Brake deceleration early warning system and method thereof

Abstract

The invention belongs to the field of brake deceleration early warning, relates to a data analysis technology, and aims to solve the problem that the early warning basis cannot be dynamically optimized by combining the driving habit of a driver and the loss state of a vehicle in the prior art, in particular to a brake deceleration early warning system and method. The deceleration early warning platform is in communication connection with a user portrait module, a loss evaluation module, a database and a plurality of terminal processors; according to the invention, an optimization processing module obtains a primary conventional early warning threshold and an advanced conventional early warning threshold from a database as initial references, wherein the numerical value of the advanced conventional early warning threshold is set to be smaller than that of the primary conventional early warning threshold so as to define the early warning level priority; according to the process, a dynamic scaling mechanism of an early warning judgment basis is realized, so that threshold adjustment independently depends on driver behavior characteristics and also independently depends on a vehicle loss state, and the optimized early warning threshold can be ensured to synchronously adapt to individual differences and vehicle performance changes.

Description

Technical Field

[0001] This invention belongs to the field of brake deceleration warning and involves data analysis technology, specifically a brake deceleration warning system and method. Background Technology

[0002] Brake warning systems are active safety technologies that use sensors and algorithms to monitor obstacles or dangerous situations ahead in real time and automatically issue warnings or brake when necessary. They detect obstacles ahead using radar or cameras and automatically trigger braking when the distance is too close to reduce the risk of collision.

[0003] The invention patent with publication number CN103303225B discloses an intelligent braking warning system. This intelligent braking warning system starts from active defense and makes up for the deficiencies in the current active safety configuration of automobiles. It has the advantages of low cost, high accuracy, flexible setting and good stability. However, this warning system cannot dynamically optimize the warning basis by combining the driver's driving habits and the wear and tear of the vehicle, which leads to a mismatch between the warning trigger result and the actual collision risk. In particular, when the warning result contradicts the user's habits, it may affect the driver's normal driving operation.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a brake deceleration warning system and method to solve the problem that the existing technology cannot dynamically optimize the warning basis by combining the driver's driving habits and the wear and tear of the vehicle. The technical problem to be solved by the present invention is: how to provide a braking deceleration warning system and method that can dynamically optimize the warning basis by combining the driver's driving habits and the wear and tear of the vehicle.

[0006] The objective of this invention can be achieved through the following technical solutions: A braking deceleration warning system includes a deceleration warning platform, which is communicatively connected to a user profiling module, a wear assessment module, a database, and several terminal processors. The user profiling module is used to perform profiling analysis on drivers who access the deceleration warning platform: the registered drivers of the vehicles corresponding to the terminal processor are marked as analysis objects, an analysis period is generated, the following data and lane-changing data of the analysis objects within the analysis period are obtained, and the analysis objects are marked as aggressive objects or normal objects based on the following data and lane-changing data. The loss assessment module is used to perform loss assessment and analysis on vehicles connected to the deceleration warning platform: the vehicle corresponding to the terminal processor is marked as the assessment object, and the braking data and tire temperature data of the assessment object within the analysis period are obtained; the assessment object is marked as a positive loss object or a negative loss object by using the braking data and tire temperature data. The terminal processor is communicatively connected to a data acquisition module, an optimization processing module, an early warning analysis module, and a controller; The data acquisition module is used to collect driving data from vehicles connected to the deceleration warning platform; The optimization processing module is used to optimize the criteria for judging the vehicle's braking warning. The early warning analysis module is used to perform real-time braking early warning analysis on the vehicle.

[0007] Furthermore, the process of acquiring following data includes: extracting the license plate number of the vehicle in front of the analysis subject using a dashcam and marking it as recorded information; marking the driving period during which the recorded information does not change for L1 seconds and the average vehicle speed is not lower than L2 km / h as the following period; marking the shortest distance between the analysis subject's vehicle and the vehicle in front during the following period as the following value of the following period; and summing and averaging the following values ​​of all following periods within the analysis period to obtain the following data. The process of acquiring lane-changing data includes: marking the process from vehicle start-up to engine shutdown as the target process; marking the ratio of the actual number of lane changes during the target process to the number of lane changes in the navigation plan as the lane-changing value of the target process; and summing and averaging the lane-changing values ​​of all target processes within the analysis period to obtain the lane-changing data.

[0008] Furthermore, the specific process of marking the analysis object as an aggressive object or a regular object includes: obtaining the following threshold and lane-changing threshold from the database, and comparing the following data and lane-changing data of the analysis object with the following threshold and lane-changing threshold respectively: if the following data is less than the following threshold or the lane-changing data is greater than or equal to the lane-changing threshold, then the corresponding analysis object is marked as an aggressive object; otherwise, the corresponding analysis object is marked as a regular object.

[0009] Furthermore, the point braking data refers to the number of point braking events of the evaluation object within the analysis period. The process of acquiring tire temperature data includes: collecting tire temperature in real time through built-in sensors while the evaluation object is driving, marking the driving time period when the tire temperature is not lower than the preset temperature threshold as the temperature difference period, and marking the ratio of the sum of the durations of all temperature difference periods within the analysis period to the total driving time as the tire temperature data.

[0010] Furthermore, the specific process of marking the assessment object as a damage-corrected object or a damage-abnormal object includes: obtaining the braking threshold and tire temperature threshold from the database, and comparing the braking data and tire temperature data of the assessment object with the braking threshold and tire temperature threshold respectively; if the braking data is greater than the braking threshold or the tire temperature data is greater than the tire temperature threshold, then the corresponding assessment object is marked as a damage-abnormal object; otherwise, the corresponding assessment object is marked as a damage-corrected object.

[0011] Furthermore, the specific process of the data acquisition module collecting driving data of vehicles connected to the deceleration warning platform includes: acquiring the vehicle speed data and obstacle data of the assessment object in real time while the assessment object is driving; not performing braking warning analysis when the vehicle speed data is less than the preset vehicle speed threshold; and sending obstacle data to the optimization processing module when the vehicle speed data is not less than the preset vehicle speed threshold. The obstacle data is the straight-line distance between the assessment object and the obstacle directly in front.

[0012] Furthermore, the specific process by which the optimization processing module optimizes the vehicle's braking warning judgment criteria includes: retrieving the primary conventional warning threshold and the advanced conventional warning threshold from the database. Both the primary conventional warning threshold and the advanced conventional warning threshold are preset numerical constants, and the advanced conventional warning threshold is less than the primary conventional warning threshold; multiplying the primary conventional warning threshold by the habitual intervention coefficient t1 and the habitual intervention coefficient t2 in sequence to obtain the primary optimized warning threshold; multiplying the advanced conventional warning threshold by the habitual intervention coefficient t1 and the habitual intervention coefficient t2 in sequence to obtain the advanced optimized warning threshold; and sending the primary optimized warning threshold and the advanced optimized warning threshold to the warning analysis module.

[0013] Furthermore, the process for determining the values ​​of t1 and t2 includes: if the object being analyzed is marked as an aggressive object, then 0.85≤t1≤0.95; if the object being analyzed is marked as a normal object, then t1=1; if the object being evaluated is marked as a damaged object, then 1.05≤t2≤1.15; if the object being evaluated is marked as a damaged object, then t2=1.

[0014] Furthermore, the specific process of the early warning analysis module performing real-time braking early warning analysis on the vehicle includes: comparing the obstacle data of the evaluation object with the primary optimized early warning threshold and the advanced optimized early warning threshold; if the obstacle data is less than the primary optimized early warning threshold, it is determined that the vehicle does not need a braking warning; if the obstacle data is greater than the advanced optimized early warning threshold, it is determined that the vehicle needs a braking warning and the warning level is advanced, an advanced processing signal is generated and sent to the controller, and the controller performs emergency braking after receiving the advanced processing signal; otherwise, it is determined that the vehicle needs a braking warning and the warning level is primary, a primary processing signal is generated and sent to the controller, and the controller provides an audible and visual warning to the driver after receiving the primary processing signal.

[0015] A braking deceleration warning method includes the following steps: Step 1: Perform profile analysis on drivers connected to the deceleration warning platform and mark the analysis subjects as aggressive or regular subjects; Step 2: Conduct a damage assessment and analysis on the vehicles connected to the deceleration warning platform and mark the assessment objects as positive or negative damage objects; Step 3: Collect driving data and obtain obstacle data in real time for vehicles connected to the deceleration warning platform; Step 4: Optimize the vehicle's braking warning criteria and generate primary optimized warning thresholds and advanced optimized warning thresholds; Step 5: Perform real-time braking warning analysis on the vehicle.

[0016] The present invention has the following beneficial effects: 1. This application uses following and lane-changing thresholds retrieved from the database as objective benchmarks. Following and lane-changing data of the analyzed object are input into a comparison logic unit for dual-dimensional verification. When the system detects that following data is below the safety threshold or lane-changing data exceeds the reasonable frequency threshold, an aggressive object marking process is automatically triggered. This precisely corresponds to the impatient tendency reflected by overly close following and the aggressive habit reflected by frequent lane changes. If both data are within the safety threshold range, a regular object marking process is executed. This fallback mechanism ensures that normal driving behavior is not misjudged. This classification logic based on dual-dimensional threshold linkage makes the behavior type determination results traceable and verifiable, providing reliable behavioral feature input for subsequent warning optimization. 2. The solution in this application collects tire temperature data in real time during the driving process of the evaluated object using built-in sensors. The system continuously compares the temperature value with a preset temperature threshold. When the temperature value reaches or exceeds the threshold, the system automatically marks the driving period as a temperature anomaly period and records the duration. Simultaneously, the system uses a brake pedal sensor to accurately count and analyze the intermittent braking events within the cycle to form intermittent braking data. Finally, the system calculates the ratio of the total duration of all temperature anomaly periods to the total driving time as the tire temperature data. This process ensures that the intermittent braking data is directly related to the operating frequency of the braking system, and the tire temperature data is normalized to quantify the cumulative effect of tire overheating. Together, they constitute a repeatable and comparable objective indicator, enabling the wear assessment module to accurately distinguish between positive and negative damage objects based on actual operating data, thereby providing a reliable data foundation for the dynamic optimization of the early warning basis. 3. This application acquires vehicle speed and obstacle data of the evaluation object in real time. First, it dynamically compares the vehicle speed data with a preset threshold. When the vehicle speed data is lower than the threshold, the early warning analysis process is actively terminated, thereby avoiding invalid calculations in low-speed scenarios. When the vehicle speed data reaches or exceeds the threshold, only the obstacle data is transmitted to the optimization processing module, so that system resources are concentrated on processing high-risk scenarios. At the same time, the obstacle data provides objective distance parameters in the form of straight-line distance, laying an accurate input foundation for subsequent early warning analysis. This forms a screening mechanism based on vehicle speed conditions, effectively coordinating the resource allocation of data collection and analysis. 4. This application uses an optimization processing module to obtain primary and advanced routine warning thresholds from the database as initial benchmarks. The advanced routine warning threshold is set to be lower than the primary routine warning threshold to clarify the priority of the warning level. Subsequently, the range of the habit intervention coefficient t1 is determined based on the driver marking results output by the user profile module, and the range of the habit intervention coefficient t2 is determined based on the vehicle marking results output by the wear assessment module. On this basis, the primary routine warning threshold is multiplied by t1 and t2 in sequence to generate the primary optimized warning threshold, and the advanced routine warning threshold is multiplied by t1 and t2 in sequence to generate the advanced optimized warning threshold. Finally, the optimized thresholds are transmitted to the warning analysis module for real-time obstacle data comparison. This process realizes a dynamic scaling mechanism for the warning judgment criteria, so that the threshold adjustment depends independently on both driver behavior characteristics and vehicle wear status, thereby ensuring that the optimized warning threshold can synchronously adapt to individual differences and vehicle performance changes, avoiding the warning timing deviation caused by fixed thresholds. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention; Figure 3 This is a flowchart of the method in Embodiment 3 of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Traditional braking warning systems often fail to dynamically adjust warning criteria based on driver habits and vehicle wear and tear, leading to a mismatch between warning triggers and actual collision risks. The root of this problem lies in the system's reliance on fixed thresholds for warning decisions, neglecting to incorporate driver behavior and vehicle physical condition as dynamic inputs. This disconnect between the warning logic and real-time driving scenarios results in the system failing to adaptively optimize its evaluation criteria when driver behavior patterns or vehicle health conditions change. Consequently, the system's stability in safety performance and operational smoothness are compromised, manifesting as a deviation between warning timing and the actual risk level.

[0021] For example, in following scenarios during peak hours on urban roads, the system monitors the distance to obstacles ahead using radar, triggering a braking warning when the distance closes to a preset threshold. However, for drivers who habitually maintain a short following distance, their operating style is misjudged as high-risk by the system, resulting in frequent warning signals. Simultaneously, if brake pad wear causes a delay in braking response, the system still calculates the warning timing based on a standard model, failing to consider the impact of increased tire temperature on the coefficient of friction. In this scenario, the warning signal conflicts with the driver's actual operating intentions, disrupting the normal following rhythm, and the vehicle's physical wear and tear is not included in the risk assessment, causing the warning results to deviate from the actual collision probability.

[0022] If these issues are not addressed, the system will continue to generate warning outputs that are inconsistent with driving behavior, leading to decreased driver trust in the warning signals and potential neglect of critical safety alerts. Furthermore, the lack of quantitative assessment of vehicle wear and tear results in a mismatch between braking response characteristics and warning logic, further increasing the risk of false or missed warnings. This technical deficiency not only weakens the effectiveness of active safety features but may also affect traffic flow stability due to unnecessary braking intervention, increasing potential accident hazards.

[0023] Example 1: As Figure 1 As shown, a braking deceleration warning system includes a deceleration warning platform, which is communicatively connected to a user profiling module, a wear assessment module, a database, and several terminal processors.

[0024] The user profiling module is used to perform profile analysis on drivers connected to the deceleration warning platform. It marks the registered drivers of the vehicles corresponding to the terminal processors as analysis targets, generates analysis periods, and acquires the following and lane-changing data of the analysis targets within the analysis period. The following data acquisition process includes: extracting the license plate number of the vehicle in front of the analysis target while driving using a dashcam and marking it as recorded information; marking the driving period during which the recorded information does not change for L1 seconds and the average vehicle speed is not lower than L2 km / h as the following period; marking the shortest distance between the analysis target's vehicle and the vehicle in front within the following period as the following value for the following period; and calculating the following values ​​for all following periods within the analysis period. The following data is obtained by averaging the results. The process of acquiring lane-changing data includes: marking the process from vehicle start-up to shutdown as the target process; marking the ratio of the actual number of lane changes in the target process to the number of lane changes in the navigation plan as the lane-changing value of the target process; summing and averaging the lane-changing values ​​of all target processes within the analysis period to obtain the lane-changing data; obtaining the following threshold and lane-changing threshold from the database; comparing the following data and lane-changing data of the analysis object with the following threshold and lane-changing threshold respectively: if the following data is less than the following threshold or the lane-changing data is greater than or equal to the lane-changing threshold, the corresponding analysis object is marked as an aggressive object; otherwise, the corresponding analysis object is marked as a normal object.

[0025] Among them, a dashcam refers to an in-vehicle visual acquisition device, which can use a wide-angle lens and image recognition algorithms to capture images of vehicles ahead in real time and extract license plate information; the recorded information can be understood as license plate data identifiers obtained from the dashcam, which can be stored in the temporary cache of the in-vehicle processor for continuous tracking; L1 second refers to a preset time threshold parameter, which can be dynamically adjusted according to the degree of road congestion to ensure the stability of the recorded information and eliminate brief interference; L2 km / h refers to a preset speed threshold parameter, which can be configured based on road type to filter effective driving scenarios to avoid the influence of low-speed conditions; the following period refers to the continuous driving interval that meets the time and speed thresholds, which can be identified by a time window detection algorithm; the following value refers to the minimum distance quantification index within the following period, which can be obtained based on radar ranging or visual depth estimation technology; the target process refers to a complete driving conversation unit, which can be defined from the vehicle start signal to the end signal; the lane change value is an index of the ratio of the actual lane change behavior to the navigation-planned lane change, which can reflect the degree of randomness of the driver's lane change behavior.

[0026] Specifically, the proposed solution continuously captures license plate information of vehicles ahead using a dashcam. When this information remains stable within L1 seconds and the average vehicle speed is not lower than L2 km / h, both L1 and L2 are numerical constants, with their specific values ​​set by administrators. The system automatically marks this period as a valid following period and extracts the minimum following distance within that period as the following value. Simultaneously, the system divides the target process into units from vehicle start-up to engine shutdown, calculating the ratio of actual lane changes to planned lane changes as the lane change value. By averaging all following and lane change values ​​within the analysis period, standardized following and lane change data are generated, providing an objective basis for driver behavior classification. This process focuses on stable driving scenarios through dual constraints of time and speed thresholds, avoiding interference from congestion or frequent lane changes; it quantifies the closeness of following by using the minimum following distance, highlighting the characteristics of risky moments; it calculates the lane change ratio using navigation planning as a reference benchmark, effectively distinguishing between planned and arbitrary lane changes; and finally, it uses statistical averaging to eliminate the randomness of individual driving, ensuring the accuracy and consistency of data collection.

[0027] As a preferred embodiment, the solution of this application is implemented as follows: The dashcam uses a standard vehicle camera module, installed in the center of the vehicle's windshield, to collect real-time images of the road ahead; during highway driving, if the system detects that the license plate information of the vehicle ahead has not changed within a reasonable time period and the vehicle speed is within the normal driving range, it is automatically marked as a following period, and the minimum distance between vehicles during this period is recorded as the following value; for a daily commuting driving session, the system defines the process from vehicle start-up to engine shutdown as the target process, counts the actual number of lane-changing operations performed and compares them with the planned lane-changing number provided by the navigation system to generate a lane-changing value; after all following values ​​and lane-changing values ​​within the analysis period are aggregated and calculated by the vehicle processor, standardized data is output for use by the user profiling module.

[0028] The following threshold is a critical parameter used to quantify the safety boundary of a driver's following behavior. It can be achieved by using statistical values ​​determined based on big data analysis of historical driving behavior or by using numerical ranges recommended by industry safety standards. Its purpose is to objectively distinguish between safe following distance and dangerous following distance. The lane-changing threshold is a critical parameter used to identify whether the frequency of a driver's lane-changing behavior is abnormal. It can be achieved by using a threshold calculated through vehicle dynamic model simulation or by using a benchmark value for lane-changing frequency verified by real vehicle testing. Its purpose is to accurately capture the aggressive driving tendency reflected by excessive lane changing. The comparison process can be understood as a logical judgment operation between the quantified behavioral data and the preset safety benchmark. Its purpose is to establish a data-driven classification mechanism and avoid biases introduced by subjective judgment.

[0029] Specifically, the proposed solution uses following and lane-changing thresholds retrieved from a database as objective benchmarks. Following and lane-changing data of the analyzed object are input into a comparison logic unit for dual-dimensional verification. When the system detects that following data is below a safe threshold or lane-changing data exceeds a reasonable frequency threshold, an aggressive object marking process is automatically triggered. This precisely corresponds to the impatient tendency reflected in overly close following and the aggressive habit reflected in frequent lane changes. If both data are within the safe threshold range, a regular object marking process is executed. This fallback mechanism ensures that normal driving behavior is not misjudged. This classification logic based on dual-dimensional threshold linkage makes the behavior type determination results traceable and verifiable, providing reliable behavioral feature input for subsequent warning optimization.

[0030] The loss assessment module is used to perform loss assessment and analysis on vehicles connected to the deceleration warning platform. It marks the vehicle corresponding to the terminal processor as the assessment object, and acquires the braking point data and tire temperature data of the assessment object within the analysis period. The braking point data represents the number of braking points of the assessment object within the analysis period. The tire temperature data acquisition process includes: real-time collection of tire temperature via built-in sensors while the assessment object is driving; marking driving time periods where the tire temperature is not lower than a preset temperature threshold as temperature anomaly periods; and marking the ratio of the sum of the durations of all temperature anomaly periods to the total driving time within the analysis period as tire temperature data. The module then obtains the braking point threshold and tire temperature threshold from the database and compares the assessment object's braking point data and tire temperature data with these thresholds. If the braking point data is greater than the braking point threshold or the tire temperature data is greater than the tire temperature threshold, the corresponding assessment object is marked as a loss-prone object; otherwise, the corresponding assessment object is marked as a loss-positive object.

[0031] Specifically, point braking data refers to the number of times the object applied the brakes during the analysis period. This can be achieved by counting rapid braking events monitored by the brake pedal sensor, aiming to quantify the frequency of driver braking behavior to objectively assess the degree of mechanical wear of the braking system. The built-in sensor can be understood as a temperature detection device integrated into the tire or wheel hub, which can be implemented using thermocouples or infrared temperature sensors. Its purpose is to continuously acquire tire temperature data to support wear status analysis. In practical applications, the temperature threshold is specifically a preset tire overheating critical point, which can be dynamically set based on tire material characteristics or environmental conditions, aiming to accurately identify high-risk periods for tire wear. Temperature anomaly periods refer to driving periods where the tire temperature is not lower than the temperature threshold, which can be achieved by comparing timestamp records with the temperature threshold, aiming to eliminate interference from normal temperature fluctuations. Tire temperature data can be understood as the ratio of the sum of the durations of temperature anomaly periods to the total driving time, which can be calculated through normalization by the data processing unit, aiming to eliminate the influence of different driving times and standardize the reflection of the degree of tire overheating accumulation.

[0032] Specifically, the solution in this application uses built-in sensors to collect tire temperature data in real time during the driving process of the evaluated object. The system continuously compares the temperature value with a preset temperature threshold. When the temperature value reaches or exceeds the threshold, the system automatically marks the driving period as a temperature anomaly period and records its duration. Simultaneously, the system uses brake pedal sensors to accurately count and analyze the intermittent braking events within the cycle to form intermittent braking data. Finally, the system calculates the ratio of the total duration of all temperature anomaly periods to the total driving time as the tire temperature data. This process ensures that the intermittent braking data is directly related to the braking system's operating frequency, and the tire temperature data is normalized to quantify the cumulative effect of tire overheating. Together, they constitute a repeatable and comparable objective indicator, enabling the wear assessment module to accurately distinguish between positive and negative damage objects based on actual operating data, thereby providing a reliable data foundation for the dynamic optimization of early warning criteria.

[0033] As a specific implementation method, the solution of this application is implemented as follows: a thermocouple sensor is embedded inside the vehicle tire as a built-in sensor for real-time tire temperature acquisition; the data acquisition module transmits the temperature data to the wear assessment module; when the temperature value reaches a preset temperature threshold, the system records this period as a temperature anomaly period; simultaneously, the rapid braking events detected by the brake pedal sensor are counted as the number of braking points; at the end of the analysis cycle, the system calculates the ratio of the total duration of the temperature anomaly period to the total driving time as the tire temperature data. This implementation method utilizes the vehicle's native hardware to achieve continuous monitoring, ensuring that the tire temperature data accurately reflects the tire overheating accumulation state.

[0034] Example 2: Figure 2 As shown, the terminal processor is connected to a data acquisition module, an optimization processing module, an early warning analysis module, and a controller.

[0035] The data acquisition module is used to collect driving data of vehicles connected to the deceleration warning platform: it acquires the vehicle speed data and obstacle data of the evaluation object in real time when the evaluation object is driving; it does not perform braking warning analysis when the vehicle speed data is less than the preset vehicle speed threshold; and it sends the obstacle data to the optimization processing module when the vehicle speed data is not less than the preset vehicle speed threshold. The obstacle data is the straight-line distance between the evaluation object and the obstacle directly in front of it.

[0036] Specifically, vehicle speed data refers to the quantified information of the vehicle's current speed, which can be obtained using vehicle speed sensors or a global positioning system module, with the aim of monitoring the vehicle's dynamic status in real time; obstacle data refers to the spatial distance parameters between the assessed object and obstacles directly in front, which can be obtained using millimeter-wave radar or stereo vision cameras, with the aim of accurately perceiving the environment ahead; the preset vehicle speed threshold refers to a pre-set speed reference benchmark, which can be configured based on road type or vehicle model, with the aim of distinguishing between low-risk and high-risk driving scenarios; not performing brake warning analysis refers to suspending the warning calculation process, which can be achieved through software logic gating mechanisms, with the aim of avoiding redundant processing in low-speed scenarios; sending obstacle data to the optimization processing module refers to data transmission behavior, which can be achieved through the vehicle controller's local area network, with the aim of ensuring timely information flow in high-risk scenarios.

[0037] Specifically, the solution proposed in this application acquires vehicle speed data and obstacle data of the evaluation object in real time. First, the vehicle speed data is dynamically compared with a preset threshold. When the vehicle speed data is lower than the threshold, the early warning analysis process is actively terminated, thereby avoiding invalid calculations in low-speed scenarios. When the vehicle speed data reaches or exceeds the threshold, only the obstacle data is transmitted to the optimization processing module, so that system resources are concentrated on the processing of high-risk scenarios. At the same time, the obstacle data provides objective distance parameters in the form of straight-line distance, laying an accurate input foundation for subsequent early warning analysis. This forms a screening mechanism based on vehicle speed conditions, effectively coordinating the resource allocation of the data collection and analysis stages.

[0038] The optimization processing module optimizes the vehicle's braking warning judgment criteria: It retrieves the primary and advanced conventional warning thresholds from the database. Both thresholds are preset numerical constants, with the advanced threshold being smaller than the primary threshold. The primary conventional warning threshold is multiplied sequentially by the habitual intervention coefficient t1 and t2 to obtain the primary optimized warning threshold. Similarly, the advanced conventional warning threshold is multiplied sequentially by the habitual intervention coefficient t1 and t2 to obtain the advanced optimized warning threshold. The determination process for t1 and t2 includes: if the analyzed object is marked as an aggressive object, then 0.85 ≤ t1 ≤ 0.95; if the analyzed object is marked as a conventional object, then t1 = 1; if the evaluated object is marked as a damaged object, then 1.05 ≤ t2 ≤ 1.15; if the evaluated object is marked as a damaged positive object, then t2 = 1. The primary optimized warning threshold and the advanced optimized warning threshold are then sent to the warning analysis module.

[0039] Among them, the primary routine warning threshold refers to the basic warning trigger benchmark preset by the system, which can be implemented using a fixed numerical constant stored in the database, with the aim of providing a standardized primary warning reference point; the advanced routine warning threshold refers to the emergency warning trigger benchmark preset by the system, which can be implemented using a fixed numerical constant lower than the primary routine warning threshold, with the aim of defining the triggering conditions for high-priority warning scenarios; the habit intervention coefficient t1 refers to a dynamic adjustment parameter reflecting the driver's behavioral characteristics, which can be implemented using a numerical range determined based on the driver profile analysis results, for example, by mapping through aggressive or routine object labels output by the user profile module, with the aim of adapting to the following habits of different drivers; the habit intervention coefficient t2 refers to the anti- The dynamic adjustment parameters reflecting the vehicle's braking performance status can be implemented using a numerical range determined based on the vehicle wear assessment results. For example, they can be mapped using the abnormal or positive damage object markers output by the wear assessment module. The purpose is to compensate for the actual wear status of the vehicle's braking system. The primary optimized warning threshold refers to the primary warning benchmark after adjustment by the habitual intervention coefficient. It can be obtained by multiplying the primary conventional warning threshold by t1 and t2 in sequence. The purpose is to personalize the warning basis to match the real-time driving scenario. The advanced optimized warning threshold refers to the advanced warning benchmark after adjustment by the habitual intervention coefficient. It can be obtained by multiplying the advanced conventional warning threshold by t1 and t2 in sequence. The purpose is to ensure that the triggering time of the emergency warning is synchronized with the vehicle status.

[0040] Specifically, the proposed solution uses an optimization processing module to obtain a primary routine warning threshold and a high-level routine warning threshold from the database as initial benchmarks. The high-level routine warning threshold is set lower than the primary routine warning threshold to clarify the warning level priority. Subsequently, the range of the habit intervention coefficient t1 is determined based on the driver marking results output by the user profiling module, and the range of the habit intervention coefficient t2 is determined based on the vehicle marking results output by the wear assessment module. Based on this, the primary routine warning threshold is multiplied sequentially by t1 and t2 to generate a primary optimized warning threshold, and the high-level routine warning threshold is multiplied sequentially by t1 and t2 to generate a high-level optimized warning threshold. Finally, the optimized thresholds are transmitted to the warning analysis module for real-time obstacle data comparison. This process implements a dynamic scaling mechanism for warning judgment criteria, ensuring that threshold adjustments depend independently on both driver behavior characteristics and vehicle wear status. This guarantees that the optimized warning thresholds can synchronously adapt to individual differences and vehicle performance changes, avoiding warning timing deviations caused by fixed thresholds.

[0041] The early warning analysis module is used to perform real-time braking early warning analysis on the vehicle: it compares the obstacle data of the evaluation object with the primary optimization early warning threshold and the advanced optimization early warning threshold: if the obstacle data is less than the primary optimization early warning threshold, it is determined that the vehicle does not need a braking warning; if the obstacle data is greater than the advanced optimization early warning threshold, it is determined that the vehicle needs a braking warning and the warning level is advanced, an advanced processing signal is generated and sent to the controller, and the controller performs emergency braking upon receiving the advanced processing signal; otherwise, it is determined that the vehicle needs a braking warning and the warning level is primary, a primary processing signal is generated and sent to the controller, and the controller provides an audible and visual warning to the driver upon receiving the primary processing signal.

[0042] The warning analysis module is a logic unit used to perform braking warning analysis. It can be implemented using an embedded processor or a dedicated integrated circuit. Its purpose is to process obstacle data in real time and generate corresponding processing signals. Obstacle data refers to the straight-line distance between the assessment object and the obstacle directly in front. It can be collected by millimeter-wave radar or camera sensors. Its purpose is to quantify the proximity of the vehicle to the obstacle in front. The primary optimized warning threshold and the advanced optimized warning threshold refer to the warning distance thresholds that have been dynamically optimized. They can be calculated based on the optimization parameters stored in the database. Their purpose is to adjust the warning sensitivity according to the driver's habits and the vehicle's wear and tear. The judgment logic refers to the rules for determining the warning level based on the comparison results of obstacle data and thresholds. It can be implemented using conditional statements. Its purpose is to achieve graded judgment of warnings. The processing signal refers to the electrical signal indicating the warning level. It can be represented as a digital signal or an analog signal. Its purpose is to trigger the corresponding action of the controller. The controller is a unit that receives the processing signal and performs braking or warning operations. It can be implemented using a vehicle electronic control unit. Its purpose is to ensure that the warning results are transformed into actual safety measures.

[0043] Example 3: Figure 3 As shown, a braking deceleration warning method includes the following steps: Step 1: Perform profile analysis on drivers connected to the deceleration warning platform and mark the analysis subjects as aggressive or regular subjects; Step 2: Conduct a damage assessment and analysis on the vehicles connected to the deceleration warning platform and mark the assessment objects as positive or negative damage objects; Step 3: Collect driving data and obtain obstacle data in real time for vehicles connected to the deceleration warning platform; Step 4: Optimize the vehicle's braking warning criteria and generate primary optimized warning thresholds and advanced optimized warning thresholds; Step 5: Perform real-time braking warning analysis on the vehicle.

[0044] A braking deceleration warning system and method are disclosed. During operation, the deceleration warning platform coordinates the data flow of various modules to ensure that the analysis results of the user profiling module and the wear assessment module are transmitted to the terminal processor in a timely manner. For example, during the implementation of the user profiling module, a driving recorder and onboard radar can work together to monitor the changes in the relative distance between the vehicle and the vehicle in front in real time. Combined with the lane-changing operation frequency recorded by the navigation system, the average following distance to lane-changing frequency ratio within the analysis period is calculated to objectively determine the driver's style type. Similarly, the wear assessment module uses tire-embedded temperature sensors and brake pedal pressure detection devices to continuously record the number of intermittent braking actions and periods of abnormal tire temperature to quantify the vehicle's wear status. Therefore, the optimization processing module adaptively corrects the preset warning threshold according to the style classification of the analyzed object and the wear status classification of the assessed object. For example, when the analyzed object is identified as an aggressive object, the system automatically lowers the warning trigger threshold to adapt to its shorter safe distance habit, while when the assessed object is marked as a damaged object, the threshold is correspondingly increased to compensate for the decrease in braking performance caused by tire wear.

[0045] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0046] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0047] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A brake deceleration warning system, characterized in that, This includes a deceleration warning platform, which is communicatively connected to a user profiling module, a loss assessment module, a database, and several terminal processors. The user profiling module is used to perform profiling analysis on drivers who access the deceleration warning platform: the registered drivers of the vehicles corresponding to the terminal processor are marked as analysis objects, an analysis period is generated, the following data and lane-changing data of the analysis objects within the analysis period are obtained, and the analysis objects are marked as aggressive objects or normal objects based on the following data and lane-changing data. The loss assessment module is used to perform loss assessment and analysis on vehicles connected to the deceleration warning platform: the vehicle corresponding to the terminal processor is marked as the assessment object, and the braking data and tire temperature data of the assessment object within the analysis period are obtained; the assessment object is marked as a positive loss object or a negative loss object by using the braking data and tire temperature data. The terminal processor is communicatively connected to a data acquisition module, an optimization processing module, an early warning analysis module, and a controller; The data acquisition module is used to collect driving data from vehicles connected to the deceleration warning platform; The optimization processing module is used to optimize the criteria for judging the vehicle's braking warning. The early warning analysis module is used to perform real-time braking early warning analysis on the vehicle.

2. The brake deceleration warning system according to claim 1, characterized in that, The process of acquiring following data includes: extracting the license plate number of the vehicle in front of the analysis subject using a dashcam and marking it as recorded information; marking the driving period during which the recorded information does not change for L1 seconds and the average vehicle speed is not less than L2 km / h as the following period; marking the shortest distance between the analysis subject's vehicle and the vehicle in front during the following period as the following value of the following period; and summing and averaging the following values ​​of all following periods within the analysis period to obtain the following data. The process of acquiring lane-changing data includes: marking the process from vehicle start-up to engine shutdown as the target process; marking the ratio of the actual number of lane changes during the target process to the number of lane changes planned in the navigation as the lane-changing value of the target process; and summing and averaging the lane-changing values ​​of all target processes within the analysis period to obtain the lane-changing data.

3. The brake deceleration warning system according to claim 2, characterized in that, The specific process of marking an analysis object as an aggressive object or a regular object includes: obtaining the following threshold and lane-changing threshold from the database, and comparing the following data and lane-changing data of the analysis object with the following threshold and lane-changing threshold respectively: if the following data is less than the following threshold or the lane-changing data is greater than or equal to the lane-changing threshold, the corresponding analysis object is marked as an aggressive object; otherwise, the corresponding analysis object is marked as a regular object.

4. A brake deceleration warning system according to claim 3, characterized in that, The braking point data refers to the number of braking points of the evaluation object within the analysis period. The process of obtaining tire temperature data includes: collecting tire temperature in real time through built-in sensors while the evaluation object is driving, marking the driving time period when the tire temperature is not lower than the preset temperature threshold as the temperature difference period, and marking the ratio of the sum of the duration of all temperature difference periods within the analysis period to the total driving time as the tire temperature data.

5. A brake deceleration warning system according to claim 4, characterized in that, The specific process of marking the assessment object as a damage-corrected object or a damage-abnormal object includes: obtaining the braking threshold and tire temperature threshold from the database, and comparing the braking data and tire temperature data of the assessment object with the braking threshold and tire temperature threshold respectively; if the braking data is greater than the braking threshold or the tire temperature data is greater than the tire temperature threshold, the corresponding assessment object is marked as a damage-abnormal object; otherwise, the corresponding assessment object is marked as a damage-corrected object.

6. A brake deceleration warning system according to claim 5, characterized in that, The specific process of the data acquisition module collecting driving data of vehicles connected to the deceleration warning platform includes: acquiring the vehicle speed data and obstacle data of the evaluation object in real time while the evaluation object is driving; not performing braking warning analysis when the vehicle speed data is less than the preset vehicle speed threshold; and sending obstacle data to the optimization processing module when the vehicle speed data is not less than the preset vehicle speed threshold. The obstacle data is the straight-line distance between the evaluation object and the obstacle directly in front.

7. A brake deceleration warning system according to claim 6, characterized in that, The specific process of optimizing the vehicle's braking warning judgment criteria by the optimization processing module includes: retrieving the primary conventional warning threshold and the advanced conventional warning threshold from the database. Both the primary conventional warning threshold and the advanced conventional warning threshold are preset numerical constants, and the advanced conventional warning threshold is less than the primary conventional warning threshold; multiplying the primary conventional warning threshold by the habitual intervention coefficient t1 and the habitual intervention coefficient t2 in sequence to obtain the primary optimized warning threshold; multiplying the advanced conventional warning threshold by the habitual intervention coefficient t1 and the habitual intervention coefficient t2 in sequence to obtain the advanced optimized warning threshold; and sending the primary optimized warning threshold and the advanced optimized warning threshold to the warning analysis module.

8. A brake deceleration warning system according to claim 7, characterized in that, The process for determining the values ​​of t1 and t2 includes: if the object being analyzed is marked as an aggressive object, then 0.85≤t1≤0.95; if the object being analyzed is marked as a normal object, then t1=1; if the object being evaluated is marked as a damaged object, then 1.05≤t2≤1.15; if the object being evaluated is marked as a damaged object, then t2=1.

9. A brake deceleration warning system according to claim 8, characterized in that, The specific process of the early warning analysis module for real-time braking early warning analysis of the vehicle includes: comparing the obstacle data of the evaluation object with the primary optimized early warning threshold and the advanced optimized early warning threshold; if the obstacle data is less than the primary optimized early warning threshold, it is determined that the vehicle does not need a braking warning; if the obstacle data is greater than the advanced optimized early warning threshold, it is determined that the vehicle needs a braking warning and the warning level is advanced, an advanced processing signal is generated and sent to the controller, and the controller performs emergency braking upon receiving the advanced processing signal; otherwise, it is determined that the vehicle needs a braking warning and the warning level is primary, a primary processing signal is generated and sent to the controller, and the controller provides an audible and visual warning to the driver upon receiving the primary processing signal.

10. A braking deceleration warning method, characterized in that, Includes the following steps: Step 1: Perform profile analysis on drivers connected to the deceleration warning platform and mark the analysis subjects as aggressive or regular subjects; Step 2: Conduct a damage assessment and analysis on the vehicles connected to the deceleration warning platform and mark the assessment objects as positive or negative damage objects; Step 3: Collect driving data and obtain obstacle data in real time for vehicles connected to the deceleration warning platform; Step 4: Optimize the vehicle's braking warning criteria and generate primary optimized warning thresholds and advanced optimized warning thresholds; Step 5: Perform real-time braking warning analysis on the vehicle.

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