Risk perception and prevention and control oriented heterogeneous road side unit optimization layout method

Abstract

The invention relates to a heterogeneous road side unit optimized layout method, in particular to a heterogeneous road side unit optimized layout method oriented to risk perception and prevention and control. The invention aims to solve the problem that the existing RSU layout scheme research is lack of consideration on risk perception, prevention and control and heterogeneous road side units. The method comprises the following steps: 1, discretizing a road area into space-time units; mapping the accident record into a corresponding space-time unit, and counting the accident frequency in each space-time unit; standardizing the accident frequency in each time-space unit to obtain the risk intensity of each time-space unit; 2, setting an effective risk prevention and control interval on a space unit # imgabs0 #; 3, acquiring a communication range and a sensing range of a # imgabs 1 # type road side unit RSU; 4, constructing a target function; and 5, solving the objective function by adopting a genetic algorithm to obtain an optimization result. The method is applied to the field of heterogeneous road side unit layout.

Description

Technical Field

[0001] The invention relates to a method for optimizing the layout of heterogeneous roadside units. Background Art

[0002] In vehicle-road collaboration scenarios, due to the limitations of onboard sensors in terms of perception capabilities and range, relying solely on single-vehicle perception alone cannot meet the requirements for safe driving in complex scenarios. Roadside Units (RSUs), as roadside sensing and communication facilities, can detect risk information and transmit this information to vehicles in real time through V2I communication, enhancing their beyond-visual-range risk perception capabilities and ensuring safe operation. For example, deploying RSUs at tunnel exits or intersections without traffic lights can provide vehicles with advance warning of potential risks, such as disabled vehicles or crossing pedestrians, enabling them to slow down, change lanes, or brake in advance, significantly enhancing driving safety.

[0003] Driven by these practical needs, scholars at home and abroad have conducted extensive research on RSU deployment in recent years. Most of this research focuses on network performance, minimizing communication latency, maximizing communication connectivity, and improving road network coverage. Various strategies for optimizing the number and location of RSUs have been proposed, and significant progress has been made.

[0004] However, existing research generally has the following two shortcomings:

[0005] (1) Lack of consideration for risk perception and prevention needs. Affected by factors such as road alignment, traffic flow, and control measures, road risks show obvious differences in temporal and spatial distribution. For example, sections of road with a high incidence of traffic accidents have higher requirements for risk perception and early warning. However, traditional RSU deployment strategies are mostly guided by communication needs, ignoring the spatial differences in risk levels. They are unable to achieve perception of high-risk areas and early warning of risks, which affects vehicle safety decisions.

[0006] (2) The deployment optimization of heterogeneous RSUs is not fully considered. In practical applications, different types of RSUs have different perception and communication ranges, forming heterogeneous RSUs. Existing deployment solutions mostly focus on the deployment of homogeneous RSUs, without targeted modeling and deployment optimization for heterogeneous RSUs.

[0007] In summary, existing RSU deployments lack consideration for risk perception and control, as well as the deployment of heterogeneous roadside units (RSUs), making it difficult to effectively support the risk perception and safe driving needs of intelligent connected vehicles in complex environments. Therefore, it is urgent to develop an optimized deployment method for heterogeneous RSUs for risk perception and control, to enhance the beyond-visual-range risk perception capabilities of intelligent connected vehicles and ensure their safe operation. Summary of the Invention

[0008] The purpose of this invention is to solve the problem that the existing RSU deployment scheme research lacks consideration of risk perception and prevention and control as well as heterogeneous roadside units, and to propose a heterogeneous roadside unit optimization deployment method for risk perception and prevention and control.

[0009] The specific process of the optimized deployment method of heterogeneous roadside units for risk perception and prevention is as follows:

[0010] Step 1: Discretize the road area into spatiotemporal units ; Map accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The accident frequency in each time and space unit Standardization to obtain the risk intensity of each time and space unit;

[0011] Step 2: Set up space units Effective prevention and control range of risks ;

[0012] Step 3: Get the The communication range and perception range of RSU-like roadside units;

[0013] Step 4: Set decision variables and set constraints based on the decision variables;

[0014] Based on decision variables, spatial units Effective prevention and control range of risks , No. The communication range and perception range of the RSU-like roadside unit and the constraints are used to construct the objective function;

[0015] Step 5: Use genetic algorithm to solve the objective function of step 4 and obtain the optimization result.

[0016] Preferably, in step 1, the road area is discretized into time and space units. ; Map accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The accident frequency in each time and space unit Standardize and obtain the risk intensity of each time and space unit; the specific process is:

[0017] Step 1: Discretize the road area into space-time units ; The specific process is:

[0018] Divide the road network into spatial units, and divide the time dimension into time units, and the length of each space unit is , constructing a set of two-dimensional space-time units ;

[0019] in, Indicates the time interval Neidi space units; , ;

[0020] Step 1 and 2: Mapping accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The specific process is:

[0021] Based on the timestamp and geographic coordinate information contained in the traffic accident data, the accident records are mapped to the corresponding spatiotemporal units. and count the accident frequency in each time and space unit ;

[0022] Step 13: The accident frequency in each time and space unit Normalize and obtain the risk intensity of each space-time unit.

[0023] Preferably, in steps 1 and 3, the accident frequency in each time-space unit is Standardize and obtain the risk intensity of each time and space unit; the specific process is:

[0024] The accident frequency in each time and space unit Perform normalization and use normalized data to represent the risk intensity of each spatiotemporal unit:

[0025]

[0026] in, Indicates the time period , space unit intensity of risk.

[0027] Preferably, in step 2, a space unit is set Effective prevention and control range of risks ; The specific process is:

[0028] Introducing spatial units Effective prevention and control range of risks ; Expressed as:

[0029]

[0030] Where, The minimum effective distance for risk prevention and control, The maximum effective distance for risk prevention and control.

[0031] Preferably, in step three, the The communication range and perception range of a roadside unit (RSU) are as follows:

[0032] Ignoring the road width and only considering the direction of vehicle travel along the road, the The communication range and perception range of a roadside unit (RSU) are expressed as follows:

[0033]

[0034] in,

[0035] For the kind, ; is the total number of roadside unit RSU classes;

[0036] For the roadside unit RSU to be deployed in the location coordinates;

[0037] For the Communication radius of roadside unit (RSU);

[0038] For the The perception radius of the roadside unit RSU;

[0039] For the Roadside unit RSU is located at Communication range;

[0040] For the Roadside unit RSU is located at range of perception.

[0041] Preferably, in step 4, decision variables are set, and constraints are set based on the decision variables;

[0042] Based on decision variables, spatial units Effective prevention and control range of risks , No. The communication range and perception range of the RSU-like roadside unit and the constraints are used to construct the objective function;

[0043] The specific process is:

[0044] Step 41: Set decision variables; the specific process is:

[0045] Divide the road into candidate deployment points, with a total of Each type of RSU has different sensing and communication ranges; the deployment variables are defined as:

[0046]

[0047] in:

[0048] , For the Class RSU in position The decision variables, For the indivual, ; Place the RSU in position coordinates;

[0049] When Roadside unit RSU is located at When the perception range ,otherwise ;

[0050] When Roadside unit RSU is located at The communication range, ,otherwise ;

[0051] Step 42: Set constraints based on the decision variables, including the number and location interval constraints of roadside units (RSUs);

[0052] The number of roadside units (RSUs) should be equal to the preset number. , as shown below:

[0053]

[0054] The location spacing constraint of the roadside unit RSU: each candidate point can be deployed with at most one type of roadside unit RSU, as shown below:

[0055]

[0056] in:

[0057] Indicates any ;

[0058] Indicates any ;

[0059] Step 43: Construct the objective function; the specific process is:

[0060] To maximize the risk value Number of vehicles effectively covered is the objective function, which is defined as follows:

[0061]

[0062] in, Represented in space-time units The perceived risk value;

[0063] Represents space-time units The number of vehicles effectively covered by the perceived risk value;

[0064] Step 44: Taking maximizing the number of vehicles effectively covered by the risk value as the objective function and the number and location interval of roadside units (RSUs) as constraints, a roadside unit optimization layout model is established to obtain the number and location of different types of roadside units.

[0065] Preferably, in step 43, in the space-time unit Perceived risk value The solution process is:

[0066] make Indicates the total roadside unit sensing range, It is obtained by taking the union of the sensing range of each roadside unit, ;

[0067] in, Indicates the communication range of the first type roadside unit RSU at position 1, Indicates the communication range of the first type roadside unit RSU at position 2, Indicates the Type RSU in position Communication range;

[0068] For space-time units , assuming that the space-time unit Within the sensing range of the total roadside unit RSU Inside, it means the risk is sensed by the vehicle. ,otherwise , as shown below:

[0069]

[0070] in, Indicates the time period , space unit the intensity of risk;

[0071] Represented in space-time units The perceived risk value.

[0072] Preferably, the space-time unit in step 43 Number of vehicles effectively covered by the risk information perceived The solution process is:

[0073] exist Time, space-time unit Number of vehicles effectively covered by the risk information perceived The calculation of is as follows:

[0074] Each roadside unit RSU has a specific communication range , take the union of the communication ranges of all roadside units RSU on the road to get the total communication range set , ;

[0075] in, Indicates the communication range of the first type roadside unit RSU deployed at position 1, Indicates the communication range of the first type roadside unit RSU deployed at position 2, Indicates the Roadside unit RSU is located at Communication range;

[0076] Defining a function , if the spatial unit on the road segment In the total communication range collection Inside, at this time , risk information can be disseminated, otherwise ,Right now:

[0077]

[0078] in, A propagation function representing risk information based on the communication range;

[0079] Let the communication radius between vehicles be The average headway is , if the average headway is less than or equal to the communication radius between any two vehicles in front and behind Represents spatial unit Risk information can be transmitted via V2V. ,otherwise , then the function Expressed as:

[0080]

[0081] in, represents the risk information transmission function based on the communication radius;

[0082] Function-based and function Sure ;

[0083]

[0084] in, Indicated in space unit Whether risk information can be transmitted;

[0085] The spatial units from the risk location to the effective risk prevention and control interval are defined as follows: ;

[0086] in All spatial units between the risk location and the effective risk prevention and control area;

[0087] For space unit Effective prevention and control range of risks Any spatial unit on ;

[0088] like arrive There are continuous space units on , then it is considered that the risk information can be transmitted to the effective range of risk prevention and control, that is, Otherwise, it is believed that the risk information will be interrupted. ; expressed as:

[0089]

[0090] in, The decision function representing the transmission of risk information to the effective interval of risk prevention and control;

[0091] based on Get Risk Number of vehicles covered ; expressed as:

[0092]

[0093] in, Represents spatial unit density; Indicates the length of the spatial unit.

[0094] Preferably, in step 44, maximizing the number of vehicles effectively covered by the risk value is used as the objective function, and the number and position interval of the roadside units (RSUs) are used as constraints. A roadside unit optimization layout model is established to obtain the layout quantity and layout location of different types of roadside units. The specific model is as follows:

[0095]

[0096] st

[0097] .

[0098] Preferably, in step five, a genetic algorithm is used to solve the objective function of step four to obtain an optimization result; the specific process is:

[0099] Step 51: Chromosome encoding and population initialization; the specific process is:

[0100] Each individual represents a layout plan, using binary encoding: for each candidate position Whether the kth type RSU is deployed is represented by 0 or 1; if deployed, it is 1, and if not deployed, it is 0;

[0101] for candidate points and The solution of the roadside unit type is a The two-dimensional matrix form is as shown below;

[0102] The chromosome length is , the initial population is generated by random layout, and the population size is set to ;

[0103]

[0104] in, is the first row and first column element in the two-dimensional matrix, is the first row and second column element in the two-dimensional matrix, is the first row and M column element in the two-dimensional matrix, is the element in the second row and first column of the two-dimensional matrix, is the second row and second column element in the two-dimensional matrix, is the element in the second row and M column of the two-dimensional matrix, is the Kth row and first column element in the two-dimensional matrix, is the Kth row and second column element in the two-dimensional matrix, is the K-th row and M-th column element in the two-dimensional matrix, is a two-dimensional matrix;

[0105] Step 52: Design the fitness function; the specific process is:

[0106] The objective function of step 4 is directly used as the fitness function :

[0107]

[0108] Step 53: Select an operation; the specific process is as follows:

[0109] From the current population, select a% individuals as parents according to their fitness values from large to small, with a ranging from 10 to 20;

[0110] Step 53: Crossover operation; the specific process is:

[0111] Among the selected parent individuals, the crossover probability Perform crossover operations, using single-point or multi-point crossover to exchange gene segments on chromosomes, thereby generating new offspring individuals that meet the layout constraints;

[0112] Step 54: Mutation operation; the specific process is:

[0113] Set a certain mutation probability , randomly changing the gene position in the chromosome (0→1 or 1→0);

[0114] Step 55: Repeat steps 53 to 54. When the termination condition is met, the current optimal chromosome is output as the final heterogeneous RSU layout plan. .

[0115] The beneficial effects of the present invention are:

[0116] This invention breaks through the limitations of existing RSU deployment methods in "only focusing on communication indicators, ignoring risk distribution" and "not considering the collaborative deployment of heterogeneous equipment", and proposes an optimized deployment strategy that integrates risk perception intensity, heterogeneous RSU performance differences and multiple communication mechanisms (V2I+V2V). It effectively improves the beyond-visual-range perception capability and active prevention and control capability of intelligent connected vehicles in high-risk areas, and has stronger practicality, adaptability and safety assurance capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 Flowchart of the present invention;

[0118] Figure 2 A map of the effective range for risk information transmission;

[0119] Figure 3 It is a risk information dissemination map;

[0120] Figure 4 It is a heat map of risk distribution;

[0121] Figure 5 This is the convergence graph of the genetic algorithm. DETAILED DESCRIPTION

[0122] Specific implementation method 1: The specific process of the method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention in this implementation is as follows:

[0123] Step 1: Discretize the road area into spatiotemporal units ; Map accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The accident frequency in each time and space unit Standardization to obtain the risk intensity of each time and space unit;

[0124] Step 2: Set up space units Effective prevention and control range of risks ;

[0125] Step 3: Get the The communication range and perception range of RSU-like roadside units;

[0126] Step 4: Set decision variables and set constraints based on the decision variables;

[0127] Based on decision variables, spatial units Effective prevention and control range of risks , No. The communication range and perception range of the RSU-like roadside unit and the constraints are used to construct the objective function;

[0128] Step 5: Use genetic algorithm to solve the objective function of step 4 and obtain the optimization result.

[0129] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in step 1, the road area is discretized into time and space units. ; Map accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The accident frequency in each time and space unit Standardization to obtain the risk intensity of each time and space unit;

[0130] The specific process is:

[0131] Step 1: Discretize the road area into space-time units ; The specific process is:

[0132] Divide the road network into spatial units (one-dimensional road length), and the time dimension is divided into time units, and the length of each space unit is (one-dimensional road length), constructing a set of two-dimensional space-time units ;

[0133] in, Indicates the time interval Neidi space units; , ;

[0134] Step 1 and 2: Mapping accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The specific process is:

[0135] Based on the timestamp and geographic coordinate information contained in the traffic accident data, the accident records are mapped to the corresponding spatiotemporal units. and count the accident frequency in each time and space unit ;

[0136] Step 13: The accident frequency in each time and space unit Normalize and obtain the risk intensity of each space-time unit.

[0137] Other steps and parameters are the same as those in the first embodiment.

[0138] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that: in step one or three, the accident frequency in each time-space unit is Standardize and obtain the risk intensity of each time and space unit; the specific process is:

[0139] The accident frequency in each time and space unit Perform normalization and use normalized data to represent the risk intensity of each spatiotemporal unit:

[0140]

[0141] in, Indicates the time period , space unit intensity of risk.

[0142] Other steps and parameters are the same as those in the first or second embodiment.

[0143] Specific embodiment 4: This embodiment differs from any one of specific embodiments 1 to 3 in that: in step 2, a space unit is set Effective prevention and control range of risks ; The specific process is:

[0144] Considering the timeliness of risk prevention and control, it is necessary to transmit the detected risk information to upstream vehicles so that the vehicles can perceive and take preventive measures in advance. If the distance between the vehicle and the risk is too short, the vehicle will not have enough time to take preventive measures. If the distance between the vehicle and the risk is too long, the risk may have been eliminated by the time the vehicle reaches the risk location. Therefore, it is necessary to set a minimum distance and a maximum distance for risk prevention and control. Figure 2 Minimum distance shown Ensure that the driver has enough time to take preventive measures if the range exceeds the maximum distance The effectiveness of risk information transmission will be reduced.

[0145] Considering the timeliness of risk prevention and control, spatial units are introduced Effective prevention and control range of risks ,like Figure 2 As shown; Expressed as:

[0146]

[0147] Where, The minimum effective distance for risk prevention and control, The maximum effective distance for risk prevention and control.

[0148] The other steps and parameters are the same as those in the first to third embodiments.

[0149] Specific implementation method 5: This implementation method is different from any one of the specific implementation methods 1 to 4 in that: in step 3, the first The communication range and perception range of a roadside unit (RSU) are as follows:

[0150] In practice, different roadside units have different sensing and communication ranges, which constitute heterogeneous roadside units. In order to describe heterogeneous roadside units, a unified model is established for heterogeneous roadside units. To simplify the modeling, the road width is ignored and only the direction of vehicle travel along the road is considered. The communication range and perception range of a roadside unit (RSU) are expressed as follows:

[0151]

[0152] in,

[0153] For the kind, ; The total number of roadside unit RSU classes (one RSU is placed at each location, and there are several RSUs in one class);

[0154] For the roadside unit RSU to be deployed in the location coordinates;

[0155] For the Communication radius of roadside unit (RSU);

[0156] For the The perception radius of the roadside unit RSU;

[0157] For the Roadside unit RSU is located at The communication range of the vehicle can directly communicate with the roadside unit.

[0158] For the Roadside unit RSU is located at The risks within the perception range can be effectively perceived by the roadside unit.

[0159] The other steps and parameters are the same as those in the first to fourth embodiments.

[0160] Specific embodiment 6: This embodiment differs from any one of specific embodiments 1 to 5 in that: in step 4, decision variables are set, and constraints are set based on the decision variables;

[0161] Based on decision variables, spatial units Effective prevention and control range of risks , No. The communication range and perception range of the RSU-like roadside unit and the constraints are used to construct the objective function;

[0162] The specific process is:

[0163] Step 41: Set decision variables; the specific process is:

[0164] Divide the road into candidate deployment points, with a total of Each type of RSU has different sensing and communication ranges; the deployment variables are defined as:

[0165]

[0166] in:

[0167] , For the Class RSU in position The decision variables, For the indivual, ; Place the RSU in position coordinates;

[0168] When Roadside unit RSU is located at When the perception range ,otherwise (empty set);

[0169] When Roadside unit RSU is located at The communication range, ,otherwise (empty set);

[0170] Step 42: Set constraints based on the decision variables, including the number and location interval constraints of roadside units (RSUs);

[0171] The number of roadside units (RSUs) should be equal to the preset number. , as shown below:

[0172]

[0173] The location spacing constraint of the roadside unit RSU: Each candidate point is equipped with at most one type of roadside unit RSU (one type of roadside unit RSU includes multiple roadside units RSU), as shown below:

[0174]

[0175] in:

[0176] Indicates any ;

[0177] Indicates any ;

[0178] Step 43: Construct the objective function; the specific process is:

[0179] In order to make risk information serve more vehicles, improve the performance of risk prevention and control, and maximize the risk value Number of vehicles effectively covered is the objective function, which is defined as follows:

[0180]

[0181] in, Represented in space-time units The perceived risk value;

[0182] Represents space-time units The number of vehicles effectively covered by the perceived risk value;

[0183] Step 44: Taking maximizing the number of vehicles effectively covered by the risk value as the objective function and the number and location interval of roadside units (RSUs) as constraints, a roadside unit optimization layout model is established to obtain the number and location of different types of roadside units.

[0184] Other steps and parameters are the same as those in Specific Implementations 1 to 5-1.

[0185] Specific embodiment seven: This embodiment differs from any one of the specific embodiments one to six in that: in step four-three, in the space-time unit Perceived risk value The solution process is:

[0186] make Indicates the total roadside unit sensing range, It is obtained by taking the union of the sensing range of each roadside unit, ;

[0187] in, Indicates the communication range of the first type roadside unit RSU at position 1, Indicates the communication range of the first type roadside unit RSU at position 2, Indicates the Type RSU in position Communication range;

[0188] For space-time units , assuming that the space-time unit Within the sensing range of the total roadside unit RSU Inside, it means the risk is sensed by the vehicle. ,otherwise , as shown below:

[0189]

[0190] in, Indicates the time period , space unit the intensity of risk;

[0191] Represented in space-time units The perceived risk value.

[0192] Other steps and parameters are the same as those in Specific Implementations 1 to 6-1.

[0193] Specific embodiment eight: This embodiment differs from any one of specific embodiments one to seven in that: the space-time unit in step four-three Number of vehicles effectively covered by the risk information perceived The solution process is:

[0194] exist Time, space-time unit Number of vehicles effectively covered by the risk information perceived The calculation of is as follows:

[0195] In actual application scenarios, each roadside unit RSU has a specific communication range ,like Figure 3 As shown, the communication ranges of all roadside units RSU on the road are combined to obtain the total communication range set , ;

[0196] in, Indicates the communication range of the first type roadside unit RSU deployed at position 1, Indicates the communication range of the first type roadside unit RSU deployed at position 2, Indicates the Roadside unit RSU is located at Communication range;

[0197] To determine the spatial unit on the road section Whether risk information is disseminated through the vehicle-to-vehicle (V2I) infrastructure within the communication range, define the function , if the spatial unit on the road segment In the total communication range collection Inside, at this time , risk information can be disseminated, otherwise ,Right now:

[0198]

[0199] in, A propagation function representing risk information based on the communication range;

[0200] For any space-time unit , define the function To judge the spatial unit exist Is it possible to transmit risk information through vehicle-to-vehicle communication (V2V) at any time? Let the communication radius between vehicles be The average headway is , if the average headway is less than or equal to the communication radius between any two vehicles in front and behind Represents spatial unit Risk information can be transmitted via V2V. ,otherwise , then the function Expressed as:

[0201]

[0202] in, represents the risk information transmission function based on the communication radius;

[0203] In order to transmit the risk to the effective risk prevention and control range, if the spatial unit on the road section Risk information can be transmitted via V2V or V2I, i.e. or , then define Indicated in space unit Risk information can be transmitted on Indicated in space unit Risk information cannot be transmitted online;

[0204] Function-based and function Sure ;

[0205]

[0206] in, Indicated in space unit Whether risk information can be transmitted;

[0207] The spatial units from the risk location to the effective risk prevention and control interval are defined as follows: ;

[0208] in All spatial units between the risk location and the effective risk prevention and control area;

[0209] For space unit Effective prevention and control range of risks Any spatial unit on ;

[0210] like arrive There are continuous space units on , then it is considered that the risk information can be transmitted to the effective range of risk prevention and control, that is, Otherwise, it is believed that the risk information will be interrupted. ; expressed as:

[0211]

[0212] in, The decision function representing the transmission of risk information to the effective interval of risk prevention and control;

[0213] based on Get Risk Number of vehicles covered ; expressed as:

[0214]

[0215] in, Represents spatial unit density; Indicates the length of the spatial unit.

[0216] The other steps and parameters are the same as those in the first to seventh embodiments.

[0217] Specific embodiment 9: This embodiment differs from specific embodiments 1 to 8 in that: in step 44, maximizing the number of vehicles effectively covered by the risk value is used as the objective function, and the number and position interval of roadside units (RSUs) are used as constraints. A roadside unit optimization layout model is established to obtain the layout number and layout location of different types of roadside units. The specific model is as follows:

[0218]

[0219] st

[0220] .

[0221] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0222] Specific embodiment 10: The difference between this embodiment and the specific embodiments 1 to 9 is that in step 5, a genetic algorithm is used to solve the objective function of step 4 to obtain the optimization result (the final heterogeneous RSU deployment plan and ); the specific process is:

[0223] The solution algorithm in step 5 uses a genetic algorithm to solve the layout model of heterogeneous roadside units. As a global optimization search method, the genetic algorithm can effectively handle the optimization problems defined in this patent with discrete decision variables and complex constraints by simulating the inheritance and selection mechanisms in the natural evolution process. The specific solution process is as follows:

[0224] Step 51: Chromosome encoding and population initialization; the specific process is:

[0225] Each individual represents a layout plan, using binary encoding: for each candidate position Whether the kth type RSU is deployed is represented by 0 or 1; if deployed, it is 1, and if not deployed, it is 0;

[0226] for candidate points and The solution of the roadside unit type is a The two-dimensional matrix form is as shown below;

[0227] The chromosome length is , the initial population is generated by random layout, and the population size is set to ;

[0228]

[0229] in, is the first row and first column element in the two-dimensional matrix, is the first row and second column element in the two-dimensional matrix, is the first row and M column element in the two-dimensional matrix, is the element in the second row and first column of the two-dimensional matrix, is the second row and second column element in the two-dimensional matrix, is the element in the second row and M column of the two-dimensional matrix, is the Kth row and first column element in the two-dimensional matrix, is the Kth row and second column element in the two-dimensional matrix, is the K-th row and M-th column element in the two-dimensional matrix, is a two-dimensional matrix;

[0230] Step 52: Design the fitness function; the specific process is:

[0231] The objective function of step 4 is directly used as the fitness function :

[0232]

[0233] Step 53: Select an operation; the specific process is as follows:

[0234] From the current population, select a% individuals as parents according to their fitness values from large to small, with a ranging from 10 to 20;

[0235] Step 54: Cross operation; the specific process is:

[0236] Among the selected parent individuals, the crossover probability Perform crossover operation, use single-point or multi-point crossover to exchange gene segments of chromosomes, thereby generating new offspring individuals, and the new offspring individuals meet the layout constraints ;

[0237] Step 55: Mutation operation; the specific process is:

[0238] In order to avoid the algorithm falling into the local optimal solution, a certain mutation probability is set , randomly changing some gene positions in the chromosome (0→1 or 1→0), thereby introducing new search directions;

[0239] The mutation operation helps maintain the diversity of the population and improves the global search capability of the algorithm.

[0240] Step 56: Repeat steps 53 to 55. When the termination condition is met, the current optimal chromosome is output as the final heterogeneous RSU layout plan (each candidate position Which type of RSU is deployed? Each candidate location only deploys one type of RSU, and one type of RSU includes multiple RSUs. .

[0241] The termination condition is: reaching the preset maximum number of iterations , or the fitness of the optimal individual for several consecutive generations does not change significantly (the change range is lower than the set threshold ).

[0242] The other steps and parameters are the same as those in the specific implementation modes 1 to 9-1.

[0243] The following examples are used to verify the beneficial effects of the present invention:

[0244] Example 1:

[0245] (1) Data preparation and parameter setting; the process is:

[0246] The experiment selected a 5km section of the I10 highway in California, USA as the test section, and conducted a layout optimization experiment based on the accident data from 2016 to 2022. Road section is calculated per 100 Divide into 50 spatial units and divide into 144 time units every 10 minutes to obtain space-time units , the traffic density is obtained based on the historical traffic.

[0247] Table 1 Parameter settings

[0248]

[0249] (2) Assessment of the spatiotemporal distribution of accident risk; the process is as follows:

[0250] The accident frequency of each space-time unit is counted in the divided spatial unit, and the risk distribution heat map of each space-time unit is obtained by standardization. The darker the color, the smaller the risk value of the accident. Figure 4 As shown:

[0251] (3) Optimization and solution results; the process is:

[0252] The genetic algorithm was used to optimize the above model. The number of RSUs of each type was limited to 4, and the candidate position of RSU was the midpoint of each spatial unit. Five experiments were conducted, and the convergence diagram of the genetic algorithm is shown in Figure 2. Figure 5 As shown, the optimal layout solution is as follows:

[0253] The layout positions are as follows:

[0254] The first type of RSU is deployed at 500m, 1700m, 3200m, and 4500m;

[0255] The second type of RSU is deployed at 1100m, 2200m, 2800m and 4200m.

[0256] The present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention, characterized by: The specific process of the method is: Step 1: Discretize the road area into spatiotemporal units ; Mapping accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The accident frequency in each time and space unit Standardization to obtain the risk intensity of each time and space unit; Step 2: Set up space units Effective prevention and control range of risks ; Step 3: Get the The communication range and perception range of RSU-like roadside units; Step 4: Set decision variables and set constraints based on the decision variables; Based on decision variables, spatial units Effective prevention and control range of risks , No. The communication range and perception range of the RSU-like roadside unit and the constraints are used to construct the objective function; Step 5: Use genetic algorithm to solve the objective function of step 4 and obtain the optimization result.

2. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 1 is characterized by: In step 1, the road area is discretized into time and space units. ; Mapping accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The accident frequency in each time and space unit Standardize and obtain the risk intensity of each time and space unit; the specific process is: Step 1: Discretize the road area into space-time units ; The specific process is: Divide the road network into spatial units, and divide the time dimension into time units, and the length of each space unit is , constructing a set of two-dimensional space-time units ; in, Indicates the time interval Neidi space units; , ; Step 1 and 2: Mapping accident records to corresponding spatiotemporal units and count the accident frequency in each time and space unit ; The specific process is: Based on the timestamp and geographic coordinate information contained in the traffic accident data, the accident records are mapped to the corresponding spatiotemporal units. and count the accident frequency in each time and space unit ; Step 13: The accident frequency in each time and space unit Normalize and obtain the risk intensity of each space-time unit.

3. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 2 is characterized by: In the steps 1 and 3, the accident frequency in each time-space unit is Standardize and obtain the risk intensity of each time and space unit; the specific process is: The accident frequency in each time and space unit Perform normalization and use normalized data to represent the risk intensity of each spatiotemporal unit: in, Indicates the time period , space unit intensity of risk.

4. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 3 is characterized by: In step 2, the space unit is set Effective prevention and control range of risks ; The specific process is: Introducing spatial units Effective prevention and control range of risks ; Expressed as: Where, The minimum effective distance for risk prevention and control, The maximum effective distance for risk prevention and control.

5. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 4 is characterized by: In step three, obtain The communication range and perception range of a roadside unit (RSU) are as follows: Ignoring the road width and only considering the direction of vehicle travel along the road, the The communication range and perception range of a roadside unit (RSU) are expressed as follows: in, For the kind, ; is the total number of roadside unit RSU classes; For the roadside unit RSU to be deployed in the location coordinates; For the Communication radius of roadside unit (RSU); For the The perception radius of the roadside unit RSU; For the Roadside unit RSU is located at Communication range; For the Roadside unit RSU is located at range of perception.

6. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 5 is characterized by: In the step 4, decision variables are set, and constraints are set based on the decision variables; Based on decision variables, spatial units Effective prevention and control range of risks , No. The communication range and perception range of the RSU-like roadside unit and the constraints are used to construct the objective function; The specific process is: Step 41: Set decision variables; the specific process is: Divide the road into candidate deployment points, with a total of Each type of RSU has different sensing and communication ranges; the deployment variables are defined as: in: , For the Class RSU in position The decision variables, For the indivual, ; Place the RSU in position coordinates; When Roadside unit RSU is located at When the perception range ,otherwise ; When Roadside unit RSU is located at The communication range, ,otherwise ; Step 42: Set constraints based on the decision variables, including the number and location interval constraints of roadside units (RSUs); The number of roadside units (RSUs) should be equal to the preset number. , as shown below: The location spacing constraint of the roadside unit RSU: each candidate point can be deployed with at most one type of roadside unit RSU, as shown below: in: Indicates any ; Indicates any ; Step 43: Construct the objective function; the specific process is: To maximize the risk value Number of vehicles effectively covered is the objective function, which is defined as follows: in, Represented in space-time units The perceived risk value; Represents space-time units The number of vehicles effectively covered by the perceived risk value; Step 44: Taking maximizing the number of vehicles effectively covered by the risk value as the objective function and the number and location interval of roadside units (RSUs) as constraints, a roadside unit optimization layout model is established to obtain the number and location of different types of roadside units.

7. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 6 is characterized by: In the step 43, in the space-time unit Perceived risk value The solution process is: make Represents the total roadside unit sensing range, It is obtained by taking the union of the sensing range of each roadside unit, ; in, Indicates the communication range of the first type roadside unit RSU at position 1, Indicates the communication range of the first type roadside unit RSU at position 2, Indicates the Type RSU in position Communication range; For space-time units , assuming that the space-time unit Within the sensing range of the total roadside unit RSU Inside, it means the risk is sensed by the vehicle. ,otherwise , as shown below: in, Indicates the time period , space unit the intensity of risk; Represented in space-time units The perceived risk value.

8. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 7 is characterized by: The space-time unit in step 43 Number of vehicles effectively covered by the risk information perceived The solution process is: exist Time, space-time unit Number of vehicles effectively covered by the risk information perceived The calculation of is as follows: Each roadside unit RSU has a specific communication range , take the union of the communication ranges of all roadside units RSU on the road to get the total communication range set , ; in, Indicates the communication range of the first type roadside unit RSU deployed at position 1, Indicates the communication range of the first type roadside unit RSU deployed at position 2, Indicates the Roadside unit RSU is located at Communication range; Defining a function , if the spatial unit on the road segment In the total communication range collection Inside, at this time , risk information can be disseminated, otherwise ,Right now: in, a propagation function representing risk information based on the communication range; Let the communication radius between vehicles be The average headway is , if the average headway is less than or equal to the communication radius between any two vehicles in front and behind Represents spatial unit Risk information can be transmitted via V2V. ,otherwise , then the function Expressed as: in, represents the risk information transmission function based on the communication radius; Function-based and function Sure ; in, Indicated in space unit Whether risk information can be transmitted; The spatial units from the risk location to the effective risk prevention and control interval are defined as follows: ; in All spatial units between the risk location and the effective risk prevention and control area; For space unit Effective prevention and control range of risks Any spatial unit on ; like arrive There are continuous space units on , it is believed that the risk information can be transmitted to the effective range of risk prevention and control, that is, Otherwise, it is believed that the risk information will be interrupted. ; expressed as: in, The decision function representing the transmission of risk information to the effective interval of risk prevention and control; based on Get Risk Number of vehicles covered ; expressed as: in, Represents spatial unit density; Indicates the length of the spatial unit.

9. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 8 is characterized by: In step 44, the objective function is to maximize the number of vehicles effectively covered by the risk value, and the number and position interval of the roadside units (RSUs) are used as constraints. An optimized roadside unit layout model is established to obtain the layout quantity and layout location of different types of roadside units. The specific model is as follows: st 。 10. The method for optimizing the deployment of heterogeneous roadside units for risk perception and prevention according to claim 9, characterized in that: In step 5, a genetic algorithm is used to solve the objective function of step 4 to obtain an optimization result; the specific process is: Step 51: Chromosome encoding and population initialization; the specific process is: Each individual represents a layout plan, using binary encoding: for each candidate position Whether the kth type RSU is deployed is represented by 0 or 1; if deployed, it is 1, and if not deployed, it is 0; for candidate points and The solution of the roadside unit type is a The two-dimensional matrix form is as shown below; The chromosome length is , the initial population is generated by random layout, and the population size is set to ; in, is the first row and first column element in the two-dimensional matrix, is the first row and second column element in the two-dimensional matrix, is the first row and M column element in the two-dimensional matrix, is the element in the second row and first column of the two-dimensional matrix, is the second row and second column element in the two-dimensional matrix, is the element in the second row and M column of the two-dimensional matrix, is the Kth row and first column element in the two-dimensional matrix, is the Kth row and second column element in the two-dimensional matrix, is the K-th row and M-th column element in the two-dimensional matrix, is a two-dimensional matrix; Step 52: Design the fitness function; the specific process is: The objective function of step 4 is directly used as the fitness function : Step 53: Select an operation; the specific process is as follows: From the current population, select a% individuals as parents according to their fitness values from large to small, with a ranging from 10 to 20; Step 54: Cross operation; the specific process is: Among the selected parent individuals, the crossover probability Perform crossover operations, using single-point or multi-point crossover to exchange gene segments on chromosomes, thereby generating new offspring individuals that meet the layout constraints; Step 55: Mutation operation; the specific process is: Set a certain mutation probability , randomly changing the gene positions in the chromosome; Step 56: Repeat steps 53 to 55. When the termination condition is met, the current optimal chromosome is output as the final heterogeneous RSU layout plan and .

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