Smart urban traffic guardrail anti-collision prompting system

Abstract

The invention relates to the technical field of traffic control, in particular to a smart city traffic guardrail anti-collision prompting system, which comprises a vehicle sensing module, which is used for detecting passing vehicles, acquiring the speed, position and direction information of the vehicles, transmitting the information to a data center in real time and storing data based on sensors mounted on two sides of a road, recording as a data set, and generating vehicle dynamic data. According to the invention, through comprehensive collection and monitoring of the vehicle driving data, accurate evaluation of vehicle behaviors is realized. The speed, the position and the direction of the vehicle are collected and analyzed in real time, and data support is provided for judging the relative position and the movement trend between the vehicle and the guardrail. And whether the vehicle may deviate from the normal driving route is effectively predicted by comparing the real-time driving route with the preset route. Through continuous tracking and immediate feedback of vehicle behaviors, the sensitivity and accuracy of the early warning system when potential collision risks are judged are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control, and in particular to a smart city traffic guardrail anti-collision prompt system. Background Art

[0002] The traffic guardrail collision avoidance warning system is a specific technical application, which is part of the intelligent transportation system and aims to improve road safety by integrating sensors and communication equipment in traffic guardrails. When the system detects that a vehicle is about to collide with the guardrail, it will issue a warning in time to prompt the driver to take evasive measures.

[0003] However, existing technologies mainly rely on sensors to directly detect the distance when the vehicle approaches the guardrail to issue a warning, but their ability to predict and accurately evaluate vehicle behavior is weak. This technology is insufficient in analyzing the history and behavior patterns of vehicle driving data, and it is difficult to cope with complex or sudden traffic situations, such as drastic lane changes or rapid approach to guardrails. Therefore, the adaptability and effectiveness of existing technologies under changing road conditions are limited, resulting in protective measures that may lag due to insufficient reaction time. The lack of long-term tracking and analysis of vehicle historical behavior limits the ability to respond to complex traffic environments. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a smart city traffic guardrail anti-collision warning system.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A smart city traffic guardrail anti-collision reminder system comprises: The vehicle perception module detects passing vehicles based on sensors installed on both sides of the road, collects vehicle speed, location and direction information, transmits the information to the data center in real time, stores the data, records it as a data set, and generates vehicle dynamic data; A data processing module, based on the vehicle dynamic data, screens outliers in the data set, removes erroneous data, integrates data of consecutive time periods in chronological order, analyzes changes in vehicle speed and position, calculates the position difference of the vehicle at each time point, and obtains the vehicle motion trajectory; The guardrail warning module calculates the vehicle's driving path based on the vehicle's motion trajectory, compares it with a preset normal driving route, determines whether the vehicle deviates from the normal route, calculates the distance between the vehicle's current position and the traffic guardrail, calculates the vehicle's speed and direction relative to the guardrail, determines whether the vehicle is moving toward the guardrail, determines whether there is a collision risk based on the distance and relative speed, and generates a collision risk assessment result; A signal prompt module, based on the collision risk assessment result, selects a warning signal of a corresponding level, controls the lighting, flashing or sound prompting of the signal device, transmits the warning signal to the driver and surrounding traffic participants, and issues a warning message; The system coordination module, based on the warning information, sends the information to the traffic management center to share data, update traffic status information, coordinate the implementation of traffic control measures, notify emergency response departments, and establish coordinated control instructions.

[0006] As a further solution of the present invention, the data set includes vehicle speed, vehicle position and vehicle direction, the vehicle motion trajectory includes the position coordinates and timestamp of the vehicle at each time point, and the collision risk assessment result includes collision probability, expected collision time and potential collision position.

[0007] As a further solution of the present invention, the vehicle perception module includes: The vehicle detection submodule detects passing vehicles based on sensors installed on both sides of the road, captures the signal changes of the sensors when the vehicles pass by, determines whether there are vehicles passing by, records the time and location of the vehicles passing by, and generates vehicle detection results; The information collection submodule measures the position of the vehicle at each time point based on the vehicle detection result, calculates the position change of the vehicle in a unit time, obtains the vehicle speed, determines the driving direction of the vehicle, collects speed, position and direction information, transmits the information to the data center in real time, and generates vehicle information data; The data storage submodule classifies the vehicle information data based on the data, organizes the data according to time and vehicle number, stores the data in a database, establishes a time series data set, saves the vehicle's speed, position and direction information, and generates vehicle dynamic data.

[0008] As a further solution of the present invention, the data processing module includes: A data cleaning submodule, based on the vehicle dynamic data, checks whether there are missing values ​​or abnormal values ​​in the data set, filters the abnormal data, removes the erroneous data, and generates a cleaned data set; A data integration submodule, based on the cleaned data set, arranges the data of the same vehicle in chronological order, integrates the data of consecutive time periods, analyzes the changes in vehicle speed and position, and generates a vehicle position sequence; The trajectory generation submodule calculates the position difference of adjacent time points based on the vehicle position sequence, obtains the displacement of the vehicle at each time point, connects each position point, and obtains the vehicle motion trajectory.

[0009] As a further solution of the present invention, the guardrail warning module includes: A path analysis submodule, based on the vehicle motion trajectory, extracts the position coordinates of the vehicle at each time point, connects the position points in chronological order, calculates the vehicle's driving path, and generates vehicle driving path data; a deviation detection submodule, based on the vehicle driving path data, comparing the vehicle driving path with a preset normal driving path, calculating the deviation between the two paths, determining whether the deviation exceeds a threshold, determining whether the vehicle deviates from the normal path, and generating vehicle deviation status information; The risk assessment submodule calculates the distance between the current position of the vehicle and the traffic guardrail based on the vehicle deviation state information, measures the speed and direction of the vehicle relative to the guardrail, determines whether the vehicle is moving toward the guardrail through a vector analysis algorithm, determines whether there is a collision risk based on the distance and relative speed, and generates a collision risk assessment result.

[0010] As a further solution of the present invention, the signal prompt module includes: A signal selection submodule, which determines the risk level based on the collision risk assessment result, selects a warning signal of a corresponding level according to each risk level, and generates a warning signal instruction; The signal control submodule activates the corresponding signal device based on the warning signal instruction, controls the lighting of the signal light, the flashing frequency or the intensity of the sound prompt, and generates a warning signal output; The information release submodule transmits the warning signal to the driver and surrounding traffic participants through signal lights and sound prompts based on the warning signal output, thereby issuing warning information.

[0011] As a further solution of the present invention, the system collaboration module includes: The information sharing submodule, based on the warning information, sends the information to the traffic management center through the network, uploads it to the sharing platform, and shares the warning information data; The status update submodule analyzes changes in traffic conditions based on the warning information data, updates traffic status information, and generates current traffic status data.

[0012] The instruction issuing submodule formulates traffic control measures based on the current traffic status data, sends instructions to related departments, notifies emergency response departments, and establishes coordinated control instructions.

[0013] As a further solution of the present invention, the vector analysis algorithm is according to the formula:

[0014] in, is the vehicle velocity vector, is the position vector from the vehicle to the guardrail, for and The angle between is the vehicle acceleration vector, is the road resistance vector, is the environmental factor impact vector, is the weight coefficient used to adjust the impact of acceleration and road resistance on the result. Represents the adjusted relationship between the vehicle and the guardrail, which is used to determine the possibility of the vehicle moving toward the guardrail.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, an accurate assessment of vehicle behavior is achieved by comprehensively collecting and monitoring vehicle driving data. The speed, position and direction of the vehicle are collected and analyzed in real time to provide data support for determining the relative position and movement trend between the vehicle and the guardrail. By comparing the real-time driving path with the preset route, it is effectively predicted whether the vehicle may deviate from the normal driving route. This continuous tracking and instant feedback of vehicle behavior significantly improves the sensitivity and accuracy of the early warning system in determining potential collision risks. The refined processing of the vehicle's motion trajectory significantly improves the response speed and prediction accuracy of the early warning system, reduces the risk of relying on the driver's reaction time, and effectively enhances the safety of road users. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] See also Figure 1 The present invention provides a technical solution: a smart city traffic guardrail anti-collision prompt system comprising: The vehicle perception module detects passing vehicles based on sensors installed on both sides of the road, collects vehicle speed, location and direction information, transmits the information to the data center in real time, stores the data, records it as a data set, and generates vehicle dynamic data; The data processing module, based on the vehicle dynamic data, screens outliers in the data set, removes erroneous data, integrates data from consecutive time periods in chronological order, analyzes changes in vehicle speed and position, calculates the position difference of the vehicle at each time point, and obtains the vehicle's motion trajectory; The guardrail warning module calculates the vehicle's driving path based on the vehicle's motion trajectory, compares it with the preset normal driving route, determines whether the vehicle deviates from the normal route, calculates the distance between the vehicle's current position and the traffic guardrail, calculates the vehicle's speed and direction relative to the guardrail, determines whether the vehicle is moving toward the guardrail, determines whether there is a collision risk based on the distance and relative speed, and generates a collision risk assessment result; The signal prompt module selects the corresponding level of warning signal based on the collision risk assessment result, controls the lighting, flashing or sound prompt of the signal device, transmits the warning signal to the driver and surrounding traffic participants, and issues warning information; The system coordination module, based on the warning information, sends the information to the traffic management center to share data, update the traffic status information, coordinate the implementation of traffic control measures, notify the emergency response department, and establish coordinated control instructions.

[0019] The data set includes vehicle speed, vehicle position and vehicle direction. The vehicle motion trajectory includes the vehicle's position coordinates and timestamp at each time point. The collision risk assessment results include collision probability, expected collision time and potential collision location.

[0020] In this embodiment, the vehicle perception module includes: The vehicle detection submodule detects passing vehicles based on sensors installed on both sides of the road, captures the signal changes of the sensors when the vehicles pass by, determines whether there are vehicles passing by, records the time and location of the vehicles passing by, and generates vehicle detection results; The information collection submodule measures the position of the vehicle at each time point based on the vehicle detection results, calculates the position change of the vehicle in a unit time, obtains the vehicle speed, determines the vehicle's driving direction, collects speed, position and direction information, transmits the information to the data center in real time, and generates vehicle information data; The data storage submodule classifies the data based on the vehicle information data, organizes it according to time and vehicle number, stores it in the database, establishes a time series data set, saves the vehicle's speed, position and direction information, and generates vehicle dynamic data.

[0021] The vehicle detection submodule uses sensors installed on both sides of the road to identify passing vehicles. When a vehicle passes, the sensor will capture the signal changes and determine the presence of the vehicle by analyzing the signal changes. In this process, the sensor's signal changes are converted into digital signals, and then the data is analyzed by algorithms to confirm the passage of the vehicle. Real-time monitoring technology and signal processing technology play a key role in this process. They ensure that sensor data can be accurately captured and converted into usable traffic flow data. The specific passing time and location information of the vehicle is obtained by comparing continuous signal changes. The data is then used to generate detailed vehicle detection results.

[0022] In the above content, the vehicle speed is calculated according to the formula Calculate the vehicle's position change per unit time to get its speed.

[0023] In the formula, represents the vehicle speed, Represents position change, Represents time change.

[0024] Detailed explanation of the formula and the process of formula calculation and derivation: The movement of a vehicle on the road can be recorded as two position points and In time and If Seconds m, in Seconds m, position change Meter, time change seconds. Therefore, the vehicle speed This result shows that the average speed of the vehicle in the inspection range is 5 meters per second.

[0025] The data storage submodule classifies and stores the collected vehicle information. During this process, the vehicle information data is sorted in the order of time and vehicle number. In this way, a time series data set is created. During the storage process, the system will store the data into the database according to the preset database model, including speed, location and direction information. These data are indexed by timestamps and vehicle identifiers to ensure that the data can be quickly retrieved and used for subsequent data analysis. Database design and data indexing technology ensure efficient data management and query, and ultimately form a complete data set that can reflect the dynamic changes of vehicles.

[0026] The data storage submodule adopts intelligent data screening and storage strategies to retain only key data fragments within a certain period of time, such as data from the last few minutes. These data are sufficient for routine purposes such as traffic flow analysis. For vehicles that may have abnormal behavior or approach guardrails and other potential risks, we will store their data at a higher frequency and in a more comprehensive manner to facilitate subsequent precise analysis and risk assessment.

[0027] Through this dynamically adjusted data storage strategy, the demand for storage capacity can be greatly reduced. At the same time, with the continuous development of storage technology, the cost of storage devices is constantly decreasing and the capacity is constantly increasing. At present, there are many high-capacity, low-cost storage solutions available on the market, such as cloud computing-based storage services, whose scalability can easily meet the storage needs of this system.

[0028] The data storage submodule also includes data compression and cleaning, which compresses vehicle data in real time before storing it, which can significantly reduce the storage space occupied by data without losing key information. For example, some efficient lossless compression algorithms can reduce the data volume by several times or even dozens of times, further alleviating storage pressure.

[0029] In this embodiment, the data processing module includes: The data cleaning submodule checks whether there are missing values ​​or abnormal values ​​in the data set based on vehicle dynamic data, filters abnormal data, removes erroneous data, and generates a cleaned data set; The data integration submodule arranges the data of the same vehicle in chronological order based on the cleaned data set, integrates the data of consecutive time periods, analyzes the changes in vehicle speed and position, and generates a vehicle position sequence; The trajectory generation submodule calculates the position difference between adjacent time points based on the vehicle position sequence, obtains the displacement of the vehicle at each time point, connects each position point, and obtains the vehicle motion trajectory.

[0030] The data cleaning submodule operates by checking for missing values ​​or outliers in the vehicle dynamics dataset. By setting thresholds and anomaly detection algorithms, it screens out data points that do not meet the standards, such as records that exceed normal speeds or positions. These data points are considered to be erroneous data and are removed from the dataset to ensure data quality. This process utilizes data verification and cleaning techniques, such as outlier detection and missing value processing, to ensure the accuracy and completeness of the dataset. The cleaned dataset generated after removing erroneous data is used for further data analysis and processing, which ensures the accuracy and reliability of data analysis. The final cleaned dataset will provide high-quality input for subsequent modules.

[0031] Based on the cleaned data set, the data integration submodule arranges the data of the same vehicle in chronological order, integrates the data of continuous time periods, and creates a model representing the vehicle position sequence by analyzing the speed and position changes of the vehicle. This process involves time series analysis and data integration technology. During the integration process, the data is sorted and aligned by timestamps to ensure the temporal continuity and accuracy of the data. By calculating the speed and position differences between consecutive time points, the vehicle's movement pattern can be observed.

[0032] In the above content, the displacement of the vehicle at each time point is calculated according to the formula: , calculate the position difference between adjacent time points.

[0033] In the formula, represents displacement, and Represent the position coordinates of consecutive time points respectively.

[0034] Detailed explanation of the formula and the process of formula calculation and derivation: Considering the vehicle at time lie in Meter, time lie in m. The position difference is calculated as rice, m. Therefore, the displacement m. This result indicates that the vehicle moved 5 m between these two time points.

[0035] In this embodiment, the guardrail warning module includes: The path analysis submodule extracts the position coordinates of the vehicle at each time point based on the vehicle motion trajectory, connects the position points in chronological order, calculates the vehicle's driving path, and generates vehicle driving path data; The deviation detection submodule compares the vehicle driving path with a preset normal driving path based on the vehicle driving path data, calculates the deviation between the two paths, determines whether the deviation exceeds a threshold, determines whether the vehicle deviates from the normal route, and generates vehicle deviation status information; The risk assessment submodule calculates the distance between the vehicle's current position and the traffic guardrail based on the vehicle deviation status information, measures the vehicle's speed and direction relative to the guardrail, and uses a vector analysis algorithm to determine whether the vehicle is moving toward the guardrail. Based on the distance and relative speed, it determines whether there is a collision risk and generates a collision risk assessment result.

[0036] The path analysis submodule calculates the vehicle's driving path by extracting the vehicle's position coordinates at each time point and connecting these position points in chronological order. This process includes the extraction of data points and the construction of the path. By using the position coordinate data, the system connects each point in chronological order to form a continuous driving trajectory. This continuous path not only reflects the actual driving trajectory of the vehicle, but can also be used for subsequent path analysis and behavior evaluation. The generation of the path depends on high-precision time and position data to ensure the accuracy and reliability of the path. The generated vehicle driving path data provides basic data for the deviation detection submodule.

[0037] In the above content, the comparison between the vehicle's driving path and the preset normal driving route is based on the formula: , calculate the deviation between two paths.

[0038] In the formula, represents the deviation between paths, The coordinate points representing the actual path of the vehicle, Coordinate points representing the normal driving route.

[0039] Detailed explanation of the formula and the process of formula calculation and derivation: For example, the coordinates of a point on the actual path of the vehicle are , and the coordinates of the corresponding points on the preset normal route are , then the path deviation is calculated as: rice.

[0040] This result shows that the vehicle's path deviation at this point is approximately 3.16 meters. Based on the threshold setting, it can be determined whether a deviation has occurred.

[0041] The risk assessment submodule calculates the distance between the vehicle's current position and the traffic guardrail based on the vehicle deviation status information, and measures the vehicle's speed and direction relative to the guardrail. In this process, the module not only calculates the distance, but also integrates the vehicle's speed and direction of movement. Through these data, it evaluates whether the vehicle is moving toward the guardrail. If the distance between the vehicle's movement trajectory and the guardrail gradually decreases, and the vehicle speed is high, the evaluation result may show a higher collision risk. The evaluation result will be dynamically calculated based on the specific speed of the vehicle and the distance between the guardrails to determine whether to trigger a safety warning to ensure driving safety. In this way, the system can evaluate and respond to potential safety threats in real time, thereby preventing possible accidents in advance.

[0042] In this embodiment, the signal prompt module includes: The signal selection submodule determines the risk level based on the collision risk assessment results, selects the corresponding level of warning signal according to each risk level, and generates a warning signal instruction; The signal control submodule activates the corresponding signal device based on the warning signal instruction, controls the lighting of the signal light, the flashing frequency or the intensity of the sound prompt, and generates a warning signal output; The information release submodule, based on the warning signal output, transmits the warning signal to the driver and surrounding traffic participants through signal lights and sound prompts to issue warning information.

[0043] The signal selection submodule operates according to the collision risk assessment results. The specific process involves judging different risk levels and selecting corresponding warning signals for each level. This process uses a risk level classification algorithm to select the type and urgency of the warning signal based on the algorithm results. In this process, the determination of the risk level depends on the calculation of the collision probability and the analysis of the severity of potential consequences. The risk level division method uses quantitative analysis techniques, such as the risk matrix. The logic for determining the warning signal is based on preset safety protocols and industry standards. The generated warning signal instructions will directly affect the response strategy of the signal control submodule in the next stage to ensure the appropriateness and timeliness of the warning measures.

[0044] In the above content, the intensity adjustment of the control signal light, flashing frequency or sound prompt is based on the formula: , calculate the output intensity of the warning signal.

[0045] In the formula, Represents the final signal strength, represents the basic strength coefficient, Represents the input warning level, represents the adjustment function of the sound or flashing frequency, Represents the result value of the risk assessment.

[0046] Detailed explanation of the formula and the process of formula calculation and derivation: For example, the basic strength coefficient , enter the warning level , risk assessment result value , and for example the function , then the signal strength adjustment is calculated as This result indicates that, according to the high level of risk assessment, the signal device needs to output a higher intensity warning signal to ensure the attention and reaction of the driver and surrounding traffic participants.

[0047] The information release submodule uses signal lights and sound prompts to release warning information to the driver and surrounding traffic participants according to the warning signal output. During this process, the signal is released through a variety of communication media, such as visual signals and auditory signals, to ensure that the information can be effectively transmitted to all traffic participants. The design of the warning signal takes into account the clarity and urgency of the information to maximize the driver's reaction time and readiness. Through an efficient communication network and control system, the signal propagation is optimized to respond to various emergency situations. In this way, the system ensures the effective communication of warning information at critical moments, thereby increasing road safety and reducing the possible occurrence of accidents.

[0048] In this embodiment, the system collaboration module includes: The information sharing submodule, based on the warning information, sends the information to the traffic management center through the network, uploads it to the sharing platform, and shares the warning information data; The status update submodule analyzes changes in traffic conditions based on warning information data, updates traffic status information, and generates current traffic status data.

[0049] The command issuing submodule formulates traffic control measures based on the current traffic status data, sends commands to related departments, notifies the emergency response department, and establishes coordinated control commands.

[0050] The information sharing submodule sends the warning information to the traffic management center through the network and uploads it to the sharing platform. During this process, the submodule is responsible for collecting various types of warning information and processing the data through encryption and compression technology to ensure the security and efficiency of transmission. The information is formatted before sending to ensure compatibility with the traffic management center's system. At the same time, the information on the data sharing platform is updated in real time to support the data needs of traffic management decisions. This information sharing mechanism not only speeds up the data circulation, but also improves the timeliness of emergency response, ensuring that relevant departments can quickly obtain and use the latest traffic conditions information.

[0051] In the above content, traffic condition changes are analyzed and traffic status information is updated according to the formula: , calculate the current traffic status.

[0052] In the formula, represents the traffic flow change distance, represents the initial velocity, Represents the time when the change occurs. Represents acceleration.

[0053] Detailed explanation of the formula and the process of formula calculation and derivation: If the initial speed m / s, change time Seconds, acceleration , then the traffic flow change distance is calculated as This result shows that considering the current speed and acceleration, the traffic flow is expected to change by 175 meters in 5 seconds. Such analysis helps the traffic management center to accurately predict traffic conditions in order to adjust traffic lights and issue relevant traffic control measures.

[0054] The command issuing submodule formulates and issues traffic control measures based on the current traffic status data, sends instructions to related departments through a dedicated communication network, and notifies the emergency response department. This submodule uses a decision support system to generate collaborative control instructions, which are dynamically adjusted according to real-time data to ensure a quick and effective response to various traffic conditions. In this way, the command issuing submodule supports close collaboration between the traffic management center and the emergency response department, enhances the overall response capability and efficiency of the urban transportation system, ensures traffic safety and smoothness, and thus reduces potential traffic congestion and accidents.

[0055] In this embodiment, the vector analysis algorithm is based on the formula:

[0056] in, is the vehicle velocity vector, is the position vector from the vehicle to the guardrail, for and The angle between is the vehicle acceleration vector, is the road resistance vector, is the environmental factor impact vector, is the weight coefficient used to adjust the impact of acceleration and road resistance on the result. Represents the adjusted relationship between the vehicle and the guardrail, which is used to determine the possibility of the vehicle moving toward the guardrail.

[0057] Execution process: First, based on the vehicle velocity vector and the position vector to the guardrail , calculate their modulus and the angle between them , use these values ​​to find the dot product, and then add the vehicle acceleration vector and the road resistance vector The adjustment term is calculated by multiplying the square root of the product of the two vectors by the weight coefficient Calculation, weight coefficient The determination depends on the vehicle type and road conditions, and finally this sum is divided by the environmental factor impact vector The modulus length is increased by one to take into account the specific influence of environmental factors. The calculation result Describes the dynamic relationship between the vehicle and the guardrail. This value can be used to assess the risk of the vehicle moving towards the guardrail.

[0058] Parameter acquisition: The magnitude of the vehicle's velocity vector : The vehicle's current speed is , converted to meters per second:

[0059] This speed is measured by the vehicle speed sensor.

[0060] The modulus of the position vector from the vehicle to the guardrail : The straight-line distance between the vehicle and the guardrail is ,therefore:

[0061] This distance is obtained by the vehicle-mounted radar rangefinder.

[0062] Velocity Vector and the position vector The angle between : The angle between the vehicle's direction of travel and the guardrail is , calculate the cosine value:

[0063] This angle is calculated by the vehicle's orientation sensors and positioning system.

[0064] The modulus of the vehicle acceleration vector : The vehicle's current deceleration is , take the absolute value:

[0065] This acceleration is measured by an accelerometer.

[0066] Module length of road resistance vector : Road resistance is calculated based on vehicle speed and road conditions, and the values ​​are:

[0067] This value is calculated from the road friction coefficient and the air resistance coefficient.

[0068] Environmental factors affect the modulus of the vector : The comprehensive impact of environmental factors such as wind speed and humidity is quantified as:

[0069] This value is aggregated from environmental sensor data.

[0070] Weight coefficient : Determined according to vehicle type and road conditions, calculation method:

[0071] Assume vehicle mass , standard quality , Road condition coefficient (wet road surface) ,but:

[0072] Calculation Adjustments :

[0073] This result represents the adjustment value for the combined effects of acceleration and road resistance.

[0074] Calculate the numerator part:

[0075] Substitute the known values:

[0076] calculate:

[0077] This result is the combined value of speed and position factors, plus the sum of the adjustment terms.

[0078] Calculate the denominator:

[0079] This result takes into account the influence of environmental factors.

[0080] Calculate the final result :

[0081] The value 49.797 represents the risk level of the vehicle moving towards the guardrail. The higher the value, the greater the risk and the necessary safety measures need to be taken.

Claims

1. A smart city traffic guardrail anti-collision reminder system, characterized in that: The system comprises: The vehicle perception module detects passing vehicles based on sensors installed on both sides of the road, collects vehicle speed, location and direction information, transmits the information to the data center in real time, stores the data, records it as a data set, and generates vehicle dynamic data; A data processing module, based on the vehicle dynamic data, screens outliers in the data set, removes erroneous data, integrates data of consecutive time periods in chronological order, analyzes changes in vehicle speed and position, calculates the position difference of the vehicle at each time point, and obtains the vehicle motion trajectory; The guardrail warning module calculates the vehicle's driving path based on the vehicle's motion trajectory, compares it with a preset normal driving route, determines whether the vehicle deviates from the normal route, calculates the distance between the vehicle's current position and the traffic guardrail, calculates the vehicle's speed and direction relative to the guardrail, determines whether the vehicle is moving toward the guardrail, determines whether there is a collision risk based on the distance and relative speed, and generates a collision risk assessment result; A signal prompt module, based on the collision risk assessment result, selects a warning signal of a corresponding level, controls the lighting, flashing or sound prompting of the signal device, transmits the warning signal to the driver and surrounding traffic participants, and issues a warning message; The system coordination module, based on the warning information, sends the information to the traffic management center to share data, update traffic status information, coordinate the implementation of traffic control measures, notify emergency response departments, and establish coordinated control instructions.

2. The smart city traffic guardrail anti-collision warning system according to claim 1 is characterized by: The data set includes vehicle speed, vehicle position and vehicle direction, the vehicle motion trajectory includes the position coordinates and timestamp of the vehicle at each time point, and the collision risk assessment result includes collision probability, collision expected time and potential collision position.

3. The smart city traffic guardrail anti-collision reminder system according to claim 1 is characterized in that: The vehicle perception module comprises: The vehicle detection submodule detects passing vehicles based on sensors installed on both sides of the road, captures the signal changes of the sensors when the vehicles pass by, determines whether there are vehicles passing by, records the time and location of the vehicles passing by, and generates vehicle detection results; The information collection submodule measures the position of the vehicle at each time point based on the vehicle detection result, calculates the position change of the vehicle in a unit time, obtains the vehicle speed, determines the driving direction of the vehicle, collects speed, position and direction information, transmits the information to the data center in real time, and generates vehicle information data; The data storage submodule classifies the vehicle information data based on the data, organizes the data according to time and vehicle number, stores the data in a database, establishes a time series data set, saves the vehicle's speed, position and direction information, and generates vehicle dynamic data.

4. The smart city traffic guardrail anti-collision warning system according to claim 1 is characterized in that: The data processing module comprises: A data cleaning submodule, based on the vehicle dynamic data, checks whether there are missing values ​​or abnormal values ​​in the data set, filters the abnormal data, removes the erroneous data, and generates a cleaned data set; A data integration submodule, based on the cleaned data set, arranges the data of the same vehicle in chronological order, integrates the data of consecutive time periods, analyzes the changes in vehicle speed and position, and generates a vehicle position sequence; The trajectory generation submodule calculates the position difference of adjacent time points based on the vehicle position sequence, obtains the displacement of the vehicle at each time point, connects each position point, and obtains the vehicle motion trajectory.

5. The smart city traffic guardrail anti-collision warning system according to claim 1 is characterized in that: The guardrail warning module includes: A path analysis submodule, based on the vehicle motion trajectory, extracts the position coordinates of the vehicle at each time point, connects the position points in chronological order, calculates the vehicle's driving path, and generates vehicle driving path data; a deviation detection submodule, based on the vehicle driving path data, comparing the vehicle driving path with a preset normal driving path, calculating the deviation between the two paths, determining whether the deviation exceeds a threshold, determining whether the vehicle deviates from the normal path, and generating vehicle deviation status information; The risk assessment submodule calculates the distance between the current position of the vehicle and the traffic guardrail based on the vehicle deviation state information, measures the speed and direction of the vehicle relative to the guardrail, determines whether the vehicle is moving toward the guardrail through a vector analysis algorithm, determines whether there is a collision risk based on the distance and relative speed, and generates a collision risk assessment result.

6. The smart city traffic guardrail anti-collision warning system according to claim 1 is characterized in that: The signal prompt module comprises: A signal selection submodule, which determines the risk level based on the collision risk assessment result, selects a warning signal of a corresponding level according to each risk level, and generates a warning signal instruction; The signal control submodule activates the corresponding signal device based on the warning signal instruction, controls the lighting of the signal light, the flashing frequency or the intensity of the sound prompt, and generates a warning signal output; The information release submodule transmits the warning signal to the driver and surrounding traffic participants through signal lights and sound prompts based on the warning signal output, thereby issuing warning information.

7. The smart city traffic guardrail anti-collision warning system according to claim 1 is characterized in that: The system collaboration module includes: The information sharing submodule, based on the warning information, sends the information to the traffic management center through the network, uploads it to the sharing platform, and shares the warning information data; The status update submodule analyzes changes in traffic conditions based on the warning information data, updates traffic status information, and generates current traffic status data.

8. The smart city traffic guardrail anti-collision warning system according to claim 7 is characterized in that: Also includes: The instruction issuing submodule formulates traffic control measures based on the current traffic status data, sends instructions to related departments, notifies emergency response departments, and establishes coordinated control instructions.

9. The smart city traffic guardrail anti-collision warning system according to claim 5 is characterized in that: The vector analysis algorithm follows the formula: ； in, is the vehicle velocity vector, is the position vector from the vehicle to the guardrail, for and The angle between is the vehicle acceleration vector, is the road resistance vector, is the environmental factor impact vector, is the weight coefficient used to adjust the impact of acceleration and road resistance on the result. Represents the adjusted relationship between the vehicle and the guardrail, which is used to determine the possibility of the vehicle moving toward the guardrail.

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