Intelligent auxiliary driving alarm system based on V2V communication

Abstract

The invention provides an intelligent auxiliary driving alarm system based on V2V communication, and relates to the technical field of data processing, and the method comprises the steps: calculating the operation stability of a target vehicle according to a standardized data set, calculating the safety vehicle distance range between the target vehicle and surrounding vehicles according to the coordinate position, the auxiliary vehicle speed and the auxiliary acceleration of the surrounding vehicles, and obtaining the safety vehicle distance range of the target vehicle; calculating an operation correction value according to the operation stability, correcting the basic safety vehicle distance threshold value to obtain a dynamic safety vehicle distance threshold value, determining a relative motion parameter set of the target vehicle and each surrounding vehicle, and monitoring the real-time vehicle distance between the target vehicle and the surrounding vehicles; and comparing the vehicle distance with a dynamic safe vehicle distance threshold value, calculating a potential collision probability, and generating an early warning signal for early warning. According to the invention, driving early warning can be realized based on data sharing of V2V communication.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent auxiliary driving alarm system based on V2V communication. Background Art

[0002] With the development of intelligent transportation systems, V2V communication technology has gradually become an important part of assisted driving and traffic safety. V2V communication uses wireless communication technology to achieve information exchange between vehicles, including sharing of key data such as vehicle speed, acceleration, and brake status, thereby providing forward collision warning, intersection collision warning, emergency brake reminder and other functions. However, the current V2V alarm system mostly uses fixed rule thresholds for warning, such as setting thresholds based on vehicle distance and relative speed to trigger alarms. This method may have the risk of false alarms or missed alarms in complex traffic environments.

[0003] For example, in urban intersection scenarios, traditional V2V alarm systems usually rely on a fixed vehicle-to-vehicle distance threshold for collision warnings. When two vehicles are relatively close to a set minimum safety distance, the system will trigger an alarm. However, this fixed threshold-based approach may result in false alarms. In congested environments, due to the short distance between vehicles, the fixed threshold cannot be flexibly adjusted, and the system may frequently trigger invalid alarms, resulting in the inability to provide accurate collision risk assessments in a timely manner, thereby increasing the possibility of accidents. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent assisted driving alarm system based on V2V communication, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] An intelligent assisted driving alarm system based on V2V communication, the system comprising:

[0007] A first calculation module is used to calculate the operation stability of the target vehicle according to the operation status data in the standardized data set to obtain an operation stability parameter;

[0008] The second calculation module is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the target vehicle data and the environmental data, and obtain the basic safe vehicle distance threshold;

[0009] A correction module is used to calculate an operation correction value according to the operation stability parameter, and to correct the basic safe vehicle distance threshold according to the operation correction value to obtain a dynamic safe vehicle distance threshold;

[0010] A relative motion module is used to determine the relative motion parameters between the target vehicle and each surrounding vehicle according to the target vehicle data and the environmental data, and obtain a relative motion parameter set;

[0011] A detection module, used to monitor the real-time vehicle distance between the target vehicle and surrounding vehicles through a relative motion parameter set, and compare it with a dynamic safety vehicle distance threshold, and generate a collision assessment instruction when the real-time vehicle distance exceeds the dynamic safety vehicle distance threshold;

[0012] An evaluation module, used to calculate the potential collision probability according to the collision evaluation instruction and obtain a collision risk index;

[0013] The alarm module is used to set the corresponding warning trigger conditions according to the collision risk index, and generate a warning signal for warning.

[0014] Furthermore, the first calculation module includes:

[0015] A data conversion unit, used for normalizing the throttle opening value, brake pedal pressure value and steering wheel steering angle in the operation state data to obtain standard operation data;

[0016] A stability analysis unit, used to calculate the throttle opening change rate, brake pedal pressure change rate and steering wheel steering angle change rate of the target vehicle per unit time according to standard operation data, and obtain an operation fluctuation data set;

[0017] A lane change analysis unit is used to determine whether the target vehicle performs a lane change operation according to the steering wheel steering angle change rate, and to count the number of lane change operations to obtain the number of lane changes;

[0018] The stability parameter generating unit is used to calculate the operation stability of the target vehicle according to the operation fluctuation data set and the number of lane changes, and obtain the operation stability parameter.

[0019] Furthermore, the calculation formula of the stable operability is: ,

[0020] in, For the operational stability of the target vehicle, is the number of times the operation status data is collected, is the index of the number of collections, For the The throttle opening value collected for each time, is the time variable, For the The throttle opening change rate during the first acquisition, For the The brake pedal pressure value collected each time, For the The brake pedal pressure change rate at the time of the first collection, For the The steering wheel angle collected For the The steering wheel angle change rate at the time of acquisition, is the number of lane changes, is the index of the number of lane changes, For the Steering wheel angle change rate for lane change, is the coefficient.

[0021] Furthermore, the second calculation module includes:

[0022] A surrounding vehicle travel distance unit, used to calculate the travel distance of surrounding vehicles within a preset time period according to the auxiliary vehicle speed and auxiliary acceleration of surrounding vehicles, and obtain a surrounding vehicle travel distance data set;

[0023] A target vehicle travel distance unit is used to calculate the travel distance of the target vehicle within a preset time period according to the main vehicle speed and main acceleration of the target vehicle, and obtain a target vehicle travel distance data set;

[0024] A minimum safety distance calculation unit is used to determine the minimum relative driving distance between the target vehicle and the surrounding vehicles according to the surrounding vehicle driving distance data set and the target vehicle driving distance data set, so as to obtain a safety reference data set;

[0025] The basic threshold value generating unit is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the safety reference data set to obtain a basic safe vehicle distance threshold value.

[0026] Furthermore, the correction module includes:

[0027] A lane change detection unit is used to count the number of lane changes of the target vehicle within a preset time window to obtain lane change frequency data;

[0028] A brake control analysis unit, used to analyze the degree of sudden change in braking of the target vehicle according to the change rate of the brake pedal pressure of the target vehicle within a preset time window, and to count the number of emergency braking times to obtain emergency braking data;

[0029] The throttle control analysis unit is used to analyze whether the target vehicle frequently accelerates suddenly according to the throttle pedal pressure change rate of the target vehicle within a preset time window, and to count the number of sudden accelerations to obtain sudden acceleration data;

[0030] an operation correction value calculation unit, for calculating an operation correction value of a target vehicle according to the lane change frequency data, the emergency braking data and the sudden acceleration data;

[0031] The threshold correction calculation unit is used to correct the basic safe vehicle distance threshold according to the operation correction value to obtain the dynamic safe vehicle distance threshold.

[0032] Furthermore, the relative motion module includes:

[0033] A relative speed calculation unit, used to calculate the relative speed between the target vehicle and surrounding vehicles according to the main vehicle speed in the target vehicle data and the auxiliary vehicle speed in the environmental data;

[0034] A relative acceleration calculation unit, used to calculate the relative acceleration between the target vehicle and surrounding vehicles based on the main acceleration in the target vehicle data and the secondary acceleration in the environmental data;

[0035] A relative position calculation unit, used to calculate the relative position between the target vehicle and surrounding vehicles according to the coordinate position in the environmental data;

[0036] The relative motion parameter set generating unit is used to combine the relative velocity, relative acceleration and relative position to generate a relative motion parameter set.

[0037] Furthermore, the detection module includes:

[0038] A real-time vehicle distance calculation unit is used to calculate the real-time driving distance between the target vehicle and each surrounding vehicle according to the relative motion parameter set to obtain a real-time vehicle distance value;

[0039] A vehicle distance threshold comparison unit is used to screen the real-time vehicle distance value according to the dynamic safety vehicle distance threshold, and determine whether the real-time vehicle distance between the target vehicle and surrounding vehicles exceeds the dynamic safety vehicle distance threshold. When the result is yes, a warning trigger signal is generated;

[0040] The target vehicle driving trend analysis unit is used to analyze the current main vehicle speed and main acceleration of the target vehicle according to the early warning trigger signal, predict the driving trend of the target vehicle within a preset time period, and generate a collision assessment instruction.

[0041] Furthermore, the evaluation module includes:

[0042] A data extraction unit, used to extract the real-time vehicle distance value, the relative speed and relative acceleration of the target vehicle and surrounding vehicles according to the collision assessment instruction, and obtain a real-time motion data set;

[0043] A collision time calculation unit is used to calculate the shortest time required for the target vehicle to collide with surrounding vehicles at the current speed according to the real-time vehicle distance value and the relative speed, and obtain a collision time threshold;

[0044] A braking response time unit, used to calculate the time required for the target vehicle to completely brake according to the braking state and main acceleration of the target vehicle, and obtain a braking time threshold;

[0045] The driving path unit is used to analyze the driving path of the target vehicle within a preset braking time according to the steering angle of the steering wheel of the target vehicle to obtain the driving path data.

[0046] Furthermore, the evaluation module also includes:

[0047] The braking success probability unit is used to calculate whether the target vehicle can complete braking before collision under different braking forces according to the collision time threshold and the braking time threshold, and obtain the braking success probability;

[0048] A path overlap analysis unit is used to calculate the probability of path overlap between the target vehicle and surrounding vehicles based on the driving path data to obtain the path overlap probability;

[0049] The potential collision probability unit is used to calculate the probability of collision between the target vehicle and surrounding vehicles according to the braking success probability and the path overlap probability, and obtain the potential collision probability.

[0050] Furthermore, the calculation formula of the potential collision probability is: ,

[0051] in, is the potential collision probability of the target vehicle, is the collision time threshold, is the path overlap probability, is the braking time threshold, is the relative speed between the target vehicle and surrounding vehicles, is the relative acceleration between the target vehicle and surrounding vehicles, is the coefficient.

[0052] The above solution of the present invention includes at least the following beneficial effects:

[0053] The present invention calculates operational stability, analyzes the driver's throttle, brake and steering wheel operating modes, quantifies driving stability, and generates operational stability parameters, which are then used to adjust the safe vehicle distance so that vehicles with stable driving behavior are allowed a smaller safe distance; traditional V2V alarm systems rely on preset fixed vehicle distance thresholds and cannot be adjusted according to the driver's operating behavior. The present invention automatically increases the safe distance for vehicles with aggressive driving styles to reduce collision risks, provides personalized safe vehicle distances for different driving styles, improves the accuracy of early warnings, and dynamically adjusts the vehicle distance threshold to reduce false alarms and missed alarms caused by different driving habits, thereby reducing the driver's distrust of the system; while adapting to different road conditions, the vehicle distance is adjusted in highway and urban congestion environments to improve the applicability of the system.

[0054] The present invention calculates the relative speed, relative acceleration and relative position of the target vehicle and the surrounding vehicles, and through these data ensures that the system not only pays attention to the static vehicle distance, but also can analyze the relative motion relationship between the two vehicles in real time. Compared with the traditional fixed vehicle distance judgment method, the system can avoid false alarms when the vehicle distance is close but the relative speed is large through comprehensive calculation of dynamic parameters between vehicles. At the same time, it can also identify risk scenarios that require early warning due to excessive speed differences. It is particularly suitable for complex traffic environments such as highways and urban intersections. In these scenarios, relying solely on fixed vehicle distances for alarms is often prone to false alarms or missed alarms. The system combines multiple motion parameters for analysis, improves the ability to accurately judge vehicle dynamic changes, and makes the warning mechanism more intelligent and accurate.

[0055] The present invention calculates the minimum safe distance between vehicles, and performs threshold correction based on this, and dynamically adjusts the safe distance according to the operational stability, the vehicle's motion state and the complexity of the road environment. The traditional fixed threshold method usually cannot take into account the differences in different driving behaviors, resulting in the alarm threshold being too conservative and frequently triggering the alarm, or failing to provide timely warnings when the driving habits are aggressive. The present system adaptively adjusts the safe distance so that vehicles with relatively stable driving behaviors can maintain a relatively small safe distance, and when the driving behavior is aggressive or the environment is complex, the system automatically increases the safe distance to ensure sufficient reaction time. This adaptive mechanism improves the flexibility and accuracy of the system, reduces the frequency of unnecessary alarms, and improves driving safety.

[0056] The present invention calculates the future driving trajectory of the target vehicle and analyzes the relative position change trend of the vehicles in combination with the collision probability calculation, so as to determine whether there is a potential risk of the paths of the two vehicles intersecting. The traditional alarm relies on the physical distance between vehicles, lacks accurate prediction of the future path of the vehicle, and is prone to false alarms or missed alarms in scenes such as ramps and intersections. The present system is more intelligent than the method that simply relies on vehicle distance calculation, especially in complex traffic environments. It can effectively reduce unnecessary alarms and ensure timely warnings when there is real danger, thereby improving the system's warning capabilities in complex environments and making it suitable for more diversified traffic scenarios.

[0057] The present invention monitors the basic motion state of the target vehicle to identify high-risk operations of the target vehicle. Traditional V2V alarm systems are usually unable to identify aggressive driving behaviors of vehicles, such as frequent lane changes, sudden acceleration or sudden braking, resulting in the system's early warnings in these situations being inaccurate and possibly missing some key risk signals. The present system dynamically adjusts the alarm threshold by detecting the number of lane changes and the steering wheel angle change rate of the target vehicle, combined with sudden acceleration and sudden braking behavior analysis. In the event of high-frequency sudden braking, sudden acceleration or frequent lane changes in the vehicle, the system automatically increases the safe vehicle distance and improves the sensitivity of the alarm trigger to ensure timely warnings, which can effectively reduce the risk of traffic accidents caused by driving style, enable the system to be intelligently adjusted, and improve overall driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flowchart of an intelligent assisted driving alarm system based on V2V communication provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0060] like Figure 1 As shown, an embodiment of the present invention proposes an intelligent assisted driving alarm system based on V2V communication, including:

[0061] A first calculation module is used to calculate the operation stability of the target vehicle according to the operation status data in the standardized data set to obtain an operation stability parameter;

[0062] The second calculation module is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the target vehicle data and the environmental data, and obtain the basic safe vehicle distance threshold;

[0063] A correction module is used to calculate an operation correction value according to the operation stability parameter, and to correct the basic safe vehicle distance threshold according to the operation correction value to obtain a dynamic safe vehicle distance threshold;

[0064] A relative motion module is used to determine the relative motion parameters between the target vehicle and each surrounding vehicle according to the target vehicle data and the environmental data, and obtain a relative motion parameter set;

[0065] A detection module, used to monitor the real-time vehicle distance between the target vehicle and surrounding vehicles through a relative motion parameter set, and compare it with a dynamic safety vehicle distance threshold, and generate a collision assessment instruction when the real-time vehicle distance exceeds the dynamic safety vehicle distance threshold;

[0066] An evaluation module, used to calculate the potential collision probability according to the collision evaluation instruction and obtain a collision risk index;

[0067] The alarm module is used to set the corresponding warning trigger conditions according to the collision risk index, and generate a warning signal for warning.

[0068] In an embodiment of the present invention, a data acquisition module is used to obtain the main vehicle speed, main acceleration and braking status of the target vehicle, obtain the target vehicle data, obtain the throttle opening value, brake pedal pressure value and steering wheel steering angle of the target vehicle, obtain the operating status data, obtain the coordinate position, auxiliary vehicle speed and auxiliary acceleration of surrounding vehicles, and obtain environmental data; by preprocessing the target vehicle data, operating status data and environmental data, removing redundant data, and obtaining a standardized data set, through multi-dimensional data acquisition, the system can construct complete vehicle status information and improve the comprehensiveness of the alarm system; the first calculation module is used to calculate the operating stability of the target vehicle according to the operating status data in the standardized data set, obtain the operating stability parameters, which can accurately identify the driving style of the target vehicle and provide a reference for the subsequent dynamic adjustment of the alarm threshold.

[0069] The second calculation module is used to calculate the safe distance range between the target vehicle and the surrounding vehicles based on the target vehicle data and environmental data, and obtain the basic safe distance threshold, which can provide a suitable safe distance range under different working conditions, avoid false alarm problems caused by fixed threshold settings, and improve the adaptability of the system; the correction module is used to calculate the operation correction value based on the operation stability parameter, and correct the basic safe distance threshold based on it to obtain the dynamic safe distance threshold, and dynamically adjust the safe distance threshold so that the system can adaptively adjust according to real-time driving data to improve the accuracy of the warning; the relative motion module is used to determine the relative motion parameters between the target vehicle and each surrounding vehicle based on the target vehicle data and environmental data, and obtain a relative motion parameter set, which can accurately identify the dynamic relationship between vehicles.

[0070] The detection module is used to monitor the real-time distance between the target vehicle and surrounding vehicles through a relative motion parameter set, and compare it with the dynamic safety distance threshold. When the real-time distance exceeds the dynamic safety distance threshold, a collision assessment instruction is generated; the assessment module is used to calculate the potential collision probability according to the collision assessment instruction, obtain the collision risk index, provide accurate collision probability calculation, and improve the intelligence of the system; the alarm module is used to set the corresponding warning trigger condition according to the collision risk index, and generate a warning signal based on it for warning, adopting a graded warning method to reduce false alarms and improve driving safety.

[0071] The standardized data set includes:

[0072] The main speed, main acceleration and braking status of the target vehicle are obtained to obtain the target vehicle data; the throttle opening value, brake pedal pressure value and steering wheel steering angle of the target vehicle are obtained to obtain the operation status data; the coordinate position, auxiliary speed and auxiliary acceleration of the surrounding vehicles are obtained to obtain the environmental data; by preprocessing the target vehicle data, operation status data and environmental data, redundant data are removed to obtain a standardized data set.

[0073] Specifically, when the system is running, the on-board electronic control unit receives the main vehicle speed, main acceleration and brake status information during the vehicle's driving process through the CAN bus. These data are provided by the vehicle's own speed sensor, accelerometer and brake pressure sensor, and are sampled at a high frequency to ensure the real-time and accuracy of the data. At the same time, the acquisition of operating status data depends on multiple sensors inside the vehicle. The throttle opening value is detected in real time by the throttle sensor, the brake pedal pressure value is obtained through the pressure sensor of the brake system, and the steering wheel steering angle is provided by the electronic steering system. These data are preliminarily processed by the ECU and form continuous time series data inside the data acquisition module for subsequent calculations.

[0074] In the process of acquiring environmental data, the data acquisition module relies on the V2V communication protocol and DSRC technology to exchange information with surrounding vehicles. Each vehicle will regularly broadcast its own coordinate position, secondary vehicle speed and secondary acceleration information. After receiving this information, the data acquisition module will perform position calibration in combination with GPS data to ensure that the position information of surrounding vehicles is consistent with the local coordinate system of the target vehicle. In addition, the system will perform redundant filtering on V2V communication data and remove data points with signal loss or excessive delay to improve data accuracy and stability.

[0075] The main tasks of data preprocessing are to format, denoise and normalize the target vehicle data, operating status data and environmental data. The formatting process ensures that all data are aligned with a unified timestamp. The denoising process uses the Kalman filter algorithm to correct abnormal data to reduce the impact of data jitter on system judgment. The normalization operation converts physical quantities from different data sources into standardized values ​​for subsequent model calculations. For example, the vehicle speed may be expressed in km / h, while the acceleration is usually expressed in m / s², so it is necessary to convert them into the same data range through a standardized method. Finally, all processed data are integrated into a standardized data set, which includes the motion information of the target vehicle, the driver's operating status and the vehicle status of the surrounding environment, providing accurate input data for subsequent collision risk assessment and warning.

[0076] The alarm module is used to set the corresponding warning trigger conditions according to the collision risk index and generate a warning signal for warning, which specifically includes:

[0077] First, after the system's evaluation module calculates the probability of collision between the target vehicle and surrounding vehicles, the alarm module classifies and processes different levels of alarms according to the preset risk threshold. When the collision risk index is low, the system will generate a mild alarm to prompt attention to the traffic conditions ahead. When the collision risk index reaches medium or high risk, the system will trigger a stronger alarm mechanism to ensure that the vehicle can respond quickly.

[0078] In the mild alarm stage, the alarm module mainly reminds the driver through visual and auditory means. For example, the warning light on the dashboard will flash yellow, and a prompt message will pop up on the central control display to remind the driver to keep a safe distance. If the collision risk index rises further to a medium level, the system will activate an audible alarm and emit a short beep through the vehicle audio system to enhance the driver's alertness. In addition, the seat vibration module may also be activated, and the bottom or backrest area of ​​the driver's seat will vibrate slightly to provide additional tactile feedback.

[0079] When the system detects a high-risk collision situation, that is, the target vehicle may actually collide with surrounding vehicles in a short period of time, the alarm module will enter emergency alarm mode. At this stage, in addition to visual and sound prompts, the system may also activate automatic driving assistance functions, such as automatic braking or automatic deceleration, to help the driver avoid a collision. Specifically, if the target vehicle still does not take braking measures in a high-risk state, the alarm module will send a braking command to the vehicle's braking system to control the braking pressure and reduce the speed. In addition, if the vehicle is in a low-speed driving state and a side collision risk is detected, the system may also apply a slight direction correction by adjusting the electronic power steering of the steering wheel to assist the driver in avoiding obstacles.

[0080] In order to optimize the alarm triggering mechanism, the system also monitors the driver's real-time operating behavior. For example, before the alarm is triggered, if the system detects that the driver has begun to actively slow down, it will automatically reduce the level of the alarm to avoid unnecessary alarms interfering with the driver's operation. On the contrary, if the driver still does not take any evasive measures in a high-risk state, the system will continue to increase the intensity of the alarm and trigger the automatic emergency braking function when necessary to minimize the risk of collision.

[0081] In a preferred embodiment of the present invention, the first calculation module includes:

[0082] A data conversion unit, used for normalizing the throttle opening value, brake pedal pressure value and steering wheel steering angle in the operation state data to obtain standard operation data;

[0083] A stability analysis unit, used to calculate the throttle opening change rate, brake pedal pressure change rate and steering wheel steering angle change rate of the target vehicle per unit time according to standard operation data, and obtain an operation fluctuation data set;

[0084] A lane change analysis unit is used to determine whether the target vehicle performs a lane change operation according to the steering wheel steering angle change rate, and to count the number of lane change operations to obtain the number of lane changes;

[0085] The stability parameter generating unit is used to calculate the operation stability of the target vehicle according to the operation fluctuation data set and the number of lane changes, and obtain the operation stability parameter.

[0086] In an embodiment of the present invention, a data conversion unit is used to normalize the throttle opening value, brake pedal pressure value and steering wheel steering angle in the operation status data to obtain standard operation data, eliminate the differences in data units and numerical ranges, and make subsequent calculations more standardized and comparable; a stability analysis unit is used to calculate the throttle opening change rate, brake pedal pressure change rate and steering wheel steering angle change rate of the target vehicle per unit time according to the standard operation data, obtain an operation fluctuation data set, and accurately quantify the driver's operation stability; a lane change analysis unit is used to determine whether the target vehicle performs a lane change operation according to the steering wheel steering angle change rate, and count the number of lane change operations to obtain the number of lane changes, so as to effectively identify the situation where the driver frequently changes lanes; a stability parameter generation unit is used to calculate the operation stability of the target vehicle according to the operation fluctuation data set and the number of lane changes, obtain an operation stability parameter, and realize the quantification of driving behavior.

[0087] In a preferred embodiment of the present invention, the calculation formula of the stable operability is: ,

[0088] in, For the operational stability of the target vehicle, is the number of times the operation status data is collected, is the index of the number of collections, For the The throttle opening value collected for each time, is the time variable, For the The throttle opening change rate during the first acquisition, For the The brake pedal pressure value collected each time, For the The brake pedal pressure change rate at the time of the first collection, For the The steering wheel angle collected For the The steering wheel angle change rate at the time of acquisition, is the number of lane changes, is the index of the number of lane changes, For the Steering wheel angle change rate for lane change, is the coefficient.

[0089] In an embodiment of the present invention, the calculation formula for stable operability quantifies the changes in multiple parameters such as the vehicle's throttle opening, brake pedal pressure, steering wheel steering angle, etc. to obtain an operational stability parameter. The calculation formula comprehensively considers the changing trend of the operating status data per unit time, and combines the number of lane changes to reflect the driver's control stability. The basic structure of the formula is to mathematically model the changes in each operating variable in a time series, and weight their influence to obtain a numerical indicator for measuring driving stability.

[0090] In a preferred embodiment of the present invention, the second calculation module includes:

[0091] A surrounding vehicle travel distance unit, used to calculate the travel distance of surrounding vehicles within a preset time period according to the auxiliary vehicle speed and auxiliary acceleration of surrounding vehicles, and obtain a surrounding vehicle travel distance data set;

[0092] A target vehicle travel distance unit is used to calculate the travel distance of the target vehicle within a preset time period according to the main vehicle speed and main acceleration of the target vehicle, and obtain a target vehicle travel distance data set;

[0093] A minimum safety distance calculation unit is used to determine the minimum relative driving distance between the target vehicle and the surrounding vehicles according to the surrounding vehicle driving distance data set and the target vehicle driving distance data set, so as to obtain a safety reference data set;

[0094] The basic threshold value generating unit is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the safety reference data set to obtain a basic safe vehicle distance threshold value.

[0095] In the embodiment of the present invention, the surrounding vehicle travel distance unit is used to calculate the travel distance of the surrounding vehicles within a preset time period according to the secondary vehicle speed and the secondary acceleration of the surrounding vehicles, obtain the surrounding vehicle travel distance data set, and predict the future travel trajectory of the surrounding vehicles by analyzing the motion parameters of the surrounding vehicles, so as to provide data support for the subsequent calculation of the safe vehicle distance of the target vehicle; the target vehicle travel distance unit is used to calculate the travel distance of the target vehicle within a preset time period according to the main vehicle speed and the main acceleration of the target vehicle, and obtain the target vehicle travel distance data set, so as to ensure that the driving trend of the target vehicle can be accurately predicted, so that the subsequent safe vehicle distance calculation is more in line with the actual driving situation; the minimum safety distance calculation unit is used to determine the minimum relative travel distance between the target vehicle and the surrounding vehicles according to the surrounding vehicle travel distance data set and the target vehicle travel distance data set, and obtain the safety reference data set. By combining the travel distance of the target vehicle and the surrounding vehicles, the accuracy in complex traffic environments is improved, and unreasonable alarms caused by simple vehicle distance judgment are avoided; the basic threshold generation unit is used to calculate the safe vehicle distance range between the target vehicle and the surrounding vehicles according to the safety reference data set, obtain the basic safety vehicle distance threshold, and convert the basic safety distance into a safe vehicle distance threshold that can be actually applied.

[0096] The basic threshold generation unit is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the safety benchmark data set to obtain the basic safe vehicle distance threshold, which specifically includes:

[0097] First, the system parses key data from the safety benchmark data set, including the driving distance of surrounding vehicles, the driving distance of the target vehicle, and the minimum relative driving distance between the two. In addition, the driver's reaction time is also an important factor affecting the calculation of safe vehicle distance. The system will compensate based on the driver's average reaction time to ensure that the warning system can provide sufficient safety distance before the driver reacts. In order to ensure the accuracy of the calculation, the system will perform integrity checks during the data parsing process, eliminate abnormal data, and ensure that all calculations are based on reliable input information.

[0098] After parsing the data, the system begins to calculate the basic safe distance. The minimum safe distance is calculated by comparing the motion trajectories of the target vehicle and surrounding vehicles in the same time window. The calculation of the minimum safe distance is based on the estimated driving distance of the two vehicles to ensure that the collision risk can be reasonably assessed in a dynamic environment. If the calculation results show that the target vehicle may overtake the surrounding vehicles within the time window, the system will adjust the warning strategy in subsequent steps to prevent misjudgment due to insufficient distance calculation. At the same time, the system will take into account the driver's reaction time and add it to the calculation as a compensation factor, so that the final basic safe distance can reflect the time interval from the driver's perception of danger to taking braking or avoidance measures. By introducing reaction time compensation, the system can effectively reduce the risk of collision caused by driver operation delays and improve the reliability of warnings.

[0099] After obtaining the preliminary safe distance calculation results, the system also needs to calculate additional safety margins to adapt to different road environments, vehicle conditions and driving styles. The calculation of the safety margin is based on multiple factors, including the current speed and acceleration of the target vehicle and the complexity of the driving environment. When driving on the highway, the vehicle speed is fast, and the system will automatically increase the safety margin to ensure sufficient reaction time. When driving at low speeds in the city, the system will appropriately reduce the safety margin to avoid affecting the driving experience due to overly conservative warnings. In addition, if the driving behavior of the target vehicle is relatively unstable, such as frequent sudden braking or acceleration, the system will dynamically increase the safety margin, improve the safety factor, and ensure that the warning is more in line with the actual situation. In this way, the system can adapt to different driving styles and road conditions, avoid the limitations of the traditional fixed threshold setting method, and make the alarm system more intelligent and personalized.

[0100] The calculation formula for the safe vehicle distance range is: ,

[0101] in, is the safe distance range, is the driving distance of surrounding vehicles, is the target vehicle’s driving distance, is the target vehicle’s speed, The driver's reaction time, is the target vehicle’s acceleration, is the coefficient.

[0102] After completing all data calculations and adjustments, the system finally generates a basic safe distance threshold and uses it as the input value of the subsequent correction module to further optimize and adjust it in combination with the driver's driving stability and environmental factors. The final basic safe distance is composed of the minimum safe distance , Driver reaction time compensation and safety margin It consists of three parts to ensure that it can maintain rationality in various driving environments.

[0103] In a preferred embodiment of the present invention, the correction module includes:

[0104] A lane change detection unit is used to count the number of lane changes of the target vehicle within a preset time window to obtain lane change frequency data;

[0105] A brake control analysis unit, used to analyze the degree of sudden change in braking of the target vehicle according to the change rate of the brake pedal pressure of the target vehicle within a preset time window, and to count the number of emergency braking times to obtain emergency braking data;

[0106] The throttle control analysis unit is used to analyze whether the target vehicle frequently accelerates suddenly according to the throttle pedal pressure change rate of the target vehicle within a preset time window, and to count the number of sudden accelerations to obtain sudden acceleration data;

[0107] an operation correction value calculation unit, for calculating an operation correction value of a target vehicle according to the lane change frequency data, the emergency braking data and the sudden acceleration data;

[0108] The threshold correction calculation unit is used to correct the basic safe vehicle distance threshold according to the operation correction value to obtain the dynamic safe vehicle distance threshold.

[0109] In an embodiment of the present invention, the lane change detection unit is used to count the number of lane changes of the target vehicle within a preset time window to obtain lane change frequency data. The detection of lane change behavior provides a basis for subsequent safety distance adjustment; the braking control analysis unit is used to analyze the degree of sudden braking of the target vehicle according to the brake pedal pressure change rate of the target vehicle within the preset time window, and count the number of emergency braking to obtain emergency braking data. By detecting the number of emergency braking, the system can determine whether the driver is responding to an emergency or whether there is a high risk of accident; the throttle control analysis unit is used to analyze whether the target vehicle frequently changes lanes according to the throttle pedal pressure change rate of the target vehicle within the preset time window. Sudden acceleration, and counting the number of sudden accelerations to obtain sudden acceleration data, identify whether the driver has sudden acceleration behavior, and judge whether his driving style is aggressive; an operation correction value calculation unit, used to calculate the operation correction value of the target vehicle according to the lane change frequency data, emergency braking data and sudden acceleration data, and generate an operation stability index by comprehensively analyzing the driver's operation mode, so that the system can accurately evaluate the driving style and driving environment, and provide a reliable basis for subsequent threshold adjustment; a threshold correction calculation unit, used to correct the basic safe vehicle distance threshold according to the operation correction value, obtain the dynamic safe vehicle distance threshold, and adaptively adjust the warning threshold according to the actual driving behavior to improve the accuracy and reliability of the system.

[0110] The operation correction value calculation unit is used to calculate the operation correction value of the target vehicle according to the lane change frequency data, the emergency braking data and the sudden acceleration data, and specifically includes:

[0111] The above data are converted into standardized parameters and a weighted calculation method is used to generate operational correction values.

[0112] For example, if the weight coefficients corresponding to the lane change frequency, emergency braking number, and rapid acceleration number are set to 0.4, 0.3, and 0.3 respectively, the operation correction value is calculated by the above formula : ,

[0113] in, is the number of lane changes, is the number of emergency brakes, The number of rapid accelerations.

[0114] In a preferred embodiment of the present invention, the relative motion module comprises:

[0115] A relative speed calculation unit, used to calculate the relative speed between the target vehicle and surrounding vehicles according to the main vehicle speed in the target vehicle data and the auxiliary vehicle speed in the environmental data;

[0116] A relative acceleration calculation unit, used to calculate the relative acceleration between the target vehicle and surrounding vehicles based on the main acceleration in the target vehicle data and the secondary acceleration in the environmental data;

[0117] A relative position calculation unit, used to calculate the relative position between the target vehicle and surrounding vehicles according to the coordinate position in the environmental data;

[0118] The relative motion parameter set generating unit is used to combine the relative velocity, relative acceleration and relative position to generate a relative motion parameter set.

[0119] In an embodiment of the present invention, a relative speed calculation unit is used to calculate the relative speed between the target vehicle and the surrounding vehicles according to the main vehicle speed in the target vehicle data and the secondary vehicle speed in the environmental data, which can avoid the limitation of relying solely on the vehicle distance for alarm and ensure that reasonable warnings can be provided under different driving conditions; a relative acceleration calculation unit is used to calculate the relative acceleration between the target vehicle and the surrounding vehicles according to the main acceleration in the target vehicle data and the secondary acceleration in the environmental data, identify the acceleration changes between vehicles, and improve the response capability to emergencies; a relative position calculation unit is used to calculate the relative position between the target vehicle and the surrounding vehicles according to the coordinate position in the environmental data, and reduce warning errors caused by changes in motion trajectory; a relative motion parameter set generation unit is used to merge the relative speed, relative acceleration and relative position to generate a relative motion parameter set, and improve the overall intelligence level of the system through data integration and trend analysis.

[0120] In a preferred embodiment of the present invention, the detection module includes:

[0121] A real-time vehicle distance calculation unit is used to calculate the real-time driving distance between the target vehicle and each surrounding vehicle according to the relative motion parameter set to obtain a real-time vehicle distance value;

[0122] A vehicle distance threshold comparison unit is used to screen the real-time vehicle distance value according to the dynamic safety vehicle distance threshold, and determine whether the real-time vehicle distance between the target vehicle and surrounding vehicles exceeds the dynamic safety vehicle distance threshold. When the result is yes, a warning trigger signal is generated;

[0123] The target vehicle driving trend analysis unit is used to analyze the current main vehicle speed and main acceleration of the target vehicle according to the early warning trigger signal, predict the driving trend of the target vehicle within a preset time period, and generate a collision assessment instruction.

[0124] In an embodiment of the present invention, a real-time vehicle distance calculation unit is used to calculate the real-time driving distance between the target vehicle and each surrounding vehicle according to a relative motion parameter set to obtain a real-time vehicle distance value. By introducing accurate real-time distance calculation, the warning accuracy of the system is improved; a vehicle distance threshold comparison unit is used to screen the real-time vehicle distance value according to a dynamic safety vehicle distance threshold, and determine whether the real-time vehicle distance between the target vehicle and the surrounding vehicles exceeds the dynamic safety vehicle distance threshold. When the result is yes, a warning trigger signal is generated, and combined with the dynamic safety vehicle distance calculation, the adaptability of the warning is improved; a target vehicle driving trend analysis unit is used to analyze the current main vehicle speed and main acceleration of the target vehicle according to the warning trigger signal, predict the driving trend of the target vehicle within a preset time period, and generate a collision assessment instruction. Through short-term trajectory prediction, the system can warn of the impending collision risk in advance.

[0125] The target vehicle driving trend analysis unit is used to analyze the current main vehicle speed and main acceleration of the target vehicle according to the warning trigger signal, predict the driving trend of the target vehicle within a preset time period, and generate a collision assessment instruction, which specifically includes:

[0126] When the system detects that the real-time vehicle distance is less than the safety threshold, the target vehicle driving trend analysis unit further analyzes the target vehicle's driving trend to ensure that the alarm will not be falsely reported due to a short vehicle distance.

[0127] This unit is mainly based on the following input data:

[0128] The current speed and acceleration of the target vehicle, the steering angle of the target vehicle, and the historical trajectory data of the target vehicle;

[0129] Based on the above data, the system uses the trajectory prediction model to calculate the future The estimated position of the target vehicle within seconds;

[0130] At the same time, combined with the motion status of surrounding vehicles, the system calculates their If the system detects that the target vehicle’s predicted trajectory will enter the safety range of surrounding vehicles within a short period of time, the system generates a collision assessment instruction.

[0131] In a preferred embodiment of the present invention, the evaluation module comprises:

[0132] A data extraction unit, used to extract the real-time vehicle distance value, the relative speed and relative acceleration of the target vehicle and surrounding vehicles according to the collision assessment instruction, and obtain a real-time motion data set;

[0133] A collision time calculation unit is used to calculate the shortest time required for the target vehicle to collide with surrounding vehicles at the current speed according to the real-time vehicle distance value and the relative speed, and obtain a collision time threshold;

[0134] A braking response time unit, used to calculate the time required for the target vehicle to completely brake according to the braking state and main acceleration of the target vehicle, and obtain a braking time threshold;

[0135] The driving path unit is used to analyze the driving path of the target vehicle within a preset braking time according to the steering angle of the steering wheel of the target vehicle to obtain the driving path data.

[0136] In an embodiment of the present invention, a data extraction unit is used to extract the real-time vehicle distance value, the relative speed and relative acceleration between the target vehicle and the surrounding vehicles according to the collision assessment instruction, obtain a real-time motion data set, extract and optimize the vehicle's motion data in real time, and ensure that the input data information is accurate; a collision time calculation unit is used to calculate the shortest time required for the target vehicle to collide with the surrounding vehicles at the current speed according to the real-time vehicle distance value and the relative speed, obtain a collision time threshold, and dynamically predict the collision time of the vehicle; a braking response time unit is used to calculate the time required for the target vehicle to completely brake according to the braking state and main acceleration of the target vehicle, obtain a braking time threshold, and accurately calculate the braking response time of the vehicle; a driving path unit is used to analyze the driving path of the target vehicle within a preset braking time according to the steering angle of the steering wheel of the target vehicle, obtain driving path data, predict the driving path of the target vehicle, and identify potential risk points before a collision occurs.

[0137] The collision time calculation unit is used to calculate the shortest time required for the target vehicle to collide with surrounding vehicles at the current speed according to the real-time vehicle distance value and relative speed, and obtain the collision time threshold, which specifically includes:

[0138] The system uses the real-time vehicle distance and relative speed to calculate the time required for the two vehicles to come into contact under the current conditions. If there is a relative acceleration between the two vehicles, the system will also consider the impact of acceleration on the collision time. The calculation formula is as follows: ,

[0139] in, is the collision time threshold, is the relative speed, is the relative acceleration, The real-time vehicle distance value.

[0140] The braking response time unit is used to calculate the time required for the target vehicle to completely brake according to the braking state and main acceleration of the target vehicle, and obtain the braking time threshold, which specifically includes:

[0141] The core goal of this unit is to calculate the time required for the target vehicle to come to a complete stop under braking. First, the target vehicle data is called to calculate the shortest time required for braking. In addition, the system also takes into account the driver's braking reaction time. , that is, the delay time from the alarm to the driver starting to brake, the final braking time is calculated as follows: ,

[0142] in, is the braking time threshold, is the braking reaction time, is the target vehicle’s speed, is the maximum braking deceleration of the target vehicle, which is determined by the brake pedal pressure and the vehicle's braking force.

[0143] The driving path unit is used to analyze the driving path of the target vehicle within the preset braking time according to the steering angle of the target vehicle's steering wheel to obtain the driving path data, which specifically includes:

[0144] During vehicle movement, the current speed and acceleration alone are not sufficient to fully assess the risk of collision, so the future driving trajectory of the target vehicle is predicted.

[0145] First, the system calls the steering wheel angle data and calculates the future time based on the current vehicle speed. Estimated driving route within: ,

[0146] in, is the future time of the target vehicle The expected driving path within is the current position of the target vehicle, is the current speed of the target vehicle, is the current acceleration of the target vehicle, is the influence factor of the steering angle on the path.

[0147] In a preferred embodiment of the present invention, the evaluation module further includes:

[0148] The braking success probability unit is used to calculate whether the target vehicle can complete braking before collision under different braking forces according to the collision time threshold and the braking time threshold, and obtain the braking success probability;

[0149] A path overlap analysis unit is used to calculate the probability of path overlap between the target vehicle and surrounding vehicles based on the driving path data to obtain the path overlap probability;

[0150] The potential collision probability unit is used to calculate the probability of collision between the target vehicle and surrounding vehicles according to the braking success probability and the path overlap probability, and obtain the potential collision probability.

[0151] In an embodiment of the present invention, a braking success probability unit is used to calculate whether the target vehicle can complete braking before collision under different braking forces according to a collision time threshold and a braking time threshold, and obtain a braking success probability. By accurately calculating the braking capacity of the target vehicle, the problem of false alarm or missed alarm caused by the setting of a fixed threshold is avoided; a path overlap analysis unit is used to calculate the probability of path overlap between the target vehicle and surrounding vehicles according to the driving path data, and obtain a path overlap probability. Through path prediction and overlap analysis, the system can identify potential collision risks in advance; a potential collision probability unit is used to calculate the probability of collision between the target vehicle and surrounding vehicles according to the braking success probability and the path overlap probability, and obtain a potential collision probability. By quantifying the potential collision risk, the system can more accurately determine whether an alarm needs to be issued.

[0152] The braking success probability unit is used to calculate whether the target vehicle can complete braking before collision under different braking forces according to the collision time threshold and the braking time threshold, and obtain the braking success probability, which specifically includes:

[0153] The system first extracts the current speed, relative speed, relative acceleration of the target vehicle and the motion state parameters of the surrounding vehicles, and analyzes them in combination with the braking performance of the target vehicle. The braking performance includes braking response time, maximum braking force, tire grip and current road conditions. The system obtains road condition information in real time through V2V communication, and calculates the shortest braking distance of the target vehicle in combination with the historical braking data of the target vehicle.

[0154] Based on the above data, the system calculates the time required for the target vehicle to brake at the current speed and maximum braking force, that is, the braking time threshold. Then, the system compares the braking time threshold with the collision time threshold between the target vehicle and the vehicle in front to determine whether the target vehicle can be completely stopped before a collision occurs. If the braking time threshold is less than the collision time threshold, the system determines that the target vehicle can brake successfully. Otherwise, it is considered that the target vehicle is at risk of collision and further risk assessment is required in combination with other parameters.

[0155] The path overlap analysis unit is used to calculate the probability of path overlap between the target vehicle and surrounding vehicles according to the driving path data to obtain the path overlap probability, which specifically includes:

[0156] Based on the calculation of the probability of successful braking, the system further analyzes the target vehicle's future driving trajectory to determine whether there is a risk of overlapping paths between the target vehicle and surrounding vehicles. First, the system uses the target vehicle's current speed, steering wheel angle, lane line data, and V2V communication data to predict the target vehicle's possible driving path within the preset braking time. The path prediction uses the Bezier curve fitting method, combined with historical driving behavior and current direction angle, to generate a possible driving trajectory.

[0157] Next, the system obtains the historical motion trajectories of surrounding vehicles and predicts their future driving trajectories based on their current direction angles, speeds, and accelerations. The system matches the predicted trajectory of the target vehicle with the predicted trajectories of surrounding vehicles, calculates whether there is a trajectory intersection within a preset time, and determines the probability value of the trajectory intersection. If the trajectory intersection falls within the braking time range of the target vehicle, the system considers that the path overlap risk is high and needs to be further combined with the collision probability calculation module for risk assessment.

[0158] In a preferred embodiment of the present invention, the calculation formula of the potential collision probability is: ,

[0159] in, is the potential collision probability of the target vehicle, is the collision time threshold, is the path overlap probability, is the braking time threshold, is the relative speed between the target vehicle and surrounding vehicles, is the relative acceleration between the target vehicle and surrounding vehicles, is the coefficient.

[0160] In the embodiment of the present invention, the calculation formula comprehensively considers multiple dynamic factors, including collision time threshold, path overlap probability, braking time threshold, relative speed and relative acceleration, etc., so as to more accurately evaluate the collision risk between the target vehicle and surrounding vehicles. Traditional collision probability calculation is usually based only on the static distance or relative speed between vehicles, while ignoring multi-dimensional dynamic influencing factors, resulting in false alarms or missed alarms under complex road conditions. The calculation formula of this system introduces multiple variables, making the calculation of collision probability more accurate, thereby improving the warning effect of the system.

[0161] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent assisted driving alarm system based on V2V communication, characterized in that: include: A first calculation module is used to calculate the operation stability of the target vehicle according to the operation status data in the standardized data set to obtain an operation stability parameter; The second calculation module is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the target vehicle data and the environmental data, and obtain the basic safe vehicle distance threshold; A correction module is used to calculate an operation correction value according to the operation stability parameter, and to correct the basic safe vehicle distance threshold according to the operation correction value to obtain a dynamic safe vehicle distance threshold; A relative motion module is used to determine the relative motion parameters between the target vehicle and each surrounding vehicle according to the target vehicle data and the environmental data, and obtain a relative motion parameter set; A detection module, used to monitor the real-time vehicle distance between the target vehicle and surrounding vehicles through a relative motion parameter set, and compare it with a dynamic safety vehicle distance threshold, and generate a collision assessment instruction when the real-time vehicle distance exceeds the dynamic safety vehicle distance threshold; An evaluation module, used to calculate the potential collision probability according to the collision evaluation instruction and obtain a collision risk index; The alarm module is used to set the corresponding warning trigger conditions according to the collision risk index, and generate a warning signal for warning.

2. The intelligent assisted driving alarm system based on V2V communication according to claim 1 is characterized in that: The first calculation module includes: A data conversion unit, used for normalizing the throttle opening value, brake pedal pressure value and steering wheel steering angle in the operation state data to obtain standard operation data; A stability analysis unit, used to calculate the throttle opening change rate, brake pedal pressure change rate and steering wheel steering angle change rate of the target vehicle per unit time according to standard operation data, and obtain an operation fluctuation data set; A lane change analysis unit is used to determine whether the target vehicle performs a lane change operation according to the steering wheel steering angle change rate, and to count the number of lane change operations to obtain the number of lane changes; The stability parameter generating unit is used to calculate the operation stability of the target vehicle according to the operation fluctuation data set and the number of lane changes, and obtain the operation stability parameter.

3. The intelligent assisted driving alarm system based on V2V communication according to claim 2 is characterized in that: The calculation formula of the stable operation is: ， in, For the operational stability of the target vehicle, is the number of times the operation status data is collected, is the index of the number of collections, For the The throttle opening value collected for each time, is the time variable, For the The throttle opening change rate during the first acquisition, For the The brake pedal pressure value collected each time, For the The brake pedal pressure change rate at the time of the first collection, For the The steering wheel angle collected For the The steering wheel angle change rate at the time of acquisition, is the number of lane changes, is the index of the number of lane changes, For the Steering wheel angle change rate for lane change, is the coefficient.

4. The intelligent assisted driving alarm system based on V2V communication according to claim 3 is characterized in that: The second calculation module includes: A surrounding vehicle travel distance unit is used to calculate the travel distance of surrounding vehicles within a preset time period according to the auxiliary vehicle speed and auxiliary acceleration of surrounding vehicles, and obtain a surrounding vehicle travel distance data set; A target vehicle travel distance unit is used to calculate the travel distance of the target vehicle within a preset time period according to the main vehicle speed and main acceleration of the target vehicle, and obtain a target vehicle travel distance data set; A minimum safety distance calculation unit is used to determine the minimum relative driving distance between the target vehicle and the surrounding vehicles according to the surrounding vehicle driving distance data set and the target vehicle driving distance data set, so as to obtain a safety reference data set; The basic threshold value generating unit is used to calculate the safe vehicle distance range between the target vehicle and surrounding vehicles according to the safety reference data set to obtain the basic safe vehicle distance threshold value.

5. The intelligent assisted driving alarm system based on V2V communication according to claim 4 is characterized in that: The correction module comprises: A lane change detection unit is used to count the number of lane changes of the target vehicle within a preset time window to obtain lane change frequency data; A brake control analysis unit, used to analyze the degree of sudden change in braking of the target vehicle according to the change rate of the brake pedal pressure of the target vehicle within a preset time window, and to count the number of emergency braking times to obtain emergency braking data; The throttle control analysis unit is used to analyze whether the target vehicle frequently accelerates suddenly according to the throttle pedal pressure change rate of the target vehicle within a preset time window, and to count the number of sudden accelerations to obtain sudden acceleration data; an operation correction value calculation unit, for calculating an operation correction value of a target vehicle according to the lane change frequency data, the emergency braking data and the sudden acceleration data; The threshold correction calculation unit is used to correct the basic safe vehicle distance threshold according to the operation correction value to obtain the dynamic safe vehicle distance threshold.

6. The intelligent assisted driving alarm system based on V2V communication according to claim 5 is characterized in that: The relative motion module comprises: A relative speed calculation unit, used to calculate the relative speed between the target vehicle and surrounding vehicles according to the main vehicle speed in the target vehicle data and the auxiliary vehicle speed in the environmental data; A relative acceleration calculation unit, used to calculate the relative acceleration between the target vehicle and surrounding vehicles based on the main acceleration in the target vehicle data and the secondary acceleration in the environmental data; A relative position calculation unit, used to calculate the relative position between the target vehicle and surrounding vehicles according to the coordinate position in the environmental data; The relative motion parameter set generating unit is used to combine the relative velocity, relative acceleration and relative position to generate a relative motion parameter set.

7. The intelligent assisted driving alarm system based on V2V communication according to claim 6 is characterized in that: The detection module comprises: A real-time vehicle distance calculation unit is used to calculate the real-time driving distance between the target vehicle and each surrounding vehicle according to the relative motion parameter set to obtain a real-time vehicle distance value; A vehicle distance threshold comparison unit is used to screen the real-time vehicle distance value according to the dynamic safety vehicle distance threshold, and determine whether the real-time vehicle distance between the target vehicle and surrounding vehicles exceeds the dynamic safety vehicle distance threshold. When the result is yes, a warning trigger signal is generated; The target vehicle driving trend analysis unit is used to analyze the current main vehicle speed and main acceleration of the target vehicle according to the early warning trigger signal, predict the driving trend of the target vehicle within a preset time period, and generate a collision assessment instruction.

8. The intelligent assisted driving alarm system based on V2V communication according to claim 7 is characterized in that: The evaluation module includes: A data extraction unit, used to extract the real-time vehicle distance value, the relative speed and relative acceleration of the target vehicle and surrounding vehicles according to the collision assessment instruction, and obtain a real-time motion data set; A collision time calculation unit is used to calculate the shortest time required for the target vehicle to collide with surrounding vehicles at the current speed according to the real-time vehicle distance value and the relative speed, and obtain a collision time threshold; A braking response time unit, used to calculate the time required for the target vehicle to completely brake according to the braking state and main acceleration of the target vehicle, and obtain a braking time threshold; The driving path unit is used to analyze the driving path of the target vehicle within a preset braking time according to the steering angle of the steering wheel of the target vehicle to obtain the driving path data.

9. The intelligent assisted driving alarm system based on V2V communication according to claim 8 is characterized in that: The evaluation module also includes: The braking success probability unit is used to calculate whether the target vehicle can complete braking before collision under different braking forces according to the collision time threshold and the braking time threshold, and obtain the braking success probability; A path overlap analysis unit is used to calculate the probability of path overlap between the target vehicle and surrounding vehicles based on the driving path data to obtain the path overlap probability; The potential collision probability unit is used to calculate the probability of collision between the target vehicle and surrounding vehicles according to the braking success probability and the path overlap probability, and obtain the potential collision probability.

10. The intelligent assisted driving alarm system based on V2V communication according to claim 9, characterized in that: The calculation formula of the potential collision probability is: ， in, is the potential collision probability of the target vehicle, is the collision time threshold, is the path overlap probability, is the braking time threshold, is the relative speed between the target vehicle and surrounding vehicles, is the relative acceleration between the target vehicle and surrounding vehicles, is the coefficient.

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