CN110667536A - Bend control decision method for train AEB system - Google Patents

Bend control decision method for train AEB system Download PDF

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Publication number
CN110667536A
CN110667536A CN201910830428.1A CN201910830428A CN110667536A CN 110667536 A CN110667536 A CN 110667536A CN 201910830428 A CN201910830428 A CN 201910830428A CN 110667536 A CN110667536 A CN 110667536A
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train
track
laser radar
curve
obstacle
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刘永涛
韩毅
王庆锋
巨洪
张红娟
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Shaanxi Jiuyu Tongchuang Track System Technology Co Ltd
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Shaanxi Jiuyu Tongchuang Track System Technology Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T7/00—Brake-action initiating means
    • B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
    • B60T7/22—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle, or by means of contactless obstacle detectors mounted on the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • B60Q9/008—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for anti-collision purposes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B60W50/16—Tactile feedback to the driver, e.g. vibration or force feedback to the driver on the steering wheel or the accelerator pedal

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a curve control decision method of a train AEB system, which comprises the following steps: s1, collecting data by the laser radar group; s2, converting data space coordinates; s3, denoising and filtering; s4, fitting an orbit curve; s5, calculating the curvature of the curve; s6, recognizing the rail obstacle; s7, correcting safety margin in real time; in the subsequent train running process, the train speed is in change, different safety margins are set for different train speeds, and the safety margins are corrected by adopting danger coefficients; s8, calculating the stroke of the obstacle; calculating the collision distance between the train and the obstacle by combining S6 and S7; and S9, making braking decision of an AEB system. According to the invention, the front laser radar and the angle laser radar which are arranged at the head of the train are used for acquiring the information of the obstacles on the track in front of the advancing line of the train and on the two sides of the track, so that the data extraction of the obstacles is realized, and the safety of the train in the curve is ensured by an automatic emergency braking decision-making method of the curve of the train.

Description

Bend control decision method for train AEB system
Technical Field
The invention relates to the technical field of train safe operation, in particular to a curve control decision method of an AEB system of a train.
Background
With the continuous increase of railway mileage in China, the passenger and freight transportation borne by trains is also increased continuously, and higher-level requirements are provided for the safety and reliability of train running. The safety management of train transportation is regarded as the central importance of daily work by all levels of departments, but due to various reasons, barriers such as personnel, vehicles, construction machinery and the like exist along part of railways, the safety of train transportation is seriously influenced, and the safety of lives and properties of people is greatly influenced. In order to improve the safety of train running and track construction, it is necessary to install an automatic emergency braking system for the train. Due to the differences of the train and the automobile in the aspects of volume, mass, braking mode and the like, the train has greater potential safety hazard when running on a curve.
Disclosure of Invention
The invention aims to provide a curve control decision method of an AEB system of a train, which solves the problem of decision-making errors of the train provided with the AEB system in the curve running process, namely the problem of brake decision-making errors caused by wrong curve brake distance calculation.
In order to achieve the purpose, the invention adopts the technical scheme that: a curve control decision method of an AEB system of a train comprises the following steps:
s1, collecting data by laser radar group
Monitoring road condition information and obstacles in front of and on two sides of the line by adopting a front laser radar set, and acquiring the road condition information;
the laser radar group comprises a front laser radar (1) and a group of angle laser radars (2);
the laser radars are arranged in the center of the middle of the train head, and the two groups of angle laser radars are arranged on two sides of the lower part of the train head;
s2, converting data space coordinates
Carrying out coordinate conversion of different coordinate systems on the collected space data and attribute data of the track and objects around the track to obtain radar point cloud data;
s3, denoising and filtering processing
Denoising and filtering the radar point cloud data;
s4, fitting track curve
Acquiring a basic shape of the track through the processed radar point cloud data;
s5, curve curvature calculation
Calculating the curvature of the track through the basic shape of the track;
s6 track obstacle recognition
And clustering the laser radar point cloud data by using a DBSCAN algorithm and extracting the outer contour of the obstacle data point by using a fuzzy line segment method.
S7, real-time correction of safety margin
In the subsequent running process of the train, the speed of the train is changed, and different safety margins are set for different speeds of the train, so that the system corrects the safety margins by adopting danger coefficients;
s8 calculating the stroke of the obstacle
Calculating the collision distance between the train and the obstacle by combining S6 and S7;
s9, AEB system braking decision
Preferably, in S4, for the sorted lidar point cloud data, a least square method is used to perform train track basic shape line fitting, X and Y respectively represent a matrix of horizontal and vertical coordinate values of the radar point data, Xk、ykCoordinate values for point k:
X＝[x1，x2，x3，...xk]T，Y＝[y1，y2，y3，...yk]T
and (3) performing train track line fitting by using a least square method, namely using an n-order polynomial expression to express a fitting curve equation, wherein the expression is as follows:
Figure BDA0002190508330000021
expressed in matrix form as: y ═ X0A, wherein:
Figure BDA0002190508330000031
by calculating the coefficient term aiThe matrix a of (a) obtains an expression of a fitted curve.
Preferably, in S5, the curvature is obtained by fitting a curve on the track side according to the following formula:
Figure BDA0002190508330000032
similarly, the curvature K of the other side of the track is calculated2，
The curvature of the track centerline is then:
Figure BDA0002190508330000033
when the track is a straight line, the curvature K of the track center line is 0.
Preferably, in S6, clustering the laser radar point cloud data by using the DBSCAN algorithm, and extracting the outline of the obstacle data point by using the fuzzy line segment method, where the DBSCAN clustering algorithm needs to input in advance an Eps field of a core point for filtering noise and a Min Pts threshold value of the minimum number of points in a neighborhood range of the core point, where the change in the Eps and Min Pts values is as follows:
Eps＝rn-1sin(ΔΦ)/sin(γ-ΔΦ)+3σr
Figure BDA0002190508330000034
in the formula: eps: scanning the radius; min Pts contains the minimum number of points; outputting obstacle point clustering data set m ═ { m ═ m1，m2，m3…mn}；rn-1Is a data point Pn-1Depth value of (d); sigmarIs the measurement error of the laser radar;
Figure BDA0002190508330000035
the angular resolution of the laser radar; gamma is a threshold parameter which determines the size of the maximum distance threshold; n is a radical ofTIs m in prepolymerizationiThe number of middle obstacle points;
Figure BDA0002190508330000036
is a threshold factor.
Preferably, in S7, for the real-time correction of the safety margin, in the subsequent train operation process, the speed of the train is changing, and different safety margins should be set for different speeds of the train, so the safety margin is corrected by using the risk coefficient, and the calculation method of the risk coefficient is as follows:
Figure BDA0002190508330000037
where v is the speed of the vehicle itself, thmimMinimum time interval allowed for driver, d is actual distance, dbrTo brake the safety distance. When the risk coefficient epsilon is more than 0, the train is in a safe state; when the epsilon is more than 0 and less than or equal to 1, the system starts alarming, and the acousto-optic alarming level is higher along with the smaller epsilon value; when the danger coefficient epsilon is less than 0, starting emergency braking; using the risk factor epsilon versus the safety margin d0And correcting, wherein the correction result is as follows:
Figure BDA0002190508330000041
preferably, in S8, referring to fig. 4, the curve braking distance is obtained by obtaining the curve angle θ according to the cosine equation:
Figure BDA0002190508330000042
preferably, in S9, when the obstacle is identified, the system controls braking using the following braking safety distance formula;
Figure BDA0002190508330000043
wherein: v, vrelRespectively the train speed and the relative speed; a is1、a2The maximum braking deceleration of the train and the maximum braking deceleration of the barrier are respectively; t is t1、t2Respectively the driver reaction time and the system delay time; d0The safety margin which needs to be kept with the barrier after the train stops;
and according to the calculated safe distance, when the actual distance between the train and the train is less than or equal to the safe distance measured by the sensor, namely collision risk exists or the train and an obstacle on the track have collision risk, the system sends out early warning and forces the train to start an automatic emergency braking system of the train.
Wherein the giving of the early warning comprises giving an early warning to a driver of the train, including giving an alarm, steering wheel vibration, seat vibration to a cab, and giving an early warning to other trains or pedestrians who are at risk of collision with the train, including an alarm or a sound that can draw attention;
wherein forcing the train to start its automatic emergency braking system comprises slowing the train or stopping the train to achieve the start of the automatic emergency braking system for collision avoidance purposes.
Compared with the prior art, the invention has the following beneficial effects:
1. the system has high real-time performance, can brake the obstacle information in front of the train and on two sides of the train in time according to the working condition of the curve, and has high detection accuracy on the obstacles.
2. The laser radar and the angle laser radar are arranged at the front of the head of the train to acquire the information of the obstacles on the track in front of the advancing route of the train and on the two sides of the track, so that the data extraction of the obstacles is realized, and the automatic emergency braking of the train is finally realized.
3. The angle laser radars are arranged on two sides below the train head and used for detecting obstacles on two sides of the train, can accurately monitor road conditions and obstacle information on two sides of the train, can be used for accurately reconstructing the surrounding environment in real time and determining the driving available area around the train; the accuracy, the practicability, the adaptability, the stability and the real-time performance of the dynamic obstacle prediction tracking are further improved.
4. When the train is in danger of collision, the system can give an alarm and prompt, and can assist the driver to brake, so that the occurrence of collision accidents is avoided, the safety of train driving is effectively improved, and the mental stress of the driver is relieved.
Drawings
FIG. 1 is a flow chart of a decision method of the present invention;
FIG. 2 is a schematic view of the installation position of a front lidar and an angle lidar of the present invention;
FIG. 3 is a schematic view of the recognition scope of the present invention;
FIG. 4 is a schematic diagram of curve curvature calculation according to the present invention.
Detailed Description
The invention is described in detail below with reference to the attached drawing figures:
the invention provides a curve control decision method of a train AEB system, which comprises the following steps:
s1, collecting data by laser radar group
The laser radar group emits different lasers to the surface of a measured object, the laser radars are received by an instrument after being reflected, a time difference is calculated at the same time, and then the angle encoder acquires the vertical angle M and the horizontal angle N of the measured object according to the distance S from the laser radar to the measured point. The radar group comprises a front lidar 1 and an angle lidar 2. The front laser radar 1 is arranged in the center of the middle part of the train head, and the two groups of angle laser radars are arranged on two sides of the lower part of the train head; the specific installation layout is shown in fig. 2.
The coordinate calculation formula of the measured point P (X, Y, Z) is obtained as follows:
S＝0.5CT
x＝ScosMcosN
Y＝ScosMsinN
Z＝SsinM
where C is the speed of light; s is the distance from the laser radar to the measured point; m is the vertical angle of the measured object obtained by the angle encoder; n is the horizontal angle of the measured object obtained by the angle encoder; and T is the time difference between the laser emission and the laser receiving.
S2, converting data space coordinates
The laser radar 1 collects scene data around the track and transmits the collected scene data around the track to the industrial personal computer. And starting the system, initializing the laser radar, and transmitting scene data around the track to an industrial personal computer system through an Ethernet port for processing and use. The coordinate transformation is performed by the following formula:
Figure BDA0002190508330000061
in the formula Xc、Yc、ZcRespectively as original space coordinates; xw、Yw、ZwThe transformed coordinates; r is a rotation matrix and t is a translation vector.
S3, data denoising and filtering:
carrying out filtering smoothing processing on the image by adopting a bilateral filtering method, and carrying out filtering output by the following formula:
Figure BDA0002190508330000062
wherein Ij、IiIs the intensity value of the pixel, so at the place (edge) with large intensity difference, the weight will be reduced, and the filtering effect will be reduced; sigmas、σrIs a smoothing parameter; kiA filter coefficient; xj、XiIs an image coordinate;
in general, bilateral filtering has a similar effect to gaussian filtering in regions where the pixel intensity does not change much, while the gradient can be maintained in regions where the intensity gradient is large, such as the image edge.
And S4, fitting an orbit curve.
And for the laser radar point cloud data after classification, performing train track basic shape line fitting by using a least square method, wherein X and Y respectively represent radar point data horizontal linesMatrix of ordinate values, xk、ykCoordinate values for point k:
X＝[x1，x2，x3，...xk]T，Y＝[y1，y2，y3，...yk]T
the least square method is utilized to carry out train track curve fitting, namely the fitting curve equation is expressed by an n-order polynomial, and the expression is as follows:
Figure BDA0002190508330000071
expressed in matrix form as: y ═ X0A, wherein:
by calculating the coefficient term aiThe matrix a of (a) obtains an expression of a fitted curve.
And S5, calculating the curvature of the curve.
The curvature is obtained by curve fitting one side of the rail according to the following formula:
similarly, the curvature K of the other side of the track is calculated2，
The curvature of the track centerline is then:
Figure BDA0002190508330000074
when the track is a straight line, the curvature K of the track center line is 0.
And S6, identifying the rail obstacle.
And clustering the laser radar point cloud by using a DBSCAN algorithm and extracting the outer contour of the obstacle data point by using a fuzzy line segment method. The DBSCAN clustering algorithm needs to input Eps neighborhood of the noise filtering core point and Min Pts threshold value of the minimum number of points in the neighborhood range of the core point in advance, and the change of the Eps and Min Pts values is as follows:
Eps＝rn-1sin(ΔΦ)/sin(γ-ΔΦ)+3σr
in the formula: eps: scanning the radius; min Pts contains the minimum number of points; outputting obstacle point clustering data set m ═ { m ═ m1，m2，m3…mn}；rn-1Is a data point Pn-1Depth value of (d); sigmarIs the measurement error of the laser radar;
Figure BDA0002190508330000081
the angular resolution of the laser radar; gamma is a threshold parameter which determines the size of the maximum distance threshold; n is a radical ofTIs m in prepolymerizationiThe number of middle obstacle points;
Figure BDA0002190508330000082
is a threshold factor and is obtained empirically.
S7, correcting safety margin in real time; in the subsequent running process of the train, the speed of the train is changed, different safety margins are set for different speeds of the train, so that the safety margins are corrected by adopting a danger coefficient, and the calculation method of the danger coefficient comprises the following steps:
Figure BDA0002190508330000083
where v is the speed of the vehicle itself, thminMinimum time interval allowed for driver, d is actual distance, dbrTo brake the safety distance. When the risk coefficient epsilon is more than 1, the train is in a safe state; when the epsilon is more than 0 and less than or equal to 1, the system starts alarming, and the acousto-optic alarming level is higher along with the smaller epsilon value; and when the danger coefficient epsilon is less than 0, starting emergency braking. Using the risk factor epsilon versus the safety margin d0And correcting, wherein the correction result is as follows:
Figure BDA0002190508330000084
and according to the calculated safe distance, when the actual distance between the train and the train is less than or equal to the safe distance measured by the sensor, namely collision risk exists, the system sends out early warning and forces the train to start an automatic emergency braking system of the train.
S8And calculating the stroke of the obstacle.
Calculating the curve distance between the barrier target and the train; referring to fig. 4, the curve angle θ is obtained according to the cosine formula, and the curve braking distance is obtained as follows:
Figure BDA0002190508330000085
S9and performing control decision-making after obstacle feature comparison according to three-dimensional data identified by the radar cloud point data, namely identifying static and dynamic obstacles such as facilities and constructors on the track, and establishing a linear Kalman filtering tracker according to the point cloud features to perform target tracking, so that the decision-making can be effectively controlled. The kalman filter and predictive estimation equations may be derived as follows:
the filter estimation equation is:
X(k|k)＝AX(k|k-1)+Kk[Zk-HAX(k|k-1)]
the filter gain equation is:
Kk＝Plk-1HT[HPlkHT+R(k)]-1
in the formula:
Plk＝APk-1AT+Q(k-1)
for the ideal single target tracking, the position of the target identified in the current data frame can be used as a measured value, and the measured value and a predicted value obtained by the tracker by using the state at the previous moment are used for updating the state vector of the tracker, the covariance of the estimation error and other results. And predicting the state of the target in the next frame according to the updated result. The essence is to reconstruct the state vector of the system from the measured values, and eliminate random and unstable interference in a mode of 'prediction-actual measurement-correction' sequential recursion and frame-by-frame downward continuation confirmation, thereby providing more accurate information. For the actual tracking of multiple targets, the target object identified in the current data frame and the object in the prediction tracker need to be associated. The target association is to perform feature matching, and regard a target with a high matching degree as the appearance of the same target at different times. Therefore, the same target can be tracked continuously.
When an obstacle is identified, the system controls braking using the following brake safety distance formula.
Figure BDA0002190508330000091
Wherein: v, vrelRespectively the train speed and the relative speed; a is1、a2The maximum braking deceleration of the train and the maximum braking deceleration of the barrier are respectively; t is t1、t2Respectively the driver reaction time and the system delay time; d0A safety margin with obstacles is required after the train stops.
And according to the calculated safe distance, when the actual distance between the train and the train is less than or equal to the safe distance measured by the sensor, namely collision risk exists or the train and pedestrians on the track exist, the system sends out early warning and forces the train to start an automatic emergency braking system of the train.
Wherein, send out the early warning include to the driver of train sends out the early warning, for example send out warning, steering wheel vibrations, seat vibrations in the driver's cabin in-car early warning, and to with there are collision risk's other barriers to send out the early warning, like the sound that the warning perhaps can arouse attention etc..
Wherein forcing the train to start its automatic emergency braking system comprises slowing the train or stopping the train to achieve the start of the automatic emergency braking system for collision avoidance purposes.
The invention utilizes the laser radar group to accurately reconstruct the surrounding environment in real time and determine the possibility of collision of the running area around the train; through angle laser radar, preceding laser radar data fusion, make the vehicle, personnel's detection, it is more accurate to trail, and can real-time supervision bend operating mode under, road conditions information and the barrier information of place ahead and both sides when the train is high-speed to go, judge the safe state that the train went, when the train has collision danger, the system can report to the police and indicate, and the system can assist the driver to brake, avoid the emergence of collision accident, effectively improve the security of train driving and alleviate driver's mental stress.

Claims (7)

1. A curve control decision method of an AEB system of a train is characterized by comprising the following steps:
s1, collecting data by laser radar group
Monitoring road condition information and barriers in front of and on two sides of the train by adopting a front laser radar set, and acquiring the road condition information;
the laser radar group comprises a front laser radar (1) and a group of angle laser radars (2);
the laser radars (1) are arranged in the center of the middle of the train head, and the group of angle laser radars (2) are arranged on two sides of the lower part of the train head;
s2, converting data space coordinates
Carrying out coordinate conversion of different coordinate systems on the collected space data and attribute data of the track and objects around the track to obtain point cloud data of the laser radar;
s3, denoising and filtering processing
Denoising and filtering the laser radar point cloud data;
s4, fitting track curve
Acquiring a basic shape of the track through the processed laser radar point cloud data;
s5, curve curvature calculation
Calculating the curvature of the track through the basic shape of the track;
s6 track obstacle recognition
Clustering laser radar point cloud data by using a DBSCAN algorithm, and extracting the outer contour of the obstacle data point by using a fuzzy line segment method;
s7, real-time correction of safety margin
In the subsequent train running process, the train speed is in change, different safety margins are set for different train speeds, and the safety margins are corrected by adopting danger coefficients;
s8 calculating the stroke of the obstacle
Calculating the collision distance between the train and the obstacle by combining S6 and S7;
s9, AEB system braking decision
2. A train AEB system curve control decision method as claimed in claim 1, characterized in that: in S4, for the laser radar point cloud data after being processed and classified, the least square method is utilized to carry out train track basic shape line fitting, X and Y respectively represent matrixes of horizontal and vertical coordinate values of the radar point cloud data, and Xk、ykCoordinate values for point k:
X＝[x1，x2，x3，...xk]T,Y＝[y1，y2，y3，...yk]T
and (3) fitting the basic shape line of the train track by using a least square method, namely the fitting curve equation is expressed by an nth-order polynomial, wherein the expression is as follows:
expressed in matrix form as: y ═ X0A, wherein:
Figure FDA0002190508320000022
by calculating the coefficient term aiThe matrix a of (a) obtains an expression of a fitted curve.
3. A train AEB system curve control decision method as claimed in claim 1, characterized in that: in S5, the train-track-side curvature is calculated as:
Figure FDA0002190508320000023
similarly, the curvature K of the other side of the track is calculated2,
The curvature of the track centerline is then:
Figure FDA0002190508320000024
when the track is a straight line, the curvature K of the track center line is 0.
4. A train AEB system curve control decision method as claimed in claim 1, characterized in that: in S6, clustering the laser radar point cloud data by using a DBSCAN algorithm, and extracting an outline of an obstacle data point by using a fuzzy line segment method, where the DBSCAN clustering algorithm needs to input an Eps field of a core point for noise filtering and a Min Pts threshold value of the minimum number of points in a neighborhood range of the core point in advance, and a change in the Eps and Min Pts values is as follows:
Eps＝rn-1sin(ΔΦ)/sin(γ-ΔΦ)+3σr
Figure FDA0002190508320000031
in the formula: eps is the scanning radius; min Pts contains the minimum number of points; outputting obstacle point clustering data set m ═ { m ═ m1,m2,m3…mn}；rn-1Is a data point Pn-1Depth value of (d); sigmarIs the measurement error of the laser radar;
Figure FDA0002190508320000032
the angular resolution of the laser radar; gamma is a threshold parameter which determines the size of the maximum distance threshold; n is a radical ofTIs m in prepolymerizationiThe number of middle obstacle points;
Figure FDA0002190508320000033
is a threshold factor.
5. A train AEB system curve control decision method as claimed in claim 1, characterized in that: in S7, for the real-time correction of the safety margin, in the subsequent train operation process, the speed is in change, and different safety margins should be set for different speeds, so the safety margin is corrected by using the risk coefficient, and the calculation method of the risk coefficient is:
Figure FDA0002190508320000034
where v is the speed of the vehicle itself, thminMinimum time interval allowed for driver, d is actual distance, dbrTo brake the safety distance. When the risk coefficient epsilon is more than 1, the train is in a safe state; when the epsilon is more than 0 and less than or equal to 1, the system starts alarming, and the acousto-optic alarming level is higher along with the smaller epsilon value; when the danger coefficient epsilon is less than 0, starting emergency braking; using the risk factor epsilon versus the safety margin d0And correcting, wherein the correction result is as follows:
Figure FDA0002190508320000035
6. a train AEB system curve control decision method as claimed in claim 1, characterized in that: in S8, calculating a braking distance;
and (3) obtaining a curve angle theta according to a cosine formula, and obtaining a curve braking distance as follows:
7. a train AEB system curve control decision method as claimed in claim 1, characterized in that: in S9, when an obstacle is identified, the system controls braking using the following braking safety distance formula;
Figure FDA0002190508320000041
wherein: v, vrelRespectively the train speed and the relative speed; a is1、a2The maximum braking deceleration of the train and the maximum braking deceleration of the barrier are respectively; t is t1、t2Respectively the driver reaction time and the system delay time; d0The safety margin which needs to be kept with the barrier after the train stops;
and according to the calculated safe distance, when the actual distance between the train and the train is less than or equal to the safe distance measured by the sensor, namely collision risk exists or the train and an obstacle on the track have collision risk, the system sends out early warning and forces the train to start an automatic emergency braking system of the train.
Wherein the giving of the early warning comprises giving an early warning to a driver of the train, including giving an alarm, steering wheel vibration, seat vibration to a cab, and giving an early warning to other trains or pedestrians who are at risk of collision with the train, including an alarm or a sound that can draw attention;
wherein forcing the train to start its automatic emergency braking system comprises slowing the train or stopping the train to achieve the start of the automatic emergency braking system for collision avoidance purposes.
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Cited By (5)

* Cited by examiner, † Cited by third party
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CN111897338A (en) * 2020-08-04 2020-11-06 安徽国钜工程机械科技有限公司 Sensing system for shield construction method automatic driving horizontal transport locomotive
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CN114396892A (en) * 2021-12-02 2022-04-26 重庆交通大学 Method for measuring curvature of curve track of track traffic
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CN111897338A (en) * 2020-08-04 2020-11-06 安徽国钜工程机械科技有限公司 Sensing system for shield construction method automatic driving horizontal transport locomotive
CN112230245A (en) * 2020-09-21 2021-01-15 卡斯柯信号有限公司 System and method for detecting active obstacles of train in tunnel based on laser radar
CN112230245B (en) * 2020-09-21 2022-06-28 卡斯柯信号有限公司 System and method for detecting active obstacles of train in tunnel based on laser radar
CN112562419A (en) * 2020-11-03 2021-03-26 南京航空航天大学 Off-line multi-target tracking-based weather avoidance zone setting method
CN112562419B (en) * 2020-11-03 2022-04-08 南京航空航天大学 Off-line multi-target tracking-based weather avoidance zone setting method
CN114396892A (en) * 2021-12-02 2022-04-26 重庆交通大学 Method for measuring curvature of curve track of track traffic
CN114396892B (en) * 2021-12-02 2023-08-25 重庆交通大学 Track curvature measuring method for track traffic curve
WO2023197535A1 (en) * 2022-04-13 2023-10-19 中国矿业大学 Slope and curve passage method for unmanned rail electric locomotive in deep limited space

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