A vehicle early warning method and system based on dynamic speed threshold for sharp curve road section

Abstract

The application relates to the technical field of intelligent transportation, and discloses a vehicle early warning method and system based on a dynamic speed threshold of a sharp curve road section. The method comprises the following steps: collecting real-time driving data of candidate target vehicles driving into the sharp curve road section in real time; performing false target filtering processing on the candidate target vehicles to obtain an effective target set; constructing a dynamic speed threshold model according to historical accident data, road geometric parameters and real-time environmental parameters of the sharp curve road section to obtain a dynamic speed threshold at the current moment; performing risk level judgment according to the vehicle state in the effective target set and the dynamic speed threshold to obtain a risk level of a local vehicle and a risk level of an opposite vehicle; and outputting a matched early warning level according to the risk level of the local vehicle and the risk level of the opposite vehicle. The application can automatically correct the risk judgment threshold according to the real-time environment of the sharp curve road section, and improve the accuracy of risk judgment.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, specifically to a vehicle warning method and system for sharp curves based on dynamic speed thresholds. Background Technology

[0002] In recent years, with the continuous improvement of the traffic capacity of rural roads, mountain roads, and county and township roads, safety issues related to sharp bends, continuous bends, and bends near water or cliffs have become increasingly prominent. Sharp bends typically feature small bend radii, insufficient visibility, numerous obstructions, and limited road width, making them high-risk areas for traffic accidents. Due to their small bend radius and limited visibility, vehicles entering bends are highly susceptible to serious accidents such as skidding, rollovers, or collisions with oncoming vehicles if they do not control their speed properly. Accurately assessing the risks of vehicles navigating bends is crucial for improving driving safety on curves.

[0003] The main problems with existing methods for determining vehicle risk on curves are: (1) Most methods use a fixed speed threshold (such as 30 km / h or 40 km / h) as the basis for risk determination. However, the actual risk of sharp curves is not only related to vehicle speed, but also closely related to factors such as rain, fog, night, slippery road surface, curve radius, and effective sight distance. A speed that is safe under dry conditions may pose a greater risk under rainy, foggy, or slippery road conditions. Fixed speed thresholds cannot accurately reflect the real curve risk under dynamic conditions, leading to underreporting in dry conditions, overreporting in rainy conditions, or false alarms. (2) Vehicle perception systems (such as millimeter-wave radar) are easily affected by roadside guardrails, trees, parked vehicles, multipath reflections, etc., in curve environments, resulting in false targets. Existing methods lack an effective filtering mechanism for curve scenarios, and false targets directly participate in risk determination, causing serious misjudgments. (3) Most existing risk assessment methods are based only on the speed of the vehicle itself, ignoring the comprehensive impact on the risk to oncoming vehicles and road geometric parameters (curving radius, sight distance, superelevation), making it difficult to accurately assess the real risks in the scenario of meeting oncoming vehicles on a curve.

[0004] Therefore, there is an urgent need for a curve vehicle warning method that can dynamically adjust the speed threshold based on real-time environmental parameters and road geometry parameters, effectively filter out false targets, and comprehensively assess the risk by combining the status of the vehicle and oncoming vehicles. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, this invention provides a vehicle warning method and system for sharp curves based on dynamic speed thresholds. The system collects vehicle driving data, environmental parameters, and road geometric parameters using an onboard millimeter-wave radar, constructs a dynamic speed threshold model, filters out false signals from perceived targets, and finally outputs a matching warning level based on the risk levels of the vehicle and oncoming vehicles for subsequent warning or active control purposes.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] In a first aspect, the present invention proposes a vehicle warning method for sharp curve sections based on dynamic speed thresholds, comprising the following steps:

[0008] Real-time driving data of candidate target vehicles entering sharp bends is collected.

[0009] The candidate target vehicles are subjected to false target filtering to obtain a valid target set;

[0010] A dynamic speed threshold model is constructed based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends to obtain the dynamic speed threshold at the current moment.

[0011] Based on the vehicle status in the set of valid targets and the dynamic speed threshold, the risk level is determined to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0012] Based on the risk level of the local vehicle and the risk level of the oncoming vehicle, a matching warning level is output.

[0013] Furthermore, the real-time driving data includes the vehicle's real-time speed, the distance between the vehicle and the curve entrance, the vehicle's azimuth angle, the vehicle's driving direction, and the target echo intensity.

[0014] Furthermore, the candidate target vehicles are subjected to false target filtering to obtain a set of valid targets, including:

[0015] The radar polar coordinates of the candidate target vehicle are converted into road coordinates to obtain the target's distance along the road and the target's lateral offset distance.

[0016] Establish lane area constraints, and determine whether the candidate target vehicle is within the effective lane area based on the lane left boundary function and right boundary function;

[0017] Establish a consistency constraint for the direction of motion, and verify whether the distance change of the candidate target is consistent with the radar velocity measurement result;

[0018] A multi-frame confirmation model is established. Within the confirmation window, the number of frames that meet the valid target conditions is counted. When the number of valid confirmation frames is reached, the candidate target vehicle is confirmed as a valid target and added to the set of valid targets.

[0019] Furthermore, a dynamic speed threshold model is constructed based on historical accident data, road geometry parameters, and real-time environmental parameters of the sharp curve section to obtain the dynamic speed threshold at the current moment, including:

[0020] The basic speed threshold for road sections is determined based on historical accident speed thresholds, road geometric safe speeds, and road speed limits.

[0021] Calculate the environmental correction factor based on rainfall intensity, visibility, road surface wetness, road surface temperature, and light intensity;

[0022] Determine the safe speed threshold based on the effective sight distance and vehicle braking distance;

[0023] Calculate the initial dynamic speed threshold based on the road segment's basic speed threshold, the environmental correction coefficient, and the safe speed threshold;

[0024] The initial dynamic velocity threshold is smoothed to obtain the dynamic velocity threshold at the current moment.

[0025] Furthermore, the method for determining the historical accident speed threshold is as follows:

[0026] The oncoming accident rate for each speed range is determined based on the accident rate and vehicle speed distribution model.

[0027] The aforementioned on-vehicle collision rate is subjected to a sliding smoothing process;

[0028] Based on preset threshold determination conditions, a critical speed is determined from the smoothed data and used as the historical accident vehicle speed threshold.

[0029] Furthermore, the method for determining the road's geometrically safe speed is as follows:

[0030] Obtain the curve radius, lateral adhesion coefficient, and superelevation rate of sharp curves;

[0031] Based on the curve radius, the lateral adhesion coefficient, and the superelevation rate, the maximum safe driving speed of the vehicle under conditions without sideslip or rollover is calculated and used as the road geometric safe speed.

[0032] Furthermore, based on rainfall intensity, visibility, road surface slipperiness, road surface temperature, and light intensity, environmental correction factors are calculated, including:

[0033] Based on rainfall intensity, visibility, road surface slipperiness, road surface temperature, and light intensity, the normalization factor corresponding to each environmental factor is calculated.

[0034] The environmental correction coefficient is obtained by weighting and fusing the normalized factors corresponding to each environmental factor.

[0035] Further, based on the vehicle status in the effective target set and the dynamic speed threshold, a risk level determination is made to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle, including:

[0036] Obtain the distance of the vehicle to the curve entrance and the vehicle's real-time speed;

[0037] When the distance is less than a preset distance threshold and the real-time speed of the vehicle is greater than the sum of the dynamic speed threshold and the hysteresis judgment speed margin, the vehicle is determined to be at a high risk level; otherwise, the vehicle is determined to be at a low risk level.

[0038] The above determination is performed on both local vehicles and oncoming vehicles in the set of valid targets to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0039] Furthermore, based on the risk level of the local vehicle and the risk level of the oncoming vehicle, a matching warning level is output, including:

[0040] When the risk level of the local vehicle is low and there are no oncoming vehicles, a safe passage reminder is output.

[0041] When the risk level of the local vehicle is high and there are no oncoming vehicles, a one-way high-risk deceleration warning is output.

[0042] When both the risk level of the local vehicle and the risk level of the oncoming vehicle are low risk, a safe passing warning is output.

[0043] When the risk level of the local vehicle is low and the risk level of the oncoming vehicle is high, or when both the risk levels of the local vehicle and the oncoming vehicle are high, a strong high-risk warning is output.

[0044] Secondly, this invention proposes a vehicle warning system for sharp curves based on dynamic speed thresholds, employing the aforementioned vehicle warning method for sharp curves based on dynamic speed thresholds, including:

[0045] The data acquisition module is used to collect real-time driving data of candidate target vehicles entering sharp bends.

[0046] The target filtering module is used to perform false target filtering on the candidate target vehicles to obtain a set of valid targets.

[0047] The threshold correction module is used to construct a dynamic speed threshold model based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends, and obtain the dynamic speed threshold at the current moment.

[0048] The risk assessment module is used to determine the risk level based on the vehicle status in the set of valid targets and the dynamic speed threshold, and to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0049] The early warning output module is used to output a matching early warning level based on the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0050] The present invention has the following beneficial effects:

[0051] (1) This invention differs from using a single fixed speed threshold. Instead, it dynamically corrects the speed threshold based on historical accident data, road geometry parameters, real-time rainfall, visibility, slippery conditions, temperature and light intensity, so that the risk assessment is more in line with the actual safety requirements of sharp curves.

[0052] (2) The present invention can filter out roadside stationary objects, guardrail reflections, multipath echoes and outgoing direction targets by using lane area constraints, motion continuity constraints, direction consistency constraints and multi-frame confirmation mechanisms, thereby reducing misjudgments caused by false targets.

[0053] (3) The present invention combines the risk levels of the vehicle and the oncoming vehicle to accurately identify one-way high-risk and two-way high-risk oncoming scenarios and output differentiated warning levels (deceleration prompt, oncoming vehicle prompt, strong warning, etc.). Attached Figure Description

[0054] Figure 1 This is a schematic diagram of a vehicle warning method for sharp curves based on dynamic speed thresholds according to the present invention. Detailed Implementation

[0055] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0056] like Figure 1 As shown, an embodiment of the present invention provides a vehicle warning method for sharp curves based on dynamic speed thresholds, comprising the following steps S1 to S5:

[0057] S1. Real-time collection of driving data of candidate target vehicles entering sharp bends;

[0058] In an optional embodiment of the present invention, step S1 uses millimeter-wave radar to collect real-time driving data of vehicles entering the curve. The collected real-time driving data includes the vehicle's real-time speed. Distance between the vehicle and the curve entrance Vehicle azimuth angle The vehicle's direction of travel and the target echo intensity, among which Indicates the target number. Indicates the sampling time.

[0059] Millimeter-wave radar transmits frequency-modulated continuous wave signals, receives target echoes, and performs mixing, filtering, and spectrum analysis to obtain the range, velocity, angle, and echo intensity of candidate targets. The corresponding millimeter-wave radar ranging model is as follows:

[0060] ;

[0061] in, Let be the distance of the i-th target at the k-th sampling time. The difference frequency signal frequency, At the speed of light, B is the frequency modulation period, and B is the frequency modulation bandwidth.

[0062] The speed measurement model is:

[0063] ;

[0064] in, λ is the Doppler frequency, and λ is the radar wavelength. This represents the vehicle's real-time speed.

[0065] Radar output candidate target set for:

[0066] ;

[0067] in, As a candidate target, The target azimuth angle, For the target echo intensity, The number of candidate targets at the k-th sampling time.

[0068] S2. Perform false target filtering on the candidate target vehicles to obtain a set of valid targets;

[0069] In an optional embodiment of the present invention, step S2 includes:

[0070] The radar polar coordinates of the candidate target vehicle are converted into road coordinates to obtain the target's distance along the road and the target's lateral offset distance.

[0071] Establish lane area constraints, and determine whether the candidate target vehicle is within the effective lane area based on the lane left boundary function and right boundary function;

[0072] Establish a consistency constraint for the direction of motion, and verify whether the distance change of the candidate target is consistent with the radar velocity measurement result;

[0073] A multi-frame confirmation model is established. Within the confirmation window, the number of frames that meet the valid target conditions is counted. When the number of valid confirmation frames is reached, the candidate target vehicle is confirmed as a valid target and added to the set of valid targets.

[0074] To avoid false triggering caused by roadside guardrails, trees, parked vehicles, pedestrians, non-motorized vehicles, reflectors on the outer side of curves, and multipath echoes, this embodiment performs false target filtering on candidate targets after the millimeter-wave radar outputs them. Specifically, it applies lane area constraints, motion continuity constraints, direction consistency constraints, and multi-frame confirmation processing to the candidate targets output by the millimeter-wave radar to obtain a set of valid targets. Only when a candidate target meets the valid target determination criteria will it participate in the subsequent dynamic velocity threshold determination. The false target filtering process is as follows:

[0075] First, convert the radar coordinates to road coordinates:

[0076] ;

[0077] in, The distance to the target along the road direction. The target's lateral offset distance.

[0078] Then establish lane area constraints:

[0079] ;

[0080] ;

[0081] in, To achieve the maximum effective detection range, and These are the functions for the left and right boundaries of the lanes on a sharp curve, respectively. The lane boundary tolerance is set; if a candidate target is located outside the effective lane area, it is considered an invalid target.

[0082] Next, establish a consistency constraint for the direction of motion:

[0083] ;

[0084] in, This is a measure of consistency in the direction of motion. The radar sampling period. When the condition is met... If the distance change of the candidate target is consistent with the radar velocity measurement result, then the target is considered to be a false target or an abnormally moving target. To ensure consistency in the direction of motion, a tolerance of 0.5 m / s is typically used.

[0085] Next, a multi-frame confirmation model is established:

[0086] ;

[0087] in, This is a flag indicating whether a candidate target satisfies the lane region constraint and motion direction consistency constraint in frame j, where M is the number of frames in the confirmation window. To effectively confirm the number of frames; when the candidate target appears in at least M consecutive frames. If a frame meets the conditions for a valid target, it is confirmed as a valid vehicle target.

[0088] The final set of effective targets is obtained as follows:

[0089] ;

[0090] Only those belonging to the valid target set Only after the target is determined can the dynamic speed threshold judgment and linkage early warning steps be entered.

[0091] S3. Construct a dynamic speed threshold model based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends to obtain the dynamic speed threshold at the current moment.

[0092] In an optional embodiment of the present invention, step S3 includes:

[0093] The basic speed threshold for road sections is determined based on historical accident speed thresholds, road geometric safe speeds, and road speed limits.

[0094] Calculate the environmental correction factor based on rainfall intensity, visibility, road surface wetness, road surface temperature, and light intensity;

[0095] Determine the safe speed threshold based on the effective sight distance and vehicle braking distance;

[0096] Calculate the initial dynamic speed threshold based on the road segment's basic speed threshold, the environmental correction coefficient, and the safe speed threshold;

[0097] The initial dynamic velocity threshold is smoothed to obtain the dynamic velocity threshold at the current moment.

[0098] The method for determining the historical accident vehicle speed threshold is as follows:

[0099] The oncoming accident rate for each speed range is determined based on the accident rate and vehicle speed distribution model.

[0100] The aforementioned on-vehicle collision rate is subjected to a sliding smoothing process;

[0101] Based on preset threshold determination conditions, a critical speed is determined from the smoothed data and used as the historical accident vehicle speed threshold.

[0102] The method for determining the road's geometrically safe speed is as follows:

[0103] Obtain the curve radius, lateral adhesion coefficient, and superelevation rate of sharp curves;

[0104] Based on the curve radius, the lateral adhesion coefficient, and the superelevation rate, the maximum safe driving speed of the vehicle under conditions without sideslip or rollover is calculated and used as the road geometric safe speed.

[0105] Among them, environmental correction factors are calculated based on rainfall intensity, visibility, road surface slipperiness, road surface temperature, and light intensity, including:

[0106] Based on rainfall intensity, visibility, road surface slipperiness, road surface temperature, and light intensity, the normalization factor corresponding to each environmental factor is calculated.

[0107] The environmental correction coefficient is obtained by weighting and fusing the normalized factors corresponding to each environmental factor.

[0108] This embodiment uses dynamic speed thresholds for tiered early warning. Specifically, the dynamic speed threshold is calculated based on historical accident data, vehicle speed distribution, road geometry parameters, and real-time environmental parameters of the target sharp curve section. The specific process is as follows:

[0109] First, a historical accident rate and vehicle speed distribution model is constructed as follows:

[0110] ;

[0111] in, Let J be the oncoming traffic accident rate corresponding to the j-th speed range. This represents the number of accidents within that speed range. This represents the number of vehicles passing through this speed range. To prevent extremely small positive numbers with a denominator of zero;

[0112] Accident rate of passing vehicles The smoothed accident rate is obtained after performing sliding smoothing. :

[0113] ;

[0114] Where q is the radius of the smoothing window. Index the data points.

[0115] The center value of the speed range that first meets the following conditions is taken as the historical accident speed threshold. :

[0116] ;

[0117] in, This represents the average accident rate in the low-speed range. The standard deviation of the accident rate.

[0118] Road geometric safe speed for:

[0119] ;

[0120] in, It is the acceleration due to gravity. The radius of the curve is 1. The lateral adhesion coefficient is for a typical dry road surface. For curve superelevation rate.

[0121] Determine the basic speed threshold for the road segment based on historical accident speed thresholds, road geometric safe speeds, and road speed limits. for:

[0122] ;

[0123] in, To find the minimum value function, The critical speed threshold determined based on the historical accident vehicle speed distribution. For road geometric safety speed, This refers to the speed limit for the road.

[0124] Secondly, environmental parameters are corrected based on rainfall intensity, visibility, road surface wetness, road surface temperature, and light intensity. Environmental conditions vary across different regions, thus requiring adjustments to the environmental parameters. Real-time environmental parameter vector. for:

[0125]

[0126] in, Rainfall intensity, For visibility, This is a sign indicating that the road surface is wet and slippery. For road surface temperature, The ambient light intensity is represented by T, which is the transpose of the light.

[0127] Calculate the rainfall normalization factor for:

[0128] ;

[0129] in, Rainfall intensity, This is the baseline value for rainfall intensity.

[0130] Calculate visibility normalization factor for:

[0131] ;

[0132] in, This is the baseline value for visibility. For visibility, , This is the function for finding the maximum value.

[0133] The normalization factor for road surface slippage is set as follows: , where 0 indicates non-slippery and 1 indicates slippery.

[0134] Calculate the low-temperature freezing risk factor for:

[0135] ;

[0136] in, This refers to the road surface temperature.

[0137] Calculate low light risk factors for:

[0138] ;

[0139] in, As the reference value for illumination, The ambient light intensity.

[0140] The environmental correction factor is calculated as follows:

[0141] ;

[0142] in, These are the correction weights for rainfall, visibility, slipperiness, temperature, and light intensity, respectively. This is the lower limit of the environmental correction factor. This is the amplitude limiting function.

[0143] Finally, the safe speed is adjusted based on the effective line-of-sight distance. The effective safe line-of-sight distance is:

[0144] ;

[0145] in, For effective and safe line of sight, The geometrically visible distance of the curve. This refers to real-time visibility.

[0146] Equivalent braking deceleration for:

[0147] ;

[0148] in, This represents the current road surface adhesion coefficient.

[0149] Obtain the safe speed threshold based on braking distance constraints for:

[0150] ;

[0151] in, This refers to the driver's reaction time.

[0152] Finally, based on the basic speed threshold of the road segment, the environmental correction factor, and the safe speed threshold, the initial dynamic speed threshold is calculated. for:

[0153] ;

[0154] in, The minimum protection threshold, This is the highest limit threshold.

[0155] To avoid frequent jumps in the dynamic velocity threshold due to instantaneous fluctuations in environmental sensors, this embodiment further smooths the initial dynamic velocity threshold to obtain the dynamic velocity threshold at the current moment. :

[0156] ;

[0157] in, For smoothing coefficients, For the previous moment The dynamic speed threshold.

[0158] S4. Determine the risk level based on the vehicle status in the set of valid targets and the dynamic speed threshold to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0159] In an optional embodiment of the present invention, step S4 includes:

[0160] Obtain the distance of the vehicle to the curve entrance and the vehicle's real-time speed;

[0161] When the distance is less than a preset distance threshold and the real-time speed of the vehicle is greater than the sum of the dynamic speed threshold and the hysteresis judgment speed margin, the vehicle is determined to be at a high risk level; otherwise, the vehicle is determined to be at a low risk level.

[0162] The above determination is performed on both local vehicles and oncoming vehicles in the set of valid targets to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0163] This embodiment addresses the effective target set. The risk level of the vehicle target is determined. A hysteresis comparator is set up to determine the risk level when the vehicle speed... satisfy When a vehicle is determined to have moved from a low-risk to a high-risk location, among which... For hysteresis determination speed margin; when satisfied When the vehicle is determined to be at a high risk level, it is exited from a low risk level; the above-mentioned delay processing avoids the warning status from frequently changing near the speed threshold.

[0164] When a vehicle meets the following conditions simultaneously:

[0165] ;

[0166] If a vehicle is deemed high-risk, it is deemed low-risk; otherwise, it is deemed low-risk.

[0167] High-risk vehicles meet the following conditions:

[0168] ;

[0169] The high-risk status will be lifted when the vehicle leaves the effective detection area.

[0170] By obtaining the risk level of oncoming vehicles through vehicle-to-vehicle or vehicle-to-infrastructure communication, and combining it with the risk level of the vehicle itself, a combination of risk levels for the current curve meeting scenario can be obtained (such as low risk in one direction, high risk in one direction, low risk in both directions, and high risk in both directions).

[0171] S5. Based on the risk level of the local vehicle and the risk level of the oncoming vehicle, output a matching warning level.

[0172] In an optional embodiment of the present invention, step S5 includes:

[0173] When the risk level of the local vehicle is low and there are no oncoming vehicles, a safe passage reminder is output.

[0174] When the risk level of the local vehicle is high and there are no oncoming vehicles, a one-way high-risk deceleration warning is output.

[0175] When both the risk level of the local vehicle and the risk level of the oncoming vehicle are low risk, a safe passing warning is output.

[0176] When the risk level of the local vehicle is low and the risk level of the oncoming vehicle is high, or when both the risk levels of the local vehicle and the oncoming vehicle are high, a strong high-risk warning is output.

[0177] This embodiment outputs a matching warning level based on the risk levels of the vehicle and oncoming vehicles, according to the following model:

[0178] Let the risk level of this vehicle be: The risk level of oncoming vehicles is ,in:

[0179] ;

[0180] Then the local warning action for:

[0181] ;

[0182] in, To avoid warning, For safe passage reminders, This is a one-way high-risk deceleration warning. For safe passing warning, It provides a high-risk warning, along with a slow-down warning and a passing warning.

[0183] The aforementioned warning level information can be transmitted to the vehicle's warning subsystem or active control subsystem to execute corresponding driver prompts or automatic vehicle deceleration interventions.

[0184] This invention provides a vehicle warning system for sharp curves based on dynamic speed thresholds, which applies the aforementioned vehicle warning method for sharp curves based on dynamic speed thresholds, including:

[0185] The data acquisition module is used to collect real-time driving data of candidate target vehicles entering sharp bends.

[0186] The target filtering module is used to perform false target filtering on the candidate target vehicles to obtain a set of valid targets.

[0187] The threshold correction module is used to construct a dynamic speed threshold model based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends, and obtain the dynamic speed threshold at the current moment.

[0188] The risk assessment module is used to determine the risk level based on the vehicle status in the set of valid targets and the dynamic speed threshold, and to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0189] The early warning output module is used to output a matching early warning level based on the risk level of the local vehicle and the risk level of the oncoming vehicle.

[0190] The invention will now be described with reference to specific examples.

[0191] This example focuses on a sharp bend in a rural road. The bend radius is small, and the inner side of the bend is obstructed by mountains or buildings, making it difficult for drivers to observe oncoming traffic before entering the bend. Relevant parameters include: bend radius. =25m, curve superelevation rate =6%, road speed limit =30 km / h, lane width 3.5m, mountainside obstruction on the inside of the curve, limiting driver visibility. This embodiment uses a 24GHz frequency-modulated continuous wave millimeter-wave radar as the data acquisition device, with a frequency modulation bandwidth B=240MHz and a frequency modulation period of... =1ms, wavelength λ=12.5mm, maximum effective detection distance =250m. General parameters are set as follows: gravitational acceleration g = 9.8 m / s². 2 Driver reaction time =1.5s, smoothing coefficient =0.2, hysteresis margin =2km / h; False target filtering parameters are set to: lane boundary tolerance =0.5m, confirmation window frame count M=5, effective confirmation frame count =4.

[0192] (1) Active safety warning vehicle data collection steps

[0193] The millimeter-wave radar scans vehicles entering the curve at fixed intervals, collecting real-time data on vehicles entering the curve, including real-time vehicle speed, distance between the vehicle and the curve entrance, vehicle azimuth angle, vehicle direction of travel, and target echo intensity. At a certain moment, the radar detected three candidate targets, and the raw measurement data are shown in Table 1.

[0194] Table 1 Radar Detection Data

[0195]

[0196] Vehicle parameters were calculated based on the distance measurement model and the speed measurement model. The calculation results are shown in Table 2.

[0197] Table 2 Distance and Speed ​​Calculation Table

[0198]

[0199] The final set of candidate targets is obtained as follows:

[0200] ;

[0201] Vehicle-to-everything (V2X) communication is used to obtain the driving status information of vehicles on both sides, and the aforementioned candidate target data is input into the false target filtering step.

[0202] (2) False target filtering steps

[0203] To avoid interference from false targets such as roadside guardrails, trees, and multipath reflections, the candidate targets output by the millimeter-wave radar are filtered out by lane area constraints, motion continuity constraints, directional consistency constraints, and multi-frame confirmation. Only when a candidate target meets the valid target determination conditions will it participate in the subsequent dynamic speed threshold determination.

[0204] First, the radar coordinates were converted into road coordinates, and the calculation results are shown in Table 3.

[0205] Table 3 Road coordinate transformation results

[0206]

[0207] Secondly, lane area constraints are established. The boundary function for the lanes in this direction for this road segment is: left boundary... =3.5m, right boundary =0m; The boundary function for the opposing lane is: left boundary =7.0m, right boundary =3.5m. The calculation and judgment results based on the lane area constraints are shown in Table 4:

[0208] Table 4 Lane boundary results

[0209]

[0210] Next, establish a consistency constraint on the direction of motion, with the following conditions:

[0211]

[0212] in, The radar sampling period is set to 0.1s. Data from the previous sampling time k−1 is used for target 1. =70.75 m, target 3 =60.7 m. Substituting into the calculation, we get:

[0213] Objective 1: To ensure consistency in the direction of motion;

[0214] Objective 3: This satisfies the consistency of the direction of motion.

[0215] Finally, a multi-frame confirmation model is established. M=5. The results of the five consecutive frames are shown in Table 5.

[0216] Table 5 Lane boundary results

[0217]

[0218] The final set of effective targets is obtained as follows:

[0219] ;

[0220] (3) Dynamic speed threshold meter correction steps

[0221] The statistical data on on-road accidents in this section of road over the past three years, as well as the results calculated using the above method, are shown in Table 6.

[0222] Table 6 Accident Rate-Vehicle Speed ​​Distribution Data

[0223]

[0224] Low-speed range definition: 10-30 km / h (a low-risk range with stable accident rates), the average accident rate in this range is calculated. The standard deviation of the accident rate is 0.000068. It is 0.000072.

[0225] Based on the critical speed determination criteria, the speed range in which the criteria are first met is 35-40 km / h. Therefore, the center value of this range is taken as the critical speed for historical accidents. .

[0226] According to the road geometric safe speed formula:

[0227] ;

[0228] Determine the base speed threshold:

[0229] ;

[0230] Calculate the environmental correction factor :

[0231] ;

[0232] The correction weights for rainfall, visibility, slipperiness, temperature, and light intensity are set to [values ​​to be filled in]. , , , , The lower limit of the environmental correction factor is taken as: .

[0233] This embodiment uses two typical working conditions—dry sunny day and slippery moderate rain—as examples to calculate the environmental correction factor:

[0234] Condition 1 (Sunny and Dry): Environmental parameters are rainfall intensity. =0 mm / h, visibility =1000 m, road surface is slippery =0, road surface temperature =25 ∘ C, Ambient light intensity =10000 lx. The corresponding baseline values ​​are respectively... =10 mm / h, =200m, =5000lx. The normalization factors for each environment were calculated as follows:

[0235] ;

[0236] ;

[0237] ;

[0238] ;

[0239] ;

[0240] Substituting, we get: .

[0241] Operating Condition 2 (Moderate Rain and Slippery Slippery Situation): Environmental parameters are rainfall intensity. =10 mm / h, visibility =200 m, road surface is slippery =1, road surface temperature =10 ∘ C, Ambient light intensity =2000 lux. The corresponding baseline values ​​are respectively... =10 mm / h, =200m, =5000lx. The normalization factors for each environment were calculated as follows:

[0242] ;

[0243] ;

[0244] ;

[0245] ;

[0246] ;

[0247] Substituting, we get: .

[0248] Calculate the safe speed threshold :

[0249] ;

[0250] in, The driver's reaction time is taken as 1.5 seconds in this embodiment.

[0251] Operating Condition 1 (Sunny and Dry): =0.6, then =9.8 × 0.6 = 5.88 m / s 2 , =80m, =1000 m, =250m, therefore Substitute into the formula:

[0252] =83.16km / h;

[0253] Operating Condition 2 (Moderate Rain, Slippery Slippery): =0.3, then =9.8 × 0.3 = 2.94 m / s 2 , Substitute into the formula:

[0254] =63.79km / h;

[0255] Based on the dynamic speed threshold formula, combined with the aforementioned basic speed threshold... =30km / h and environmental correction factor ,have:

[0256]

[0257] in, The function limits the value to the minimum protection threshold. =20km / h and maximum limit threshold = Between 30km / h.

[0258] Operating Condition 1 (Sunny and Dry): =30×1=30km / h, =83.16km / h, take the minimum value =30km / h, after limiting the speed. =30km / h.

[0259] Operating Condition 2 (Moderate Rain, Slippery Slippery): =30×0.5=15km / h, =63.79km / h, take the minimum value =15km / h, after limiting the speed. =20km / h (because the lower limit is 20 km / h).

[0260] To avoid frequent jumps in the dynamic speed threshold due to instantaneous fluctuations in the environmental sensor, this embodiment further performs smoothing processing:

[0261]

[0262] in, The smoothing coefficient is set to 0.2 in this embodiment. This is the threshold value after smoothing from the previous time step.

[0263] Condition 1 (Sunny and Dry): Smoothing threshold at the previous moment =29km / h, then:

[0264] =0.2×29+0.8×30=5.8+24=29.8km / h≈30km / h

[0265] Condition 2 (Moderate Rain and Slippery ... =15.5km / h, then:

[0266] =0.2×15.5+0.8×20=3.1+16=19.1km / h

[0267] (4) Risk level determination steps

[0268] Configure a hysteresis comparator and hysteresis margin. =2km / h. Substitute the valid target data to determine:

[0269] Operating Condition 1 (Sunny and Dry): Smoothing Threshold =30km / h, then , .

[0270] Local target vehicle 1: Speed ​​of local target vehicle 1 =30km / h, distance =70m < 250m. Based on the judgment criteria, the vehicle is classified as low risk.

[0271] Oncoming target vehicle 3: Speed ​​of oncoming target vehicle 3 =27km / h, distance =60m < 250m. Based on the judgment criteria, the vehicle is classified as low risk.

[0272] Operating Condition 2 (Moderate Rain and Slippery Slippery Slope): Smoothing Threshold =19.1km / h, then , .

[0273] Local target vehicle 1: Speed ​​of local target vehicle 1 =30km / h, distance =70m < 250m. Based on the judgment criteria, the vehicle is classified as high-risk.

[0274] Oncoming target vehicle 3: Speed ​​of oncoming target vehicle 3 =27km / h, distance =60m < 250m. Based on the judgment criteria, the vehicle is classified as high-risk.

[0275] By obtaining the risk level of oncoming vehicles through vehicle-to-vehicle communication and combining it with the risk level of the vehicle itself, a combination of risk levels for the current curve-crossing scenario is obtained.

[0276] (5) Early warning level output

[0277] Operating Condition 1 (Sunny and Dry): , , It outputs a two-way safe passing warning.

[0278] Operating Condition 2 (Moderate Rain, Slippery Slippery): , , It outputs two-way high-intensity early warnings.

[0279] In experimental applications on sharp curves, compared to the fixed triggering method of "warning upon the presence of a vehicle," this invention can dynamically adjust the speed threshold based on the real-time environment, effectively filter out false targets, accurately determine the risk level of the vehicle and oncoming vehicles, and output differentiated warning levels. This method is applicable to all types of vehicles equipped with millimeter-wave radar and vehicle-to-vehicle communication capabilities, and can significantly improve driving safety on sharp curves.

[0280] The present invention transforms the early warning system for sharp curves from being triggered by a single sensor to a comprehensive proactive safety warning process involving "effective target confirmation, dynamic threshold correction, risk level determination, and two-way linkage warning," thereby better adapting to complex traffic safety scenarios on sharp curves.

[0281] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0282] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0283] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0284] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0285] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for vehicle warning on a sharp curve road section based on a dynamic speed threshold, characterized in that, Includes the following steps: Real-time driving data of candidate target vehicles entering sharp bends is collected. The candidate target vehicles are subjected to false target filtering to obtain a valid target set; A dynamic speed threshold model is constructed based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends to obtain the dynamic speed threshold at the current moment. Based on the vehicle status in the set of valid targets and the dynamic speed threshold, the risk level is determined to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle. Based on the risk level of the local vehicle and the risk level of the oncoming vehicle, a matching warning level is output.

2. The method of claim 1, wherein the method further comprises: The real-time driving data includes the vehicle's real-time speed, the distance between the vehicle and the curve entrance, the vehicle's azimuth angle, the vehicle's driving direction, and the target echo intensity.

3. The vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 1, characterized in that, The candidate target vehicles are subjected to false target filtering to obtain a set of valid targets, including: The radar polar coordinates of the candidate target vehicle are converted into road coordinates to obtain the target's distance along the road and the target's lateral offset distance. Establish lane area constraints, and determine whether the candidate target vehicle is within the effective lane area based on the lane left boundary function and right boundary function; Establish a consistency constraint for the direction of motion, and verify whether the distance change of the candidate target is consistent with the radar velocity measurement result; A multi-frame confirmation model is established. Within the confirmation window, the number of frames that meet the valid target conditions is counted. When the number of valid confirmation frames is reached, the candidate target vehicle is confirmed as a valid target and added to the set of valid targets.

4. The vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 1, characterized in that, A dynamic speed threshold model is constructed based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends to obtain the dynamic speed threshold at the current moment, including: The basic speed threshold for road sections is determined based on historical accident speed thresholds, road geometric safe speeds, and road speed limits. Calculate the environmental correction factor based on rainfall intensity, visibility, road surface wetness, road surface temperature, and light intensity; Determine the safe speed threshold based on the effective sight distance and vehicle braking distance; Calculate the initial dynamic speed threshold based on the road segment's basic speed threshold, the environmental correction coefficient, and the safe speed threshold; The initial dynamic velocity threshold is smoothed to obtain the dynamic velocity threshold at the current moment.

5. A vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 4, characterized in that, The method for determining the historical accident vehicle speed threshold is as follows: The oncoming accident rate for each speed range is determined based on the accident rate and vehicle speed distribution model. The aforementioned on-vehicle collision rate is subjected to a sliding smoothing process; Based on preset threshold determination conditions, a critical speed is determined from the smoothed data and used as the historical accident vehicle speed threshold.

6. A vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 4, characterized in that, The method for determining the geometrically safe speed of the road is as follows: Obtain the curve radius, lateral adhesion coefficient, and superelevation rate of sharp curves; Based on the curve radius, the lateral adhesion coefficient, and the superelevation rate, the maximum safe driving speed of the vehicle under conditions without sideslip or rollover is calculated and used as the road geometric safe speed.

7. A vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 4, characterized in that, Environmental correction factors are calculated based on rainfall intensity, visibility, road surface wetness, road surface temperature, and light intensity, including: Based on rainfall intensity, visibility, road surface slipperiness, road surface temperature, and light intensity, the normalization factor corresponding to each environmental factor is calculated. The environmental correction coefficient is obtained by weighting and fusing the normalized factors corresponding to each environmental factor.

8. A vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 1, characterized in that, Risk levels are determined based on the vehicle status in the set of valid targets and the dynamic speed threshold, resulting in the risk levels of local vehicles and oncoming vehicles, including: Obtain the distance of the vehicle to the curve entrance and the vehicle's real-time speed; When the distance is less than a preset distance threshold and the real-time speed of the vehicle is greater than the sum of the dynamic speed threshold and the hysteresis judgment speed margin, the vehicle is determined to be at a high-risk level; otherwise, the vehicle is determined to be at a low-risk level. The above determination is performed on both local vehicles and oncoming vehicles in the set of valid targets to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle.

9. A vehicle warning method for sharp curves based on dynamic speed thresholds according to claim 1, characterized in that, Based on the risk level of the local vehicle and the risk level of the oncoming vehicle, a matching warning level is output, including: When the risk level of the local vehicle is low and there are no oncoming vehicles, a safe passage reminder is output. When the risk level of the local vehicle is high and there are no oncoming vehicles, a one-way high-risk deceleration warning is output. When both the risk level of the local vehicle and the risk level of the oncoming vehicle are low risk, a safe passing warning is output. When the risk level of the local vehicle is low and the risk level of the oncoming vehicle is high, or when both the risk levels of the local vehicle and the oncoming vehicle are high, a strong high-risk warning is output.

10. A vehicle warning system for sharp curves based on dynamic speed thresholds, employing the vehicle warning method for sharp curves based on dynamic speed thresholds as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to collect real-time driving data of candidate target vehicles entering sharp bends. The target filtering module is used to perform false target filtering on the candidate target vehicles to obtain a set of valid targets. The threshold correction module is used to construct a dynamic speed threshold model based on historical accident data, road geometry parameters, and real-time environmental parameters of sharp bends, and obtain the dynamic speed threshold at the current moment. The risk assessment module is used to determine the risk level based on the vehicle status in the set of valid targets and the dynamic speed threshold, and to obtain the risk level of the local vehicle and the risk level of the oncoming vehicle. The early warning output module is used to output a matching early warning level based on the risk level of the local vehicle and the risk level of the oncoming vehicle.

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