Dynamic visual induction marked line based on flash rate principle and layout method thereof

Abstract

The invention designs a dynamic visual induction marking line based on a flash rate principle and a layout method thereof, which are suitable for a key guide area in front of a guide lane. According to the invention, the area in front of the guide lane line is divided into a visual deceleration area and an R area, and solid line driving behavior guidance is realized through dynamic virtual marking lines. The visual deceleration area adopts white variable-spacing marking lines, the spacing is calculated based on the flashing rate principle, rhythm sensation in the deceleration process is simulated, and a driver is guided to stably decelerate; the R area dynamically adjusts equal-interval marking lines and pattern types according to the risk factor R, and the prompt rhythm is optimized in real time in combination with parameters such as the vehicle speed, the acceleration and the visibility. The marking line is virtually projected through AR equipment and gradually coincides with a real lane line along with approaching of a vehicle, and a virtual-real fusion guiding effect is formed. The method can be extensively applied to ramps, schools, intersections and other scenes needing reinforced guidance, and the traffic safety and the driving compliance are improved.

Description

Technical Field

[0001] This invention belongs to the fields of road traffic sign technology and traffic management, and particularly relates to a dynamic visual guidance marking based on the flash rate principle and its layout method. Background Technology

[0002] With the development of intelligent transportation systems and autonomous driving technology, lane guidance markings, as an important component of road traffic, have expanded their functions beyond traditional lane division and traffic guidance to include visual guidance, risk warning, and behavioral intervention. In today's complex and ever-changing urban traffic environment, improving the guidance efficiency and adaptability of lane markings has become a crucial research direction in the field of road safety management.

[0003] In recent years, some studies have attempted to regulate driving behavior through changes in visual rhythm, such as gradually changing directional markings and width-gradient markings. These designs, to some extent, utilize the human visual system's sensitivity to changes in image density and frequency, creating a visual deceleration effect. However, most of these methods use fixed patterns and static parameters, lacking the ability to perceive actual traffic behavior and environmental variables, resulting in significant limitations in their guiding effect.

[0004] Meanwhile, the field of traffic management has proposed a series of concepts such as behavioral scoring to incentivize drivers to obey traffic laws and improve overall safety. However, most of these mechanisms rely on post-event recording and penalties, lacking real-time linkage with traffic facilities and proactive early warning, making it difficult to guide and intervene before driving behavior occurs.

[0005] Furthermore, with the gradual popularization of vehicle-mounted display technologies such as HUD (Head-Up Display) and AR (Augmented Reality), vehicle-road cooperation has become an important development direction for future road guidance systems. However, existing road marking systems generally lack structured data interfaces and executable pattern generation mechanisms, making it difficult to link with vehicle-mounted intelligent devices, resulting in fragmented visual guidance information and low collaborative efficiency.

[0006] The existing technology has the following main shortcomings: 1. Road marking designs are static and fixed, lacking adaptability to dynamic scenarios. Current standards mostly use fixed lengths, fixed spacing, and uniform patterns, making it difficult to meet the diverse needs of different road conditions, vehicle speeds, driving styles, and other factors.

[0007] 2. Lack of risk perception mechanisms and personalized guidance strategies. Existing lane marking systems fail to incorporate variables such as driving behavior data and environmental state parameters for intelligent adjustment, and are unable to provide targeted guidance and intervention in high-risk situations.

[0008] 3. Difficulty in cooperating with in-vehicle AR or HUD systems. The current lane marking system does not provide data formats and control logic that can be parsed by the terminal, and does not have the ability to dynamically generate and project patterns for AR display devices, making it difficult to support an immersive, multi-dimensional lane guidance experience.

[0009] 4. Visual rhythm design lacks systematic modeling and parameter quantification support. Some rhythmic markings lack scientific model support and fail to establish a unified and controllable visual model based on driver perception behavior characteristics (such as flash rate, attention maintenance, perception speed, etc.), so the design results cannot be generalized or automatically generated. Summary of the Invention

[0010] To address the shortcomings of existing technologies, the purpose of this invention is to provide a dynamic visual guidance marking system and its deployment method based on the flash rate principle. By introducing driving behavior data and environmental perception parameters, calculating the risk factor R, and combining the flash rate principle with a perception speed model, the spacing and pattern of the guidance markings can be dynamically generated and controlled, thereby improving driving guidance effectiveness, traffic safety level, and vehicle-road cooperation capabilities.

[0011] To achieve the above objectives, the technical solution of the present invention is as follows: A dynamic visual guidance marking based on the flash rate principle is suitable for vehicle-mounted AR devices. It divides the area before the guide lane line into a visual deceleration zone and an R zone, each using different marking patterns. The dynamic visual guidance marking is not continuously displayed, but is activated when the vehicle is about to enter the visual deceleration zone and R zone before the guide lane line, and is virtually projected by the AR device.

[0012] The display frequency and spacing of the dynamic visual guidance markings are dynamically adjusted according to the vehicle's driving status and the driver's violations.

[0013] The R area is further divided into low-risk, medium-risk, and high-risk R areas, each using different marking patterns.

[0014] A dynamic visual guidance marking layout method based on the flash rate principle, used for the above-mentioned marking layout, includes the following steps: S1: Collect driving behavior and environmental data; During driving, four types of driving behavior and environmental data are collected through vehicle-mounted devices and cloud platforms, including: vehicle operating parameters, driving behavior parameters, road segment attribute parameters, and external environmental parameters; these data are then quantified as the basis for subsequent dynamic lane marking generation.

[0015] S2: Calculate dynamic visual guidance marking parameters when approaching the guidance area of ​​the guide lane; When approaching the guide lane area, the spacing between the lane markings is no longer fixed, but is dynamically generated based on comprehensive data of traffic environment, vehicle status and driving behavior.

[0016] S3: Select the corresponding dynamic marking pattern type based on the dynamic visual guidance marking parameters; S4: Dynamic line marking display; After the dynamic visual guidance marking data is calculated, the dynamic visual guidance marking is displayed through AR-HUD / vehicle display screen.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) It can calculate the risk factor R based on real-time driving behavior and environmental variables, and use it as the core variable for dynamically setting up guidance markings, so as to realize the personalized generation and responsive control of guidance patterns; (2) By optimizing the visual guidance effect through the flash rate model and the perception speed theory, the driver’s perception of the guidance rhythm is enhanced, thereby improving the operational safety; (3) Provides a structured pattern data format that can be parsed by AR terminals to achieve vehicle-road cooperative guidance interaction; (4) Construct a complete closed-loop system from data collection to risk perception, pattern generation and terminal presentation, which is intelligent, scalable and engineering feasible, and applicable to various environments such as smart highways, urban main roads and vehicle-road cooperative autonomous driving scenarios. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the dynamic marking scheme design according to an embodiment of the present invention; Figure 2 This is a flowchart of the dynamic marking layout based on the flash rate principle in an embodiment of the present invention; Figure 3 This is an example diagram of the basic pattern of the third type of road marking according to an embodiment of the present invention; Figure 4 This is an example diagram of the visual deceleration area markings designed based on the flash rate principle in an embodiment of the present invention; Figure 5 This is an example diagram of full-area marking based on the flash rate principle in an embodiment of the present invention; Figure 6 This is a practical schematic diagram of the vehicle-mounted AR-HUD lane marking layout method according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0020] A dynamic visual guidance marking based on the flash rate principle is suitable for vehicle-mounted AR devices. It divides the area before the guide lane line into a visual deceleration zone and an R zone, each using different marking patterns. The dynamic visual guidance marking is not continuously displayed, but is activated when the vehicle is about to enter the visual deceleration zone and R zone before the guide lane line, and is virtually projected by the AR device.

[0021] The display frequency and spacing of the dynamic visual guidance markings are dynamically adjusted according to the vehicle's driving status and the driver's violations.

[0022] The R region is further divided into low-risk, medium-risk, and high-risk R regions.

[0023] Specifically, the dynamic lane marking layout strategy is shown in Table 1: Table 1 Dynamic Marking Layout Strategy Further explanation is as follows: The "Standard for Setting Urban Road Traffic Signs and Markings GB51038-2015" stipulates that the length of the guide lane lines at intersections should preferably be no less than 30 meters. In actual road sections, the length of the guide lane lines is generally 30-70 meters. Therefore, in this invention, 70m is taken as the design area for lane-changing guide markings. The first 30m is the guide lane line, and the 10m after the guide lane line is designated as the R area; the 30m after the R area is designated as the visual deceleration zone.

[0024] Based on the distance between the vehicle and the guide lane lines, current speed, and historical driving patterns, the system determines that the vehicle is about to enter the guidance area and begins to dynamically display AR lane markings. As the vehicle approaches the guide lane lines, the dynamic virtual lane markings overlap with the real-world solid white lane lines, enhancing the driver's perception of lane line changes and their willingness to control the vehicle, thereby achieving the goal of "virtual-real fusion guidance for return".

[0025] This invention primarily targets the visual deceleration zone and R area in front of the guide lane lines for marking design and dynamic guidance. However, its design concept and algorithm are also applicable to other key areas that require guidance for deceleration and improved attention, such as: ramp sections, in front of schools, complex intersections, temporary construction areas, etc., demonstrating good adaptability and scalability.

[0026] Furthermore, the dynamic visual guidance markings include three basic marking patterns: Pattern A, Pattern B, and Pattern C. (e.g.) Figure 3 ) Pattern A is a white solid line segment used to guide the lane area, visual deceleration area, and low-risk R area; Pattern B is a yellow dotted segment used in the medium-risk R area; The pattern C is a red arrow segment used for high-risk R areas; The details are shown in Table 2.

[0027] Table 2 Basic Patterns for Three Types of Road Markings In this invention, a white line segment, i.e. pattern A, is fixed in the visual deceleration area, while the pattern type (A / B / C) is dynamically selected in the R area according to the risk factor R and its rate of change. The core of this differentiated design is that the two areas serve completely different driving guidance objectives.

[0028] Specifically, the core function of the visual deceleration zone is to create a gradual visual rhythm through varying spacing of the markings, guiding drivers to smoothly decelerate before entering the critical area. Standardized white lines are used in this stage because the deceleration itself already requires drivers to invest significant attention in processing the visual signals of changing spacing. Introducing varied pattern types at this stage could distract drivers from speed adjustments and even lead to cognitive overload due to pattern complexity. White, as the base color for road markings, has become muscle memory through long-term driving habits, allowing the varying density of dashed lines to naturally evoke the intention to decelerate without adding extra cognitive burden.

[0029] In contrast, the design goal of the R zone shifts from speed control to risk warning. After a vehicle has decelerated, drivers tend to enter a relaxed state, requiring visual stimulation to reawaken their awareness of environmental risks. A dynamic pattern selection mechanism (such as yellow dotted lines indicating medium risk and red arrows indicating high risk) utilizes principles of color psychology and morphological differences to create strong visual hierarchy signals. The intermittent nature of the yellow dotted lines effectively attracts attention without triggering panic, while the directionality and high contrast of the red arrows are suitable for high-risk scenarios requiring immediate action. This design not only enhances the targeted nature of risk warnings but also helps drivers quickly understand the urgency of the current road condition through the intuitive association between pattern changes and risk levels.

[0030] In summary, the synergy between the two forms a complete visual guidance chain, from physical behavioral guidance to psychological intervention. The single pattern in the visual deceleration zone ensures the smoothness and predictability of the deceleration process, while the multi-modal pattern in the R zone ensures the flexibility and intensity of the risk warning.

[0031] Furthermore, in the visual deceleration zone, based on the flash rate principle, the most suitable lane marking spacing value was calculated according to vehicle speed, line-of-sight distance, and driver reaction time.

[0032] set up Indicates the location of the first traffic marking in the deceleration zone. , arrive This indicates the position of traffic markings from the 2nd to the nth, where L represents the distance between two adjacent traffic markings; the vehicle speed is v( The travel time is t( The car's acceleration is a( If the position of the nth traffic mark is given, then the position of the nth traffic mark is given. Spacing between traffic markings Set to: , The lane change guidance markings designed in this way utilize the flash rate principle, which is based on visual stimuli and the driver's psychological response to determine the spacing values ​​of the markings. Flash rate refers to the frequency with which traffic markings appear in the driver's field of vision.

[0033] To achieve the aforementioned high visibility effect, this invention incorporates a gradually decreasing spacing of traffic markings before the entrance to the guide lane. This design increases the density of markings seen by a driver traveling at a constant speed within their field of vision, thereby guiding the driver to consciously reduce speed through visual stimulation. It not only alerts the driver to the approaching critical area but also enhances the intuitive response to deceleration through visual changes.

[0034] Studies have shown that traffic markings are most effective at guiding drivers to slow down when their flashing rate reaches 4 times per second (4 bps). This invention calculates the optimal spacing between traffic markings based on vehicle speed, line-of-sight distance, and driver reaction time to achieve the best visual flashing rate.

[0035] For example, based on the braking capacity a car should have, the average deceleration of the car during normal braking should be: Furthermore, the standard "Road Traffic Signs and Markings Part 3: Road Traffic Markings GB 5768.3-2009" stipulates that the design speed is less than... The road can cross the dividing line of the same direction of traffic lanes and the interval lengths are respectively and Therefore, we will take an example here. ; ; ; The obtained spacing values ​​are shown in Table 3 below, and the results are as follows. Figure 4 .

[0036] Table 3 Examples of visual deceleration zone markings based on the flash rate principle Furthermore, in region R, the spacing between the markings... Based on the current vehicle speed acceleration Risk factors Time period and visibility Parameters are dynamically generated to form a rhythmic visual cue that changes with the scene, driving pre-adjustment of driving behavior.

[0037] Marking spacing Satisfy the following formula: The foundation spacing is as follows: , The target flash rate. As an example, set... It is 4 times per second.

[0038] Introducing risk factors The spacing after that is calculated as follows: , This is the risk adjustment coefficient. In this embodiment, Take 0.5.

[0039] This is the acceleration adjustment coefficient; This is the deviation angle adjustment coefficient; , These are environmental correction factors for road type, traffic congestion level, time period, and visibility, respectively.

[0040] The comprehensive risk factor R is calculated based on the weight and frequency of driving behavior penalties, and the calculation formula is as follows: in, For each behavior, the weight coefficient is... For the frequency of occurrence of the corresponding behavior, This represents the maximum risk threshold.

[0041] The visual marking patterns include three types: white solid lines, yellow dotted lines, and red arrow lines, which are used for low-risk, medium-risk, and high-risk R areas, respectively.

[0042] Based on risk factors Its rate of change The R region is divided into risk segments to determine the visual elements of each line segment. The specific rules are as follows: in: Risk change rate threshold, set ; : The gradient of the risk factor along the vehicle's direction of travel (unit: increment per meter).

[0043] Pattern A, Pattern B, and Pattern C are selected for low-risk, medium-risk, and high-risk R areas, respectively.

[0044] Additional explanation regarding The principles and significance of: Risk factors The rate of spatial change during vehicle movement is used to sense trends of sudden risk changes.

[0045] High values ​​indicate a sharp increase in risk, such as entering a curve with limited visibility, an area prone to accidents ahead, or approaching a high-risk area (such as a school entrance). Based on the risk estimation curve using a sliding window, the risk gradient is calculated using first-order differencing: in, This refers to the vehicle's current spatial coordinates (distance along the direction of travel, in meters). This refers to a small displacement increment (e.g., it can be set to a fixed value of 0.5m).

[0046] For example, currently It belongs to medium risk, corresponding to type B, but The system will directly upgrade the inducement level to Type C, triggering a strong alert in advance. That is, if... Value and If the judgment results are inconsistent, the higher-level result shall prevail to ensure that the system responds quickly.

[0047] Working principle explanation: The visual deceleration region uses segment spacing. The progressively decreasing spacing of the lane markings simulates the physical rhythm of a vehicle deceleration. By adjusting the lane spacing based on the vehicle's dynamic state (such as real-time speed and acceleration), a synchronized visual and driving behavior is created, helping the driver develop a smooth and natural psychological expectation of deceleration and promoting safe vehicle slowdown. The adjustment of the lane spacing is done continuously and smoothly to ensure the visual rhythm is not interrupted and to avoid confusing the driver.

[0048] After the vehicle completes deceleration, it enters the subsequent risk warning zone, or R zone. This zone uses equally spaced lane markings, but the spacing is dynamically adjusted based on the real-time risk factor R, creating a stable and rhythmic visual flashing pattern to continuously maintain the driver's risk perception and attention. This design ensures that the driver remains alert even after deceleration, preventing safety hazards caused by decreased vigilance.

[0049] The design difference between the visual deceleration zone and the R zone reflects the phased evolution of driving behavior. The variable spacing design of the deceleration zone emphasizes the continuity of visual rhythm and the coordination of physical deceleration, while the R zone focuses on risk level indication and maintaining attention. Although the dynamic adjustment of the risk factor R is crucial to the effectiveness of risk indication, directly applying it to the visual deceleration zone may lead to discontinuous changes in lane spacing, disrupting the stability of the visual rhythm during deceleration and hindering the driver's psychological adaptation to the deceleration rhythm. Therefore, placing the risk factor adjustment in the R zone after deceleration is completed is more conducive to forming a scientific, reasonable, and hierarchical visual guidance system, achieving a smooth transition from "gradual deceleration guidance" to "constant risk indication," thereby effectively improving driving safety.

[0050] A method for setting up dynamic visual guidance markings based on the flash rate principle includes the following steps: (Note that the quantified values ​​are for reference only and can be modified according to actual conditions) S1: Collect driving behavior and environmental data During driving, four types of driving behavior and environmental data are collected through vehicle-mounted devices and cloud platforms, including: vehicle operating parameters, driving behavior parameters, road segment attribute parameters, and external environmental parameters; these data are then quantified as the basis for subsequent dynamic lane marking generation.

[0051] The specific parameters are explained below: (1) Vehicle operating parameters (real-time data from the vehicle) (2) Driving behavior parameters (core source of risk) Among them, regarding Explanation: Based on the traffic management department's penalty rules, fine amounts, and pre-deduction standards, the corresponding behavioral weights and frequency are converted into a comprehensive risk threshold. For example, in a certain region, five consecutive instances of crossing the line or three instances of forced lane changes will trigger severe penalties. If the cumulative weighted value of a driver's violations within a certain period reaches or exceeds [a certain threshold], [the driver's violation will be considered as a violation]. When the risk factor R reaches 1, the system will trigger the highest level of visual guidance adjustment and related pre-deduction measures to strengthen safety prompts and behavioral correction.

[0052] (3) Road segment attribute parameters (vehicle-road cooperation / map matching) about Explanation: According to the "Road Traffic Congestion Evaluation Method" (GAT 115-2020), traffic congestion level is used to measure the degree of traffic congestion and is divided into four levels: Level I, Level II, Level III, and Level IV, which represent severe congestion, moderate congestion, light congestion, and smooth traffic flow, respectively.

[0053] (4) External environmental parameters S2: When approaching the guide lane guidance area, calculate the dynamic visual guidance marking parameters (e.g., Figure 2 ) When approaching the guide lane area, the spacing between the lane markings is no longer fixed, but is dynamically generated based on comprehensive data of traffic environment, vehicle status and driving behavior.

[0054] Specifically, first, the visual deceleration zone is distinguished from the low-risk, medium-risk, and high-risk R-zones, and then the dynamic visual guidance line parameters are calculated.

[0055] The calculation process is based on the vehicle's current speed, combined with risk factors and various correction parameters, to determine the final deployment spacing, thereby achieving precise guidance and risk control for driving behavior.

[0056] definition The baseline spacing (m) between two road markings shows that the most significant effect on driver deceleration is achieved when the flashing rate of traffic markings reaches 4 times per second (4 bps). To maintain the flashing rate at the target flashing rate... That is, a fixed value of 4 visual stimuli per second should be provided, which should satisfy: Simply put: the slower the vehicle speed, the smaller the distance between the two lane markings should be, in order to ensure that the driver sees the lane markings at a rate of 4 times per second and maintains the guiding rhythm.

[0057] The 4bps rhythm design used in this invention has been proven in multiple independent experiments and under different road conditions to be the most stable and effective flashing frequency. It has demonstrated good behavioral guidance in various scenarios such as urban roads, curves, and highway exits. Specifically, Katz et al.'s road tests in New York, Mississippi, and Texas showed that when the markings are set at a frequency of 4bps, the average vehicle speed can decrease by up to 59% in the short term and maintain a 24% decrease in the long term. The comparative real-vehicle tests of the Virginia Tech Smart Road project further confirmed that in the simulated scenario of highway exit ramps, the 4bps marking design can reduce the average vehicle speed by up to 42% compared to 2bps and traditional baseline conditions, and the guidance effect is significantly better than other frequencies. Introducing risk factors Then, the system will dynamically adjust the induction intensity. If the risk is high... If the risk is low, increase the induction density and shorten the spacing between the markings; if the risk is low, the induction density will be increased and the spacing between the markings will be shortened. This reduces the induced density while maintaining traffic efficiency. The adjusted spacing is calculated as follows: ,in, Here, we take the risk adjustment coefficient. To ensure that when the highest risk occurs The flash rate should not exceed twice the base value (i.e., 8bps) to avoid speed misjudgment caused by doubling the frequency.

[0058] To better reflect reality, factors such as road conditions were also incorporated and adjusted to arrive at the final spacing calculation. : in, When the magnitude of acceleration increases (rapid acceleration or sudden braking), the distance is shortened to improve concentration. This is the deviation angle adjustment coefficient, which reflects the deviation between the actual running direction of the vehicle and the direction of the lane. The greater the deviation, the stronger the induction intensity needs to be.

[0059] S3: Select the corresponding dynamic marking pattern type based on the dynamic visual guidance marking parameters. Based on the calculation results of S2, and according to the risk factors Its rate of change The R region is divided into risk segments to determine the visual elements of each line marking. The specific pattern type selection rules are as follows: in: Risk change rate threshold, set here. ; : The gradient of the risk factor along the vehicle's direction of travel (unit: increment per meter).

[0060] Additional explanation regarding The principles and significance of: Risk factors The rate of spatial change during vehicle movement is used to sense trends of sudden risk changes.

[0061] High values ​​indicate a sharp increase in risk, such as entering a curve with limited visibility, an area prone to accidents ahead, or approaching a high-risk area (such as a school entrance). Based on the risk estimation curve using a sliding window, the risk gradient is calculated using first-order differencing. : in: Vehicle spatial coordinates (along the direction of travel, unit: m), from The calculation shows that, The total length is fixed, including a 30m visual deceleration zone, a 10m R zone, and a 30m guide lane line zone. This represents the remaining distance from the vehicle to the end of the guide lane line.

[0062] : Small displacement increment (can be set to a fixed value of 0.5m) to balance calculation accuracy and real-time performance.

[0063] S4: Dynamic lane markings display Specifically, for vehicles equipped with AR-HUD, the focus is on analyzing the spatial coordinates and flashing frequency data of road markings. A high-precision positioning system then accurately overlays the virtual road markings onto the actual road location in the driver's field of vision. Figure 6 For vehicles using traditional in-vehicle displays, the pattern type and color information of the road markings are extracted and converted into a simplified two-dimensional guide line display. Although the display format is simplified, it can still accurately convey the risk level and guidance intent.

[0064] Although the two types of display terminals present information in different ways, they are both based on the same data source and calculation model to ensure the consistency of the guiding information.

[0065] To address dynamic changes in vehicle operating conditions and road conditions, the system is equipped with a real-time synchronization control mechanism to promptly update the lane marking layout strategy when vehicle speed, acceleration, deviation angle, and environmental parameters change. If vehicle speed changes significantly, the system recalculates. Update and distribute the structure; if A sudden increase in value, or If the threshold is exceeded, the guidance level will switch immediately (e.g., from pattern B to pattern C); if the vehicle is about to leave the guidance area, the system will preload the data for the next segment to ensure the continuity of the markings.

[0066] This mechanism ensures that the induction effect is synchronized with driving behavior, enhancing the timeliness and adaptability of behavioral intervention.

[0067] The system continuously collects driver behavior responses under visual guidance through vehicle-mounted sensors, such as deceleration magnitude, whether the driver crosses lane lines, and whether alertness increases. It then dynamically adjusts risk factors and correction coefficients based on these behavioral responses. The updated parameters influence the lane marking layout in subsequent sections, enabling continuous evolution and adaptive optimization of the guidance model. This closed-loop mechanism enhances the system's long-term effectiveness and personalized adaptability, making it particularly suitable for vehicle-to-infrastructure (V2I) and intelligent driving scenarios.

[0068] Furthermore, considering uncontrollable factors such as sensor failure, network interruption, and data loss, this invention introduces a redundant control mechanism to enhance system stability and anti-interference capabilities. When key parameters (such as v and R) are lost or fluctuate abnormally, the system will activate a default guidance mode, such as fixed-interval white dashed lines, to ensure basic safety guidance. In the event of data interruption, the most recent guidance structure cached locally is used as an emergency alternative to ensure continuity. The system monitors abnormal state cycles; if they continue to exceed limits, a system alarm or prompt for manual intervention will be generated. Through the above fault-tolerant design, the system has the ability to operate stably in complex traffic environments, ensuring the continuity of guidance strategies and driver trust.

[0069] The system integrates the marking parameters of the visual deceleration zone and the R zone into a unified data structure, supporting standard protocols such as JSON and XML, which facilitates real-time access and display by vehicle-side devices. Specific data structure fields are described in Table 4 below: Table 4. Description of the Data Structure for Generating Dynamic Markings The method described in this invention is mainly applicable to the following scenarios and parameter ranges: (1) Explanation of vehicle speed range This method is applicable to design speed ranges of [specific speed range]. Urban traffic sections. Below... In slow-moving areas (such as internal parking lots and residential roads), the rhythmic regulation effect of dynamic visual guidance markings is not significant due to the slow pace of vehicle movement and the concentrated attention of drivers; in areas higher than... On high-speed sections, the high rhythm density may cause information redundancy or even interference. Furthermore, AR projection requires a higher refresh rate and positioning accuracy, making it unsuitable for direct application of the standard version of this method.

[0070] (2) Typical application scenarios This method is recommended for one-way multi-lane scenarios with lane-changing capability, but is not suitable for areas without guide lane markings or single-lane areas. It is geared towards key sections in urban traffic segments with significant needs for adjusting driving behavior and providing strong guidance risk warnings, and is particularly suitable for the typical scenarios shown in Table 5. Furthermore, to adapt to a wider range of road types and equipment configuration differences, this method supports controllable rollout through parameter constraints and module customization.

[0071] Table 5 Applicable Road Scenarios Figure 5 This demonstrates a dynamic lane marking example designed based on the flash rate principle, which includes a visual deceleration zone and an R zone, and connects to the guide lane line area.

[0072] This embodiment sets several key parameters, assuming... , The dynamic marking values ​​of the visual deceleration zone and the R zone were calculated, clearly demonstrating the effect of the dynamic visual guidance markings. The generated marking segment data are summarized in Table 6: Table 6 Example Data Table A dynamic visual guidance marking system based on the flash rate principle, suitable for vehicle-mounted AR devices, includes: 1. Input module: Used to collect and receive source input data, including: (1) Vehicle operating parameters, such as real-time vehicle speed, longitudinal acceleration, and deviation angle; (2) Driving behavior parameters, such as the frequency of violations and weighting coefficients; (3) Road segment attribute parameters, such as road type and traffic congestion level; (4) External environmental parameters, such as time period and visibility.

[0073] 2. Risk Factor Calculation Module: By setting a weighted calculation model and integrating the above input parameters, a normalized risk factor R (within the range of 0 to 1) is generated as the core control parameter for the dynamic guidance line layout.

[0074] 3. Dynamic Deployment Module: Based on the aforementioned risk factor R and its rate of change, combined with the principle of visual flash rate and the perception speed model, the spacing between guide line segments is dynamically calculated to generate the rhythm of the guide line pattern. (1) When the risk is high, the system automatically reduces the segment spacing, increases the pattern density, and increases the frequency of visual guidance; (2) When the risk is low, the interval between segments can be appropriately widened to reduce interference and information redundancy; The lane marking area is divided into two phases: a visual deceleration zone and a risk warning zone. The former guides deceleration gradually through non-equidistant patterns, while the latter guides deceleration in a consistent manner with vehicle movements through equidistant patterns.

[0075] 4. Pattern Generation and Data Structure Output Module: This module encapsulates the generated line marking patterns (including coordinates, spacing, rhythm type, segment length, etc.) into a structured data format for terminal devices to parse and display. Data formats include, but are not limited to: line marking length, spacing type, spacing value, pattern type, and color.

[0076] 5. Terminal display adaptation module: The structured pattern data can be projected to AR devices for presentation, forming a dynamic visual guidance effect that is synchronized with the vehicle path in real time.

[0077] In summary, the "Dynamic Visual Guidance Markings and Their Deployment Method Based on the Flash Rate Principle" proposed in this invention not only possesses high flexibility and data-driven characteristics in marking generation, but also constructs a complete system closed loop in terms of execution, feedback, and adaptive evolution after data distribution. This solution is particularly suitable for intelligent connected roads, refined urban traffic management, and autonomous driving assistance scenarios, and can effectively improve the intelligence level of traffic guidance and traffic safety assurance capabilities.

Claims

1. A dynamic visual guidance marking based on the flash rate principle, suitable for vehicle-mounted AR devices, characterized in that, The area approaching the guide lane line is divided into a visual deceleration zone and an R zone, each using different marking patterns. The dynamic visual guidance markings are not continuously displayed, but are activated when the vehicle is about to enter the visual deceleration zone and R zone before the guide lane line, and are virtually projected by an AR device. The display frequency and spacing of the dynamic visual guidance markings are dynamically adjusted according to the vehicle's driving status and the driver's violations. The R area is further divided into low-risk, medium-risk, and high-risk R areas, each using different marking patterns.

2. The dynamic visual guidance marker based on the flash rate principle according to claim 1, characterized in that, The dynamic visual guidance markings include three basic marking patterns: Pattern A, Pattern B, and Pattern C; Pattern A is a white solid line segment used to guide the lane area, visual deceleration area, and low-risk R area; Pattern B is a yellow dotted segment used in the medium-risk R area; The pattern C is represented by a red arrow segment, used for high-risk R areas.

3. The dynamic visual guidance marker based on the flash rate principle according to claim 1, characterized in that, In the visual deceleration zone, based on the flash rate principle, the most suitable lane marking spacing value is calculated according to vehicle speed, line-of-sight distance and driver reaction time; set up Indicates the location of the first traffic marking in the deceleration zone. , arrive This indicates the position of traffic markings from the 2nd to the nth, where L represents the distance between two adjacent traffic markings; the vehicle speed is v( The travel time is t( The car's acceleration is a( If the position of the nth traffic mark is given, then the position of the nth traffic mark is given. Spacing between traffic markings Set to: ， 。 4. The dynamic visual guidance marker based on the flash rate principle according to claim 1, characterized in that, In region R, the gradation spacing Based on the current vehicle speed acceleration Risk factors Time period and visibility Parameters are dynamically generated to create a rhythmic visual cue that changes with the scene, driving pre-adjustment of driving behavior; Marking spacing Satisfy the following formula: The foundation spacing is as follows: , Target flash rate; Introducing risk factors The spacing after that is calculated as follows: , This is the risk adjustment coefficient; This is the acceleration adjustment coefficient; This is the deviation angle adjustment coefficient; , These are environmental correction factors for road type, traffic congestion level, time period, and visibility, respectively.

5. The dynamic visual guidance marker based on the flash rate principle according to claim 4, characterized in that, The comprehensive risk factor R is calculated based on the weight and frequency of driving behavior penalties, and the calculation formula is as follows: in, For each behavior, the weight coefficient is... For the frequency of occurrence of the corresponding behavior, This represents the maximum risk threshold.

6. The dynamic visual guidance marker based on the flash rate principle according to claim 5, characterized in that, Based on risk factors Its rate of change The R region is divided into risk segments to determine the visual elements of each line segment. The specific rules are as follows: in: Risk change rate threshold, set ; : This represents the gradient of the risk factor along the vehicle's direction of travel; Pattern A, Pattern B, and Pattern C are selected for low-risk, medium-risk, and high-risk R areas, respectively. Based on the risk estimation curve using a sliding window, the risk gradient is calculated using first-order difference. ,as follows: in, This refers to the vehicle's current spatial coordinates; Refers to a tiny displacement increment.

7. The dynamic visual guidance marker based on the flash rate principle according to claim 4, characterized in that, set up It is 4 times per second.

8. A method for deploying dynamic visual guidance markings based on the principle of flash rate, used for deploying the dynamic visual guidance markings as described in any one of claims 1-7, characterized in that, Includes the following steps: S1: Collect driving behavior and environmental data; During driving, four types of driving behavior and environmental data are collected through vehicle-mounted devices and cloud platforms, including: vehicle operating parameters, driving behavior parameters, road segment attribute parameters, and external environmental parameters; and these data are quantified as the basis for subsequent dynamic lane marking generation. S2: Calculate dynamic visual guidance marking parameters when approaching the guidance area of ​​the guide lane; When approaching the guide lane area, the spacing between the lane markings is no longer fixed, but is dynamically generated based on comprehensive data of traffic environment, vehicle status and driving behavior; S3: Select the corresponding dynamic marking pattern type based on the dynamic visual guidance marking parameters; S4: Dynamic line marking display; After the dynamic visual guidance marking data is calculated, the dynamic visual guidance marking is displayed through AR-HUD / vehicle display screen.

9. The method for setting up dynamic visual guidance markings based on the flash rate principle according to claim 8, characterized in that, In step S3, based on risk factors Its rate of change The R region is divided into risk segments to determine the visual elements of each line segment.

10. The method for setting up dynamic visual guidance markings based on the flash rate principle according to claim 8, characterized in that, In step S4, after the dynamic visual guidance marking data calculation is completed, the dynamic visual guidance marking is displayed through AR-HUD / vehicle display screen; Specifically, for vehicles equipped with AR-HUD, the focus is on analyzing the spatial coordinates and flashing frequency data of the road markings, and using a high-precision positioning system to accurately overlay the virtual road markings onto the actual road location in the driver's field of vision; for vehicles using traditional in-vehicle displays, the pattern type and color information of the road markings are extracted and converted into simplified two-dimensional guide lines for display. Both types of display terminals are based on the same data source and computing model to ensure the consistency of the guidance information.

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