CN109878509B - Fuzzy logic-based multi-source information fusion rollover early warning method for integral tank car - Google Patents
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Abstract
The invention provides an integral tank car multi-source information fusion rollover early warning method based on fuzzy logic. The rollover early warning method provided by the invention does not need to consider complex kinetic equations and vehicle body parameters, only needs to redundantly process information of a plurality of low-cost sensors, and obtains the estimated optimal probability of rollover through a probability calculation model based on fuzzy logic. The method has comprehensive analysis data, accurately quantifies rollover risks in a probability form, can accurately and timely give an early warning when the integral tank car has small rollover risks, enables a driver to take preventive measures as early as possible, and reduces rollover accidents.
Description
Technical Field
The invention relates to a rollover early warning method for an integral tank car, in particular to a multi-source information fusion rollover early warning method for the integral tank car based on fuzzy logic, and belongs to the technical field of automobile safety.
Background
In recent years, with the vigorous development of road transportation industry, the integral tank car has become an important carrier for road transportation of dangerous goods. Due to the fact that the gravity center is high, the wheel track is relatively small and is easily disturbed by liquid, the integral tank car is easy to turn over on one side under the working conditions of ice and snow wet and slippery road surfaces, small turning radius, high running speed and the like, and accordingly dangerous goods are leaked, environmental pollution and traffic jam are caused, and life and social property are seriously damaged. According to the relevant statistical data of the United states road traffic safety administration, the damage degree of the rollover accident is only second to the collision accident in all the large passenger cars or transport vehicle traffic accidents, and the rollover accident is located at the 2 nd position. Therefore, the research on the rollover early warning method of the integral tank car has great social significance and practical value on road traffic safety.
In the field of rollover early warning of integral tank cars, rollover characteristic parameters commonly used include a roll angle, lateral acceleration and a transverse load transfer rate, most of the existing rollover early warning methods are based on a single rollover characteristic parameter, an actual value of the rollover characteristic parameter is compared with a preset threshold value when a vehicle runs, and early warning is performed when the actual value exceeds the threshold value. Because the posture of the integral tank car is changed quickly and the stability is low when the integral tank car runs, and the side turning factors are more, although the methods can play a role in early warning to a certain extent, the early warning is inaccurate, and meanwhile, if the preset side turning threshold value is large, when the actual value is close to the threshold value, the integral tank car already has the side turning danger, so that the problem that the early warning is not timely can occur when the actual value exceeds the threshold value and then the early warning is carried out.
Disclosure of Invention
Aiming at the problems of inaccurate and untimely rollover warning in the running process of the integral tank car, the multi-source information fusion rollover warning method for the integral tank car based on the fuzzy logic is provided. The method has comprehensive analysis data, accurately quantifies rollover risks, and can accurately and timely perform hierarchical early warning under different working conditions, so that the rollover risk is reduced, and the driving safety is improved.
In order to achieve the purpose, the invention provides the following technical scheme:
the fuzzy logic-based multi-source information fusion rollover early warning method for the integral tank car comprises the following steps:
the method comprises the following steps: defining three rollover representation parameters and respectively calculating corresponding predicted rollover occurrence probability
The rollover characteristic parameters of the integral tank truck are as follows: the lateral inclination angle alpha, the lateral acceleration A and the transverse transfer rate L of the pressure of the plate spring are calculated according to the following formula:
in the formula, FliIs the pressure on the left leaf spring of the ith axle of the integral tank car, FriIs the steel plate spring on the right side of the ith axle of the integral tank carThe received pressure i is the axle position number of the integral tank car, i is 1,2, …, n, n is the total axle number of the integral tank car;
the selected sensors comprise an MEMS gyroscope and a plurality of pressure sensors, the roll angle alpha and the lateral acceleration A are obtained according to the output information of the MEMS gyroscope, and the transverse transfer rate L of the pressure of the plate spring is obtained through calculation according to the output information of the pressure sensors and the formula (1); the MEMS gyroscope is fixed in the center of the chassis of the integral tank car, the pressure sensors are arranged at the joints of the leaf springs and the wheels, and the number of the pressure sensors depends on the number of the leaf springs on the two sides of the integral tank car;
defining the probability of judging the occurrence of the rollover based on the roll angle α as P1，
Wherein α is the side angle of the integral tank car, αTFor a predetermined roll angle threshold, αT＞0，0≤P1≤1，P1Two valid digits are reserved after the decimal point;
defining the probability of judging the rollover occurrence based on the lateral acceleration A as P2，
Wherein A is the lateral acceleration of the integral tank car, ATFor a predetermined lateral acceleration threshold, AT＞0，0≤P2≤1，P2Two valid digits are reserved after the decimal point;
defining the probability of judging the occurrence of the rollover based on the transverse transfer rate L of the pressure of the plate spring as P3，
Wherein L is the transverse transfer rate of the pressure of the plate spring of the integral tank car, L is more than or equal to 0, and LTIs a preset plate spring pressure transverse transfer rate threshold value, LT＞0，0≤P3≤1，P3Two valid digits are reserved after the decimal point;
step two: establishing probability calculation model based on fuzzy logic
1) Unambiguous input and output variables
P in the step one1、P2And P3The estimated optimal probability of the rollover occurrence is used as an output variable as an input variable of the calculation model;
2) fuzzification of precise quantities
Fuzzification is a process of converting an input variable value into membership degrees of each fuzzy set, is the first step of fuzzy logic, and needs to consider the following problems during fuzzification;
1. fuzzy set of selected input variables
Selecting three fuzzy sets, namely small, medium and large, wherein the letter representation is S, M, B in sequence;
2. determining membership functions for fuzzy sets
The input variables are all in the range of 0-1, so that the membership functions corresponding to the three fuzzy sets are defined as follows:
in the formula (f)S(x) Membership function, f, of a fuzzy set SM(x) Membership function, f, of fuzzy set MB(x) The membership function is a membership function of the fuzzy set B, two effective numbers are reserved after the decimal point of three membership function values, x represents the probability corresponding to each input variable, and x is more than or equal to 0 and less than or equal to 1;
for three input variables P1、P2And P3And obtaining the following according to the membership function: f. ofS(P1)、fM(P1)、fB(P1)，fS(P2)、fM(P2)、fB(P2)，fS(P3)、fM(P3)、fB(P3)；
3) Fuzzy inference
The fuzzy rule is based on the mature experience of the driver and the rollover simulation result, and when more than two of the three input probabilities reach the B set, the probability of the predicted rollover occurrence reaches the B set; when two probabilities reach the M set and one probability reaches the B set, the probability of the predicted rollover occurrence is considered to reach the B set; when the three probabilities all reach the M set, the probability of the predicted rollover occurrence is considered to reach the M set; when two probabilities are in the S set and one probability reaches the B set, the probability of the predicted rollover occurrence is considered to reach the M set; when two probabilities reach the M set and one probability is in the S set, the probability of the predicted rollover occurrence is considered to reach the M set; one probability reaches the B set, one probability reaches the M set, and the other probability reaches the S set, and the probability of the predicted rollover occurrence is considered to reach the M set; if two probabilities are in the S set and one probability is in the M or S set, the probability of the predicted rollover occurrence is considered to reach the S set;
because the fuzzy condition statements are combined by using AND operation in the rules, the membership degree of the output result of each rule is calculated by a min function; rule 1: if P is1At S set and P2At S set and P3In S set, the output result is that the probability of the predicted rollover occurrence is in S set, because P1Membership of the S set is fS(P1)，P2Membership of the S set is fS(P2)，P3Membership of the S set is fS(P3) So that the output result has a membership of min (f)S(P1)、fS(P2)、fS(P3) Other rules are analogized in the same way, and the total number of the rules is 27;
4) determining a disambiguation strategy
The gravity center method is adopted as a fuzzy solving strategy, and the calculation formula is as follows:
wherein R is the output variable of the calculation model, namely the estimated optimal probability of rollover occurrence, FSjIs the membership, OW, of the output result of the jth rule in the fuzzy rulejThe weights are weights of fuzzy sets in the output result of the jth rule in the fuzzy rule, and the weights usually take the intermediate values of each set, that is, ow(s) is 0.25, ow (m) is 0.5, and ow (b) is 0.75;
step three: calculating the optimal probability of pre-estimated rollover occurrence and carrying out early warning in a grading manner
Will P1、P2And P3Inputting the probability calculation model established in the second step into three estimated rollover probability to obtain the estimated optimal rollover probability R, and performing early warning according to the R size in grades, wherein the early warning module consists of a voice prompt unit and a buzzer and is fixed in a cab, and the early warning rules are as follows:
when the alarm is not given, the voice prompt unit and the buzzer do not work; when the first-level alarm is given, the voice prompt unit plays: 'please drive safely', the buzzer vibrates with low frequency; and when the secondary alarm is performed, the voice prompt unit plays: please drive cautiously, the buzzer vibrates in medium frequency; when three-level alarming is carried out, the voice prompt unit plays: "danger, about to happen to turn on one's side", the buzzer shakes with high frequency.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the early warning method disclosed by the invention has the advantages of low cost of the used sensor, clear calculation method and convenience for large-scale popularization.
2. The invention has comprehensive analysis data, so that the early warning method can monitor the rollover stability of the integral tank car on line and is well suitable for various possible environments.
3. The early warning method accurately quantifies the rollover danger in a probability mode, and can accurately and timely carry out hierarchical early warning when the integral tank car has smaller rollover danger.
Drawings
Fig. 1 is a general design flowchart of the warning method provided by the present invention.
Fig. 2 is a schematic diagram of the installation position of the early warning device.
Fig. 3 is a schematic view of a single pressure sensor mounting location.
FIG. 4 is a graph of membership functions for a fuzzy set.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention.
The invention provides an integral tank car multi-source information fusion rollover early warning method based on fuzzy logic. The rollover early warning method provided by the invention does not need to consider complex kinetic equations and vehicle body parameters, only needs to redundantly process information of a plurality of low-cost sensors, and obtains the estimated optimal probability of rollover through a probability calculation model based on fuzzy logic. The method has comprehensive analysis data, accurately quantifies rollover risks in a probability form, can accurately and timely give an early warning when the integral tank car has small rollover risks, enables a driver to take preventive measures as early as possible, and reduces rollover accidents. The overall design flow of the early warning method is shown in fig. 1, and comprises the following specific steps:
the method comprises the following steps: defining three rollover representation parameters and respectively calculating corresponding predicted rollover occurrence probability
When the roll angle and the lateral acceleration of the integral tank car are increased to certain values, the integral tank car can turn over, the two side turning characterization parameters can visually evaluate the transverse stability of the integral tank car, and the acquisition mode is simple, so that the roll angle alpha and the lateral acceleration A of the integral tank car are selected as the side turning characterization parameters. The lateral load transfer rate is a commonly used rollover characterizing parameter, however, the load measurement of the wheel under the high-speed running state is very difficult, and the lateral load transfer rate is not accurately calculated. For the integral tank car using the leaf spring suspension, because the pressure borne by the leaf spring at the wheel has a certain corresponding relation with the load of the wheel, and the leaf spring is divided into left and right symmetrical parts, and the pressure borne by the leaf spring can also be directly obtained by a sensor, the invention uses the pressure borne by the leaf spring to replace the load of the wheel, and provides a rollover characterization parameter of the leaf spring pressure transverse transfer rate L, wherein the calculation formula is as follows:
in the formula, FliIs the pressure that the left leaf spring of the ith axle of the integral tank car is stressed, FriThe pressure of a steel plate spring on the right side of the ith axle of the integral tank car is applied, i is the position number of the axle of the integral tank car, and i is 1,2, …, n and n are the total number of the axles of the integral tank car.
The sensors selected by the invention comprise an MEMS gyroscope and a plurality of pressure sensors. And obtaining a roll angle alpha and a lateral acceleration A according to the output information of the MEMS gyroscope, and calculating the transverse transfer rate L of the pressure of the plate spring according to the output information of the pressure sensor and the formula (1). The installation position of the sensor is shown in figure 2, the MEMS gyroscope is fixed in the center of the chassis of the integral tank car, the pressure sensors are installed at the joint of the steel plate springs and the wheels, the number of the pressure sensors depends on the number of the steel plate springs on the two sides of the integral tank car, and the specific installation position of a single pressure sensor is shown in figure 3.
Defining the probability of judging the occurrence of the rollover based on the roll angle α as P1，
Wherein α is the side angle of the integral tank car, αTFor a predetermined roll angle threshold, αT＞0，0≤P1≤1，P1Two significant digits are retained after the decimal point.
Defining the probability of judging the rollover occurrence based on the lateral acceleration A as P2，
Wherein A is the lateral acceleration of the integral tank car, ATFor a predetermined lateral acceleration threshold, AT＞0，0≤P2≤1，P2Two significant digits are retained after the decimal point.
Defining the probability of judging the occurrence of the rollover based on the transverse transfer rate L of the pressure of the plate spring as P3，
Wherein L is the transverse transfer rate of the pressure of the plate spring of the integral tank car, L is more than or equal to 0, and LTIs a preset plate spring pressure transverse transfer rate threshold value, LT＞0，0≤P3≤1，P3Two significant digits are retained after the decimal point.
Step two: establishing probability calculation model based on fuzzy logic
1) Unambiguous input and output variables
The purpose of establishing the calculation model is to fuse and process the estimated rollover occurrence probability corresponding to the three rollover representation parameters, so that the rollover risk can be accurately and timely estimated, and therefore P in the step one is used1、P2And P3And taking the estimated optimal probability of the rollover occurrence as an output variable as an input variable of the calculation model.
2) Fuzzification of precise quantities
Fuzzification is a process of converting input variable values into membership degrees of each fuzzy set, is the first step of fuzzy logic, and needs to consider the following problems during fuzzification.
1. Fuzzy set of selected input variables
When more fuzzy sets are used for describing each input variable, the accuracy of the probability calculation model can be improved, however, the fuzzy rule is relatively complex to make, so that simplicity and flexibility are considered when the fuzzy sets are selected.
Since the input variables are probability values in the present invention, three fuzzy sets, small, medium, and large, are selected, with S, M, B being the alphabetical representations in order.
2. Determining membership functions for fuzzy sets
The steeper the shape of the membership function is, the higher the resolution is, and the higher the output sensitivity is; the slower the change of the membership function is, the lower the sensitivity is, and because the ranges of the input variables in the invention are all 0-1, the membership functions corresponding to the three fuzzy sets are defined as follows:
in the formula (f)S(x) Membership function, f, of a fuzzy set SM(x) Membership function, f, of fuzzy set MB(x) The fuzzy set B is a membership function, two effective numbers are reserved after the decimal point of three membership function values, x represents the probability corresponding to each input variable, x is more than or equal to 0 and less than or equal to 1, and the membership function of the fuzzy set is shown in figure 4.
For three input variables P1、P2And P3And obtaining the following according to the membership function: f. ofS(P1)、fM(P1)、fB(P1)，fS(P2)、fM(P2)、fB(P2)，fS(P3)、fM(P3)、fB(P3)。
3) Fuzzy inference
Fuzzy conditional statements are usually written in fuzzy logic as a fuzzy rule table based on practical experience. When two or more than two of the three input probabilities reach the B set, the probability of predicted rollover occurrence is considered to reach the B set; when two probabilities reach the M set and one probability reaches the B set, the probability of the predicted rollover occurrence is considered to reach the B set; when the three probabilities all reach the M set, the probability of the predicted rollover occurrence is considered to reach the M set; when two probabilities are in the S set and one probability reaches the B set, the probability of the predicted rollover occurrence is considered to reach the M set; when two probabilities reach the M set and one probability is in the S set, the probability of the predicted rollover occurrence is considered to reach the M set; one probability reaches the B set, one probability reaches the M set, and the other probability reaches the S set, and the probability of the predicted rollover occurrence is considered to reach the M set; and if two probabilities are in the S set and one probability is in the M or S set, the probability of the predicted rollover occurrence is considered to reach the S set. The specific fuzzy rules are shown in the following table:
the fuzzy rule table determines twenty-seven rules, and as the fuzzy condition statements are combined by using AND operation in the rules, the membership degree of each rule output result is calculated by a min function. Rule 1: if P is1At S set and P2At S set and P3In S set, the output result is that the probability of the predicted rollover occurrence is in S set, because P1Membership of the S set is fS(P1)，P2Membership of the S set is fS(P2)，P3Membership of the S set is fS(P3) So that the output result has a membership of min (f)S(P1)、fS(P2)、fS(P3) Other rules and so on.
4) Determining a disambiguation strategy
The output of fuzzy inference is a fuzzy set, whereas the output of fuzzy logic must be a deterministic value. And taking a single value which is relatively most representative of the fuzzy set in the fuzzy set obtained by inference, and referring to the fuzzy solution or fuzzy decision. The two most commonly used methods of deblurring are the maximum membership method and the center of gravity method. The maximum membership method is to take the value with the maximum membership in all fuzzy sets or membership functions as output, and the method is simple to implement, but does not consider the influence of other values with small membership and has poor representativeness. The output result of the gravity center method is more reasonable, a smooth output curved surface is easy to generate, and the robustness of the calculation model is improved. The invention adopts a gravity center method as a fuzzy solving strategy, and the calculation formula is as follows:
wherein R is the output variable of the calculation model, namely the estimated optimal probability of rollover occurrence, FSjIs the membership, OW, of the output result of the jth rule in the fuzzy rule tablejThe weights are weights of fuzzy sets in the output result of the rule of j in the fuzzy rule table, and the weights are usually intermediate values of each set, that is, ow(s) is 0.25, ow (m) is 0.5, and ow (b) is 0.75.
Step three: calculating the optimal probability of pre-estimated rollover occurrence and carrying out early warning in a grading manner
Will P1、P2And P3Inputting the probability calculation model established in the second step into three estimated rollover probability to obtain the estimated optimal rollover probability R, and performing early warning according to the R size in grades, wherein the early warning module consists of a voice prompt unit and a buzzer and is fixed in a cab, and the early warning rules are as follows:
when the alarm is not given, the voice prompt unit and the buzzer do not work; when the first-level alarm is given, the voice prompt unit plays: 'please drive safely', the buzzer vibrates with low frequency; and when the secondary alarm is performed, the voice prompt unit plays: please drive cautiously, the buzzer vibrates in medium frequency; when three-level alarming is carried out, the voice prompt unit plays: "danger, about to happen to turn on one's side", the buzzer shakes with high frequency.
Claims (1)
1. The fuzzy logic-based multi-source information fusion rollover early warning method for the integral tank car is characterized by comprising the following steps of:
the method comprises the following steps: defining three rollover representation parameters and respectively calculating corresponding estimated rollover occurrence probabilities;
the rollover characteristic parameters of the integral tank truck are as follows: the lateral inclination angle alpha, the lateral acceleration A and the transverse transfer rate L of the pressure of the plate spring are calculated according to the following formula:
in the formula, FliIs the pressure on the left leaf spring of the ith axle of the integral tank car, FriThe pressure of a steel plate spring on the right side of the ith axle of the integral tank car is received, i is the position number of the axle of the integral tank car, and i is 1,2, …, n and n are the total number of the axles of the integral tank car;
the selected sensors comprise an MEMS gyroscope and a plurality of pressure sensors, the roll angle alpha and the lateral acceleration A are obtained according to the output information of the MEMS gyroscope, and the transverse transfer rate L of the pressure of the plate spring is obtained through calculation according to the output information of the pressure sensors and the formula (1); the MEMS gyroscope is fixed in the center of the chassis of the integral tank car, the pressure sensors are arranged at the joints of the leaf springs and the wheels, and the number of the pressure sensors depends on the number of the leaf springs on the two sides of the integral tank car;
defining the probability of judging the occurrence of the rollover based on the roll angle α as P1，
Wherein α is the side angle of the integral tank car, αTFor a predetermined roll angle threshold, αT＞0，0≤P1≤1，P1After decimal pointTwo significant digits are reserved;
defining the probability of judging the rollover occurrence based on the lateral acceleration A as P2，
Wherein A is the lateral acceleration of the integral tank car, ATFor a predetermined lateral acceleration threshold, AT＞0，0≤P2≤1，P2Two valid digits are reserved after the decimal point;
defining the probability of judging the occurrence of the rollover based on the transverse transfer rate L of the pressure of the plate spring as P3，
Wherein L is the transverse transfer rate of the pressure of the plate spring of the integral tank car, L is more than or equal to 0, and LTIs a preset plate spring pressure transverse transfer rate threshold value, LT＞0，0≤P3≤1，P3Two valid digits are reserved after the decimal point;
step two: establishing a probability calculation model based on fuzzy logic;
1) input variables and output variables are defined;
p in the step one1、P2And P3The estimated optimal probability of the rollover occurrence is used as an output variable as an input variable of the calculation model;
2) fuzzification of the precise amount;
fuzzification is a process of converting an input variable value into membership degrees of each fuzzy set, is the first step of fuzzy logic, and needs to consider the following problems during fuzzification;
i) selecting a fuzzy set of input variables;
selecting three fuzzy sets, namely small, medium and large, wherein the letter representation is S, M, B in sequence;
ii) determining a membership function of the fuzzy set;
the input variables are all in the range of 0-1, so that the membership functions corresponding to the three fuzzy sets are defined as follows:
in the formula (f)S(x) Membership function, f, of a fuzzy set SM(x) Membership function, f, of fuzzy set MB(x) The membership function is a membership function of the fuzzy set B, two effective numbers are reserved after the decimal point of three membership function values, x represents the probability corresponding to each input variable, and x is more than or equal to 0 and less than or equal to 1;
for three input variables P1、P2And P3And obtaining the following according to the membership function: f. ofS(P1)、fM(P1)、fB(P1)，fS(P2)、fM(P2)、fB(P2)，fS(P3)、fM(P3)、fB(P3)；
3) Fuzzy reasoning;
the fuzzy rule is based on the mature experience of the driver and the rollover simulation result, and when more than two of the three input probabilities reach the B set, the probability of the predicted rollover occurrence reaches the B set; when two probabilities reach the M set and one probability reaches the B set, the probability of the predicted rollover occurrence is considered to reach the B set; when the three probabilities all reach the M set, the probability of the predicted rollover occurrence is considered to reach the M set; when two probabilities are in the S set and one probability reaches the B set, the probability of the predicted rollover occurrence is considered to reach the M set; when two probabilities reach the M set and one probability is in the S set, the probability of the predicted rollover occurrence is considered to reach the M set; one probability reaches the B set, one probability reaches the M set, and the other probability reaches the S set, and the probability of the predicted rollover occurrence is considered to reach the M set; if two probabilities are in the S set and one probability is in the M or S set, the probability of the predicted rollover occurrence is considered to reach the S set;
because the fuzzy condition statements are combined by using AND operation in the rules, the membership degree of the output result of each rule is calculated by a min function; rule 1: if P is1At S set and P2At S set and P3In S set, the output result is that the probability of the predicted rollover occurrence is in S set, because P1Membership of the S set is fS(P1)，P2Membership of the S set is fS(P2)，P3Membership of the S set is fS(P3) So that the output result has a membership of min (f)S(P1)、fS(P2)、fS(P3) Other rules are analogized in the same way, and the total number of the rules is 27;
4) determining a fuzzy solving strategy;
the gravity center method is adopted as a fuzzy solving strategy, and the calculation formula is as follows:
wherein R is the output variable of the calculation model, namely the estimated optimal probability of rollover occurrence, FSjIs the membership, OW, of the output result of the jth rule in the fuzzy rulejThe weights are weights of fuzzy sets in the output result of the jth rule in the fuzzy rule, and the weights usually take the intermediate values of each set, that is, ow(s) is 0.25, ow (m) is 0.5, and ow (b) is 0.75;
step three: calculating the optimal probability of pre-estimated rollover occurrence and carrying out early warning in a grading manner
Will P1、P2And P3Inputting the probability calculation model established in the second step into the three estimated rollover probability to obtain the optimal probability R of the estimated rollover, and carrying out early warning according to the grade of R, wherein the early warning module consists of a voice prompt unit and a buzzerFixed in the cab, the early warning rules are as follows:
when the alarm is not given, the voice prompt unit and the buzzer do not work; when the first-level alarm is given, the voice prompt unit plays: 'please drive safely', the buzzer vibrates with low frequency; and when the secondary alarm is performed, the voice prompt unit plays: please drive cautiously, the buzzer vibrates in medium frequency; when three-level alarming is carried out, the voice prompt unit plays: "danger, about to happen to turn on one's side", the buzzer shakes with high frequency.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101336183A (en) * | 2006-02-02 | 2008-12-31 | 五十铃自动车株式会社 | Device for judging rollover risk of vehicle |
| CN102529960A (en) * | 2012-02-15 | 2012-07-04 | 三一重工股份有限公司 | Control method and control system for preventing side overturn and mixing transport vehicle |
| CN103419736A (en) * | 2013-09-11 | 2013-12-04 | 重庆望江工业有限公司 | Rollover alarm device of self-discharging lorry |
| CN104401323A (en) * | 2014-11-04 | 2015-03-11 | 河北工程大学 | Rollover warning method and rollover warning device for heavy vehicle |
| WO2015187083A1 (en) * | 2014-06-03 | 2015-12-10 | Autoliv Development Ab | Trailer fifth wheel coupling emergency release arrangement |
| CN106004870A (en) * | 2016-06-23 | 2016-10-12 | 吉林大学 | Vehicle stability integrated control method based on variable-weight model prediction algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20120158247A1 (en) * | 2010-12-15 | 2012-06-21 | William Robert Norris | Perception Model For Trajectory Following Autonomous And Human Augmented Steering Control |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101336183A (en) * | 2006-02-02 | 2008-12-31 | 五十铃自动车株式会社 | Device for judging rollover risk of vehicle |
| CN102529960A (en) * | 2012-02-15 | 2012-07-04 | 三一重工股份有限公司 | Control method and control system for preventing side overturn and mixing transport vehicle |
| CN103419736A (en) * | 2013-09-11 | 2013-12-04 | 重庆望江工业有限公司 | Rollover alarm device of self-discharging lorry |
| WO2015187083A1 (en) * | 2014-06-03 | 2015-12-10 | Autoliv Development Ab | Trailer fifth wheel coupling emergency release arrangement |
| CN104401323A (en) * | 2014-11-04 | 2015-03-11 | 河北工程大学 | Rollover warning method and rollover warning device for heavy vehicle |
| CN106004870A (en) * | 2016-06-23 | 2016-10-12 | 吉林大学 | Vehicle stability integrated control method based on variable-weight model prediction algorithm |
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