Road safety early warning system based on vehicle-road cooperation

Abstract

The invention discloses a road safety early warning system based on vehicle-road cooperation, and the system comprises a data collection module which is used for forming a standardized driving environment data set; the multi-dimensional environment feature construction module is used for extracting a driving environment feature set with key influence on driving safety through a feature selection method; the quantum genetic variation optimization module is used for generating an optimized driving environment feature set; the multi-target driving early warning optimization module is used for solving the multi-target driving early warning optimization model to obtain an optimal driving early warning strategy; and the early warning information transmission module is used for transmitting the optimal driving early warning strategy to a vehicle control system or a driver terminal in real time in a multi-node and multi-path data transmission mode so as to realize vehicle-road cooperative road safety early warning. According to the invention, real-time optimization and adjustment can be carried out according to the traffic flow state and the driving behavior, so that the early warning system can quickly respond to emergencies, and excessive interference to a driver can be avoided.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a highway safety warning system based on vehicle-road collaboration. Background Art

[0002] In recent years, with the development of intelligent transportation technology, vehicle-road cooperative technology has become an important means to improve highway safety. Through information exchange between vehicles, road infrastructure and cloud platforms, vehicle-road cooperative technology can effectively improve the efficiency of traffic flow and reduce the accident rate, especially in low visibility environments. However, the current highway safety warning system still faces many technical challenges in low visibility environments.

[0003] At present, traditional highway safety warnings mainly rely on the following methods: First, environmental perception based on fixed monitoring equipment. They can provide information on road conditions and weather conditions, but due to the limited monitoring range, it is difficult to dynamically reflect the global driving environment in real time; Second, the warning system based on single-vehicle intelligence can perceive surrounding obstacles and driving environment, but is limited by the vehicle's own sensing capabilities. The detection accuracy and reliability are greatly reduced in low-visibility environments; Third, the centralized warning solution based on the traffic control center relies on the data collected by the monitoring center for global analysis, and transmits information to the driver through broadcasting and dynamic display screens, but there are problems with delayed data transmission and insufficient personalization, making it difficult to adapt to sudden changes in the driving environment in real time.

[0004] Traditional technologies have significant defects in practical applications: First, information lag: Due to the centralized processing mode of the traffic control center, there is a time delay in the generation and transmission of warning signals, which cannot meet the needs of rapid response; Second, poor environmental adaptability: The warning capability of a single vehicle is limited by the perception range and environmental adaptability of the on-board sensors, and its performance is greatly reduced in severe weather; Third, insufficient global optimization capabilities: Existing warning systems based on fixed monitoring equipment or single-vehicle intelligence are difficult to comprehensively consider the overall traffic flow, vehicle status and environmental changes of the road, resulting in a single warning strategy and a lack of optimization and adjustment capabilities. In addition, existing technologies often lack intelligent feature extraction and optimization strategies, which makes it difficult to improve the accuracy and reliability of warning strategies.

[0005] Therefore, the existing technology has great limitations in driving safety warning in low visibility environments. There is an urgent need for a technical solution that combines multi-source environmental data, optimizes driving warning strategies and can transmit warning information in real time to improve highway driving safety. Summary of the invention

[0006] One purpose of the present invention is to propose a highway safety warning system based on vehicle-road collaboration. The present invention can perform real-time optimization and adjustment according to traffic flow status and driving behavior to ensure that the warning system can not only respond quickly to emergencies but also avoid excessive interference to drivers.

[0007] A highway safety warning system based on vehicle-road collaboration according to an embodiment of the present invention includes the following modules: A data acquisition module is used to collect a driving environment data set in a low visibility environment, and perform time synchronization, noise filtering and standardization on the driving environment data set to form a standardized driving environment data set; A multi-dimensional environmental feature construction module, used to construct a multi-dimensional environmental feature model based on the standardized driving environment data set, and extract a set of driving environment features that have a key impact on driving safety through a feature selection method; A quantum genetic mutation optimization module, used to use a quantum genetic mutation algorithm to perform global search and optimization on the driving environment feature set to generate an optimized driving environment feature set; A multi-objective traffic warning optimization module is used to construct a multi-objective traffic warning optimization model based on the optimized traffic environment feature set, solve the multi-objective traffic warning optimization model, and obtain the optimal traffic warning strategy; The warning information transmission module is used to transmit the optimal driving warning strategy to the vehicle control system or the driver terminal in real time in a multi-node, multi-path data transmission mode, so as to realize highway safety warning of vehicle-road collaboration.

[0008] The present invention also discloses a highway safety early warning method based on vehicle-road collaboration, which is applied to a highway safety early warning system based on vehicle-road collaboration, and includes the following steps: S1. Collect a driving environment data set in a low visibility environment, perform time synchronization, noise filtering and standardization on the driving environment data set to form a standardized driving environment data set; S2. Based on the standardized driving environment data set, a multidimensional environmental feature model is constructed, and a set of driving environment features that have a key impact on driving safety is extracted through a feature selection method; S3. Use a quantum genetic mutation algorithm to perform a global search and optimization on the driving environment feature set to obtain an optimized driving environment feature set; S4. Based on the optimized driving environment feature set, construct a multi-objective driving warning optimization model; S5. Solve the multi-objective traffic warning optimization model to obtain the optimal traffic warning strategy; S6. The optimal driving warning strategy is transmitted in real time to the vehicle control system or the driver terminal in a multi-node, multi-path data transmission manner to realize vehicle-road collaborative highway safety warning.

[0009] Optionally, the S1 includes the following steps: S11. Collecting driving environment data sets in low visibility environments : in, Indicates Driving environment data, is the total amount of driving environment data collected, is the timestamp corresponding to the driving environment data, is the numerical information of driving environment data, It is the device identifier of the source of driving environment data. Category labels for driving environment data, including vehicle dynamic data collected by on-board sensors, road condition and environment data collected by road infrastructure, meteorological data collected by meteorological equipment, and traffic flow data collected by traffic monitoring systems; S12. Time synchronization is performed on the driving environment data set to construct a unified time reference, and the time deviation of each data source is corrected by interpolation and time alignment methods to obtain a time-synchronized driving environment data set; S13. According to the category labels of the driving environment data in the driving environment data set, the vehicle dynamic data, road condition and environment data, meteorological data and traffic flow data are noise filtered respectively, and an adaptive filtering method is used to remove outliers to obtain a driving environment data set after noise filtering; S14. Use the standardization method to normalize the driving environment data set after noise filtering, unify the numerical scales of various types of driving environment data, and obtain the standardized driving environment data set ; S15. Remove redundant features from the standardized driving environment data set based on the correlation analysis of the driving environment data, calculate the driving environment data correlation matrix, which is used to quantify the correlation between the driving environment data, and obtain the final standardized driving environment data set : in, The final standardized Driving environment data, Represents the adjusted standard timestamp, Represents the numerical information of the driving environment data after removing redundant features.

[0010] Optionally, S2 includes the following steps: S21. Final standardized driving environment dataset Each piece of driving environment data Perform feature extraction and construct multi-dimensional environment feature vector : ;in, Indicates The meteorological features extracted from the driving environment data are Indicates The road conditions and environmental features extracted from the driving environment data are Indicates Traffic flow features extracted from driving environment data, Indicates Vehicle dynamic features extracted from driving environment data; S22. Constructing a multi-dimensional environmental feature model ;

[0011] S23. Using feature selection method to select the multidimensional environment feature model The multi-dimensional environmental feature vectors in the image are processed to extract the driving environment feature set that has a key impact on driving safety. : in, is the kth key feature selected from the multidimensional environmental feature model, Represents a collection of key feature indexes.

[0012] Optionally, S3 includes the following steps: S31. Based on driving environment feature set Encode quantum superposition states and construct characteristic populations of quantum environments , each quantum individual in the quantum environment feature population represents a combination of driving environment features: ;in, is the quantum population size, Indicates quantum individuals, including the combination of driving environment characteristics in low visibility environments, and All are Among the quantum individuals The quantum amplitude coefficient corresponding to each key feature; S32. Characteristic population of quantum environment Perform quantum measurements to obtain the individual set of classical driving environment characteristics in, After measurement, the Individual driving environment characteristics, It is the feature index set selected after measurement; For each driving environment feature individual Calculate fitness value : in, Based on the driving environment characteristics The calculated warning response time, The shortest warning response time is Driving environment characteristic individuals The accuracy of early warning Driving environment characteristic individuals The calculation results of the road environment complexity are as follows: It is a dynamic weight parameter, which is adjusted according to the current low-visibility environmental characteristics to improve the warning optimization effect under different environmental conditions; S33. Based on driving dynamic perception, quantum rotation update rules are constructed, and the search direction of quantum individuals is dynamically adjusted and updated in combination with the road information flow provided by the vehicle-road cooperative platform: ;in, Indicates the t+1th algebra A quantum entity, represents the tth algebra A quantum entity, It is an adaptive quantum revolving door based on vehicle-road cooperative environmental factors. is the step size control parameter, is the random perturbation factor of the Lévy distribution, is the quantum state difference between the current quantum individual and the optimal individual in the population, which is used to enhance the search capability. The calculation is as follows: in, is the quantum rotation step control parameter, represents the low visibility environment impact factor, is the maximum low visibility environmental impact factor, is the complexity of the road environment, is the maximum road complexity, The strength of road monitoring information provided to the vehicle-road collaboration platform, is the optimal fitness value in the current population; S34. Determine whether the quantum population converges based on the optimization convergence criterion. If the convergence condition is met, output the final optimized driving environment feature set. , otherwise return to step S32 to continue iterative optimization.

[0013] Optionally, the S34 includes the following steps: S341. Calculate the fitness mean of the current population based on the quantum environment characteristic population and fitness value and the mean fitness of the previous generation , and calculate the fitness change rate : in, is the quantum population size, For the Driving environment characteristics In the The fitness value of the generation; S342. Define the quantum population diversity metric function , calculate the population mean square error based on the amplitude vector of the current generation of quantum individuals: in, The characteristic dimension encoded for the quantum entity, and All are Among the quantum individuals The amplitude parameter of the feature dimension, and are the amplitude mean values ​​of all quantum individuals in this characteristic dimension, Reflects the diversity of the quantum population. The smaller the value, the more the population tends to converge. S343. According to the fitness change rate and the quantum population diversity measurement function, the convergence judgment parameter of the quantum population is calculated: in, and Determine the weight parameter for convergence; S344. Define optimization termination threshold , when the following conditions are met, the quantum population is judged to have reached the optimal convergence state: Otherwise, return to step S341 to continue optimization iteration; S345. When the optimization convergence conditions are met, select the individual with the highest fitness from the current quantum population As the final optimized driving environment feature set: ;in, In the tth optimization iteration, the qth driving environment characteristic individual in the population, It represents the complete population of individual driving environment characteristics in the tth optimization iteration, and argmax is the fitness function to be found.

[0014] Optionally, S4 includes the following steps: S41. Based on driving environment feature set , define a multi-objective traffic warning optimization model The optimization objectives of the multi-objective traffic warning optimization model include minimizing the warning time, maximizing the warning accuracy, and optimizing driving comfort. The objective function of the multi-objective traffic warning optimization model is defined as follows: in, For the traffic warning strategy to be optimized, is the warning time of the driving warning strategy, is the warning accuracy of the driving warning strategy, is the interference degree of driving warning strategy on the driver, is the multi-objective weight parameter; S42. Define the warning time minimization target and the warning time of the driving warning strategy Affected by the driving environment feature set and low visibility environment influencing factors: in, Indicates the detectable distance in the current low visibility environment. Indicates the transmission speed of the traffic warning signal. Processing time for the vehicle control system; S43. Define the goal of maximizing the early warning accuracy. Affected by the driving environment feature set and the road monitoring information provided by the vehicle-road collaboration platform: in, Indicates the number of false positives or false negatives in the current driving environment. is the total number of warnings; S44. Define driving comfort optimization goals and driving comfort interference levels Affected by the warning frequency and the driver's warning response time: in, Indicates the number of warnings triggered per unit time. Indicates the average driver reaction time to warning signals; S45. Build an adaptive weight adjustment mechanism and define dynamic weight adjustment parameters Adapt dynamic weighting parameters to different low-visibility environments: in, is the optimized weight parameter of the t+1th generation, is the weight parameter of the tth generation, is the learning rate, is the low visibility environment impact factor, is the maximum low visibility environmental impact factor, The strength of road monitoring information provided to the vehicle-road collaboration platform, It is the maximum road monitoring information intensity.

[0015] Optionally, S3 includes the following steps: S31. Based on driving environment feature set Encode quantum superposition states and construct characteristic populations of quantum environments , each quantum individual in the quantum environment feature population represents a combination of driving environment features: ;in, is the quantum population size, Indicates quantum individuals, including the combination of driving environment characteristics in low visibility environments, and All are Among the quantum individuals The quantum amplitude coefficient corresponding to each key feature; S32. Characteristic population of quantum environment Perform quantum measurements to obtain the individual set of classical driving environment characteristics in, After measurement, the Individual driving environment characteristics, It is the feature index set selected after measurement; For each driving environment feature individual Calculate fitness value : in, Based on the driving environment characteristics The calculated warning response time, The shortest warning response time is Driving environment characteristic individuals The warning accuracy rate Driving environment characteristic individuals The calculation results of the road environment complexity are as follows: It is a dynamic weight parameter, which is adjusted according to the current low visibility environment characteristics to improve the warning optimization effect under different environmental conditions; S33. Based on driving dynamic perception, quantum rotation update rules are constructed, and the search direction of quantum individuals is dynamically adjusted and updated in combination with the road information flow provided by the vehicle-road cooperative platform: ;in, Indicates the t+1th algebra A quantum entity, represents the tth algebra A quantum entity, It is an adaptive quantum revolving door based on vehicle-road cooperative environmental factors. is the step size control parameter, is the random perturbation factor of the Lévy distribution, is the quantum state difference between the current quantum individual and the optimal individual in the population, which is used to enhance the search capability. The calculation is as follows: in, is the quantum rotation step control parameter, represents the low visibility environment impact factor, is the maximum low visibility environmental impact factor, is the complexity of the road environment, is the maximum road complexity, The strength of road monitoring information provided to the vehicle-road collaboration platform, is the optimal fitness value in the current population; S34. Determine whether the quantum population converges based on the optimization convergence criterion. If the convergence condition is met, output the final optimized driving environment feature set. , otherwise return to step S32 to continue iterative optimization.

[0016] Optionally, the S34 includes the following steps: S341. Calculate the fitness mean of the current population based on the quantum environment characteristic population and fitness value and the mean fitness of the previous generation , and calculate the fitness change rate : in, is the quantum population size, For the Driving environment characteristics In the The fitness value of the generation; S342. Define the quantum population diversity metric function , calculate the population mean square error based on the amplitude vector of the current generation of quantum individuals: in, The characteristic dimension encoded for the quantum entity, and All are Among the quantum individuals The amplitude parameter of the feature dimension, and are the amplitude mean values ​​of all quantum individuals in this characteristic dimension, Reflects the diversity of the quantum population. The smaller the value, the more the population tends to converge. S343. According to the fitness change rate and the quantum population diversity measurement function, the convergence judgment parameter of the quantum population is calculated: in, and Determine the weight parameter for convergence; S344. Define optimization termination threshold , when the following conditions are met, the quantum population is judged to have reached the optimal convergence state: Otherwise, return to step S341 to continue optimization iteration; S345. When the optimization convergence conditions are met, select the individual with the highest fitness from the current quantum population As the final optimized driving environment feature set: ;in, In the tth optimization iteration, the qth driving environment characteristic individual in the population, It represents the complete population of individual driving environment characteristics in the tth optimization iteration, and argmax is the fitness function to be found.

[0017] Optionally, S4 includes the following steps: S41. Based on driving environment feature set , define a multi-objective traffic warning optimization model The optimization objectives of the multi-objective traffic warning optimization model include minimizing the warning time, maximizing the warning accuracy, and optimizing driving comfort. The objective function of the multi-objective traffic warning optimization model is defined as follows: in, For the traffic warning strategy to be optimized, is the warning time of the driving warning strategy, is the warning accuracy of the driving warning strategy, is the interference degree of driving warning strategy on the driver, is the multi-objective weight parameter; S42. Define the warning time minimization target and the warning time of the driving warning strategy Affected by the driving environment feature set and low visibility environment influencing factors: in, Indicates the detectable distance in the current low visibility environment. Indicates the transmission speed of the traffic warning signal. Processing time for the vehicle control system; S43. Define the goal of maximizing the early warning accuracy. Affected by the driving environment feature set and the road monitoring information provided by the vehicle-road collaboration platform: in, Indicates the number of false positives or false negatives in the current driving environment. is the total number of warnings; S44. Define driving comfort optimization goals and driving comfort interference levels Affected by the warning frequency and the driver's warning response time: in, Indicates the number of warnings triggered per unit time. Indicates the average driver reaction time to warning signals; S45. Build an adaptive weight adjustment mechanism and define dynamic weight adjustment parameters Adapt dynamic weighting parameters to different low-visibility environments: in, is the optimized weight parameter of the t+1th generation, is the weight parameter of the tth generation, is the learning rate, is the low visibility environment impact factor, is the maximum low visibility environmental impact factor, The strength of road monitoring information provided to the vehicle-road collaboration platform, It is the maximum road monitoring information intensity.

[0018] The beneficial effects of the present invention are: The present invention uses a quantum genetic mutation algorithm to perform global search and optimization on the driving environment feature set. Through quantum superposition state encoding and quantum rotation update rules, it can efficiently explore the optimal driving environment feature combination in a multi-dimensional feature space. By introducing the probabilistic search mechanism of quantum computing, quantum individuals can simultaneously explore multiple feature combinations and dynamically adjust the search direction, thereby ensuring the global optimality of the optimization result. The present invention proposes a multi-objective traffic warning optimization model. The multi-objective traffic warning optimization model comprehensively considers the three core indicators of minimizing warning time, maximizing warning accuracy and optimizing driving comfort, and achieves dynamic balance through an adaptive weight adjustment mechanism. By dynamically adjusting the multi-objective weight parameters, real-time optimization and adjustment can be made according to traffic flow status and driving behavior in low visibility environments, ensuring that the warning system can both respond quickly to emergencies and avoid excessive interference to drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a highway safety warning system based on vehicle-road collaboration proposed by the present invention. DETAILED DESCRIPTION

[0020] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0021] refer to Figure 1 , a highway safety warning system based on vehicle-road collaboration, including the following modules: A data acquisition module is used to collect a driving environment data set in a low visibility environment, and perform time synchronization, noise filtering and standardization on the driving environment data set to form a standardized driving environment data set; A multi-dimensional environmental feature construction module, used to construct a multi-dimensional environmental feature model based on the standardized driving environment data set, and extract a set of driving environment features that have a key impact on driving safety through a feature selection method; A quantum genetic mutation optimization module, used to use a quantum genetic mutation algorithm to perform global search and optimization on the driving environment feature set to generate an optimized driving environment feature set; A multi-objective traffic warning optimization module is used to construct a multi-objective traffic warning optimization model based on the optimized traffic environment feature set, solve the multi-objective traffic warning optimization model, and obtain the optimal traffic warning strategy; The warning information transmission module is used to transmit the optimal driving warning strategy to the vehicle control system or the driver terminal in real time in a multi-node, multi-path data transmission mode, so as to realize highway safety warning of vehicle-road collaboration.

[0022] A highway safety early warning method based on vehicle-road collaboration is applied to a highway safety early warning system based on vehicle-road collaboration, comprising the following steps: S1. Collect a driving environment data set in a low visibility environment, perform time synchronization, noise filtering and standardization on the driving environment data set to form a standardized driving environment data set; S2. Based on the standardized driving environment data set, a multidimensional environmental feature model is constructed, and a set of driving environment features that have a key impact on driving safety is extracted through feature selection methods; S3. Use the quantum genetic mutation algorithm to perform global search and optimization on the driving environment feature set to obtain an optimized driving environment feature set; S4. Based on the optimized driving environment feature set, a multi-objective driving warning optimization model is constructed; S5. Based on the optimized driving environment feature set, the system builds a multi-objective driving warning optimization model, whose core goal is to minimize the warning time , maximize the accuracy of early warning , while reducing driver distraction The system adopts an adaptive weight adjustment mechanism to dynamically adjust and optimize target weights based on real-time traffic flow information and environmental conditions. , through the heuristic solution algorithm, a global search is performed in the multi-objective optimization space to generate the optimal early warning strategy ,This process ensures that under different visibility conditions and traffic density, the warning scheme can respond quickly and avoid excessive alarms, thus improving the stability and adaptability of the overall warning system; S6. The system will solve the optimal early warning strategy Through multi-node and multi-path data transmission strategies, data is distributed to the vehicle control system and driver terminal in real time. The system uses V2X vehicle networking communication to ensure that data can reach the target vehicle quickly and reliably, and triggers warning instructions within 0.1 seconds. If the vehicle is in automatic driving mode, the system directly sends speed limit, lane change or brake signals to the vehicle controller; if it is in manual driving mode, the driver terminal will remind the driver to take corresponding actions in the form of voice alarm + HUD head-up display. This strategy ensures low latency and high reliability, effectively reducing accidents in low visibility environments.

[0023] In this implementation, S1 includes the following steps: S11. Collecting driving environment data sets in low visibility environments :

[0024] in, Indicates Driving environment data, is the total amount of driving environment data collected, is the timestamp corresponding to the driving environment data, is the numerical information of driving environment data, It is the device identifier of the source of driving environment data. Category labels for driving environment data, including vehicle dynamic data collected by on-board sensors, road condition and environment data collected by road infrastructure, meteorological data collected by meteorological equipment, and traffic flow data collected by traffic monitoring systems; S12. Perform time synchronization on the driving environment dataset, build a unified time reference, correct the time deviation of each data source by interpolation and time alignment methods, and obtain a time-synchronized driving environment dataset; S13. According to the category labels of the driving environment data in the driving environment data set, the vehicle dynamic data, road condition and environment data, meteorological data and traffic flow data are noise filtered respectively, and an adaptive filtering method is used to remove outliers to obtain a driving environment data set after noise filtering; S14. Use the standardization method to normalize the driving environment data set after noise filtering, unify the numerical scales of various types of driving environment data, and obtain the standardized driving environment data set ; S15. Remove redundant features from the standardized driving environment data set based on the correlation analysis of the driving environment data, calculate the driving environment data correlation matrix, which is used to quantify the correlation between the driving environment data, and obtain the final standardized driving environment data set :

[0025] in, The final standardized Driving environment data, Represents the adjusted standard timestamp, Represents the numerical information of the driving environment data after removing redundant features.

[0026] In this embodiment, step S2 includes the following steps: S21. Final standardized driving environment dataset Each piece of driving environment data Perform feature extraction and construct multi-dimensional environment feature vector :

[0027] in, Indicates The meteorological features extracted from the driving environment data are Indicates The road conditions and environmental features extracted from the driving environment data are Indicates Traffic flow features extracted from driving environment data, Indicates Vehicle dynamic features extracted from driving environment data; S22. Constructing a multi-dimensional environmental feature model

[0028]

[0029] S23. Using feature selection methods to model multidimensional environmental features The multi-dimensional environmental feature vectors in the image are processed to extract the driving environment feature set that has a key impact on driving safety. :

[0030] in, is the kth key feature selected from the multidimensional environmental feature model, Represents a collection of key feature indexes.

[0031] In this implementation, the S3 step includes the following steps: S31. Based on driving environment feature set Encode quantum superposition states and construct characteristic populations of quantum environments , each quantum individual in the quantum environment feature population represents a combination of driving environment features: ;in, is the quantum population size, Indicates quantum individuals, including the combination of driving environment characteristics in low visibility environments, and All are Among the quantum individuals The quantum amplitude coefficient corresponding to each key feature; S32. Characteristic population of quantum environment Perform quantum measurements to obtain the individual set of classical driving environment characteristics in, After measurement, the Individual driving environment characteristics, It is the feature index set selected after measurement; For each driving environment feature individual Calculate fitness value : in, Based on the driving environment characteristics The calculated warning response time, The shortest warning response time is Driving environment characteristic individuals The accuracy of early warning Driving environment characteristic individuals The calculation results of the road environment complexity are as follows: It is a dynamic weight parameter, which is adjusted according to the current low-visibility environmental characteristics to improve the warning optimization effect under different environmental conditions; S33. Based on driving dynamic perception, quantum rotation update rules are constructed, and the search direction of quantum individuals is dynamically adjusted and updated in combination with the road information flow provided by the vehicle-road cooperative platform: ;in, Indicates the t+1th algebra A quantum entity, represents the tth algebra A quantum entity, It is an adaptive quantum revolving door based on vehicle-road cooperative environmental factors. is the step size control parameter, is the random perturbation factor of the Lévy distribution, is the quantum state difference between the current quantum individual and the optimal individual in the population, which is used to enhance the search capability. The calculation is as follows: in, is the quantum rotation step control parameter, represents the low visibility environment impact factor, is the maximum low visibility environmental impact factor, is the complexity of the road environment, is the maximum road complexity, The strength of road monitoring information provided to the vehicle-road collaboration platform, is the optimal fitness value in the current population; S34. Determine whether the quantum population converges based on the optimization convergence criterion. If the convergence condition is met, output the final optimized driving environment feature set. , otherwise return to step S32 to continue iterative optimization.

[0032] In this embodiment, step S34 includes the following steps: S341. Calculate the fitness mean of the current population based on the quantum environment characteristic population and fitness value and the mean fitness of the previous generation , and calculate the fitness change rate : in, is the quantum population size, For the Driving environment characteristics In the The fitness value of the generation; Practical significance: Mean fitness What is calculated is the overall fitness of the current quantum population, which measures the overall performance of the population in this round of optimization. The higher the fitness, the better the quality of the optimized early warning strategy.

[0033] Fitness change rate It reflects the degree of improvement of the current round of optimization compared with the previous round of optimization: if It is still large, indicating that the optimization is still in progress and the algorithm has not converged, so iterative optimization should continue.

[0034] if When it is close to 0, it means that the optimization is stable and the optimization can be terminated.

[0035] Practical application scenarios: In a certain optimization process, the initial fitness mean is , after 3 rounds of optimization, it was improved to , , indicating that the optimization is still valid and the algorithm continues to execute.

[0036] When the optimization reaches the 10th round, It is only 0.5%, which means that the optimization has been basically completed, the system has entered the convergence state, and finally the optimal driving warning strategy is selected.

[0037] S342. Define the quantum population diversity metric function , calculate the population mean square error based on the amplitude vector of the current generation of quantum individuals: in, The characteristic dimension encoded for the quantum entity, and All are Among the quantum individuals The amplitude parameter of the feature dimension, and are the amplitude mean values ​​of all quantum individuals in this characteristic dimension, Reflects the diversity of the quantum population. The smaller the value, the more the population tends to converge. Practical significance: Quantum population diversity It reflects the current distribution of individuals in the population: if If it is large, it means that the individuals in the population are still relatively scattered, the optimization is still in the exploration stage, and the optimization search can continue.

[0038] if When it approaches 0, it means that the population has converged to a certain optimal solution, and the optimization can be terminated at this time.

[0039] Practical application scenarios: In the early warning optimization process, the initial (The population characteristics vary greatly), indicating that the search space is still extensive; After multiple rounds of optimization, , indicating that the optimization strategy of the population has stabilized, the algorithm terminates, and the optimal early warning strategy is output.

[0040] S343. According to the fitness change rate and the quantum population diversity measurement function, the convergence judgment parameter of the quantum population is calculated: in, and Determine the weight parameter for convergence; S344. Define optimization termination threshold , when the following conditions are met, the quantum population is judged to have reached the optimal convergence state: Otherwise, return to step S341 to continue optimization iteration; S345. When the optimization convergence conditions are met, select the individual with the highest fitness from the current quantum population As the final optimized driving environment feature set: ;in, In the tth optimization iteration, the qth driving environment characteristic individual in the population, It represents the complete population of individual driving environment characteristics in the tth optimization iteration, and argmax is the fitness function to be found.

[0041] In this implementation, step S4 includes the following steps: S41. Based on driving environment feature set , define a multi-objective traffic warning optimization model The optimization objectives of the multi-objective traffic warning optimization model include minimizing the warning time, maximizing the warning accuracy, and optimizing driving comfort. The objective function of the multi-objective traffic warning optimization model is defined as follows: in, For the driving warning strategy to be optimized, is the warning time of the driving warning strategy, is the warning accuracy of the driving warning strategy, is the interference degree of driving warning strategy on the driver, is the multi-objective weight parameter; Practical significance: The optimization model takes into account three objectives: : Warning time (goal: minimize), : Early warning accuracy (goal: maximize), : Driving interference level (goal: minimize) Dynamic weight adjustment mechanism Enables the system to adjust and optimize direction according to the current road environment: If the current haze is severe (low visibility), the system will increase the weight, making the system pay more attention to the accuracy of warning; If the current traffic conditions are relatively stable, the system will reduce , reducing interference to the driver.

[0042] Practical application scenarios: In a certain warning optimization, the traditional method only focuses on the warning time, which may lead to excessively frequent alarms and make the driver tired. However, the present invention shortens the warning time and improves the accuracy through multi-objective optimization, while reducing driving interference, thereby improving the overall driving experience.

[0043] S42. Define the warning time minimization target and the warning time of the driving warning strategy Affected by the driving environment feature set and low visibility environment influencing factors: in, Indicates the detectable distance in the current low visibility environment. Indicates the transmission speed of the traffic warning signal. Processing time for the vehicle control system; S43. Define the goal of maximizing the early warning accuracy. Affected by the driving environment feature set and the road monitoring information provided by the vehicle-road collaboration platform: in, Indicates the number of false positives or false negatives in the current driving environment. is the total number of warnings; S44. Define driving comfort optimization goals and driving comfort interference levels Affected by the warning frequency and the driver's warning response time: in, Indicates the number of warnings triggered per unit time. Indicates the average driver reaction time to warning signals; S45. Build an adaptive weight adjustment mechanism and define dynamic weight adjustment parameters Adapt dynamic weighting parameters to different low-visibility environments: in, is the optimized weight parameter of the t+1th generation, is the weight parameter of the tth generation, is the learning rate, is the low visibility environment impact factor, is the maximum low visibility environmental impact factor, The strength of road monitoring information provided to the vehicle-road collaboration platform, It is the maximum road monitoring information intensity.

[0044] Practical significance: Automatically adjust weights according to the environment: When the low visibility environment is severe ( ),but , at this time, the weight is reduced, and the system pays more attention to dynamic adjustment capabilities; when the road monitoring information is insufficient ( ),but , at this time, the system enhances the optimization weight and improves the adaptive ability.

[0045] This mechanism enables the system to proactively increase its focus on safety warnings and improve the accuracy of alarms in bad weather. When monitoring data is more complete, it reduces unnecessary adjustments, reduces false alarms, and improves the driving experience.

[0046] Practical application scenarios: On a certain section of mountain highway, dense fog caused low visibility ( ), while the road monitoring capability is low ( ), the system adjusts the weights through this formula, so that the early warning system focuses more on high accuracy and stability, reduces false alarms, and effectively reduces the risk of accidents.

[0047] Embodiment 1: At 4 a.m. on XX / XX / XXXX, visibility on a mountainous section of the G72 National Highway dropped sharply to below 30 meters due to sudden fog, causing many heavy trucks to slow down. The section was on a long downhill section. During the winter of the past three years, there had been six serious chain rear-end collisions there, involving more than 50 vehicles, causing significant property losses and casualties. The local traffic management department had installed traditional fixed monitoring equipment and high-speed cameras on the section, but the high-speed cameras were severely affected by the fog and had limited monitoring capabilities, so traditional methods were unable to provide timely and effective safety warnings.

[0048] At 4:12 a.m., the G72 Expressway Vehicle-Road Collaborative Highway Safety Warning System was activated. The system collects real-time driving environment data in low-visibility environments through vehicle sensors, road infrastructure sensors, and traffic flow monitoring systems, including: Meteorological information: fog concentration 92%, air humidity 97%, wind speed 5.3m / s; vehicle information: a heavy truck (license plate number "AA") was traveling at a speed of 68km / h, and the distance to the vehicle in front was shortened to 18 meters (the system default safe distance should be more than 45 meters); road information: the road surface in this area is slippery, and the road friction coefficient is reduced to 0.42 (normal is 0.65).

[0049] At 4:13 a.m., the system detected that a heavy truck with license plate number "AA" failed to reduce its speed in a low-visibility environment, and the distance to the vehicle in front was shortened sharply, posing a potential risk of rear-end collision. The system optimized the calculation through the quantum genetic mutation algorithm, conducted a comprehensive analysis of the current road conditions and driving data, and generated an early warning report: Warning time: 04:13:27, December 15, 2024; Warning type: Risk of high-speed rear-end collision in low-visibility environment; Trigger reason: The current vehicle distance is lower than the safety threshold and the weather conditions are severe; Recommended measures: It is recommended that the vehicle slow down to 40km / h and maintain a safe distance of at least 50 meters; Driver warning level: Level 3 (medium risk).

[0050] At 4:13:30 in the morning, the system sent the warning information to the driver's smart terminal of Guangdong B8 truck in real time through a multi-node and multi-path data transmission strategy, and pushed it to the on-board terminals of all connected vehicles in the area, and synchronized it to the highway control center. The system also automatically updated the information on the smart electronic traffic sign 2 kilometers ahead: "Heavy fog ahead, the distance between vehicles is too close, please slow down to 40km / h."

[0051] At 4:14 a.m., the driver of the truck with license plate number "AA" received a warning from the on-board terminal and confirmed that there was severe fog ahead. He then took measures to slow down to 42 km / h, while gradually increasing the distance between the vehicle and the vehicle in front.

[0052] At 4:14:50 in the morning, an SUV (license plate number "BB") behind the truck also received the warning message pushed by the system. It actively reduced its speed from 72km / h to 50km / h at a distance of 45 meters behind the truck and adjusted the cruise control mode.

[0053] At 4:15 a.m., the highway control center confirmed that the overall traffic on the section had slowed down, avoiding a series of rear-end collisions caused by low visibility. The system maintained real-time monitoring throughout the process and continued to push road safety status information.

[0054] In order to verify the effectiveness of the present invention, a two-month measured data comparison experiment was carried out on this road section. The experiment was divided into a "traditional method group" and a "method group of the present invention". The key comparison indicators included warning response time, accident rate, false alarm rate, and driver response rate.

[0055] Table 1 Comparison of key indicators between the method group of the present invention and the traditional method group Test indicators Traditional method (fixed monitoring + single vehicle intelligence) The method of the present invention (vehicle-road collaborative warning) Warning response time (seconds) 5.8 3.9 Accident rate (times / month) 8.2 2.7 False alarm rate (%) 12.5 6.8 Driver response rate (%) 78.3 94.6 System overall warning accuracy (%) 83.4 92.8 The test results in Table 1 show that the vehicle-road cooperative highway safety warning system of the present invention shortens the warning response time by 27.3% in low visibility environments, reduces the accident rate by 67.1%, reduces the false alarm rate by 45.6%, and increases the driver response rate by 16.3%, thereby greatly improving highway driving safety.

[0056] This embodiment fully demonstrates the effectiveness of the quantum genetic variation optimization algorithm, multi-objective traffic warning optimization model, and multi-node data transmission strategy technology of the present invention in actual highway safety warning. Through real-time data perception, intelligent analysis and optimization calculation of vehicle-road collaboration, the present invention can provide driving safety warnings more quickly and accurately than traditional methods, reduce traffic accidents in low-visibility environments, and provide a safer and more efficient solution for intelligent traffic management.

[0057] The present invention adopts a quantum genetic mutation algorithm to perform global search and optimization on the driving environment feature set. Through quantum superposition state encoding and quantum rotation update rules, it can efficiently explore the optimal driving environment feature combination in a multi-dimensional feature space. By introducing the probabilistic search mechanism of quantum computing, quantum individuals can explore multiple feature combinations at the same time and dynamically adjust the search direction, thereby ensuring the global optimality of the optimization results.

[0058] The present invention proposes a multi-objective traffic warning optimization model. The multi-objective traffic warning optimization model comprehensively considers the three core indicators of minimizing warning time, maximizing warning accuracy and optimizing driving comfort, and achieves dynamic balance through an adaptive weight adjustment mechanism. By dynamically adjusting the multi-objective weight parameters, real-time optimization and adjustment can be made according to traffic flow status and driving behavior in low visibility environments, ensuring that the warning system can both respond quickly to emergencies and avoid excessive interference to drivers.

[0059] The present invention adopts a multi-node and multi-path data transmission strategy in the warning information transmission link, which can effectively reduce network congestion and data loss problems, and improve the transmission efficiency and reliability of warning information. In the vehicle-road cooperative system, the timeliness of warning information is crucial. By constructing an adaptive routing optimization mechanism, the optimal transmission path is dynamically selected according to the road information flow provided by the vehicle-road cooperative platform, and data redundancy backup is achieved among multiple paths, ensuring that the warning information can be transmitted to the driver terminal or vehicle control system with low latency and high reliability.

[0060] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A highway safety warning system based on vehicle-road collaboration, characterized in that: Includes the following modules: A data acquisition module is used to collect a driving environment data set in a low visibility environment, and perform time synchronization, noise filtering and standardization on the driving environment data set to form a standardized driving environment data set; A multi-dimensional environmental feature construction module, used to construct a multi-dimensional environmental feature model based on the standardized driving environment data set, and extract a set of driving environment features that have a key impact on driving safety through a feature selection method; A quantum genetic mutation optimization module, used to use a quantum genetic mutation algorithm to perform global search and optimization on the driving environment feature set to generate an optimized driving environment feature set; A multi-objective traffic warning optimization module is used to construct a multi-objective traffic warning optimization model based on the optimized traffic environment feature set, solve the multi-objective traffic warning optimization model, and obtain the optimal traffic warning strategy; The warning information transmission module is used to transmit the optimal driving warning strategy to the vehicle control system or the driver terminal in real time in a multi-node, multi-path data transmission mode, so as to realize highway safety warning of vehicle-road collaboration.

2. A highway safety warning method based on vehicle-road collaboration, applied to a highway safety warning system based on vehicle-road collaboration as claimed in claim 1, characterized in that: The following steps are included: S1. Collect a driving environment data set in a low visibility environment, perform time synchronization, noise filtering and standardization on the driving environment data set to form a standardized driving environment data set; S2. Based on the standardized driving environment data set, a multidimensional environmental feature model is constructed, and a set of driving environment features that have a key impact on driving safety is extracted through a feature selection method; S3. Use a quantum genetic mutation algorithm to perform a global search and optimization on the driving environment feature set to obtain an optimized driving environment feature set; S4. Based on the optimized driving environment feature set, construct a multi-objective driving warning optimization model; S5. Solve the multi-objective traffic warning optimization model to obtain the optimal traffic warning strategy; S6. The optimal driving warning strategy is transmitted in real time to the vehicle control system or the driver terminal in a multi-node, multi-path data transmission manner to realize vehicle-road collaborative highway safety warning.

3. The highway safety early warning method based on vehicle-road collaboration according to claim 2 is characterized in that: The S1 comprises the following steps: S11. Collecting driving environment data sets in low visibility environments : in, Indicates Driving environment data, is the total amount of driving environment data collected, is the timestamp corresponding to the driving environment data, is the numerical information of driving environment data, It is the device identifier of the source of driving environment data. Category labels for driving environment data, including vehicle dynamic data collected by on-board sensors, road condition and environment data collected by road infrastructure, meteorological data collected by meteorological equipment, and traffic flow data collected by traffic monitoring systems; S12. Time synchronization is performed on the driving environment data set to construct a unified time reference, and the time deviation of each data source is corrected by interpolation and time alignment methods to obtain a time-synchronized driving environment data set; S13. According to the category labels of the driving environment data in the driving environment data set, the vehicle dynamic data, road condition and environment data, meteorological data and traffic flow data are noise filtered respectively, and an adaptive filtering method is used to remove outliers to obtain a driving environment data set after noise filtering; S14. Use the standardization method to normalize the driving environment data set after noise filtering, unify the numerical scales of various types of driving environment data, and obtain the standardized driving environment data set ; S15. Remove redundant features from the standardized driving environment data set based on the correlation analysis of the driving environment data, calculate the driving environment data correlation matrix, which is used to quantify the correlation between the driving environment data, and obtain the final standardized driving environment data set : in, The final standardized Driving environment data, Represents the adjusted standard timestamp, Represents the numerical information of the driving environment data after removing redundant features.

4. The highway safety early warning method based on vehicle-road collaboration according to claim 3 is characterized in that: The S2 comprises the following steps: S21. Final standardized driving environment dataset Each piece of driving environment data Perform feature extraction and construct multi-dimensional environment feature vector : ;in, Indicates The meteorological features extracted from the driving environment data are Indicates The road conditions and environmental features extracted from the driving environment data are Indicates Traffic flow features extracted from driving environment data, Indicates Vehicle dynamic features extracted from driving environment data; S22. Constructing a multidimensional environmental feature model ; S23. Using feature selection method to select the multidimensional environment feature model The multi-dimensional environmental feature vectors in the image are processed to extract the driving environment feature set that has a key impact on driving safety. : in, is the kth key feature selected from the multidimensional environmental feature model, Represents a collection of key feature indexes.

5. The highway safety early warning method based on vehicle-road collaboration according to claim 4 is characterized in that: The S3 comprises the following steps: S31. Based on driving environment feature set Encode quantum superposition states and construct characteristic populations of quantum environments , each quantum individual in the quantum environment feature population represents a combination of driving environment features: ;in, is the quantum population size, Indicates quantum individuals, including the combination of driving environment characteristics in low visibility environments, and All are Among the quantum individuals The quantum amplitude coefficient corresponding to each key feature; S32. Characteristic population of quantum environment Perform quantum measurements to obtain the individual set of classical driving environment characteristics in, After measurement, the Individual driving environment characteristics, It is the feature index set selected after measurement; For each driving environment feature individual Calculate fitness value : in, Based on the driving environment characteristics The calculated warning response time, The shortest warning response time is Driving environment characteristic individuals The warning accuracy rate Driving environment characteristic individuals The calculation results of the road environment complexity are as follows: It is a dynamic weight parameter, which is adjusted according to the current low-visibility environmental characteristics to improve the warning optimization effect under different environmental conditions; S33. Based on driving dynamic perception, quantum rotation update rules are constructed, and the search direction of quantum individuals is dynamically adjusted and updated in combination with the road information flow provided by the vehicle-road cooperative platform: ;in, represents the t+1th algebra A quantum entity, represents the tth algebra A quantum entity, It is an adaptive quantum revolving door based on vehicle-road cooperative environmental factors. is the step size control parameter, is the random perturbation factor of the Lévy distribution, is the quantum state difference between the current quantum individual and the optimal individual in the population, which is used to enhance the search capability. The calculation is as follows: in, is the quantum rotation step control parameter, represents the low visibility environment impact factor, is the maximum low visibility environmental impact factor, is the complexity of the road environment, is the maximum road complexity, The strength of road monitoring information provided to the vehicle-road collaboration platform, is the optimal fitness value in the current population; S34. Determine whether the quantum population converges based on the optimization convergence criterion. If the convergence condition is met, output the final optimized driving environment feature set. , otherwise return to step S32 to continue iterative optimization.

6. A highway safety early warning method based on vehicle-road collaboration according to claim 5, characterized in that: The S34 comprises the following steps: S341. Calculate the fitness mean of the current population based on the quantum environment characteristic population and fitness value and the mean fitness of the previous generation , and calculate the fitness change rate : in, is the quantum population size, For the Driving environment characteristics In the The fitness value of the generation; S342. Define the quantum population diversity metric function , calculate the population mean square error based on the amplitude vector of the current generation of quantum individuals: in, The characteristic dimension encoded for the quantum entity, and All are Among the quantum individuals The amplitude parameter of the feature dimension, and are the amplitude mean values ​​of all quantum individuals in this characteristic dimension, Reflects the diversity of the quantum population. The smaller the value, the more the population tends to converge. S343. According to the fitness change rate and the quantum population diversity measurement function, the convergence judgment parameter of the quantum population is calculated: in, and Determine the weight parameter for convergence; S344. Define optimization termination threshold , when the following conditions are met, the quantum population is judged to have reached the optimal convergence state: Otherwise, return to step S341 to continue optimization iteration; S345. When the optimization convergence conditions are met, select the individual with the highest fitness from the current quantum population As the final optimized driving environment feature set: ;in, In the tth optimization iteration, the qth driving environment characteristic individual in the population, It represents the complete population of individual driving environment characteristics in the tth optimization iteration, and argmax is the fitness function to be found.

7. The highway safety early warning method based on vehicle-road collaboration according to claim 6 is characterized in that: The S4 comprises the following steps: S41. Based on driving environment feature set , define a multi-objective traffic warning optimization model The optimization objectives of the multi-objective traffic warning optimization model include minimizing the warning time, maximizing the warning accuracy, and optimizing driving comfort. The objective function of the multi-objective traffic warning optimization model is defined as follows: in, For the driving warning strategy to be optimized, is the warning time of the driving warning strategy, is the warning accuracy of the driving warning strategy, is the interference degree of driving warning strategy on the driver, is the multi-objective weight parameter; S42. Define the warning time minimization target and the warning time of the driving warning strategy Affected by the driving environment feature set and low visibility environment factors: in, Indicates the detectable distance in the current low visibility environment. Indicates the transmission speed of the traffic warning signal. Processing time for the vehicle control system; S43. Define the goal of maximizing the early warning accuracy. Affected by the driving environment feature set and the road monitoring information provided by the vehicle-road collaboration platform: in, Indicates the number of false positives or false negatives in the current driving environment. is the total number of warnings; S44. Define driving comfort optimization goals and driving comfort interference levels Affected by the warning frequency and the driver's warning response time: in, Indicates the number of warnings triggered per unit time. Indicates the average driver reaction time to warning signals; S45. Build an adaptive weight adjustment mechanism and define dynamic weight adjustment parameters Adapt dynamic weighting parameters to different low-visibility environments: in, is the optimized weight parameter of the t+1th generation, is the weight parameter of the tth generation, is the learning rate, is the low visibility environment impact factor, is the maximum low visibility environmental impact factor, The strength of road monitoring information provided to the vehicle-road collaboration platform, It is the maximum road monitoring information intensity.

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