CN116631221B - Monte Carlo simulation-based on-road vehicle running risk quantitative calculation method - Google Patents
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Abstract
The invention discloses a method for quantitatively calculating the running risk of an on-road vehicle based on Monte Carlo simulation, which comprises the following steps: step 1) defining an emergency event which happens according to probability according to specific working conditions; step 2): providing reasonable assumption and describing the motion process of the vehicle in the collision process, establishing a kinematic equation and deducing the collision judgment condition; step 3) counting the distribution of variables involved in the motion process of the host vehicle and the traffic participant under each working condition from unmanned aerial vehicle or driving simulation data; step 4): monte Carlo simulation was used to calculate the incidence of collision events. The risk measurement index based on the risk perception of the driver and the perception characteristic of the human ear to the sound is defined in the calculation method, and compared with the existing risk measurement index, the index provided by the invention is more in line with the visual perception of the driver to the risk.
Description
Technical Field
The invention belongs to the field of transportation engineering, and particularly relates to an on-road vehicle running risk quantification calculation method based on Monte Carlo simulation.
Background
The national road traffic safety and science and technology action plan of the science and technology department, the public security department and the traffic department, which were exported in 2008, encouraged the research and application of the road traffic safety guarantee technology, so as to achieve the goals of gradually decreasing the number of dead road traffic accidents, further reducing the extra-large road traffic accidents, approaching the level of medium developed countries in ten thousand car mortality, and the like. But since 2011, road traffic deaths begin to exhibit stable fluctuation, and the rate of decline of vehicle mortality begins to slow, and there is still a gap from moderately developed countries. These phenomena suggest that the protective capabilities of existing control measures have reached a limit and that new methods are being explored to further improve the level of safety protection. As important content of national strategy of traffic, traffic safety is mentioned to an unprecedented level.
Road traffic safety research is an important direction of current exploration and gradually becomes a research hotspot. The definition of risks in the dictionary is: people in production construction and daily life are subjected to natural disasters, accidents and other unexpected events that can lead to personal casualties, property damage and other economic losses. The international organization for standardization (ISO) defines risk as "impact of uncertainty on targets". Therefore, the risk of road traffic operation can be attributed to the possibility of a driver encountering traffic accidents in daily driving activities, and is affected by random variations in road conditions, traffic participant behaviors, vehicle conditions, meteorological environments, and the like.
Related studies of road traffic running risk can be roughly divided into qualitative and quantitative aspects, the former focusing on risk source discrimination, accident formation mechanism and the like, and the latter focusing on accident rate (including severity) and risk assessment. Obviously, quantitative research is more helpful for distinguishing the dominant factors of the running risk formation, and practical and effective prevention and control countermeasures are provided. However, in terms of quantitative analysis, the indexes are not in one dimension, and are poor in comparability with each other, so that consensus is difficult to form, and one of the important reasons is the lack of basic theory and methods capable of quantitatively analyzing and characterizing the random variation from traffic behaviors to the running risk forming process.
Disclosure of Invention
Aiming at the problems, the invention provides a method for quantitatively calculating the risk of an on-road vehicle based on probability generalization and collision detection, which is based on a risk formation mechanism, considers the uncertainty of road traffic environment and behavior main body judgment and control, predicts the specific behavior change and potential collision probability of a driver through the behavior characteristics of the driver, and defines a risk measurement index which accords with visual perception of people in a decibel form based on the similarity of the risk perception of the driver and the sound perception of human ears.
The on-road vehicle risk quantification calculation method provided by the first aspect of the invention is characterized in that probability distribution of indexes such as speed, relative distance and the like under various working conditions is obtained according to driving behavior data, and the occurrence rate of collision events is calculated based on probability generalization and Monte Carlo simulation for collision detection. The method comprises the following steps:
step 1) defining emergency events which occur according to probability according to specific working conditions;
step 2) reasonable assumption is put forward and the motion process of the vehicle in the collision process is described, a kinematic equation is established, and the collision judgment condition is deduced;
step 3) counting the distribution of variables involved in the motion process of the host vehicle and the traffic participant under each working condition from unmanned aerial vehicle or driving simulation data;
step 4) calculating the occurrence rate of the collision event by adopting Monte Carlo simulation.
The risk measurement index based on the risk perception of the driver and the perception characteristic of the human ear to the sound provided by the second aspect of the invention is characterized in that the risk measurement index is defined by referring to the perception characteristic of the human ear to the sound, the risk measurement index is more in line with the visual perception of the driver to the risk, and a calculation formula of the risk measurement index is defined as follows:
wherein:
R individual -single sample risk perception index, in dB;
P individual -individual collision probability calculated using the initial state of the single sample;
P base -a base risk, using an average collision probability calculated from the full sample initial state distribution sampling values under typical conditions.
R individual The physical meaning of (c) is: deducing according to the initial state of the current sample, and issuingThe probability of a collision is the fundamental riskMultiple times.
The invention has the following advantages:
1. based on probability characteristics of risks, risk perception of a driver is quantized into evaluation of possibility of collision accidents of a vehicle, and a calculation method for in-transit vehicle risk quantization is provided based on probability generalization and collision detection.
2. Based on the driver risk perception and the perception characteristics of the human ear to the sound, a measurement index which accords with the visual perception of people is defined.
Drawings
FIG. 1 is a flowchart of the overall method for calculating the risk of in-transit vehicle operation based on Monte Carlo simulation;
FIG. 2 is a schematic diagram of a lane change condition in the same direction in an embodiment of the present invention;
FIG. 3 is a graph showing a significant brake sample deceleration time profile in an embodiment of the present invention;
FIG. 4 is a probability mass function diagram of the distance between the head of the host vehicle and the tail of the host vehicle in front of the current lane at the lane change start point in the embodiment of the invention;
FIG. 5 is a probability mass function diagram of a preceding vehicle emergency braking deceleration in an embodiment of the invention;
FIG. 6 is a probability mass function diagram of the deceleration time of a preceding vehicle in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the scope of the invention. The specific implementation mode is as follows:
in the course of the lane change of the same-direction multi-lane highway, the collision risk mainly comes from the collision risk of the host vehicle and the front vehicle of the current lane and the collision risk of the host vehicle and the rear vehicle of the target lane, as shown in fig. 2. The following description uses the calculation of the risk quantization model of the host vehicle and the front vehicle of the current lane in the course of the lane change of the same-direction multi-lane highway as an embodiment:
1) Definition of emergency events
The sudden event assumption of the vehicle and the vehicle in front of the current lane is as follows: before and after lane changing, the front vehicle suddenly and emergently brakes, so that the vehicle is not avoided and is crashed.
2) Reasonable assumption is put forward and the motion process of the vehicle in the collision process is described, a kinematic equation is established, and the judgment condition of the collision is deduced
With acceleration a of the vehicle<-1.5m·s -2 And the motor vehicle is regarded as sudden braking, the deceleration process of the motor vehicle is simplified into uniform deceleration motion, the deceleration can be the average value of a in the deceleration process, and the method for simplifying calculation is as follows:
a mean ≈0.5a max
in the method, in the process of the invention,
a mean average deceleration during deceleration of vehicle, unit m.s -2 ；
a max Constant, which is the maximum value of the deceleration in the braking process, unit m.s -2 。
According to the principle of kinematics, the driving distance of the front vehicle in the process of decelerating to the lowest speed point can be calculated by the following formula:
S(t)＝v f,0 t dec +0.25a max t dec 2
in the method, in the process of the invention,
v f,0 -initial speed before front vehicle deceleration, unit m.s -1 ；
t dec -the deceleration time of the preceding vehicle, in s.
The remaining symbols have the same meaning as before.
When the front vehicle suddenly brakes, the speed reduction process of the vehicle can be divided into 2 stages, wherein the first stage is a driver reaction stage, and the motion state of the vehicle does not change greatly in the stage; the second phase is a braking phase, where the driver of the vehicle will step on the brake to reduce the vehicle speed. The driver design reaction time length in the parking sight distance formula of highway route design rule is 2.5s, and the driver is at high speedWhen the expressway is driven, the vigilance is high, the actual reaction time is lower than the value, the invention uses the Scaner simulator to test the reaction time of the driver, and the measured reaction time is taken as t r ＝0.6s。
The duration of deceleration of the apparent brake samples in the drone data was calculated and counted, with the results shown in fig. 3. The design computer program searches the extreme point or the curvature minimum point of the track coordinates from the lane id change point forwards and backwards respectively to serve as the starting and ending point of the lane change. When the program cannot search the starting point and the ending point, the starting point and the ending point are manually determined by using a dynamic graph tool. After the vehicle body of the vehicle passes through the lane boundary and enters the target lane, the risk of collision with the front vehicle is immediately dissipated, and the proportion of the time length of the line pressing from the start of lane changing to the rear wheel line pressing in the opposite side direction of lane changing of the vehicle in the unmanned aerial vehicle data set to the total lane changing time length is counted, so that the average value of the samples is 0.65. Assuming that the minimum deceleration value and the acceleration variation form of the vehicle are consistent with those of the preceding vehicle, the motion equation of the vehicle is as follows:
S B,f ＝v B,f (0.65t h )+0.25a max (0.65t h -t r ) 2
in the method, in the process of the invention,
S B,f the unit m is the distance traveled by the vehicle from the deceleration of the front vehicle to the entering of the vehicle into the opposite lane;
v B,f the initial speed of the vehicle before deceleration, namely the speed at the start point of lane change, with the unit of m.s -1 ；
t h -total lane change duration of the host vehicle, unit s.
The remaining symbols have the same meaning as before.
Deceleration time and t r +0.65t h It can be assumed that the front vehicle speed just reaches the lowest point when the body of the vehicle passes through the line, and then the motion equation of the front vehicle becomes correspondingly:
S f ＝v f (0.65t h )+0.25a max (0.65t h ) 2
in the method, in the process of the invention,
S f -the front vehicle starts from self-decelerationDistance to the vehicle driving in the process of entering the opposite lane is in unit of m;
v f speed of the front vehicle of the vehicle at the start point of lane change, unit m.s -1 。
The other symbols have the same meaning as before.
Finally, the judging condition of collision of two vehicles can be obtained:
D f +S f -S B,f ≤0
3) Counting the distribution of variables involved in the movement process of the host vehicle and the traffic participant under each working condition from unmanned plane or driving simulation data
(1) Initial state variable of own vehicle
The initial state variable of the vehicle is only the distance D between the vehicle and the front vehicle tail at the lane change starting point f . Counting lane change starting point D of post-vehicle group of target lane in unmanned aerial vehicle data f . The probability mass of each segment was calculated with a step size of 10m, and the result is shown in fig. 4. D when calculating the basic risk f Sampling from this function, assuming that samples etc. in each paragraph might be extracted, a uniform distribution of 0.5 steps is added to the sampled value.
D f,sample ＝D f,draw +ε Df
ε Df ～Uniform(-5,5)
In the method, in the process of the invention,
D f,sample ——D f the unit m;
D f,draw -D extracted from probability mass function f Value, unit m;
ε Df -obeying a uniformly distributed random term, unit m;
form (-5, 5) -lower bound is-5 and upper bound is Uniform distribution of 5.
(2) Interactive vehicle initial state variables
The initial state variables of the interactive vehicle comprise the maximum deceleration a of the front vehicle of the own lane max And a deceleration time t dec . The two variables should be sampled from the following 2 probability mass functions when computing the base risk.
A. Maximum deceleration a max Probability mass function of (2)
The acceleration of the full sample track of the unmanned aerial vehicle data is selected, and the maximum deceleration value a of each track is extracted max . At 1 m.s -2 Counting the segments a for step size max The distribution of which is shown in table 1 and fig. 5.
TABLE 1 probability mass function values for sudden braking of a front vehicle
| Paragraph of (c) | Probability of | Paragraph center value/(m.s) -2 ) |
| 1 | 0.987176 | -0.5 |
| 2 | 0.010779 | -2 |
| 3 | 0.001617 | -3 |
| 4 | 0.000344 | -4 |
| 5 | 6.50E-05 | -5 |
| 6 | 9.29E-06 | -6 |
| 7 | 9.29E-06 | -7 |
Since the center of the segment is discrete, in order to increase the randomness of the samples, a is given by adding a uniform distribution of 0.5 step size to each segment, assuming equal probability sampling within each segment max Is determined according to the following formula:
a max，sample ＝a max，draw +ε am
ε am ～Uniform(-0.5,0.5)
in the method, in the process of the invention,
a max，sample ——a max final sample value in m.s -2 ；
a max，draw -a extracted from probability mass function min Value, unit m.s -2 ；
ε am -random term subject to uniform distribution, unit m.s -2 ；
Form (-0.5, 0.5) -lower bound is-0.5 and upper bound is a Uniform distribution of 0.5.
B. Time of deceleration t dec Probability mass function of (2)
The deceleration duration of each deceleration sample trajectory is counted. Statistical analysis to confirm t dec And a max There is no obvious linear relationship and thus each can be sampled independently. The deceleration time samples are segmented with 0.5s as step length, and the probability mass of each segment is calculated, and the result is shown in fig. 6. At the time of sampling, assuming that samples of each statistical section and the like can be extracted, a uniform distribution of 0.5 times step length is added to the sampling value, namely:
t dec，sample ＝t dec，draw +ε td
ε td ～Uniform(-0.25,0.25)
t dec，sample ——t dec final sample value, unit s;
t dec，draw -t extracted from probability mass function dec Value, unit s;
ε td -following a uniformly distributed random term, unit s;
form (-0.25, 0.25) -lower bound is-0.25 and upper bound is a Uniform distribution of 0.25.
(3) Variables associated with initial state
The variables related to the initial state comprise the speed difference v between the host vehicle and the front vehicle at the lane change starting point f -v B,f And a lane change time t h 。
A. Speed difference v between own vehicle and front vehicle f -v B,f Is calculated by the method of (a)
Front-rear vehicle speed difference (v f -v b ) D at the start of lane change f There is a linear relationship. Let v d,f ＝v f -v B,f In v d,f As dependent variable, D f A regression model was built for the independent variables, resulting in a regression equation as shown in table 2.
TABLE 2 Linear regression model for speed difference between host vehicle and front vehicle
| Coefficients of | Standard deviation of | t statistics | p value | |
| Constant term | 0.746 | 0.229 | 3.265 | 0.001 |
| Distance from the vehicle to the front and rear of the vehicle | 0.072 | 0.004 | 19.740 | <0.001 |
The standard deviation of the residual was 2.37 in order to restore v as much as possible d,f Where the residual term needs to be considered in sampling, v d,f The sample value of (2) can be calculated by the following formula:
v d,f,sample ＝0.072D f,sample +0.746+ε vdf
ε vdf ～Normal(0,2.37)
in the method, in the process of the invention,
v d,f,sample ——v d,f the final sample value of (2) in m.s -1 ；
ε vdf -normally distributing residual terms;
normal (0,2.37) -Normal distribution with standard deviation of 2.37, expected to be 0.
The remaining symbols have the same meaning as before.
B. Time t of lane change h Is calculated by the method of (a)
In D f As independent variable, change channel time t h The results of the unary regression analysis for the dependent variables are shown in Table 3.
TABLE 3 Linear regression equation for lane change time
| Coefficients of | Standard deviation of | t statistics | p value | |
| Constant term | 4.985 | 0.679 | 7.224 | <0.001 |
| Distance from the vehicle to the front and rear of the vehicle | 0.0185 | 0.008 | 2.39 | 0.021 |
The standard deviation of the residual was 2.26. Also, in order to restore the true distribution of the channel change time as much as possible, the residuals need to be put into the equation for sampling. Then t h The sample value of (2) can be calculated by the following formula:
t h,sample ＝0.0185D f,sample +4.985+ε th
ε th ～Normal(0,2.26)
t h,sample ——t h units s;
ε th -normally distributing residual terms;
normal (0,2.26) -Normal distribution with standard deviation of 2.26, expected to be 0.
The remaining symbols have the same meaning as before.
4) Computing the incidence of collision events using Monte Carlo simulation
(1) Basic risk of typical conditions
Adopting Monte Carlo simulation, programming a simulation program to calculate basic risks under specific working conditions, wherein the sampling times of simulation are 10 8 Secondly, obtaining a basic risk value of 1.5 multiplied by 10 of collision between the vehicle and the front vehicle of the current lane -7 . The base risk refers to the average level of collision accidents between the vehicle and other traffic participants in a typical operating condition under sudden event conditions. The numerical value represents 10 8 In the subsampling simulation, the ratio of the number of samples meeting the collision condition to the total number of simulation samples is achieved.
(2) Single sample risk based on driving behavior spectrum
In the multi-lane change working condition, the variables and the value method related to single sample risk calculation of the collision front vehicle are shown in the following table:
table 4 variable and value method for calculating risk of multi-lane change behavior sheet sample
In an initial state D f 67m, the speed of the vehicle is 37.4 m.s -1 Current speed of vehicle before lane 22.2 m.s -1 v d,f ＝15.2m·s -1 Taking 10 samples of the driving sample 8 Next, a collision probability of 1.06X10 was obtained -4 Whereas the basic risk of a collision front is 1.5 x 10 -7 Substituting the risk metric index formula to calculate the single sample risk value to be 2.85dB.
The specific embodiments described above are merely illustrative of the invention so that those skilled in the art can understand and apply the invention, and are not intended to limit the invention. Any modification, improvement, etc. of the above embodiments according to the technical substance of the present invention should be within the scope of the present invention as those skilled in the art fall within the spirit and principles of the present invention.
Claims (3)
1. The on-road vehicle running risk quantitative calculation method based on Monte Carlo simulation is characterized by comprising the following steps of: the method comprises the following steps:
step 1) defining an emergency event which happens according to probability according to specific working conditions;
step 2) reasonable assumption is put forward and the motion process of the vehicle in the collision process is described, a kinematic equation is established, and the collision judgment condition is deduced;
step 3) counting the distribution of variables involved in the motion process of the host vehicle and the traffic participant under each working condition from unmanned aerial vehicle or driving simulation data; wherein the variable distribution is a sampling space used to determine variables in the model
Step 4) adopting Monte Carlo simulation to calculate the occurrence rate of collision event;
the calculation of the occurrence rate refers to the perception characteristics of human ears to sound to define risk measurement indexes, the risk measurement indexes more accord with the visual perception of a driver to risks, and a calculation formula is as follows:
wherein R is individual The unit dB is a single sample risk perception index; p (P) individual An individual collision probability calculated for an initial state employing a single sample; p (P) base The average collision probability calculated by using the initial state distribution sampling value of the whole sample under the typical working condition is taken as a basic risk; r is R individual The physical meaning of (c) is: deducing according to the initial state of the current sample, wherein the probability of collision is basic riskMultiple times.
2. The method for quantitatively calculating the running risk of the on-road vehicle based on Monte Carlo simulation according to claim 1, wherein the method comprises the following steps of: the specific working condition in the step 1) refers to the working condition under the specific traffic conflict of the specific road section.
3. The method for quantitatively calculating the running risk of the on-road vehicle based on Monte Carlo simulation according to claim 1, wherein the method comprises the following steps of: the kinematic equation in the step 2) is constructed according to the motion characteristics of each traffic participant in the process that the vehicle avoids other traffic participants under the emergency.
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