A rescue emergency lamp linkage control method combined with a vehicle machine system

Abstract

The present application relates to the technical field of intelligent traffic control, and particularly relates to a rescue emergency light linkage control method combined with a vehicle system, comprising: S1: environment perception, collecting road topological structure, potential congestion obstacle distribution, visibility level and vehicle density value in real time through the vehicle system of a rescue vehicle; S2: dynamic decision, generating an emergency light control instruction based on the output of S1; S3: multi-vehicle linkage, predicting a future path based on the position and historical trajectory of the rescue vehicle, and broadcasting a V2X instruction containing real-time coordinates, path vector and avoidance direction identifier to vehicles within a preset radius; S4: closed-loop execution, issuing the instruction of S2 to a light control unit, and capturing an actual flashing sequence through a camera for time domain matching verification, and triggering instruction retransmission when the matching degree is lower than a reliability threshold. The method not only improves traffic safety and rescue efficiency, but also enhances the intelligence and reliability of the system, and adapts to modern rescue needs.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic control technology, and in particular to a method for linkage control of emergency lights in conjunction with a vehicle-mounted system. Background Technology

[0002] In the field of intelligent traffic control, emergency lighting systems for rescue vehicles are core devices for ensuring safe passage during emergency missions. Current mainstream technologies have the following limitations:

[0003] 1. The emergency light control mode is out of sync with dynamic road conditions;

[0004] Existing emergency lights use a flashing scheme with fixed frequency and brightness, which cannot respond to spatiotemporal changes in road conditions.

[0005] Warnings fail in heterogeneous road topologies: low-frequency, long-period flashing is needed in highway scenarios to improve long-distance recognition stability, while high-frequency flashing is needed at urban intersections with multiple forks to enhance turn warnings, but fixed modes cannot be switched adaptively.

[0006] Insufficient adaptability to harsh environments: Under low visibility conditions such as fog, haze, and heavy rain, traditional brightness values ​​are lower than the human visual recognition threshold, resulting in a reduction of warning distance by more than 30%.

[0007] Lack of response to high-density traffic flow: In congested areas, single-color flashing lights are easily overwhelmed by the lights of surrounding vehicles, and there is a lack of dynamic pattern enhancement mechanisms based on traffic flow density.

[0008] 2. The warning information transmission mechanism is one-way and rigid;

[0009] Surrounding vehicles only passively perceive hazard light signals visually, and critical path information fails to form a collaborative avoidance loop.

[0010] Lack of rescue route prediction: Receiving vehicles cannot know the future trajectory of the rescue vehicle (such as turning left at the intersection 300 meters ahead), resulting in incorrect avoidance direction or untimely yielding. According to traffic accident report statistics, secondary accidents caused by this reason account for 17.3%.

[0011] Spatial discretization of avoidance commands: Existing V2X broadcast protocols (such as SAE J2735) only support vehicle status sharing and do not perform spatial relationship calculation between the rescue path vector and the receiving vehicle position, thus failing to generate directional avoidance guidance.

[0012] Therefore, there is an urgent need for a method to link emergency lights with vehicle infotainment systems to solve the above problems. Summary of the Invention

[0013] To achieve the above objectives, the present invention provides a method for linkage control of emergency lights in conjunction with a vehicle infotainment system, comprising:

[0014] S1: Environmental perception, which uses the vehicle's infotainment system to collect real-time data on road topology, distribution of potential congestion obstacles, visibility level, and vehicle density; among which:

[0015] The road topology was obtained based on the matching and analysis of GPS coordinates and high-precision maps.

[0016] The distribution of potential congestion obstacles is determined by detecting the speed of moving targets using millimeter-wave radar and combining it with road speed limits.

[0017] The visibility level is determined by the grayscale distribution of images captured by the roof-mounted camera.

[0018] The vehicle density value is calculated by receiving broadcast information from vehicles within a preset radius through the vehicle-mounted network communication module.

[0019] S2: Dynamic decision-making, generating emergency light control commands based on the output of S1, including:

[0020] The basic scintillation frequency is selected based on the road topology, so that the frequency at urban intersections with multiple intersections is higher than that on highways.

[0021] When the visibility level is lower than the preset standard, the brightness enhancement and supplemental lighting modes are activated.

[0022] When the vehicle density value exceeds a critical threshold, the dual-color alternating flashing mode is activated.

[0023] S3: Multi-vehicle linkage, predicts future paths based on the location and historical trajectory of rescue vehicles, and broadcasts V2X commands containing real-time coordinates, path vectors and avoidance direction markings to vehicles within a preset radius;

[0024] S4: Closed-loop execution, sends the instruction from S2 to the lighting control unit, and performs time-domain matching verification by capturing the actual flashing sequence through the camera. When the matching degree is lower than the reliability threshold, the instruction is resent.

[0025] Preferably, the parsing process of the road topology in S1 includes:

[0026] The system performs real-time matching between GPS coordinates and the road network topology of high-precision maps to extract data on the number of lanes, distance between adjacent intersections, and ramp locations of the current road.

[0027] The detection angle range of the millimeter-wave radar is dynamically adjusted according to the number of lanes. The adjustment rule is as follows: with the reference number of lanes, the detection angle range is expanded by a preset ratio for each unit increase in the number of lanes. The reference number of lanes and the ratio coefficient are determined by analyzing the obstacle miss rate in multi-lane scenarios in historical accident data.

[0028] Preferably, the calculation process for the vehicle density value in S1 includes:

[0029] The vehicle-mounted network communication module receives the unique identifiers and location coordinates broadcast by all vehicles within a preset radius.

[0030] Calculate the effective road area of ​​the current road segment based on high-precision map data: extract the coordinate point sequence of the road network boundary and generate the area value of the closed area through a polygon area algorithm;

[0031] The density value is obtained by dividing the number of vehicles by the effective road area. The preset radius is determined by real vehicle testing in typical urban roads and highway scenarios to achieve the optimal communication coverage for avoidance response time.

[0032] Preferably, the selection rule for the basic flicker frequency in S2 includes:

[0033] Establish a mapping database between road topology types and frequencies: associate highways, urban intersections, and roundabouts with low-frequency, mid-frequency, and high-frequency ranges, respectively;

[0034] The process of determining the specific frequency value is as follows: In real vehicle tests of various topological scenarios, the effective avoidance distance of surrounding vehicles at different frequencies is collected, and the frequency value that maximizes the avoidance distance and meets the comfort of the human eye is selected.

[0035] When vehicles continuously pass through heterogeneous topology road segments, a weighted smoothing algorithm is used to transition the frequency value, with the weighting coefficients dynamically calculated based on the road segment length and vehicle speed.

[0036] Preferably, the generation process of the avoidance direction marker in S3 includes:

[0037] Obtain the real-time location coordinates of the receiving vehicle and calculate its vertical distance to the rescue path prediction vector;

[0038] Define the direction of the normal vector of the path vector: with the path's forward direction as the reference, rotate 90 degrees clockwise to get the positive direction;

[0039] A "give way to the right" sign is generated when the vertical distance value is positive, and a "give way to the left" sign is generated when the value is negative.

[0040] The critical threshold for vertical distance is determined by testing the safety score of avoidance maneuvers for different distance values ​​under typical road widths.

[0041] Preferably, the specific process of time-domain matching verification in S4 includes:

[0042] The expected control command is converted into a standard square wave signal, and the actual LED brightness sequence is acquired by the camera to generate a monitoring square wave signal.

[0043] Calculate the correlation coefficient between two signals: within a fixed time window, generate the degree of matching based on the ratio of the covariance of the discrete point sequence of the signals to their respective standard deviations;

[0044] The process of determining the reliability threshold is as follows: simulate different levels of electromagnetic interference and mechanical vibration in a laboratory environment, statistically analyze the correlation curve between the matching degree and the instruction execution error rate, and select the matching degree critical value corresponding to the point of sudden increase in error rate.

[0045] Preferably, the strategy for retransmitting the instruction includes:

[0046] When the number of consecutive times the matching degree falls below the reliability threshold reaches the retransmission trigger threshold, the instruction retransmission process is initiated.

[0047] When retransmitting, the adjustment range of the LED drive current is dynamically increased, and the increase ratio is negatively correlated with the matching degree: the lower the matching degree, the greater the current adjustment range.

[0048] The retransmission trigger threshold is set by statistically analyzing the minimum number of stable recovery attempts required for the command transmission link under different fault scenarios.

[0049] Preferably, the detection angle range of the dynamically adjustable millimeter-wave radar further includes:

[0050] When the location of the ramp is identified, the coverage area of ​​the lateral detection angle is increased.

[0051] The lateral expansion angle is calculated by predicting the radar beam coverage angle required for the potential collision risk area based on the radius of curvature of the ramp and the speed of the rescue vehicle.

[0052] The radius of curvature is obtained by extracting the coordinate sequence of the ramp centerline from a high-precision map and calculating the instantaneous curvature using a curve fitting algorithm.

[0053] Preferably, the activation rules for the brightness enhancement and supplementary lighting mode further include:

[0054] Establish a mapping table between visibility level and baseline brightness, and calibrate the mapping relationship through human driver recognition distance tests in foggy conditions;

[0055] The baseline brightness is dynamically compensated based on the vehicle density value: the higher the density value, the larger the compensation coefficient, and the slope of the compensation coefficient is fitted by the warning effect feedback data under different density scenarios.

[0056] Preferably, the V2X instruction data encapsulation method includes:

[0057] A layered coding structure is adopted: the first layer is the message type identifier, the second layer is the coordinate data and path vector, and the third layer is the avoidance direction coding;

[0058] Data compression strategy: The path vector point sequence is compressed using the Douglas-Puk algorithm, and the compression threshold is dynamically adjusted according to the location update frequency of the receiving vehicle.

[0059] The beneficial effects of this invention are:

[0060] 1. This invention dynamically adjusts the flashing frequency and brightness of emergency lights by introducing real-time traffic data and an environmental perception system to adapt to different road topologies and traffic conditions. On highways, low-frequency, long-period flashing enhances long-distance recognition stability, while in complex urban intersection scenarios, high-frequency flashing strengthens turn signal warnings, ensuring that all types of vehicles on the road receive clear signals in a timely manner.

[0061] 2. This invention can automatically adjust the brightness according to real-time visibility data under adverse weather conditions, ensuring that the warning effect of the emergency light is not affected by factors such as fog, haze, and heavy rain, effectively avoiding the failure of traditional systems in low visibility conditions, and ensuring an extended warning distance.

[0062] 3. The present invention also improves signal visibility by using a dynamic pattern enhancement mechanism based on traffic density to avoid the problem of monochromatic flashing being drowned out by the lights of other vehicles in congested road sections.

[0063] 4. This invention integrates the vehicle-to-everything (V2X) system with V2X communication technology, enabling the rescue vehicle to broadcast its preset path and future trajectory to surrounding vehicles, thus solving the problem that receiving vehicles cannot predict the rescue vehicle's movement trajectory. Through this technology, receiving vehicles can promptly learn of the rescue vehicle's impending turning, lane-changing, and other actions, accurately identify the avoidance direction, and prevent secondary accidents caused by incorrect or hesitant avoidance, significantly reducing the incidence of such accidents.

[0064] 5. This invention innovatively combines the V2X broadcast protocol, breaking through the limitation of traditional protocols that only support vehicle status sharing. By solving spatial relationships, it associates the path of the rescue vehicle with the location of the receiving vehicle, generates directional avoidance guidance instructions, and ensures that surrounding vehicles can accurately and timely avoid the rescue vehicle, effectively forming a collaborative avoidance closed loop. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0067] Figure 2This is a flowchart illustrating the specific steps of the time-domain matching verification process in method S4 of the present invention.

[0068] Figure 3 This is a flowchart illustrating the steps of the instruction retransmission strategy in the method of the present invention.

[0069] Figure 4 This is a schematic diagram of an emergency light in a specific embodiment of the present invention. Detailed Implementation

[0070] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0071] Please see Figures 1-4 This invention provides a method for linkage control of emergency lights in conjunction with a vehicle-mounted system. Based on four key steps—real-time environmental perception, dynamic decision-making, multi-vehicle linkage, and closed-loop execution—it effectively improves the traffic safety of rescue vehicles in various complex traffic environments.

[0072] In step 1, environmental perception is a fundamental step in this method, involving the integration of multiple sensors and vehicle systems:

[0073] Road topology acquisition: Using a combination of Global Positioning System (GPS) and high-precision map data, road structure information at the location of the rescue vehicle is analyzed in real time. The vehicle navigation system accurately matches the direction, shape, road signs, and traffic facilities of the surrounding roads, providing foundational data for subsequent emergency light control.

[0074] Potential congestion obstacle distribution detection: Millimeter-wave radar detects moving objects in the surrounding environment in real time, such as other vehicles, pedestrians, and obstacles. Combined with road speed limit information, it filters out obstacles that may potentially obstruct the passage of rescue vehicles. Millimeter-wave radar has strong penetrating power and can effectively detect obstacles in low-visibility environments such as fog, haze, rain, or snow.

[0075] Visibility Level Assessment: The system uses a roof-mounted camera to collect image data of the surrounding environment and analyzes the grayscale distribution of the images. Based on the image's brightness and contrast, the system assesses the current visibility level. When visibility is low, it automatically activates supplemental lighting and brightness enhancement functions to ensure the warning effect of the emergency lights.

[0076] Vehicle density calculation: The vehicle-mounted network communication module receives broadcast information from other vehicles within a preset radius and calculates the surrounding vehicle density based on this information. This step, through real-time analysis of the position and speed of surrounding vehicles, determines the degree of traffic congestion and provides a basis for subsequent dynamic decision-making.

[0077] Based on environmental perception, in step 2, the system makes intelligent decisions based on real-time traffic data:

[0078] Basic flashing frequency selection: Based on the road topology, the flashing frequency of emergency lights at urban intersections with multiple intersections is set to high frequency to increase the visibility of turn warnings, while low-frequency long-period flashing is used on straight roads such as highways to ensure the stability of long-distance warnings.

[0079] Brightness Boost and Complementary Lighting Mode: When the visibility level is lower than the preset standard (such as in severe weather such as fog or heavy rain), the system automatically activates the brightness boost and supplementary lighting mode. By increasing the brightness of the lights and the range of the light source, the visibility of the emergency lights is improved, ensuring that even in poor visibility conditions, surrounding vehicles can see the warning signals in time.

[0080] Dual-color alternating flashing mode: When the vehicle density value exceeds a set threshold, the dual-color alternating flashing mode is activated. The dual-color flashing mode enhances the warning effect by increasing the variation in light color and avoids the single-color flashing light being confused with or ignored by the lights of surrounding vehicles in high-density traffic environments.

[0081] In step 3, after the rescue vehicle activates its hazard lights, it broadcasts its real-time location, predicted path, path vector, and avoidance direction to surrounding vehicles via the vehicle-to-everything (V2X) communication system. This information helps surrounding vehicles understand the rescue vehicle's trajectory and future movements, allowing them to prepare for avoidance in advance. Through integration with the vehicle information system, receiving vehicles can accurately receive avoidance commands, reducing misoperation and the occurrence of secondary accidents.

[0082] As is understood, V2X refers to the exchange of information between vehicles and other traffic elements, encompassing communication between vehicles and other vehicles (V2V), infrastructure (V2I), pedestrians (V2P), etc. In this invention, V2X commands include the real-time location, path information, and avoidance direction of the rescue vehicle, ensuring that surrounding vehicles can receive timely avoidance guidance.

[0083] Step 4 is responsible for executing emergency light control commands and verifying them through the actual monitoring feedback system:

[0084] Command execution: Based on the control commands from the dynamic decision-making stage, the commands are sent to the lighting control unit to adjust the flashing frequency, brightness, and mode of the emergency lights in real time.

[0085] In this embodiment, the emergency light has the specific shape shown in Figure 4 and can be detachably installed on the roof or other locations on the vehicle body.

[0086] Time-domain matching verification: The system captures the flashing sequence of the emergency lights in real time using cameras and vehicle sensors, and performs time-domain matching between the actual lighting and the preset flashing sequence. If the system detects that the flashing effect is inconsistent with the expectation (e.g., due to malfunctions, equipment problems, etc., preventing correct execution), it triggers a command retransmission to ensure the accuracy and reliability of emergency light control.

[0087] This invention effectively enhances the emergency response capabilities of rescue vehicles in complex traffic environments through innovative methods such as environmental perception, dynamic decision-making, V2X multi-vehicle linkage, and closed-loop execution. By adjusting the flashing frequency, brightness, and mode of the emergency lights in real time, it not only improves their visibility and warning effect under different road topologies, inclement weather, and high-density traffic conditions, but also effectively reduces secondary accidents caused by misjudgments or slow responses through accurate path prediction and avoidance instruction broadcasting. Furthermore, the closed-loop execution mechanism ensures the accurate execution of instructions and the reliability of equipment, greatly improving traffic safety and rescue efficiency.

[0088] In one possible implementation, this invention combines the emergency light linkage control method of the vehicle-mounted infotainment system with the road topology analysis process in environmental perception, further optimizing the process. By introducing a combination of high-precision maps and Global Positioning System (GPS) data, accurate acquisition of the road topology is achieved. Simultaneously, the detection angle range of the millimeter-wave radar is dynamically adjusted to adapt to road conditions with varying numbers of lanes, improving the emergency light control performance of the rescue vehicle in complex environments.

[0089] The following steps are mainly involved in the analysis of road topology:

[0090] The system utilizes the Global Positioning System (GPS) to obtain the current and real-time location information of the rescue vehicle. Detailed road information, such as lane numbers, intersection layouts, and ramp locations, provided by high-precision maps is then matched and analyzed in real time. This step ensures the system accurately understands the road environment in which the rescue vehicle is currently located, providing foundational data for subsequent emergency light control decisions.

[0091] Information such as the number of lanes, the distance between adjacent intersections, and the location of ramps are extracted from high-precision maps. This information is the basis for judging the complexity of the road and setting the emergency light control mode.

[0092] Based on real-time information on the number of road lanes, the system dynamically adjusts the detection angle range of the millimeter-wave radar. The specific adjustment rules are as follows:

[0093] With reference to the baseline number of lanes, the detection angle range of the millimeter-wave radar expands by a preset ratio for each additional unit of lane number.

[0094] Baseline number of lanes: This usually refers to the number of lanes that are commonly found on a road. It may be obtained through historical data statistics, such as the standard number of two-lane or four-lane lanes.

[0095] Proportional coefficient: This coefficient is determined when analyzing historical accident data, with particular attention to the probability of missed obstacle detection in multi-lane scenarios. By analyzing historical data, the required angular range for millimeter-wave radar to extend under different numbers of lanes is determined, thereby improving detection accuracy and reducing missed detections.

[0096] By analyzing historical accident data, particularly the obstacle miss rate in multi-lane scenarios, the detection angle settings of millimeter-wave radar are optimized. This ensures that the radar's detection range can be dynamically adjusted according to different road conditions, reducing blind spots caused by road changes (such as an increase in the number of lanes), thereby improving overall emergency response capabilities.

[0097] By combining GPS and high-precision mapping technology and dynamically adjusting the detection angle range of millimeter-wave radar, this invention achieves more accurate environmental perception in complex traffic environments. Through real-time analysis of lane numbers and road layout, the control strategy for emergency lights is optimized, enhancing their warning effect and reducing the risk of traffic accidents caused by radar blind spots, thus providing effective protection for road safety and traffic efficiency.

[0098] In one possible implementation, this embodiment of the invention incorporates the vehicle density calculation process in the emergency lighting linkage control method of the vehicle-mounted system. This involves receiving location information of surrounding vehicles via an onboard network communication module and calculating the effective road area and vehicle density value of the current road segment based on high-precision map data and vehicle distribution. The key to this process lies in how to dynamically calculate and update the road density to ensure that the system can respond promptly and accurately to changes in the road environment during emergency rescue operations.

[0099] Specifically, the vehicle-to-everything (V2X) communication module obtains real-time information about surrounding vehicles by receiving unique identifiers (IDs) and their location coordinates broadcast by all vehicles within a preset radius. This information, including each vehicle's unique identifier and GPS coordinates, helps the system understand the distribution of surrounding vehicles and provides necessary data support.

[0100] The preset radius is an adjustable range. The system determines the area of ​​vehicles it can detect by using a pre-set communication coverage radius. Depending on the scenario (such as urban roads and highways), the size of this radius will also be adjusted to ensure that vehicles within a certain range can be covered.

[0101] Based on high-precision map data, the system first extracts the sequence of boundary coordinate points for the current road segment. These boundary coordinate points define the geometry of the road, including the actual edges of the road surface.

[0102] Using a polygon area algorithm, these coordinate points are used to generate area values ​​for enclosed regions, thereby calculating the effective road area for that road segment. This step ensures that the actual passable area is taken into account, rather than just the physical space of the entire road, so the resulting area value accurately reflects the actual road area available for traffic.

[0103] The formula for calculating vehicle density is: number of vehicles / effective road area. The system counts the number of all vehicles within a preset radius and calculates the ratio between this number and the calculated effective road area to obtain the vehicle density value for the current road segment.

[0104] This density value reflects the current level of traffic congestion on the road segment, helping the system assess whether the response strategy of the emergency lights needs to be adjusted.

[0105] The selection of the preset radius needs to be optimized based on actual testing in different road scenarios. For example, in typical urban road and highway scenarios, the optimal communication coverage range is determined through real-vehicle testing to achieve the best avoidance response time. Urban roads may require a smaller radius, while highways may require a larger radius to ensure that the vehicle density calculation accurately reflects road conditions in different scenarios.

[0106] By combining vehicle-to-everything (V2X) communication with high-precision map technology, this invention provides an efficient and dynamic method for calculating vehicle density. By sensing the distribution of surrounding vehicles in real time and combining this with the calculation of effective road area, the method can accurately determine the traffic conditions of the current road segment. Optimized preset radius and density calculation strategies ensure optimal response in different road scenarios, thereby improving the accuracy and timeliness of emergency light linkage control and providing strong protection for the safe passage of rescue vehicles.

[0107] In one possible implementation, the optimal frequency value is determined by establishing a mapping relationship between different road topology types and flashing frequencies, conducting real-vehicle tests, and using a weighted smoothing algorithm to transition the frequency, thereby ensuring effective avoidance and visual comfort in various scenarios.

[0108] To better adapt to different types of road environments, the system first established a mapping database between road topology types and flicker frequencies. Specifically, the system associates different types of roads (such as highways, urban intersections, and roundabouts) with different flicker frequency ranges.

[0109] Among them, for highways: because the traffic flow on highways is relatively smooth and the speed is relatively fast, the system allocates them to a lower frequency range (low frequency) to avoid unnecessary visual interference caused by frequent flickering.

[0110] Urban intersections: At complex urban intersections, where vehicles travel at slower speeds and traffic flows frequently, the system assigns them to the mid-frequency range (mid-frequency) to ensure that they can effectively attract the attention of surrounding vehicles without being too visually distracting.

[0111] Roundabouts: In this type of scenario, vehicles are concentrated and their speeds vary greatly. The system assigns them to a higher frequency range (high frequency) and improves the warning effect by flashing more frequently, thus ensuring traffic safety.

[0112] When determining the base flicker frequency, the system conducted extensive real-vehicle tests, collecting data on different road topology types, especially the effective avoidance distance of surrounding vehicles at different frequencies.

[0113] During testing, the system judged the effectiveness of the flashing frequency by monitoring the reaction speed and avoidance distance of surrounding vehicles. A frequency value was selected that maximizes the avoidance distance while remaining comfortable for the human eye, ensuring that the flashing frequency effectively alerted other vehicles to avoid the flashing without causing excessively frequent or intense visual stimulation that could lead to driver discomfort.

[0114] Human eye comfort: This means avoiding excessively high frequencies that cause visual fatigue, while avoiding excessively low frequencies that fail to attract the attention of surrounding vehicles in time, thus affecting the avoidance effect.

[0115] When a vehicle passes through multiple heterogeneous road segments consecutively (e.g., from a city road onto a highway, or from a roundabout into a city intersection), the system dynamically adjusts the flashing frequency according to the characteristics of each segment. A weighted smoothing algorithm is then used to ensure a smooth transition of frequency values ​​between different segments, avoiding abrupt changes that could cause discomfort.

[0116] Dynamic calculation of weighting coefficients: The smoothness of the transition frequency is dynamically calculated based on two main factors:

[0117] Road segment length: The frequency transition on longer road segments will be smoother, avoiding unnecessary impacts caused by frequent changes.

[0118] Vehicle speed: When the vehicle speed is high, the frequency transition should be smoother to avoid the frequency change being too fast and affecting the driver's visual perception and reaction speed.

[0119] By combining road topology type and dynamically adjusting frequency, the basic flashing frequency selection rule in this embodiment of the invention effectively improves the linkage control effect of emergency lights. While ensuring effective avoidance by surrounding vehicles, it also considers the driver's visual comfort, avoiding visual fatigue caused by excessive flashing frequency. Through a smooth transition algorithm, the frequency adjustment is natural and stable, improving the driver's sense of security and comfort, ultimately enhancing the responsiveness and adaptability of the entire emergency light linkage control system, ensuring that rescue vehicles can pass efficiently and safely in different road environments.

[0120] In one possible implementation, the direction of avoidance is calculated by obtaining the relationship between the location of the receiving vehicle and the rescue path, and a clear avoidance sign is generated to instruct the receiving vehicle on the specific avoidance maneuver to be performed. This process aims to improve the responsiveness of surrounding vehicles to the rescue vehicle through precise directional guidance, ensuring the smooth progress of the rescue process.

[0121] The system first obtains the current location of the receiving vehicle in real time through onboard positioning devices (such as GPS, vehicle positioning systems, etc.). This location coordinates are crucial in the entire system because they provide real-time data for subsequent avoidance operations.

[0122] The vertical distance between the real-time location coordinates of the receiving vehicle and the predicted rescue path vector is crucial for calculating the avoidance direction. The predicted rescue path vector is the driving path of the rescue vehicle pre-calculated by the system based on data such as traffic environment, road structure, and traffic flow. By calculating the vertical distance between the receiving vehicle and this predicted path vector, the system can determine the relative position of the receiving vehicle and the rescue vehicle, and thus infer the direction that needs to be avoided.

[0123] To determine the avoidance direction, the system first defines the direction of the normal vector of the path vector. The path's forward direction is the baseline, and the system determines the positive direction of the normal vector by rotating the path's forward direction 90 degrees clockwise. This setting provides a directional standard for subsequent avoidance decisions, making avoidance markings more consistent and standardized across different roads or scenarios.

[0124] Once the vertical distance between the receiving vehicle and the rescue path is calculated, the system determines the avoidance direction based on the sign of the vertical distance value:

[0125] A positive vertical distance indicates that the receiving vehicle should move to the right to give way, so the system generates a "give way to the right" sign.

[0126] A negative vertical distance indicates that the receiving vehicle should move to the left to give way, thus generating a "Left-behind" sign.

[0127] This judgment method is based on the road geometry and the real-time dynamics of the relative paths of vehicles, ensuring that drivers are provided with clear and intuitive avoidance direction prompts.

[0128] The system sets a critical threshold to determine when to generate a yield sign. This threshold was obtained through experiments and tests on typical road widths. During testing, the system simulates yield maneuvers of receiving vehicles at different distances and combines this with the safety score of the yield maneuver to determine the most suitable threshold. This threshold reflects the safety score of the yield maneuver at different distances and is used to optimize the generated yield sign to ensure high safety of yield indications within critical distance ranges.

[0129] By calculating the vertical distance between the receiving vehicle and the rescue path, and combining this with the definition of the path prediction vector normal direction, the avoidance direction marking generation process of this invention achieves accurate and efficient avoidance instructions. The system dynamically generates "avoid to the left" or "avoid to the right" markings based on real-time data and safety scores, providing drivers of receiving vehicles with intuitive and clear avoidance operation guidance, ensuring that rescue vehicles can pass smoothly in different road environments, improving rescue efficiency and ensuring traffic safety.

[0130] In one possible implementation, the system verifies the matching degree between the expected control commands and the actual LED brightness sequence to ensure that the control commands of the emergency lighting system can be executed correctly and effectively reflect the expected effect in a real environment. The key to the time-domain matching verification process is to ensure the accuracy and stability of the system under various conditions through signal comparison, correlation coefficient calculation, and the setting of reliability thresholds.

[0131] The system first converts the user- or control system's preset control commands into standard square wave signals. This square wave signal represents the ideal on / off switching mode of the LED emergency light. For example, if the LED light is expected to flash at a certain fixed frequency, the control command will be converted into a square wave signal with a frequency and duty cycle that match that flashing mode.

[0132] The system uses an onboard camera to capture real-time sequences of actual brightness changes in LED lights. After processing, the images captured by the camera extract data on brightness variations, reflecting the actual operating status of the LED lights. Subsequently, based on the collected brightness data, a monitoring square wave signal is generated. This signal represents the actual performance of the LED lights, reflecting their flicker frequency and brightness variations.

[0133] Within a fixed time window, the system compares the expected control square wave signal with the actual monitored square wave signal. Specifically, it calculates the correlation coefficient between them, a key indicator of the degree of matching. The correlation coefficient is calculated as the ratio of the covariance to the standard deviation of each discrete point sequence of the signal. The covariance measures the common variation of the signals, while the standard deviation measures the volatility of the signals. This ratio can be used to determine the degree of matching between the actual signal and the ideal signal.

[0134] The matching degree, a result of the correlation coefficient, reflects the consistency between the expected square wave signal and the actual monitored signal. The closer the matching degree is to 1, the closer the actual LED brightness change is to the expected control command, and the higher the accuracy of command execution.

[0135] To ensure the system operates stably in practical applications, a reliability threshold needs to be determined. This threshold is used to decide whether to accept the execution of the current control command. The process for determining the reliability threshold is as follows:

[0136] In a laboratory environment, we simulated different levels of external factors such as electromagnetic interference and mechanical vibration, and observed the effects of these interferences on signal transmission and LED brightness changes.

[0137] The relationship between statistical matching degree and instruction execution error rate under different interference conditions. Execution error rate refers to the frequency of deviations or errors that occur during actual execution.

[0138] Plot the correlation curve between matching degree and error rate to identify the point where the execution error rate rises sharply when the matching degree falls below a certain threshold. The matching degree corresponding to this point is the reliability threshold, which is the standard by which the system determines whether control commands are executed accurately.

[0139] By performing time-domain matching verification and analyzing the matching degree between the expected control commands and the actual LED brightness change signals, this embodiment of the invention achieves high-precision emergency light linkage control. The system not only ensures the consistency between control commands and actual execution, but also improves the system's stability and accuracy in complex environments through anti-interference testing and the setting of reliability thresholds, providing reliable assurance for emergency response.

[0140] In one possible implementation, during the time-domain matching verification process, the system monitors in real time the degree of matching between the actual LED execution effect (by monitoring the square wave) and the expected control command.

[0141] If the system repeatedly finds that the matching degree is lower than the preset reliability threshold, it is considered that the following problems may exist:

[0142] Instructions may not be transmitted correctly or may be interfered with; the LED control module may not respond adequately; or the external environment (such as electromagnetic interference or temperature and humidity changes) may cause execution distortion.

[0143] The system sets a retransmission trigger threshold (e.g., 3 times). When the matching degree is lower than the threshold for N consecutive times and does not recover automatically, the command retransmission process is initiated.

[0144] During the command retransmission phase, in order to improve the LED's response to the control signal, the system will increase the adjustment range of the LED drive current, thereby enhancing the actual physical effect of the control signal.

[0145] The current adjustment range is not fixed, but changes dynamically according to the current matching degree:

[0146] The larger the matching degree falls below the threshold, the more severe the deviation in the system's instruction execution judgment, and the more significant the increase in drive current. This is a negative correlation mechanism, which can be expressed mathematically as follows:

[0147] ΔI = k × (1 - ρ), where ρ is the matching degree and k is the adjustment coefficient.

[0148] For example, if the match is 0.7, the adjustment range is 30% of the base value; if the match drops to 0.4, the adjustment range increases to 60%.

[0149] This method can improve the problem of poor LED lighting or flickering response by increasing the current pulse gain, improve the consistency of optical output, and enhance the visual recognition effect.

[0150] The threshold is not set based on experience, but is derived through precise modeling using statistical data methods:

[0151] In a controlled experimental environment, the system simulates various fault scenarios, such as packet loss in communication links, unstable power supply, and delayed response of the controller. For each fault type, the minimum number of times the minimum matching degree is required for the automatic recovery of the control command is repeatedly tested. The minimum number of times that can be recovered in most scenarios is found in the statistical sample and then set as the trigger threshold value (e.g., if 95% of the scenarios can be recovered within 3 times, the threshold is 3).

[0152] This threshold value represents an optimal balance between response sensitivity and system fault tolerance.

[0153] By constructing a closed-loop control mechanism of "matching degree monitoring - trigger threshold judgment - dynamic current adjustment," the vehicle emergency lighting system is equipped with intelligent retransmission capability and current enhancement compensation mechanism. This method not only improves the response accuracy and robustness of emergency commands under harsh conditions, but also optimizes the sensitivity and stability of the control strategy through data-driven threshold setting, demonstrating high engineering practical value and promising prospects for widespread application.

[0154] In one possible implementation, while the vehicle is in motion, the system first compares the information with the onboard navigation and real-time map to identify whether it is about to enter or is already in the ramp area.

[0155] Ramp location identification is based on high-precision map data, including the ramp geometry (such as radius of curvature) and its connection to the main lanes, combined with GPS signals or positioning systems to confirm the vehicle's specific location on the ramp.

[0156] The process of identifying ramps is usually based on comparing the vehicle's trajectory with map data, and can be verified through an onboard system or a preliminary scan of millimeter-wave radar signals.

[0157] When a vehicle is detected entering the ramp, the system dynamically adjusts the detection angle range of the millimeter-wave radar, focusing on expanding the lateral detection angle, especially at the curve of the ramp.

[0158] Traditional millimeter-wave radar typically focuses on detecting objects in front of the vehicle. However, when entering a ramp, the vehicle's trajectory changes significantly, especially at the curve, where the potential risk of lateral collisions becomes more pronounced. By increasing the lateral detection angle, any objects or obstacles from outside the ramp can be effectively detected, ensuring that the onboard system can react promptly in this area.

[0159] The radius of curvature and vehicle speed are key parameters for determining the lateral detection angle. The radius of curvature indicates the degree of curvature of the ramp turn, while vehicle speed affects the actual trajectory of the vehicle when turning on the ramp. Based on these two parameters, the system can predict potential collision risk areas and determine the coverage angle of the radar beam accordingly.

[0160] The specific calculation method is as follows:

[0161] θ side = f(R, v);

[0162] Where, θ side R is the radius of curvature of the ramp, and v is the current speed of the rescue vehicle.

[0163] For example, if the curvature of the ramp is small, it means that the vehicle needs to make a sharp turn. The system will increase the lateral extension angle of the radar beam to ensure coverage of the sharp turn area. If the vehicle is traveling at a high speed, the system will also expand the detection range in a timely manner to deal with potential dangers.

[0164] The method for obtaining the radius of curvature relies on extracting the centerline coordinate sequence of the ramp from high-precision maps. These high-precision maps provide detailed geometric information about the ramps, and with accurate geographic coordinate data, the system can obtain the shape and curvature properties of the ramps in real time.

[0165] Using a curve fitting algorithm, the system can calculate the instantaneous curvature of the ramp based on the centerline coordinate sequence. Fitting algorithms often use quadratic curves or Bézier curves to approximate the actual curve shape of the ramp, thereby calculating the radius of curvature at each point. The radius of curvature is closely related to the vehicle's trajectory on the ramp; therefore, this calculation is crucial.

[0166] Based on the real-time calculated radius of curvature, the system can dynamically adjust the detection angle of the millimeter-wave radar to ensure coverage of all potential obstacles when turning.

[0167] By dynamically adjusting the detection angle of millimeter-wave radar and optimizing the lateral detection coverage in real time based on the curvature of the ramp and the speed of the rescue vehicle, the system's ability to identify potential collision risks in the ramp area can be effectively improved. This technology has significant advantages in improving system safety, emergency response timeliness, and intelligent perception capabilities, and is particularly suitable for precise operations in highway, mountainous, or emergency rescue scenarios.

[0168] In one possible implementation, the system obtains the vehicle's current location in real time through an in-vehicle navigation system or a high-precision map, and combines this with GPS signals to confirm whether the vehicle is about to enter the ramp area.

[0169] When entering a ramp, the system extracts the ramp's geometric information from a high-precision map, including the ramp's curvature, curve radius, and how it intersects with the main lane.

[0170] By extracting the coordinate sequence of the ramp centerline, the instantaneous radius of curvature of the ramp is calculated using a curve fitting algorithm (such as a quadratic curve or a Bézier curve).

[0171] Based on this, the system monitors the speed of rescue vehicles in real time and combines the speed with the radius of curvature to predict the vehicle's trajectory and potential collision risks when turning on the ramp.

[0172] After detecting a vehicle entering the ramp, the system dynamically adjusts the detection angle of the millimeter-wave radar based on the real-time calculated radius of curvature and vehicle speed.

[0173] The system adds a lateral detection angle to ensure that the radar can cover obstacles on both sides and around the vehicle during turns. This lateral extension angle is calculated based on the radius of curvature of the ramp and the vehicle speed, ensuring that the radar beam can adapt to different road conditions.

[0174] As the detection angle increases, the radar's coverage extends to the sides of the vehicle, especially sharp turns on ramps, further enhancing the ability to monitor areas with potential collision risks.

[0175] Based on real-time feedback from radar signals, the system can predict the distance to obstacles in front or to the side and promptly warn the driver or initiate emergency response measures.

[0176] By combining high-precision maps, vehicle speed, and ramp curvature, the detection angle of millimeter-wave radar is intelligently and dynamically adjusted, significantly improving the vehicle's collision warning capabilities in complex environments such as ramps. This technology effectively enhances the timeliness and accuracy of emergency response, strengthens the vehicle's intelligent perception capabilities, and is particularly suitable for special scenarios such as highways, mountainous areas, and emergency rescue, providing reliable support for rescue operations and driving in high-risk environments.

[0177] In one possible implementation, the first-layer data structure is used to define the basic types of V2X commands, such as emergency assistance, vehicle avoidance, or driving routes. Through message type identifiers, the receiving vehicle can quickly understand the basic intent of the command. Different types of commands have different identifiers, which helps to quickly parse information during communication.

[0178] The second layer contains coordinate data and path vectors, used to identify the current location and planned travel route of the rescue vehicle. Using GPS or other positioning technologies, the system acquires the precise coordinates of the rescue vehicle in real time and converts them into path vectors. The path vectors describe the vehicle's direction of travel and speed, allowing the receiving vehicle to plan its avoidance route in advance.

[0179] The third layer contains avoidance direction coding information, indicating which direction the receiving vehicle should steer to avoid the obstacle. This information can be calculated in real time based on the positional relationship between the receiving vehicle and the rescue vehicle. The avoidance direction coding indicates the avoidance action (such as shifting left or right) that the receiving vehicle should take in a specific situation to ensure that the passage of the rescue vehicle is not obstructed.

[0180] To reduce data volume and improve communication efficiency, the path vector point sequence is compressed using the Douglas-Pock algorithm. This algorithm simplifies the path vector sequence, removes redundant points, and retains only the necessary path points, thereby reducing data transmission volume. The compressed data can be transmitted to the receiving vehicle more quickly.

[0181] The compression threshold is dynamically adjusted based on the frequency of location updates from the receiving vehicle. When the receiving vehicle is moving quickly, the system lowers the compression threshold to ensure that frequent path updates are transmitted in a timely manner; while when the vehicle is relatively stationary, the system appropriately raises the threshold to reduce unnecessary path updates and ensure communication efficiency.

[0182] By employing a hierarchical coding structure and the Douglas-Puk algorithm to compress path vector data, coupled with a dynamically adjusted compression threshold strategy, the transmission efficiency and real-time performance of V2X commands are effectively improved, communication latency and bandwidth pressure are reduced, and the accurate and timely transmission of commands during rescue operations is ensured. This technology is suitable for scenarios such as emergency rescue and traffic management, and it has significant safety and emergency response advantages, especially on highways and in complex road conditions.

[0183] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0184] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for linkage control of emergency lights in conjunction with a vehicle infotainment system, characterized in that, include: S1: Environmental perception, which uses the vehicle's infotainment system to collect real-time data on road topology, distribution of potential congestion obstacles, visibility level, and vehicle density; among which: The road topology was obtained based on the matching and analysis of GPS coordinates and high-precision maps. The distribution of potential congestion obstacles is determined by detecting the speed of moving targets using millimeter-wave radar and combining it with road speed limits. The visibility level is determined by the grayscale distribution of images captured by the roof-mounted camera. The vehicle density value is calculated by receiving broadcast information from vehicles within a preset radius through the vehicle-mounted network communication module. S2: Dynamic decision-making, generating emergency light control commands based on the output of S1, including: The basic scintillation frequency is selected based on the road topology, so that the frequency at urban intersections with multiple intersections is higher than that on highways. When the visibility level is lower than the preset standard, the brightness enhancement and supplemental lighting modes are activated. When the vehicle density value exceeds a critical threshold, the dual-color alternating flashing mode is activated. S3: Multi-vehicle linkage, predicts future paths based on the location and historical trajectory of rescue vehicles, and broadcasts V2X commands containing real-time coordinates, path vectors and avoidance direction markings to vehicles within a preset radius; S4: Closed-loop execution, sends the S2 command to the lighting control unit, and captures the actual flashing sequence through the roof camera for time-domain matching verification. When the matching degree is lower than the reliability threshold, the command is resent. The specific process of time-domain matching verification in S4 includes: The expected control command is converted into a standard square wave signal, and the actual LED brightness sequence is acquired by the camera to generate a monitoring square wave signal. Calculate the correlation coefficient between two signals: within a fixed time window, generate the degree of matching based on the ratio of the covariance of the discrete point sequence of the signals to their respective standard deviations; The process of determining the reliability threshold is as follows: simulate different levels of electromagnetic interference and mechanical vibration in a laboratory environment, statistically analyze the correlation curve between the matching degree and the instruction execution error rate, and select the matching degree critical value corresponding to the point of sudden increase in error rate. The strategy for retransmitting the instruction includes: When the number of consecutive times the matching degree falls below the reliability threshold reaches the retransmission trigger threshold, the instruction retransmission process is initiated. When retransmitting, the adjustment range of the LED drive current is dynamically increased, and the increase ratio is negatively correlated with the matching degree: the lower the matching degree, the greater the current adjustment range. The retransmission trigger threshold is set by statistically analyzing the minimum number of stable recovery attempts required for the command transmission link under different fault scenarios.

2. The emergency lighting linkage control method combined with a vehicle infotainment system according to claim 1, characterized in that, The analytical process for the road topology in S1 includes: The system performs real-time matching between GPS coordinates and the road network topology of high-precision maps to extract data on the number of lanes, distance between adjacent intersections, and ramp locations of the current road. The detection angle range of the millimeter-wave radar is dynamically adjusted according to the number of lanes. The adjustment rule is as follows: with the reference number of lanes, the detection angle range is expanded by a preset ratio for each unit increase in the number of lanes. The reference number of lanes and the ratio coefficient are determined by analyzing the obstacle miss rate in multi-lane scenarios in historical accident data.

3. The emergency lighting linkage control method combined with a vehicle infotainment system according to claim 1, characterized in that, The calculation process for the vehicle density value in S1 includes: The vehicle-mounted network communication module receives the unique identifiers and location coordinates broadcast by all vehicles within a preset radius. Calculate the effective road area of ​​the current road segment based on high-precision map data: extract the coordinate point sequence of the road network boundary and generate the area value of the closed area through a polygon area algorithm; The density value is obtained by dividing the number of vehicles by the effective road area. The preset radius is determined by real vehicle testing in typical urban roads and highway scenarios to achieve the optimal communication coverage for avoidance response time.

4. The emergency lighting linkage control method combined with a vehicle infotainment system according to claim 1, characterized in that, The selection rules for the basic flicker frequency in S2 include: Establish a mapping database between road topology types and frequencies: associate highways, urban intersections, and roundabouts with low-frequency, mid-frequency, and high-frequency ranges, respectively; The process of determining the specific frequency value is as follows: In real vehicle tests of various topological scenarios, the effective avoidance distance of surrounding vehicles at different frequencies is collected, and the frequency value that maximizes the avoidance distance and meets the comfort of the human eye is selected. When vehicles continuously pass through heterogeneous topology road segments, a weighted smoothing algorithm is used to transition the frequency value, with the weighting coefficients dynamically calculated based on the road segment length and vehicle speed.

5. The emergency lighting linkage control method combined with a vehicle infotainment system according to claim 1, characterized in that, The process of generating the avoidance direction marker in S3 includes: Obtain the real-time location coordinates of the receiving vehicle and calculate its vertical distance to the rescue path prediction vector; Define the direction of the normal vector of the path vector: with the path's forward direction as the reference, rotate 90 degrees clockwise to get the positive direction; A "Get Out of the Way to the Right" flag is generated when the vertical distance value is positive, and a "Get Out of the Way to the Left" flag is generated when the vertical distance value is negative. The critical threshold for vertical distance is determined by testing the safety score of avoidance maneuvers for different distance values ​​under typical road widths.

6. The emergency rescue light linkage control method combined with the vehicle infotainment system according to claim 2, characterized in that, The detection angle range of the dynamically adjustable millimeter-wave radar also includes: When the location of the ramp is identified, the coverage area of ​​the lateral detection angle is increased. The lateral expansion angle is calculated by predicting the radar beam coverage angle required for the potential collision risk area based on the radius of curvature of the ramp and the speed of the rescue vehicle. The radius of curvature is obtained by extracting the coordinate sequence of the ramp centerline from a high-precision map and calculating the instantaneous curvature using a curve fitting algorithm.

7. The emergency rescue light linkage control method combined with a vehicle infotainment system according to claim 4, characterized in that, The activation rules for the brightness enhancement and fill light modes also include: Establish a mapping table between visibility level and baseline brightness, and calibrate the mapping relationship through human driver recognition distance tests in foggy conditions; The baseline brightness is dynamically compensated based on the vehicle density value: the higher the density value, the larger the compensation coefficient, and the slope of the compensation coefficient is fitted by the warning effect feedback data under different density scenarios.

8. The emergency lighting linkage control method combined with a vehicle infotainment system according to claim 3, characterized in that, The data encapsulation method for V2X instructions includes: A layered coding structure is adopted: the first layer is the message type identifier, the second layer is the coordinate data and path vector, and the third layer is the avoidance direction coding; Data compression strategy: The path vector point sequence is compressed using the Douglas-Puk algorithm, and the compression threshold is dynamically adjusted according to the location update frequency of the receiving vehicle.

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