specifically, in vehicle-mounted time-sensitive networks, network performance is key to ensuring efficient transmission. The comprehensive performance of two parameters, load balance (LoadBalance) and end-to-end delay (EndtoEndDelay), is used to measure network performance, and network efficiency is used as a factor in the main path evaluation.

[0095] Load balancing ensures that there are no overloaded or idle nodes in the network, allowing information flow to be transmitted efficiently without increasing delay and packet loss rate. It is crucial to reduce data packet loss, avoid network congestion and delay. b Use the following formula to calculate:

[0096]

[0097] Where, L i Indicates the load utilization on link i corresponding to the specified port, is the average load utilization of all designated ports, and N is the number of designated ports. In this model, N is 19.

[0098] End-to-end latency is the total time it takes for a data packet to travel from its source to its destination in a network. It is a key indicator of data flow efficiency. Due to the large size of in-vehicle time-sensitive network models and the large number of information flows, different routing schemes can significantly vary end-to-end latency, impacting the network's real-time performance and stability. Therefore, an end-to-end latency fitness function is established to guide and evaluate routing strategy design.

[0099] Assume that the information flow F i From the sending port through k i Jump to the receiving port, F i The end-to-end delay T i,ki It can be expressed as:

[0100]

[0101] Where, Representative flow F i transmission delay; Representative flow F i Propagation delay at the nth port; Delegates processing delay; represents the queue delay; T i others Represents other delays.

[0102] Taking into account the average end-to-end delay and the maximum end-to-end delay, the system's end-to-end delay fitness function formula is as follows:

[0103]

[0104] Where: λ1 and λ2 are weighting coefficients.

[0105] Based on the above derivation, the goal of network performance optimization should be to minimize the values ​​of the load balancing function and the end-to-end delay fitness function. The formula for the dual-objective optimization function is as follows:

[0106]

[0107] When executing step 105, the specific steps may be as follows:

[0108] Based on the information flow in each link after priority marking, a certain number of routing tables are randomly generated as an initial population; each routing table contains the decisions of all paths in the network.

[0109] For each routing table, the load balancing degree and end-to-end delay of the routing table are calculated.

[0110] According to the load balancing degree and end-to-end delay of each routing table, the routing tables are divided into several levels of frontiers based on non-dominance relationships; the routing tables in each level do not dominate each other.

[0111] The current population is iteratively optimized using selection, crossover, and mutation methods, and the newly generated routing table replaces part or all of the old routing table in the population to obtain a new population until the stopping condition is met.

[0112] After the iteration, all non-dominated solutions are extracted from the final population to form the Pareto front.

[0113] Based on specific requirements, one or more routing tables are selected in the Pareto frontier as the main path routing tables.

[0114] Specifically, the overall process of generating the main path routing table using the NSGA2 algorithm is as follows:

[0115] 1. Input parameters and initialization. The specific steps are as follows:

[0116] 1) Input network topology. Based on the given directed graph G, a network topology is constructed, where each node is assigned a unique identifier id(s i ), representing different hardware components in the network, such as switches, domain controllers, and sensors.

[0117] 2) Input information flow parameters. Based on the given information flow F k Parameters are used to obtain the sending node, receiving node, single frame length, cycle, deadline and priority corresponding to each information flow.

[0118] 3) Algorithm parameter initialization: Set the number of optimization objective functions nobj = 2 (i.e., load balancing and end-to-end delay adaptability), the initial population size npop = 30, the maximum number of iterations maxit = 200, the crossover probability pc = 0.8, and the mutation probability mu = 0.05.

[0119] 2. Generate the initial population. The specific steps are as follows:

[0120] Create an initial population (pop), which consists of N individuals (pop = {I_1, I_2, ..., I_N}), where each individual represents a set of routing paths for all information flows in the network. In the current network model, this embodiment sets the number of individuals N = 30. Each individual contains M paths corresponding to information flows. For each information flow F j (where j = 1, 2, ..., M), a random path is generated from the sending start point flow_start to the receiving end point endpoint. This path must not only ensure connectivity but also avoid loops to ensure the validity of the path.

[0121] The information flow path generation formula is as follows:

[0122]

[0123] Where: P i,j represents the path of the jth information flow in the i-th individual, start and end represent the starting and ending nodes respectively. neighbors(G,node) represents all neighbor nodes of node node in graph G. path i,j-1 represents the set of nodes whose (j-1)th node is already in the path for the i-th individual. \ represents set subtraction to ensure that no loop is formed. rand(neighbors) represents randomly selecting a node from the neighbors set.

[0124] 3. Fitness calculation, the specific steps are as follows:

[0125] Fitness evaluation is performed for each individual in the population. Each individual in the population will be evaluated based on the network routing configuration it represents to ensure the effectiveness of the provided path and the satisfaction of the optimization goal.

[0126] 4. Non-dominated sorting and congestion calculation, the specific steps are as follows:

[0127] 1) Non-dominated sorting. Non-dominated sorting is performed based on the degree to which each individual solution is not dominated by other solutions. That is, a solution is considered to dominate another solution if it is not inferior to the other solution in all objectives and is superior to the other solution in at least one objective. Based on this principle, this embodiment divides the population into several levels of frontiers, starting with F1 (the first-level optimal frontier) and sorting them sequentially. Individuals within each level do not dominate each other.

[0128] Let pop represent the entire population, which contains all individuals, i.e., the routing table. Let the load balancing function be f i (x), the end-to-end delay fitness function is f y (x). For any two routing tables x,y∈pop, if the following conditions are met: f i (x)≤f i (y) holds for all i; Satisfy f j (x)<f j (y); then routing table x is said to dominate routing table y, denoted as This means that under the same or lower cost conditions, routing table x is at least as good as routing table y in terms of load balancing and end-to-end delay adaptability, and is better than routing table y in at least one performance indicator. By comparing the dominance relationships between all routing tables in the population, this embodiment can determine the dominance rank Rank(x) of each routing table. Routing tables with the same rank form a frontier.

[0129] 2) Crowding calculation. After the non-dominated sorting, in order to ensure the diversity of the solution space and prevent the algorithm from focusing on the local optimal solution too early, the crowding calculation step will be performed for each routing table in the advantage front. Crowding is a quantification of the local density of individual solutions in the function target space. The higher the value, the stronger the relative isolation of the routing table in the target space, that is, there are fewer competitors around. For the mth objective function of the i-th routing table, the crowding d im It can be expressed as:

[0130] d im =f m (x i+1 )-f m (x i-1 ).

[0131] Let M be the number of objective functions and the total congestion D i is the sum of the distances in all objective function directions and can be expressed as:

[0132]

[0133] When performing population selection, if two routing tables are equal in non-dominated ordering level, the routing table with higher congestion will be given priority.

[0134] Through the above process, the algorithm will continue to iterate and update the population until it finds a Pareto optimal solution set that meets the conditions, so that the routing table tends to balance the network load while the end-to-end communication delay is as small as possible.

[0135] 5. Iterate population update. The specific steps are as follows:

[0136] Update the population using tournament selection (TournamentSelect), crossover (CrossOver), and mutation operations (Mutate), and then generate the next generation of population (pop').

[0137] pop'={TournamentSelect(pop),CrossOver(pop'),Mutate(pop')}.

[0138] 6. Store and analyze the optimal value of the optimization target. The specific steps are as follows:

[0139] For each iteration, the algorithm stores a set of routing tables that approximately represent the set of Pareto optimal solutions:

[0140]

[0141] Where: is the set of first-level non-dominated solutions after the t-th iteration, is the nth routing table (individual) in the set, N f is the number of non-dominated solutions.

[0142] For every non-dominated solution Record the corresponding load balancing degree and end-to-end delay adaptability and calculate the optimal value for each indicator. This can be done by extracting The minimum value of the corresponding objective function in the non-dominated set is achieved:

[0143]

[0144] The quality of the routing table is evaluated by monitoring the progress of load balancing and end-to-end delay adaptability.

[0145] 7. Generate the Pareto frontier. The specific steps are as follows:

[0146] In the final Pareto front, each routing table provides a relatively optimal trade-off (load versus delay) on the corresponding objective function, such as Figure 3 shown.

[0147] 8. In order to find a solution that can balance the load balancing degree (L b ) and end-to-end delay adaptability (E d ) routing scheme, set the main path routing decision function, and use a weighted decision mechanism to evaluate and select paths to ensure communication efficiency and effective use of network resources. The network efficiency decision function is as follows:

[0148]

[0149] Where: The function normalize(x) is used to normalize the load balancing degree (L b ) and end-to-end delay adaptability (E d ) are normalized to a value range of [0, 1]. ω1 and ω2 are weight coefficients that can be dynamically adjusted based on the actual network performance requirements. For example, in time-sensitive networks with higher real-time requirements, the weight ω2 for end-to-end delay adaptability should be larger. A smaller decision value D indicates a more even network load and lower end-to-end delay under that route.

[0150] Based on the above formula, all routing tables in the Pato front are traversed to find the routing table with the highest score, which is the final main path routing table. It is considered to provide the routing selection that provides the best balance between load balancing and end-to-end delay adaptability while meeting specific network performance requirements.

[0151] After executing the above steps, a main path routing table that best meets the current network status and performance requirements can be obtained, thereby ensuring the efficient use of network resources and the timeliness of data transmission in the in-vehicle TSN.

[0152] When executing step 106, the specific steps may be as follows:

[0153] The weighted directed graph of the intelligent connected vehicle is weighted to obtain a weighted directed graph; the weighted directed graph contains a weight function; the weight function is a function that maps each link in the link set to a non-negative real number to represent the unreliability weight of the link.

[0154] Based on the weighted directed graph, the unreliability probability of each link path is calculated.

[0155] For any link path, the improved Dijkstra algorithm is used to update the link path to obtain a path with the minimum unreliability.

[0156] Based on the path with the least unreliability, an antilogarithmic transformation is used to obtain the reliability probability of the path.

[0157] Record the path reliability probabilities under different links and generate a redundant path routing table.

[0158] Specifically, to find the path with the highest reliability probability as redundancy in in-vehicle TSN, an improved Dijkstra algorithm is proposed. By redefining the concept of "shortest path" as "minimum unreliability," the improved Dijkstra algorithm can be used to find the path with the highest reliability.

[0159] The problem of solving the reliability probability of the network is mapped to the analysis problem of the weighted directed graph G, with the reliability probability of each node and its connected links as the weight consideration. Based on formula (1), the weight W is introduced, and the specific weight mapping function is defined as: W: E→R + ∪{0}, the weighted directed graph G formula can be obtained as follows:

[0160]

[0161] Where: W(e ij ) is to connect each link e in the link set E ij A(e) is a function that maps to a non-negative real number to represent the unreliability weight of the link. ij ) represents link e ij The reliability probability, which is a value between 0 and 1 (A(e ij )∈(0,1]). To adapt to the positive value requirement of Dijkstra’s algorithm, a negative logarithmic transformation strategy is used to transform the original reliability probability maximization problem into a shortest path problem.

[0162] The solution process of using the improved Dijkstra algorithm to calculate the redundant path routing table is as follows: Figure 4 Specifically, the path selection process using the improved Djkstra algorithm is as follows:

[0163] First, the nodes are initialized. Next, a set of unprocessed nodes is constructed, and the unreliability scores of adjacent nodes are updated. Afterwards, processed nodes are removed, and a check is performed to determine whether all nodes have been processed or whether a minimum node has been found. If either condition is met, a minimum unreliability path is constructed; if not, the unreliability scores of adjacent nodes are updated again.

[0164] In addition, after step 106 is performed, the method further includes:

[0165] Import the routing table and conduct simulation tests, including:

[0166] Domain-centralized electrical and electronic architecture ( Figure 2) is the object, and the algorithms in step 105 and step 106 are used to generate the main path routing table and the redundant path routing table respectively. In a simple driving scenario, the node sends and receives only one message, and there are 7 information flows in the system, such as Figure 5 As shown, taking this model as an example, the generated main path routing table and redundant path routing table are Table 1 and Table 2 respectively.

[0167] Table 1 corresponds to the main path routing table in the example scenario

[0168] Information flow sequence number Main Path reliability Meets expectations 1 Lidar1→1→3→MDC 99.86％ yes 2 Radar2→1→2→3→MDC 99.82％ yes 3 Camera11→1→4→3→MDC 99.82％ yes 4 VIU1→1→2→3→MDC 99.81％ no 5 VIU1→1→3→MDC 99.85％ yes 6 CDC→1→2→3→MDC 99.81％ no 7 CDC→1→3→MDC 99.85％ yes

[0169] Table 2: Redundant path routing table corresponding to the example scenario

[0170]

[0171]

[0172] In this scenario, the reliability of information flows 4 and 6 does not meet the expected standard (as shown in Table 1), indicating potential data or packet loss risks. The algorithm then generates the necessary redundant routes for these flows (as shown in Table 2), providing high-reliability paths as alternative routes to ensure stable information transmission.

[0173] The routing table was imported into MATLAB for simulation testing. The routing table was also imported into a domain controller for hardware testing. The experimental results demonstrate that the proposed routing planning method can effectively improve network efficiency and reliability under various operating conditions by utilizing a link redundancy transmission mechanism. The simulation and hardware test results are highly consistent, demonstrating its practicality.

[0174] Example 2

[0175] like Figure 6 As shown, this embodiment provides a time-sensitive network routing planning system for intelligent connected vehicles, including:

[0176] The model acquisition module 601 is used to obtain the domain-centralized electronic and electrical architecture network topology model of the intelligent connected vehicle; the domain-centralized electronic and electrical architecture network topology model is composed of lidar, millimeter-wave radar, camera, domain controller and functional domain controller as nodes.

[0177] Construction module 602 is used to construct a weighted directed graph of the intelligent connected vehicle based on the domain-centralized electronic and electrical architecture network topology model; the weighted directed graph includes each node in the domain-centralized electronic and electrical architecture network topology model and the links between each node.

[0178] The probability calculation module 603 is used to calculate the reliability probability of each link path based on the links between the nodes in the weighted directed graph and the link evaluation model; the link evaluation model is used to calculate the reliability probability of each link based on the average repair time of the domain controller, sensor or link.

[0179] The marking module 604 is used to mark the priority of the information flow in each link according to the information flow in each link and the reliability probability of each link based on the Ethernet frame marking method of the virtual local area network; the Ethernet frame marking method of the virtual local area network is based on the IEEE802.1Q standard.

[0180] The primary path routing table module 605 is used to generate a primary path routing table based on the information flows in each link after priority marking using the NSGA2 algorithm; the primary path routing table is used to provide routing selection with optimal load balancing and end-to-end delay adaptability based on specific network performance requirements.

[0181] The redundant path routing table module 606 is used to determine a redundant path routing table based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link using an improved Dijkstra algorithm; the redundant path routing table is used to record the path reliability probability of each information flow.

[0182] In summary, this application has the following beneficial effects:

[0183] 1) This application designs a network topology model based on a domain-centralized electrical and electronic architecture. Secondly, to analyze link failures, a mathematical calculation method for evaluating link reliability is proposed, which accurately reflects the reliability levels of links in different systems. Finally, based on quality of service standards, an effective classification of different types of network information flows is implemented. This results in a comprehensive in-vehicle network framework, encompassing architecture models, link models, and information flow models.

[0184] 2) This application uses the NSGA2 algorithm to obtain the final main path routing table, which provides routing selection with optimal load balancing and end-to-end delay adaptability while meeting specific network performance requirements, thereby ensuring the efficient use of network resources and the timeliness of data transmission in the in-vehicle TSN.

[0185] 3) This application uses an improved Dijkstra algorithm to obtain a redundant path routing table, and selectively enables redundant paths according to information flow conditions during transmission, thereby fully ensuring the reliability of redundant path transmission and avoiding resource waste caused by redundancy.

[0186] 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.

[0187] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A time-sensitive network routing planning method for intelligent connected vehicles, characterized in that: The time-sensitive network routing planning method includes: Obtaining a domain-centralized electrical and electronic architecture network topology model of an intelligent connected vehicle; the domain-centralized electrical and electronic architecture network topology model is composed of a laser radar, a millimeter-wave radar, a camera, a domain controller, and a functional domain controller as nodes; Based on the domain-centralized electrical and electronic architecture network topology model, constructing a weighted directed graph of the intelligent connected vehicle; the weighted directed graph includes each node in the domain-centralized electrical and electronic architecture network topology model and the links between the nodes; Calculating the reliability probability of each link path based on the links between the nodes in the weighted directed graph and a link evaluation model; the link evaluation model is used to calculate the reliability probability of each link based on the average repair time of the domain controller, sensor or link; According to the information flow in each link and the reliability probability of each link, the information flow in each link is marked with priority based on the Ethernet frame marking method of the virtual local area network; the Ethernet frame marking method of the virtual local area network is marked based on the IEEE802.1Q standard; Based on the priority-tagged information flows in each link, the NSGA2 algorithm is used to generate a primary path routing table; the primary path routing table is used to provide routing selection with optimal load balancing and end-to-end delay adaptability based on specific network performance requirements; Based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link, an improved Dijkstra algorithm is used to determine a redundant path routing table; the redundant path routing table is used to record the path reliability probability of each information flow.

2. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 1, characterized in that: The formula expression of the weighted directed graph of the intelligent connected vehicle is specifically: In the formula, V represents the set of nodes, E represents the set of links, and e ij Represents the slave node s i To node s j links, n represents the number of nodes, s n Represents node s n .

3. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 2, characterized in that: The formula expression of the link evaluation model is: Where A is the reliability probability, MTTF is the mean time to failure, and MTTR is the mean time to repair based on the domain controller, sensor, or link.

4. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 3, characterized in that: Before generating the primary path routing table using the NSGA2 algorithm based on the priority-tagged information flows on each link, the following steps are also performed: Based on the information flow in each link after priority marking, load balancing and end-to-end delay adaptability are selected as evaluation indicators of network performance; the network performance is a time-sensitive network for intelligent connected vehicles; A dual-objective optimization function is determined based on the load balancing degree and the end-to-end delay adaptability.

5. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 4, characterized in that: The calculation formula for the load balancing degree is: Where, L i Indicates the load utilization on link i corresponding to the designated port, L- is the average load utilization of all designated ports, N is the number of designated ports, L b The load balancing degree.

6. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 5, characterized in that: The calculation formula of the end-to-end delay adaptability is: Where: λ1 and λ2 are weighting coefficients, Represents information flow F i transmission delay; Represents information flow F i Propagation delay at the nth port; Delegates processing delay; represents queue delay; Represents other delays, k i is the information flow F i The number of hops from the sending port to the receiving port.

7. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 6, characterized in that: Based on the priority-tagged information flows on each link, the NSGA2 algorithm is used to generate a primary path routing table. Specifically, the table includes: Based on the information flow in each link after priority marking, a certain number of routing tables are randomly generated as an initial population; each routing table contains the decision of all paths in the network; For each routing table, calculating the load balancing degree and end-to-end delay of the routing table; According to the load balancing degree and end-to-end delay of each routing table, the routing table is divided into several levels of frontiers based on non-dominance relationship; the routing tables in each level do not dominate each other; The current population is iteratively optimized using selection, crossover, and mutation methods, and the newly generated routing table replaces part or all of the old routing table in the population to obtain a new population until the stopping condition is met. After the iteration, all non-dominated solutions are extracted from the final population to form the Pareto frontier; Based on specific requirements, one or more routing tables are selected in the Pareto frontier as the main path routing tables.

8. The time-sensitive network routing planning method for intelligent connected vehicles according to claim 7, characterized in that: Based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link, an improved Dijkstra algorithm is used to determine a redundant path routing table; the redundant path routing table is used to record the path reliability probability of each information flow, specifically including: The weighted directed graph of the intelligent connected vehicle is weighted to obtain a weighted directed graph; the weighted directed graph contains a weight function; the weight function is a function that maps each link in the link set to a non-negative real number to represent the unreliability weight of the link; Based on the weighted directed graph, the unreliability probability of each link path is calculated; For any link path, the improved Dijkstra algorithm is used to update the link path to obtain the path with the minimum unreliability; Based on the path with the least unreliability, an antilogarithmic transformation is used to obtain the reliability probability of the path; Record the path reliability probabilities under different links and generate a redundant path routing table.

9. A time-sensitive network routing planning method for intelligent connected vehicles according to claim 8, characterized in that: The formula expression of the weighted directed graph after weighting is: Where, W(e ij ) is to connect each link e in the link set E ij A function that maps to a non-negative real number to represent the unreliability weight of the link; A(e ij ) represents link e ij The reliability probability, A(e ij )∈(0,1].

10. A time-sensitive network routing planning system for intelligent connected vehicles, characterized in that: include: A model acquisition module is used to obtain a domain-centralized electronic and electrical architecture network topology model of an intelligent connected vehicle; the domain-centralized electronic and electrical architecture network topology model is composed of a lidar, a millimeter-wave radar, a camera, a domain controller, and a functional domain controller as nodes; A construction module, configured to construct a weighted directed graph of the intelligent connected vehicle based on the domain-centralized electrical and electronic architecture network topology model; the weighted directed graph includes nodes in the domain-centralized electrical and electronic architecture network topology model and links between nodes; a probability calculation module for calculating the reliability probability of each link path based on the links between the nodes in the weighted directed graph and a link evaluation model; the link evaluation model is used to calculate the reliability probability of each link based on the mean repair time of the domain controller, sensor or link; a marking module for marking the priority of the information flow in each link based on the information flow in each link and the reliability probability of each link, based on the Ethernet frame marking method of the virtual local area network; the Ethernet frame marking method of the virtual local area network is based on the IEEE802.1Q standard for marking; A primary path routing table module is used to generate a primary path routing table based on the priority-tagged information flows in each link using the NSGA2 algorithm. The primary path routing table is used to provide routing selection with optimal load balancing and end-to-end delay adaptability based on specific network performance requirements. The redundant path routing table module is used to determine a redundant path routing table based on the weighted directed graph of the intelligent connected vehicle and the reliability probability of each link using an improved Dijkstra algorithm; the redundant path routing table is used to record the path reliability probability of each information flow.

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