Curve slope road truck driving risk early warning and linear optimization method

Abstract

The invention relates to a curve slope road truck driving risk early warning and linear optimization method, which comprises the following steps of collecting road design parameters and truck parameters; the truck parameters are coupled with the three-dimensional curved surface model, a quasi-static monorail dynamic model is constructed, and braking force distribution calculation and load transfer analysis are carried out; extracting a core risk index from a truck driving stability analysis result, and constructing a three-dimensional curved surface feature and driving risk association model; calculating a risk index value of each stake mark road section based on the correlation model, dividing risk levels according to a preset threshold value, generating a whole road section risk thermodynamic diagram, and marking a risk type and a core cause of a high-risk road section; and for the identified high-risk road section, adjusting the road horizontal and vertical parameters, substituting the road horizontal and vertical parameters into the three-dimensional curved surface model, and repeatedly executing until the risk level is reduced to a low-risk interval, thereby forming a final horizontal and vertical coupling safety optimization scheme, and improving the driving safety level of trucks on the curved slope road section.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic road engineering, in particular to a truck driving risk early warning and linear optimization method for curved slope sections. BACKGROUND

[0002] The curved slope section is a high-risk section for driving safety in the highway network. Due to the characteristics of high load and high center of gravity, the risk of brake failure, skidding and rollover of trucks on this section is significantly higher than that on ordinary sections.

[0003] However, the existing truck driving safety guarantee and road design technology for curved slope sections have the following technical problems:

[0004] Traditional road design mostly uses two-dimensional horizontal and vertical separation modeling methods, which do not fully couple the three-dimensional curved surface geometric characteristics of curved slope sections, and it is difficult to accurately reflect the influence of actual linearization on driving; the existing truck stability analysis often simplifies the core factors such as brake force distribution and trajectory deviation, and the analysis results have large deviation from the actual driving state; there is a lack of quantitative correlation model between road geometric characteristics and driving risk, and the risk assessment relies on experience, resulting in a lack of accurate basis for safety optimization of road horizontal and vertical coupling. SUMMARY

[0005] In view of the problems existing in the prior art, the purpose of the present application is to provide a truck driving risk early warning and linear optimization method for curved slope sections, which can accurately reflect the influence of actual linearization on driving, the truck stability analysis results have small deviation from the actual driving state, and there is a quantitative correlation model between road geometric characteristics and driving risk, thereby improving the driving safety level of trucks on curved slope sections.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0007] A truck driving risk early warning and linear optimization method for curved slope sections, comprising the following steps,

[0008] S1, collecting parameters: obtaining road design parameters, including road horizontal and vertical section design parameters and cross section design parameters of the curved slope section; and truck parameters, including total mass, axle load distribution ratio, center of mass height and wheelbase parameters of the truck;

[0009] S2, constructing a three-dimensional curved surface model: based on the road design parameters collected in step S1, establishing a local coordinate system with the road centerline as the reference, generating a three-dimensional curved surface equation with stake number and transverse distance as parameters, and obtaining the spatial coordinate information and three-dimensional linearization parameters of any point on the road surface;

[0010] S3, truck driving stability analysis: coupling the truck parameters obtained in step S1 with the three-dimensional curved surface model constructed in step S2, constructing a quasi-static monorail dynamics model, performing brake force distribution calculation and load transfer analysis, under the constraints of lateral force balance and high gravity center rollover, calculating the lateral and vertical offsets of the actual driving trajectory of the truck relative to the road centerline;

[0011] S4, constructing a three-dimensional curved surface feature and driving risk correlation model: extracting the lateral force overrun degree and rollover risk index from the truck driving stability analysis results in step S3 as the core risk index, establishing a quantitative correlation between the three-dimensional linear parameters and the core risk index;

[0012] S5, driving risk quantitative evaluation and early warning: based on the correlation model established in step S4, calculating the risk index value of each stake section, dividing the risk level according to the preset threshold, generating a full-section risk heat map, and marking the risk type and core cause of high-risk sections, outputting a risk evaluation table and early warning information;

[0013] S6, longitudinal coupling safety optimization design: for the high-risk sections identified in step S5, adjust the road horizontal and vertical parameters, substitute the adjusted parameters into the three-dimensional curved surface model in step S2, and repeat steps S3-S5 until the risk level is reduced to the low risk interval, forming the final horizontal and vertical coupling safety optimization scheme.

[0014] Further, the three-dimensional curved surface modeling in step S2 includes:

[0015] S21, establishing a road centerline equation: based on the horizontal linear parameters and the longitudinal section linear parameters, determining the three-dimensional coordinates of the road centerline (x(l), y(l), z(l)), where stake number l is the mileage along the centerline;

[0016] S22, defining a local coordinate system: establishing a local coordinate system composed of tangent vector T(l), horizontal normal vector N(l) and vertical vector V(l) at any point on the road centerline;

[0017] S23, establishing a cross-section shape equation: according to the road crown slope and superelevation slope, defining the vertical offset h(s) of any point on the cross-section relative to the centerline, where s is the lateral distance;

[0018] S24, generating a three-dimensional curved surface parameter equation: calculating the spatial coordinates of any point on the road surface through X(l,s)=x(l)+sNx+h(s)Vx, Y(l,s)=y(l)+sNy+h(s)Vy, Z(l,s)=z(l)+sNz+h(s)Vz, where stake number l is the mileage along the centerline and s is the lateral distance;

[0019] S25, calculate three-dimensional linear parameters: including spatial curvature Ks(l), spatial torsion τ(l), longitudinal slope i(l), lateral slope i(l, s), planar curve radius R(l, s), vertical curve radius R(l) and vertical curve radius R(l). h h v

[0020] Further, the truck driving stability analysis in step S3 includes:

[0021] S31, construct a brake force distribution model, based on the total mass of the truck and the axle load distribution ratio parameter, distribute the brake force according to the axle load ratio in the non-emergency braking state, and limit the increase of the rear axle brake force in the emergency braking state;

[0022] S32, calculate the increase of the front axle load and the decrease of the rear axle load during braking according to the brake deceleration, the height of the center of mass and the wheelbase, and correct the front axle normal force Fz 前 and the rear axle normal force Fz 后 ;

[0023] S33, calculate the lateral force limit capacity of the front and rear axles using the corrected front axle normal force Fz 前 and the rear axle normal force Fz 后 , and then obtain the total lateral force, if the total force is less than or equal to zero, it is determined that the truck has the risk of deviating to the central separation zone;

[0024] S34, set the lateral acceleration upper limit through the roll stability coefficient, and determine that there is a risk of rollover when the actual lateral acceleration exceeds the upper limit;

[0025] S35, under the constraint condition of the limit of the lateral force of the road surface, combined with the determination results of steps S33 and S34, solve the lateral and vertical deviation amounts of the actual driving track of the truck relative to the road centerline.

[0026] Further, the brake force distribution model in step S31 is:

[0027] When |F x |≦mgf max , distribute the brake force to the front axle F x前 =kF x and the rear axle F x后 =(1-k)F x according to the axle load distribution ratio k;

[0028] When the total brake force |F x |≥mgf max , the front axle brake force is kept as F x前 =k·F x , and the rear axle brake force is limited to F x后 =(1-k)·mgf​​​max +0.3(1-k)(|F x |-mgf max ), ensure that the rear axle braking force increase does not exceed 30%;

[0029] wherein |F x | is the total braking force, mg is the total mass of the truck, f max is the maximum adhesion coefficient.

[0030] Further, in step S34, the lateral acceleration upper limit ≤ SSF−|i n |−0.1; wherein SSF=Tr / (2h cg ), T r is the wheelbase, h cg is the height of the center of mass, i n is the vector resultant slope of the longitudinal and lateral slopes of the road surface, and 0.1 is a safety margin.

[0031] Further, in step S5, the driving risk quantitative evaluation and warning includes:

[0032] S51, risk index calculation: based on the stability analysis result of step S3, the lateral force overrun degree and rollover risk index value are calculated, wherein the greater the lateral force overrun degree value, the higher the risk of hitting the central divider, and the difference between the actual lateral acceleration coefficient and the rollover threshold value is the rollover risk index, and a positive value indicates that there is a rollover risk;

[0033] S52, risk classification: compare the calculated risk index value with the preset low risk, medium risk, high risk, and extremely high risk four-level thresholds to determine the risk level of each stake number section;

[0034] S53, generate risk heat map: take the stake number as the horizontal coordinate and the lateral distance as the vertical coordinate to draw the risk distribution heat map of the whole section, and visually display the high risk mileage section;

[0035] S54, mark risk inducement: for the high risk section, mark its main risk type and core inducement, including excessive longitudinal slope, insufficient adhesion coefficient, small planar curve radius, or insufficient super-elevation;

[0036] S55, output warning information: generate a risk evaluation table containing risk level, risk type, core inducement, and suggested measures, and issue a warning prompt for medium risk and above sections.

[0037] Further, in step S6, the planar and longitudinal coupling safety optimization design includes:

[0038] S61, set optimization target: for the high risk sections identified in step S5, set the optimization target of reducing the risk level to the low risk interval;

[0039] S62, adjusting horizontal and vertical parameters: according to the core cause analysis result marked in step S54, the horizontal and vertical parameters are adjusted, including adjusting the horizontal curve radius, the vertical slope gradient, the vertical curve radius, or the cross-section superelevation value;

[0040] S63, parameter verification loop: the adjusted horizontal and vertical parameters are input into the three-dimensional curved surface model in step S2, and steps S3-S5 are executed in sequence to determine whether the risk level meets the standard;

[0041] S64, iterative optimization: if the risk level does not meet the standard, return to step S62 to continue adjusting the parameters to form a closed loop iteration until the optimization target is met, and output the final optimized horizontal and vertical parameter combination.

[0042] A truck driving risk early warning and linear optimization device for a curved and sloping road section, comprising:

[0043] A parameter acquisition module is configured to acquire road design parameters, including road horizontal and vertical section design parameters and cross-section design parameters of the curved and sloping road section, and truck parameters, including total mass, axle load distribution ratio, center of mass height, and wheelbase parameters of the truck;

[0044] A three-dimensional curved surface model construction module is configured to establish a local coordinate system based on the collected road design parameters, generate a three-dimensional curved surface equation with stake number and transverse distance as parameters, and obtain spatial coordinate information and three-dimensional linear parameters of any point on the road surface;

[0045] A stability analysis module is configured to couple the obtained truck parameters with the three-dimensional curved surface model, construct a quasi-static single-track dynamics model, perform braking force distribution calculation and load transfer analysis, and calculate the transverse and vertical offset amounts of the actual driving track of the truck relative to the road centerline under the constraints of transverse force balance and high center of mass rollover;

[0046] An association model construction module is configured to extract the transverse force overrun degree and rollover risk index from the truck driving stability analysis results as core risk indexes, and establish a quantitative association between the three-dimensional linear parameters and the core risk indexes;

[0047] An evaluation and early warning module is configured to calculate the risk index value of each stake section based on the established association model, divide the risk level according to a preset threshold, generate a full-section risk heat map, mark the risk type and core cause of the high-risk section, and output a risk evaluation table and early warning information;

[0048] An optimization module is configured to adjust the road horizontal and vertical parameters for the identified high-risk sections, input the adjusted parameters into the three-dimensional curved surface model, and repeatedly perform the stability analysis and risk evaluation processes until the risk level is reduced to a low risk interval, forming a final horizontal and vertical coupling safety optimization scheme.

[0049] An electronic device comprises a processor and a memory, the memory stores a computer program, and the processor implements the curved slope road section truck driving risk early warning and line optimization method when executing the computer program.

[0050] A storage medium stores a computer program, and the computer program is executed by a processor to implement the curved slope road section truck driving risk early warning and line optimization method.

[0051] Overall, the present application has the following advantages:

[0052] A quantitative correlation model between the three-dimensional curved surface geometric characteristics of the curved slope road section and the truck driving risk is established, solving the problem of disconnection between three-dimensional characteristics and risk in traditional technology.

[0053] Improve the accuracy of risk assessment: through three-dimensional accurate modeling and fine dynamics analysis, the core risk indicators such as trajectory deviation and transverse force overrun are quantified, replacing traditional experience judgment, and the risk assessment error is significantly reduced.

[0054] Realize accurate early warning: based on the quantitative risk level, the targeted early warning scheme of the truck end and the road end is generated, which provides clear guidance for the driver and traffic control, and effectively prevents accidents from happening.

[0055] Optimize road design: form a safety optimization method of coupling of plane and vertical, which can directly guide the design of curved slope road section reconstruction, reduce high risk through parameter adjustment, and improve the scientificity and economy of road design.

[0056] Reduce the accident rate: through the application of the whole link technology system, the risks such as truck skidding, overturning and deviation collision on the curved slope road section are accurately controlled, the truck accident rate on this kind of road section is significantly reduced, and technical support is provided for safety design in the field of traffic engineering. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The method flowchart of the present application.

[0058] Figure 2 The reconstruction road optimization flowchart of the present application.

[0059] Figure 3 The curved slope road section truck driving risk early warning and line optimization device structure block diagram of the present application.

[0060] Figure 4 The electronic device structure block diagram of the present application. DETAILED DESCRIPTION

[0061] The present application will be further described in detail as follows.

[0062] Example 1

[0063] A truck driving risk early warning and linear optimization method for a curved slope section, comprising the following steps,

[0064] S1, collecting parameters: obtaining road design parameters, including road plan and longitudinal section design parameters and cross section design parameters of the curved slope section; and truck parameters, including total mass, axle load distribution ratio, mass center height and wheelbase parameters of the truck;

[0065] Parameter collection includes: 1. Scanning the existing road by laser radar to collect existing road laser point cloud data; 2. Then using B-spline and concave hull algorithm to restore the planar linear, and using total least squares method to extract the linear parameters; the spatial curvature and deflection are calculated by the formula in Table 1. The above completes the collection of parameter data.

[0066] Table 1: Calculation of spatial curvature and deflection

[0067]

[0068] S2, constructing a three-dimensional curved surface model: based on the road design parameters collected in step S1, establishing a local coordinate system based on the road centerline, generating a three-dimensional curved surface equation with stake number and transverse distance as parameters, obtaining the spatial coordinate information and three-dimensional linear parameters of any point on the road surface, including:

[0069] S21, establishing a road centerline equation: based on the planar linear parameters and longitudinal section linear parameters, the curved slope section is divided into several segments, each segment has a separate curved surface equation, the road centerline is the "reference line" of the road surface, and its three-dimensional coordinates (x(l), y(l), z(l)) are determined by the planar linear and longitudinal section linear, wherein the stake number l is the mileage along the centerline;

[0070] First, for the planar linear part (x(l), y(l)):

[0071] Straight line segment: x(l) = x0 + lcosθ, y(l) = y0 + lsinθ, wherein x0, y0 are the coordinates of the straight line starting point, and θ is the straight line direction angle;

[0072] Circular curve segment: , where R h is the circular curve radius, and l a is the mileage from the starting point of the circular curve to the starting point of the road;

[0073] Transition curve segment: , where A is the transition curve parameter, and R h is the circular curve connection radius;

[0074] Then for the longitudinal section linear part (z(l)):

[0075] Longitudinal slope segment: z(l) = z0+ il, where z0 is the starting elevation, i is the longitudinal slope;

[0076] Vertical curve segment: where R is the vertical curve radius, l is the distance from the starting point of the vertical curve to the starting point of the road, and i1 is the rearview slope; v b

[0077] S22, define the local coordinate system: establish a local coordinate system composed of tangent vector T(l), horizontal normal vector N(l), and vertical vector V(l) at any point of the road centerline;

[0078] The road surface is a curved surface that expands laterally along the centerline, and a local coordinate system at any point of the centerline is needed to describe the position of the cross section:

[0079] Tangent direction T: the unit tangent vector of the centerline, derived by taking the derivative of the centerline coordinates: (r(l) = (x(l), y(l), z(l)));

[0080] Normal direction N: the horizontal direction perpendicular to T (the centerline normal in the plane, negative on the left and positive on the right);

[0081] Vertical direction V: determined by T x N (vertical upward, corresponding to the road surface elevation direction).

[0082] S23, establish the cross section shape equation: define the vertical offset h(s) of any point of the cross section relative to the centerline according to the road crown slope and superelevation slope;

[0083] The shape of the road surface cross section is determined by the road crown (lateral slope of normal road segments) and superelevation (lateral slope adjustment of circular curve segments), and the vertical offset h(s) of any point of the cross section relative to the centerline (s is the lateral distance, −B / 2 ≤ s ≤ B / 2, B is the road width) needs to be defined;

[0084] Normal road segment (road crown): h(s) = −ks 2 k is the road crown coefficient);

[0085] Superelevation road segment (circular curve segment): h(s) = −i s |s|;

[0086] Superelevation road segment (transition curve segment): ;

[0087] ​​S24, generating three-dimensional curved surface parameter equation: combine "midline coordinates" and "cross-section offset", calculate the spatial coordinates of any point on the road surface through X(l,s)=x(l)+sNx+h(s)Vx, Y(l,s)=y(l)+sNy+h(s)Vy, Z(l,s)=z(l)+sNz+h(s)Vz;

[0088] S25, calculate three-dimensional linear parameters: including spatial curvature Ks(l), spatial torsion τ(l), longitudinal slope i(l), transverse slope i h (l,s), planar curve radius R h (l), and vertical curve radius R v (l).

[0089] Three-dimensional linear parameters embedded in curved surface model:

[0090] Decided by the three-dimensional line of the midline, and s is irrelevant, directly use the previously derived midline parameters:

[0091] Longitudinal slope i(l): the slope of the longitudinal section of the midline, (The vertical curve segment is variable, and the slope segment is a fixed value);

[0092] Planar curve radius R h (l): straight line segment R h =∞, circular curve segment R h is a fixed value, and the easement curve segment

[0093] (A is the easement curve parameter);

[0094] Vertical curve radius R v (l): slope segment R v =∞, vertical curve segment R v is a fixed value;

[0095] Spatial curvature K s (l,s)≈K s (l): the transverse offset of the cross section has little effect on the curvature (which can be ignored in engineering), and the curvature K s (l) of the midline is directly used;

[0096] Spatial torsion τ(l,s)≈τ(l): for the same reason, the torsion τ(l) of the midline is approximated;

[0097] Parameters related to both "mileage l+transverse distance s":

[0098] Transverse slope i h (l,s): indicating the "transverse inclination degree of the road surface at this point relative to the horizontal plane" (positive for right side high, negative for left side high);

[0099] Normal section (crown): ;

[0100] Superelevation section (circular curve):i h (l, s) = i s (l);

[0101] Transition section (superelevation transition): ;

[0102] The integration results in Table 2:

[0103] Table 2: Integration results

[0104] S3, truck driving stability analysis: coupling the truck parameters obtained in step S1 with the three-dimensional curved surface model constructed in step S2, a quasi-static monorail dynamics model is constructed, brake force distribution calculation and load transfer analysis are carried out, under the constraints of lateral force balance and high gravity center rollover, the lateral and vertical displacement of the actual driving track of the truck relative to the road centerline is calculated, including:

[0105] S31, construct a brake force distribution model, based on the total mass of the truck, the axle load distribution ratio parameter, under the condition of non-emergency braking, the brake force is distributed according to the axle load ratio, and under the condition of emergency braking, the increase of rear axle brake force is limited;

[0106] Essence of monorail model: simplify the truck as a "front and rear axle single wheel + center of mass connecting line" rigid body, ignore the force difference between left and right wheels, and only focus on the longitudinal and lateral force balance of front and rear axles;

[0107] Core reserved items: brake force distribution and front and rear axle load transfer (affecting lateral force bearing capacity), high gravity center rollover constraint (indirectly represented by SSF, not involving left and right wheels);

[0108] First define the coordinate system:

[0109] x-axis: parallel to the driving direction, forward as positive; y-axis: perpendicular to x-axis, left as positive; z-axis: perpendicular to x-y plane, upward as positive;

[0110] Brake force distribution:

[0111] Non-emergency braking (|F x |≦mgf max ): distribute F x前 =kF x , F x后 =(1-k)F x ;

[0112] Emergency braking (|F x |≥mgf max): limit the increase of rear axle braking force (maximum increase of 30%): F x前 =kF x ,

[0113] F x后 =(1-k)mgf max +0.3(1-k)(|F x |-mgf max ), ensure that the increase of rear axle braking force does not exceed 30%;

[0114] wherein |F x | is the total braking force, mg is the total mass of the truck, f max is the maximum adhesion coefficient.

[0115] S32, calculate the increase of front axle load and the decrease of rear axle load during braking according to the braking deceleration, the height of the center of mass and the wheelbase, and correct to obtain the front axle normal force Fz 前 and the rear axle normal force Fz 后 ;

[0116] Braking force load transfer: the front axle load increases and the rear axle load decreases during braking, which directly affects the lateral force carrying capacity; ;

[0117] a x =F x / m, L f / L r is the distance from the center of mass to the front and rear axles, h cg is the height of the center of mass, i z is the longitudinal slope gradient, wherein i z , a x are negative.

[0118] S33, calculate the lateral force limit carrying capacity of the front and rear axles using the corrected front axle normal force Fz 前 and the rear axle normal force Fz 后 , and then obtain the total lateral force, if the total force is less than or equal to zero, it is determined that the truck has the risk of shifting to the central separation zone;

[0119] Lateral force balance: integrate the "resultant slope component + centrifugal force" to calculate the total lateral force and determine whether to shift to the central separation zone. If F y合 is less than or equal to zero, the truck has the risk of shifting to the central separation zone, wherein i h is the lateral slope gradient.

[0120] ;

[0121] S34, set the lateral acceleration upper limit by the roll stability factor, and determine the risk of rollover when the actual lateral acceleration exceeds the upper limit;

[0122] Lateral acceleration upper limit ≤ SSF - |i n | - 0.1; wherein SSF = Tr / (2h cg ), T r is the wheelbase, h cg is the height of the center of mass, i n is the vector resultant slope of the longitudinal and lateral slopes of the road surface, and 0.1 is the safety margin.

[0123] S35, under the constraint of the limit of the lateral force of the road surface, combine the determination results of steps S33 and S34 to solve the lateral and vertical offset amounts of the actual driving trajectory of the truck relative to the centerline of the road.

[0124] High center of gravity rollover constraint: set the lateral acceleration upper limit by the roll stability factor (SSF) to avoid rollover caused by high center of gravity;

[0125] ;

[0126] wherein: , T r is the wheelbase, and a safety margin of 0.1 is reserved.

[0127] Road surface lateral force limit constraint: the upper limit of the lateral force of the front and rear axles is determined by their respective normal forces and the road surface friction coefficient, wherein f max is the maximum static friction coefficient.

[0128] ; .

[0129] S4, build a three-dimensional curved surface feature and driving risk correlation model: extract the lateral force overrun degree and rollover risk indicators from the truck driving stability analysis results in step S3 as the core risk indicators, and establish a quantitative correlation between the three-dimensional linear parameters and the core risk indicators;

[0130] Select features directly related to truck risk from the first step;

[0131] Geometric features: curve radius R, longitudinal slope i z , super-elevation i h , resultant slope , spatial curvature K s ;

[0132] Extract core risk indicators: from the "truck driving stability analysis", extract the quantitative indicators that can represent the "risk of hitting the central median strip": offset risk indicators: lateral force overrun degree;

[0133] The greater the value, the higher the risk;

[0134] Roll-over risk index: the difference between the actual lateral acceleration coefficient and the roll-over threshold

[0135] A positive value indicates a risk of rollover.

[0136] Determine risk quantification classification criteria, such as Table 3.

[0137] Table 3: Risk Quantification

[0138]

[0139] S5, driving risk quantification evaluation and early warning: based on the correlation model established in step S4, the risk index value of each stake number section is calculated, the risk level is divided according to the preset threshold, the full section risk heat map is generated, and the risk type and core inducement of the high risk section are marked, and the risk evaluation table and early warning information are output, including:

[0140] S51, risk index calculation: based on the stability analysis results of step S3, the lateral force overrun degree and rollover risk index value are calculated, wherein the greater the lateral force overrun degree value, the higher the risk of hitting the central divider, the rollover risk index is the difference between the actual lateral acceleration coefficient and the rollover threshold, and a positive value indicates a rollover risk;

[0141] S52, risk classification: compare the calculated risk index value with the preset low risk, medium risk, high risk, and extremely high risk four-level threshold to determine the risk level of each stake number section;

[0142] S53, generate risk heat map: take stake number as horizontal coordinate and lateral distance as vertical coordinate to draw the full section risk distribution heat map, and visualize the high risk mileage section;

[0143] S54, mark risk inducement: for high risk sections, mark their main risk types and core inducements, including excessive longitudinal slope, insufficient adhesion coefficient, small plane curve radius, or insufficient super-elevation;

[0144] S55, output early warning information: generate a risk evaluation table containing risk level, risk type, core inducement, and recommended measures, and issue a warning prompt for medium risk and above sections, as shown in Table 4.

[0145] Table 4: Early warning scheme

[0146] Risk level Warning content (truck side) Warning content (road side) Low risk Keep current speed, normal driving Normal driving Medium risk Slow down to 60~70km / h, avoid sharp steering Control speed High risk Slow down to ≤60km / h, switch to outer lane Control speed + lane change control

[0147] In this embodiment, the curved surface characteristic parameters of each mileage section are substituted into the correlation model to calculate the risk index value, and the risk level is divided into low, medium, high and extremely high risk levels according to the preset threshold value, and a full road section risk heat map is generated, the risk type and core inducement (such as longitudinal slope and adhesion coefficient) of the high-risk mileage section are marked, and finally a visual risk assessment table and early warning information are output.

[0148] S6, flat longitudinal coupling safety optimization design: for the high-risk road section identified in step S5, adjust the road flat longitudinal parameters, substitute the adjusted parameters into the three-dimensional curved surface model of step S2, and repeat steps S3-S5 until the risk level is reduced to the low risk interval, forming the final flat longitudinal coupling safety optimization scheme, including:

[0149] S61, set optimization target: for the high-risk road section identified in step S5, set the optimization target of reducing the risk level to the low risk interval;

[0150] S62, adjust flat longitudinal parameters: according to the analysis result of the core inducement marked in step S54, adjust the flat curve radius, longitudinal slope, vertical curve radius or cross section super-elevation value;

[0151] S63, parameter verification cycle: re-input the adjusted flat longitudinal parameters into the three-dimensional curved surface model of step S2, and execute steps S3-S5 in turn to determine whether the risk level meets the standard;

[0152] S64, iterative optimization: if the risk level does not meet the standard, return to step S62 to continue adjusting the parameters to form a closed loop iteration until the optimization target is met, and output the final optimized flat longitudinal parameter combination.

[0153] In this embodiment, the optimization target of the high-risk road section is first set (such as reducing to the low risk interval), and then the road flat longitudinal parameters are adjusted according to the risk causes (such as increasing the curve radius and reducing the longitudinal slope), the adjusted parameters are substituted into the three-dimensional curved surface model, and the effect is verified by repeating the stability analysis and risk assessment process until the risk meets the standard, forming the final flat longitudinal coupling safety optimization scheme.

[0154] The present application realizes the following objectives by constructing a three-dimensional curved surface model of a curved slope road section and coupling a heavy truck quasi-static single-track dynamics model: analyzing the truck driving stability on the curved slope road section and quantifying the trajectory deviation; establishing a quantitative correlation model between three-dimensional curved surface geometric characteristics and driving risk; realizing the classification evaluation and early warning of the driving risk on the curved slope road section; forming a road flat longitudinal coupling safety optimization design method based on risk feedback, and providing a basis for improving the safety of the risk curved slope road section during reconstruction and expansion.

[0155] For the road to be reconstructed and expanded, when the linear shape of the curved slope road section needs to be adjusted, the following method can be used for the design of the curved slope road section and the evaluation of the truck driving risk.

[0156] First, the point cloud data of the target curved slope section is collected by using the vehicle-mounted or airborne laser radar, at this time a large number of unordered points are collected. Then, the B-spline and the concave hull algorithm are used to restore the plane linear, and the total least squares method is used to extract the linear parameters, so as to integrate the discrete points into the actual road linear.

[0157] Then, three-dimensional surface modeling is carried out, and each linear parameter and spatial coordinate are obtained. Then, a spatial coordinate-linear parameter equation set is obtained. That is, by using the equation set, the road linear at this space can be obtained through the stake number. The specific method is described above.

[0158] After obtaining the three-dimensional surface equation, the vehicle driving stability is analyzed. Through the load transfer of the braking force distribution, then through the lateral force balance and the high gravity center rollover constraint, the lateral force overrun degree formula and the rollover risk index formula are obtained.

[0159] If the risk is medium risk and above, adjust the linear and repeat the process until the risk is low. At this time, the linear scheme of the curved slope section for truck expansion can be implemented.

[0160] Embodiment 2

[0161] A curved slope section truck driving risk early warning and linear optimization device, comprising:

[0162] A parameter acquisition module is configured to obtain road design parameters, including road plan and longitudinal section design parameters and cross section design parameters of the curved slope section, and truck parameters, including total mass, axle load distribution ratio, mass center height and wheelbase parameters of the truck;

[0163] A three-dimensional surface model construction module is configured to establish a local coordinate system based on the collected road design parameters, generate a three-dimensional surface equation with stake number and lateral distance as parameters, and obtain spatial coordinate information and three-dimensional linear parameters of any point on the road surface based on the road centerline;

[0164] A stability analysis module is configured to couple the obtained truck parameters with the three-dimensional surface model constructed in step S2, construct a quasi-static single-track dynamics model, perform braking force distribution calculation and load transfer analysis, and calculate the lateral and vertical offset of the actual driving track of the truck relative to the road centerline under the conditions of lateral force balance and high gravity center rollover constraint;

[0165] An association model construction module is configured to extract the lateral force overrun degree and the rollover risk index from the truck driving stability analysis results as the core risk index, and establish a quantitative association relationship between the three-dimensional linear parameters and the core risk index;

[0166] An evaluation and early warning module is configured to calculate a risk index value of each pile number road section based on the established correlation model, divide a risk level according to a preset threshold, generate a full road section risk heat map, mark a risk type and a core cause of a high-risk road section, and output a risk evaluation table and early warning information.

[0167] An optimization module is configured to adjust road horizontal and vertical parameters for the identified high-risk road section, substitute the adjusted parameters into the three-dimensional curved surface model, and repeatedly execute the stability analysis and risk evaluation processes until the risk level is reduced to a low risk interval, thereby forming a final horizontal and vertical coupling safety optimization scheme.

[0168] The present application is realized by relying on an integrated system composed of the parameter collection module, the three-dimensional curved surface model construction module, the stability analysis module, the correlation model construction module, the evaluation and early warning module, and the optimization module. The system cooperates with each module to complete the full-link process from parameter input to optimization scheme output.

[0169] The specific implementation of each module in the embodiment can be referred to the embodiment 1 described above, which will not be repeated here; it should be noted that the device provided in the embodiment is only exemplified by the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.

[0170] Embodiment 3

[0171] An electronic device includes a processor 602, a memory, an input device 603, a display 604 and a network interface 605 connected through a system bus 601. The processor 602 is configured to provide computing and control capabilities, the memory includes a non-volatile storage medium 606 and an internal memory 607, the non-volatile storage medium 606 stores an operating system, a computer program and a database, the internal memory 607 provides an environment for the operating system and the computer program in the non-volatile storage medium 606 to run, and the computer program is executed by the processor 602 to realize the above-mentioned truck driving risk early warning and linear optimization method on curved and sloping road sections.

[0172] Embodiment 4

[0173] A storage medium, which is a computer readable storage medium, stores a computer program, and the computer program is executed by a processor to realize the above-mentioned truck driving risk early warning and linear optimization method on curved and sloping road sections.

[0174] The storage medium described in the embodiment can be a medium such as a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a U disk, a mobile hard disk, and the like.

[0175] The present application aims at the deficiencies of "two-dimensional longitudinal separation modeling, stability analysis simplification, and risk assessment empiricism" in the conventional design of curved slope sections, and constructs a whole-link system of "three-dimensional modeling-stability analysis-risk correlation-optimization design", the significance of which includes:

[0176] 1. Filling the technical gap of the quantitative correlation between the three-dimensional curved surface characteristics of curved slope sections and the truck driving risk;

[0177] 2. Providing tool support for the accurate assessment and early warning of the truck driving risk of curved slope sections;

[0178] 3. The formed horizontal-vertical coupling optimization method can directly guide the road design, effectively reduce the truck accident rate of curved slope sections, and at the same time improve the scientificity and economy of road design, providing a technical reference for the safety design in the field of traffic engineering.

[0179] The above embodiment is a preferred embodiment of the present application, but the embodiments of the present application are not limited by the above embodiment, and any change, modification, substitution, combination, simplification made without departing from the spirit and principle of the present application shall be an equivalent replacement mode, and all shall be included in the protection scope of the present application.

Claims

1. A method for early warning of truck driving risks and alignment optimization on curved and sloping road sections, characterized in that: Includes the following steps, S1. Collect parameters: Obtain road design parameters, including the horizontal and vertical profile design parameters and cross-sectional design parameters of curved and sloping road sections; and truck parameters, including the total mass of trucks, axle load distribution ratio, center of gravity height and wheel track parameters; S2. Construct a three-dimensional surface model: Based on the road design parameters collected in step S1, establish a local coordinate system with the road centerline as the reference, generate a three-dimensional surface equation with station number and lateral distance as parameters, and obtain the spatial coordinate information and three-dimensional alignment parameters of any point on the road surface. S3. Truck driving stability analysis: Couple the truck parameters obtained in step S1 with the three-dimensional curved surface model constructed in step S2 to construct a quasi-static monorail dynamic model, perform braking force distribution calculation and load transfer analysis, and calculate the lateral and vertical offset of the actual driving trajectory of the truck relative to the road centerline under the conditions of lateral force balance and high center of gravity rollover constraint. S4. Construct a three-dimensional surface feature and driving risk correlation model: Extract the degree of lateral force exceedance and rollover risk index from the truck driving stability analysis results in step S3 as core risk indicators, and establish a quantitative correlation between three-dimensional linear parameters and core risk indicators; S5. Quantitative assessment and early warning of driving risks: Based on the correlation model established in step S4, calculate the risk index value of each road segment at each station, classify the risk level according to the preset threshold, generate a risk heat map of the entire road segment, and mark the risk type and core causes of high-risk road segments, and output the risk assessment table and early warning information. S6. Horizontal and vertical coupling safety optimization design: For the high-risk road sections identified in step S5, adjust the horizontal and vertical parameters of the road, substitute the adjusted parameters into the three-dimensional surface model in step S2, and repeat steps S3-S5 until the risk level is reduced to the low-risk range, forming the final horizontal and vertical coupling safety optimization scheme.

2. The method for early warning of truck driving risks and alignment optimization on curved and sloping road sections according to claim 1, characterized in that: Step S2, 3D surface modeling, includes: S21. Establish the road centerline equation: Based on the horizontal alignment parameters and the vertical alignment parameters, determine the three-dimensional coordinates of the road centerline. x(l),y(l),z(l) ), where the station number l This refers to the distance along the center line; S22. Define a local coordinate system: Establish a local coordinate system at any point on the road centerline using the tangent vector. T(l) Horizontal normal vector N(l) and vertical vector V(l) The local coordinate system formed; S23. Establish the cross-sectional shape equation: Based on the road camber slope and superelevation slope, define the vertical offset of any point in the cross-section relative to the centerline. h(s) ,in s Horizontal distance; S24. Generate the parametric equations of the three-dimensional surface: through... X(l,s)=x(l)+sNx+h(s)Vx , Y(l,s)=y(l)+sNy+h(s) Vy , Z(l,s)=z(l)+sNz+h(s)Vz Calculate the spatial coordinates of any point on the road surface, where l The distance along the center line, s Horizontal distance; S25. Calculate three-dimensional linear parameters: including spatial curvature. Ks(l) Spatial torsion τ(l) , longitudinal slope i(l) Cross slope i h (l, s) Radius of plane curve R h (l) and vertical curve radius R v (l) .

3. The method for early warning of truck driving risks and alignment optimization on curved and sloping road sections according to claim 1, characterized in that: Step S3, the truck driving stability analysis, includes: S31. Construct a braking force distribution model. Based on the total mass of the truck and the axle load distribution ratio parameters, distribute the braking force according to the axle load ratio in non-emergency braking state, and limit the increase of the rear axle braking force in emergency braking state. S32. Calculate the increase in front axle load and the decrease in rear axle load during braking based on braking deceleration, center of gravity height, and wheelbase, and then correct to obtain the front axle normal force. Fz 前 and rear axle normal force Fz 后 ; S33, Utilizing the modified front axle normal force Fz 前 and rear axle normal force Fz 后 Calculate the ultimate bearing capacity of the lateral force of the front and rear axles, and then obtain the total resultant lateral force. If the resultant force is less than or equal to zero, it is determined that the truck is at risk of shifting towards the central divider. S34. Set an upper limit for lateral acceleration using a rollover stability coefficient. When the actual lateral acceleration exceeds the upper limit, it is determined that there is a risk of rollover. S35. Under the limit constraint of the lateral force on the road surface, and in combination with the judgment results of steps S33 and S34, solve for the lateral and vertical offset of the actual driving trajectory of the truck relative to the centerline of the road.

4. The method for early warning of truck driving risks and alignment optimization on curved and sloping road sections according to claim 3, characterized in that: The braking force distribution model in step S31 is as follows: When |F x |≦ mgf max At that time, according to the axle load distribution ratio k Distribute braking force to the front axle F x前 = k·F x and rear axle F x后 = (1-k)· F x ; When the total braking force |F x |≥ mgf max At that time, the front axle braking force remains F x前 = k·F x The rear axle braking force is limited to F x后 = (1-k) mgf max +0.3(1-k)(|F x |- mgf max To ensure that the increase in rear axle braking force does not exceed 30%; Among them, |F x | is the total braking force. mg The total mass of the truck. f max This represents the maximum adhesion coefficient.

5. The method for early warning of truck driving risks and alignment optimization on curved and sloping road sections according to claim 3, characterized in that: In step S34, the upper limit of lateral acceleration is ≤ SSF -| i n |−0.1; where SSF = Tr / (2 h cg ), T r The wheelbase is the distance between the wheels. h cg For the height of the center of mass, i n The slope is the vector composite slope of the road's longitudinal and transverse slopes, with 0.1 representing a safety margin.

6. The method for early warning of truck driving risks and alignment optimization on curved and sloping road sections according to claim 1, characterized in that: Step S5, the quantitative assessment and early warning of driving risks, includes: S51. Risk index calculation: Based on the stability analysis results of step S3, calculate the degree of lateral force exceeding the limit and the rollover risk index value. The larger the value of the lateral force exceeding the limit, the higher the risk of hitting the central divider. The rollover risk index is the difference between the actual lateral acceleration coefficient and the rollover threshold. A positive value indicates that there is a risk of rollover. S52. Risk Classification: The calculated risk index values ​​are compared with the preset four-level thresholds of low risk, medium risk, high risk, and extremely high risk to determine the risk level of each road segment at each station number. S53. Generate a risk heat map: Using the station number as the horizontal axis and the horizontal distance as the vertical axis, draw a risk distribution heat map of the entire road section and visualize the high-risk mileage section. S54. Mark the risk factors: For high-risk road sections, mark their main risk types and core factors, including excessive longitudinal slope, insufficient adhesion coefficient, excessively small horizontal curve radius or insufficient superelevation. S55. Output early warning information: Generate a risk assessment table that includes risk level, risk type, core causes and recommended measures, and issue early warning prompts for road sections with medium or higher risk.

7. The method for early warning of truck driving risks and alignment optimization on curved and sloping road sections according to claim 1, characterized in that: Step S6, the horizontal and vertical coupling safety optimization design includes: S61. Set optimization goals: For the high-risk road sections identified in step S5, set optimization goals to reduce the risk level to the low-risk range; S62. Adjust horizontal and vertical parameters: Based on the core cause analysis results marked in step S54, adjust the horizontal circular curve radius, longitudinal slope, vertical curve radius or cross section superelevation value accordingly. S63, Parameter Verification Loop: Re-input the adjusted horizontal and vertical parameters into the 3D surface model of step S2, and execute steps S3-S5 in sequence to determine whether the risk level meets the standard. S64. Iterative optimization: If the risk level does not meet the standard, return to step S62 to continue adjusting the parameters to form a closed loop iteration until the optimization objective is met, and output the final optimized combination of horizontal and vertical parameters.

8. A device for early warning and alignment optimization of truck driving risks on curved and sloping road sections, characterized in that, include: The parameter acquisition module is used to acquire road design parameters, including the horizontal and vertical profile design parameters and cross-sectional design parameters for curved and sloping road sections. And truck parameters, including the truck's gross weight, axle load distribution ratio, center of gravity height, and wheel track parameters; The 3D surface model construction module is used to establish a local coordinate system based on the collected road design parameters and the road centerline, generate a 3D surface equation with station number and lateral distance as parameters, and obtain the spatial coordinate information and 3D linear parameters of any point on the road surface. The stability analysis module is used to couple the acquired truck parameters with the three-dimensional curved surface model to construct a quasi-static monorail dynamic model, perform braking force distribution calculation and load transfer analysis, and calculate the lateral and vertical offset of the actual driving trajectory of the truck relative to the road centerline under the conditions of lateral force balance and high center of gravity rollover constraint. The correlation model construction module is used to extract the degree of lateral force exceedance and rollover risk indicators from the truck driving stability analysis results as core risk indicators, and to establish a quantitative correlation between the three-dimensional linear parameters and the core risk indicators. The assessment and early warning module is used to calculate the risk index values ​​of each road segment based on the established correlation model, classify the risk level according to the preset threshold, generate a risk heat map of the entire road segment, mark the risk type and core causes of high-risk road segments, and output a risk assessment table and early warning information. The optimization module is used to adjust the horizontal and vertical parameters of the identified high-risk road sections, substitute the adjusted parameters into the three-dimensional surface model, and repeatedly execute the stability analysis and risk assessment process until the risk level drops to the low-risk range, thus forming the final horizontal and vertical coupled safety optimization scheme.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the method for warning of truck driving risks and optimizing alignment on curved and sloping road sections as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method for warning of truck driving risks and optimizing alignment on curved and sloping road sections as described in any one of claims 1-7.

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