CN112800939A - Internet-of-wire control chassis vehicle comprehensive motion prediction method - Google Patents

Internet-of-wire control chassis vehicle comprehensive motion prediction method Download PDF

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CN112800939A
CN112800939A CN202110102245.5A CN202110102245A CN112800939A CN 112800939 A CN112800939 A CN 112800939A CN 202110102245 A CN202110102245 A CN 202110102245A CN 112800939 A CN112800939 A CN 112800939A
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surrounding
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黄云丰
赵万忠
邹松春
王春燕
章波
胡犇
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a comprehensive motion prediction method for a network-connected-wire chassis vehicle, which comprises the following steps: acquiring current state information of surrounding vehicles and information of surrounding environment; establishing a driving behavior set for all surrounding vehicles; predicting the intention of drivers of surrounding vehicles by adopting a game theory method according to the surrounding environment information and the driving behavior set to obtain the prediction probability of the driving intention of the surrounding vehicles; carrying out behavior recognition on surrounding vehicles to obtain the behavior recognition probability of the surrounding vehicles; respectively obtaining the intention prediction track of the driver of the surrounding vehicle and the behavior recognition track of the surrounding vehicle by adopting a polynomial method; and (3) fusing the two tracks by a Nash-optimization method by taking the track error as an objective function to obtain a comprehensive predicted track of the motion of the surrounding vehicle. The method solves the problem that the prediction result is inaccurate due to the fact that only the intention of a driver or the state of the vehicle is considered in the prior art.

Description

Internet-of-wire control chassis vehicle comprehensive motion prediction method
Technical Field
The invention belongs to the technical field of intelligent vehicle decision making, and particularly relates to a comprehensive motion prediction method for an internet control chassis vehicle.
Background
The intelligent networked automobile is a research hotspot nowadays, and key technologies of the intelligent networked automobile mainly comprise: the method comprises three parts of motion prediction of surrounding vehicles, intelligent networking automobile decision and vehicle control. Therefore, in order to realize unmanned driving, it is necessary to be able to understand surrounding traffic well and predict the future movement behavior of the surrounding vehicle.
Currently, methods of predicting the motion of a surrounding vehicle mainly include prediction based on the intention of a driver and prediction based on the state of the vehicle. The driver's intention represents the driver's preference before taking driving action, the result of which has a direct influence on the vehicle movement, and can be predicted mainly using vehicle internal information such as accelerator pedal pressure, brake pedal pressure, steering wheel angle, etc., or vehicle external information such as vehicle speed, lateral displacement, distance, etc. And the prediction based on the vehicle state predicts the future motion of the vehicle by the current state of the vehicle. The movement of the vehicle is controlled on the one hand by the driver and on the other hand is dependent on the state of the vehicle since the change in the movement of the vehicle is continuous, i.e. the future movement of the vehicle is determined by the intention of the driver together with the state of the vehicle. However, although many researchers have conducted intensive research on the driver intention prediction and the vehicle behavior prediction, the research focuses on either the intention of the driver or the behavior state of the vehicle, and few researchers comprehensively consider both, which may result in inaccurate prediction results and fail to satisfy both the driving purpose of the driver and the vehicle motion continuity.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for predicting vehicle comprehensive movement by internet-of-wire control chassis, which integrates driver intention prediction and vehicle behavior recognition together to solve the problem of inaccurate prediction result caused by considering only driver intention or vehicle state in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses a comprehensive motion prediction method of a network-connected-wire chassis vehicle, which comprises the following steps:
step 1: acquiring current state information of surrounding vehicles and information of surrounding environment;
step 2: the method comprises the steps that a driving behavior set M ═ LCL LK LCR } is established for all surrounding vehicles, LCL represents that the vehicles change lanes on the left, LK represents that the vehicles keep driving in lanes, and LCR represents that the vehicles change lanes on the right;
and step 3: predicting the intention of the drivers of the surrounding vehicles by adopting a game theory method according to the surrounding environment information and the driving behavior set to obtain the prediction probability P of the driving intention of the surrounding vehiclesint；
And 4, step 4: according to the current state information and the driving behavior set of the surrounding vehicles, performing behavior recognition on the surrounding vehicles to obtain surrounding vehicle behavior recognition probability Prec；
And 5: predicting probability P by the obtained surrounding vehicle driving intentionintAnd surrounding vehicle behavior recognition probability PrecRespectively obtaining the predicted trajectories T of the driver intentions of the surrounding vehicles by adopting a polynomial methodint(T) and surrounding vehicle behavior recognition trajectory Trec(t)；
Step 6: and (3) fusing the two tracks by a Nash-optimization method by taking the track error as an objective function to obtain a comprehensive predicted track T (t) of the motion of the surrounding vehicle.
Preferably, the current state information of the surrounding vehicle in the step 1 includes a vehicle position and a vehicle speed; the surrounding environment information includes a lane center line position, a lane width, and a lane speed limit.
Preferably, the step 3 specifically includes:
3.1, establishing a revenue function, specifically:
Figure BDA0002916403740000021
wherein TTC is collision time, TH is headway, vlimIs a vehicleThe maximum speed limit of a vehicle target lane, v is the speed of a target vehicle, and d is the distance that the vehicle can travel in front;
TTC, TH and d are specifically as follows:
Figure BDA0002916403740000022
Figure BDA0002916403740000023
Figure BDA0002916403740000024
in the formula, xfIs the position of the leading vehicle, x is the target vehicle position, DmIs the visible distance of the human eye, vfV is the speed of the preceding vehicle and the speed of the target vehicle, respectively;
3.2 calculate the income corresponding to each driving intention:
Figure BDA0002916403740000025
in the formula, miIs the corresponding driving behavior;
3.3 calculating the driving intention prediction probability:
Figure BDA0002916403740000026
in the formula (I), the compound is shown in the specification,
Figure BDA0002916403740000027
the intention probabilities of the left lane change, the lane keeping and the right lane change are respectively;
the probabilities are specifically:
Figure BDA0002916403740000031
preferably, the step 4 adopts an interactive multi-model algorithm to identify the motion of the vehicle, and specifically comprises the following steps:
4.1, establishing a lane model, wherein the lane model obeys normal distribution:
Figure BDA0002916403740000032
wherein, k represents the k-th lane,
Figure BDA0002916403740000033
respectively, the mean and variance of normal distribution;
4.2 model transfer matrix pi of vehicle at time tt,kjIs a 3-order matrix, specifically:
Figure BDA0002916403740000034
Figure BDA0002916403740000035
in the formula, subscripts k, j are lane serial numbers,
Figure BDA0002916403740000036
is the lateral speed of the vehicle and,
Figure BDA0002916403740000037
for the initial model transfer matrix, phi is the Gaussian cumulative distribution function, rhokj,
Figure BDA0002916403740000038
Respectively the mean value and the variance of the transfer criterion;
phi is specifically:
Figure BDA0002916403740000039
4.3 probability of behavior recognition by time t-1
Figure BDA00029164037400000310
And the lateral position y of the vehicle at time ttThe recognition probability P at the time t is obtained through updatingrec。
Preferably, in the step 5, a cubic polynomial is adopted for trajectory planning, specifically:
5.1 trajectory planning equations and boundary conditions are:
Figure BDA00029164037400000311
Figure BDA0002916403740000041
in the formula (x)t,yt) As the t-time coordinate of the surrounding vehicle, (x)f,yf) Is the coordinates of the target;
5.2 obtaining the predicted track T of the driver intention of the surrounding vehicleint(T) and vehicle behavior recognition trajectory Trec(t)：
Figure BDA0002916403740000042
ηint(t+i)＝(xint,t+i,yint,t+i)
And
Figure BDA0002916403740000043
ηrec(t+i)＝(xrec,t+i,yrec,t+i)
In the formula, TpTo predict the time domain, TsIs the sampling time, ηint,ηrecIdentifying trace points on the trace for driver intent predicted trace and vehicle behavior, respectively, (x)int,yint),(xrec,yrec) Respectively as a track point etaintAnd ηrecThe coordinates of (a).
Preferably, the step 6 specifically includes:
6.1 obtaining a comprehensive predicted track:
Figure BDA0002916403740000044
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
in the formula, λ1,λ2Is a weight coefficient, and eta is a track point on the comprehensive prediction track;
6.2 establishing an objective function, specifically:
Figure BDA0002916403740000045
Figure BDA0002916403740000051
6.3 Nash-optimization method for lambda1,λ2Optimizing:
Figure BDA0002916403740000052
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
And
Figure BDA0002916403740000053
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
6.4 obtaining an optimized comprehensive predicted track:
Figure BDA0002916403740000054
Figure BDA0002916403740000055
the invention has the beneficial effects that:
the invention fuses the driver intention prediction and the vehicle behavior recognition through a Nash-optimization method to obtain the comprehensive prediction result of the surrounding vehicle motion, solves the problem that the prediction result cannot simultaneously meet the driver purpose and the vehicle state continuity only by considering the driver intention or the current vehicle state in the current research, and improves the accuracy and the rationality of the prediction of the surrounding vehicle motion.
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FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the trajectory fusion of the present invention.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 1, the method for predicting the comprehensive motion of the online control chassis vehicle of the invention comprises the following steps:
step 1: acquiring current state information of surrounding vehicles and information of surrounding environment;
the current state information of the surrounding vehicles in the step 1 comprises vehicle positions, speeds and the like; the surrounding environment information includes information such as a lane center line position, a lane width, a lane speed limit, and the like.
Step 2: the method comprises the steps that a driving behavior set M ═ LCLLKLCR is established for all surrounding vehicles, LCL represents that the vehicles change lanes on the left, LK represents that the vehicles keep driving in lanes, and LCR represents that the vehicles change lanes on the right;
and step 3: according to the surrounding environment information and the driving behavior set, the intention of drivers of surrounding vehicles is predicted by adopting a game theory method, and the driving intention of the surrounding vehicles is obtainedGraph prediction probability Pint；
3.1, establishing a revenue function, specifically:
Figure BDA0002916403740000061
wherein TTC is collision time, TH is headway, vlimThe maximum speed limit of a vehicle target lane is defined, v is the target vehicle speed, and d is the distance that the vehicle can travel in front of the vehicle;
TTC, TH and d are specifically as follows:
Figure BDA0002916403740000068
Figure BDA0002916403740000062
Figure BDA0002916403740000063
in the formula, xfIs the position of the leading vehicle, x is the target vehicle position, DmIs the visible distance of the human eye, vfV is the speed of the preceding vehicle and the speed of the target vehicle, respectively;
3.2 calculate the income corresponding to each driving intention:
Figure BDA0002916403740000064
in the formula, miIs the corresponding driving behavior;
3.3 calculating the driving intention prediction probability:
Figure BDA0002916403740000065
in the formula (I), the compound is shown in the specification,
Figure BDA0002916403740000066
the intention probabilities of the left lane change, the lane keeping and the right lane change are respectively;
the probabilities are specifically:
Figure BDA0002916403740000067
and 4, step 4: according to the current state information and the driving behavior set of the surrounding vehicles, performing behavior recognition on the surrounding vehicles to obtain surrounding vehicle behavior recognition probability Prec；
The method adopts an interactive multi-model algorithm to identify the motion of the vehicle, and specifically comprises the following steps:
4.1, establishing a lane model, wherein the lane model obeys normal distribution:
Figure BDA0002916403740000071
wherein, k represents the k-th lane,
Figure BDA0002916403740000072
respectively, the mean and variance of normal distribution;
4.2 model transfer matrix pi of vehicle at time tt,kjIs a 3-order matrix, specifically:
Figure BDA0002916403740000073
Figure BDA0002916403740000074
in the formula, subscripts k, j are lane serial numbers,
Figure BDA0002916403740000075
is the lateral speed of the vehicle and,
Figure BDA0002916403740000076
for the initial model transfer matrix, phi is the Gaussian cumulative distribution function, rhokj,
Figure BDA0002916403740000077
Respectively the mean value and the variance of the transfer criterion;
phi is specifically:
Figure BDA0002916403740000078
4.3 probability of behavior recognition by time t-1
Figure BDA0002916403740000079
And the lateral position y of the vehicle at time ttThe recognition probability P at the time t is obtained through updatingrec。
And 5: predicting probability P by the obtained surrounding vehicle driving intentionintAnd surrounding vehicle behavior recognition probability PrecRespectively obtaining the predicted trajectories T of the driver intentions of the surrounding vehicles by adopting a polynomial methodint(T) and surrounding vehicle behavior recognition trajectory Trec(t)；
Adopting a cubic polynomial to plan the track, specifically:
5.1 trajectory planning equations and boundary conditions are:
Figure BDA00029164037400000710
Figure BDA0002916403740000081
in the formula (x)t,yt) As the t-time coordinate of the surrounding vehicle, (x)f,yf) Is the coordinates of the target;
5.2 obtaining the predicted track T of the driver intention of the surrounding vehicleint(T) and vehicle behavior recognition trajectory Trec(t)：
Figure BDA0002916403740000082
ηint(t+i)＝(xint,t+i,yint,t+i)
And
Figure BDA0002916403740000083
ηrec(t+i)＝(xrec,t+i,yrec,t+i)
In the formula, TpTo predict the time domain, TsIs the sampling time, ηint,ηrecIdentifying trace points on the trace for driver intent predicted trace and vehicle behavior, respectively, (x)int,yint),(xrec,yrec) Respectively as a track point etaintAnd ηrecThe coordinates of (a).
Step 6: fusing the two tracks by a Nash-optimization method by taking the track error as a target function to obtain a comprehensive predicted track T (t) of the motion of the surrounding vehicle; as shown with reference to FIG. 2;
6.1 obtaining a comprehensive predicted track:
Figure BDA0002916403740000084
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
in the formula, λ1,λ2Is a weight coefficient, and eta is a track point on the comprehensive prediction track;
6.2 establishing an objective function, specifically:
Figure BDA0002916403740000091
Figure BDA0002916403740000092
6.3 Nash-optimization method for lambda1,λ2Optimizing:
Figure BDA0002916403740000093
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
And
Figure BDA0002916403740000094
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
6.4 obtaining an optimized comprehensive predicted track:
Figure BDA0002916403740000095
Figure BDA0002916403740000096
while the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (5)

1. A comprehensive motion prediction method for a network-controlled chassis vehicle is characterized by comprising the following steps:
step 1: acquiring current state information of surrounding vehicles and information of surrounding environment;
step 2: the method comprises the steps that a driving behavior set M ═ LCL LK LCR } is established for all surrounding vehicles, LCL represents that the vehicles change lanes on the left, LK represents that the vehicles keep driving in lanes, and LCR represents that the vehicles change lanes on the right;
and step 3: predicting the intention of drivers of surrounding vehicles by adopting a game theory method according to the surrounding environment information and the driving behavior set to obtain the prediction probability of the driving intention of the surrounding vehicles;
and 4, step 4: according to the current state information and the driving behavior set of the surrounding vehicles, performing behavior recognition on the surrounding vehicles to obtain the behavior recognition probability of the surrounding vehicles;
and 5: respectively obtaining the intention prediction track of the driver of the surrounding vehicle and the behavior recognition track of the surrounding vehicle by adopting a polynomial method according to the obtained driving intention prediction probability and the behavior recognition probability of the surrounding vehicle;
step 6: and (3) fusing the two tracks by a Nash-optimization method by taking the track error as an objective function to obtain a comprehensive predicted track of the motion of the surrounding vehicle.
2. The method for predicting the comprehensive motion of the internet-controlled chassis vehicle according to claim 1, wherein the step 3 specifically comprises:
3.1, establishing a revenue function, specifically:
Figure FDA0002916403730000011
wherein TTC is collision time, TH is headway, vlimThe maximum speed limit of a vehicle target lane is defined, v is the target vehicle speed, and d is the distance that the vehicle can travel in front of the vehicle;
TTC, TH and d are specifically as follows:
Figure FDA0002916403730000012
Figure FDA0002916403730000013
Figure FDA0002916403730000014
in the formula, xfIs the position of the leading vehicle, x is the target vehicle position, DmIs the visible distance of the human eye, vfV is the speed of the preceding vehicle and the speed of the target vehicle, respectively;
3.2 calculate the income corresponding to each driving intention:
Figure FDA0002916403730000015
in the formula, miIs the corresponding driving behavior;
3.3 calculating the predicted probability P of Driving intentionint：
Figure FDA0002916403730000021
In the formula (I), the compound is shown in the specification,
Figure FDA0002916403730000022
the intention probabilities of the left lane change, the lane keeping and the right lane change are respectively;
the probabilities are specifically:
Figure FDA0002916403730000023
3. the method for predicting the comprehensive motion of the online control chassis vehicle according to claim 1, wherein in the step 4, a vehicle is subjected to motion recognition by adopting an interactive multi-model algorithm, and the method specifically comprises the following steps:
4.1, establishing a lane model, wherein the lane model obeys normal distribution:
Figure FDA0002916403730000024
wherein, k represents the k-th lane,
Figure FDA0002916403730000025
respectively, the mean and variance of normal distribution;
4.2 model transfer matrix pi of vehicle at time tt,kjIs a 3-order matrix, specifically:
Figure FDA0002916403730000026
Figure FDA0002916403730000027
in the formula, subscripts k, j are lane serial numbers,
Figure FDA0002916403730000028
is the lateral speed of the vehicle and,
Figure FDA0002916403730000029
for the initial model transfer matrix, phi is the Gaussian cumulative distribution function, rhokj,
Figure FDA00029164037300000210
Respectively the mean value and the variance of the transfer criterion;
phi is specifically:
Figure FDA0002916403730000031
4.3 probability of behavior recognition by time t-1
Figure FDA0002916403730000032
And the lateral position y of the vehicle at time ttThe recognition probability P at the time t is obtained through updatingrec。
4. The method for predicting the comprehensive motion of the online control chassis vehicle according to claim 1, wherein in the step 5, a cubic polynomial is adopted for trajectory planning, and specifically:
5.1 trajectory planning equations and boundary conditions are:
Figure FDA0002916403730000033
Figure FDA0002916403730000034
in the formula (x)t,yt) As the t-time coordinate of the surrounding vehicle, (x)f,yf) Is the coordinates of the target;
5.2 obtaining the predicted track T of the driver intention of the surrounding vehicleint(T) and vehicle behavior recognition trajectory Trec(t)：
Figure FDA0002916403730000035
In the formula, TpTo predict the time domain, TsIs the sampling time, ηint,ηrecIdentifying trace points on the trace for driver intent predicted trace and vehicle behavior, respectively, (x)int,yint),(xrec,yrec) Respectively as a track point etaintAnd ηrecThe coordinates of (a).
5. The method for predicting the comprehensive motion of the internet-controlled chassis vehicle according to claim 1, wherein the step 6 specifically comprises:
6.1 obtaining a comprehensive predicted track:
T(t)＝[η(t+1),η(t+2),…,η(t+Tp/Ts)]
η(t+i)＝λ1ηint(t+i)+λ2ηrec(t+i)
in the formula, λ1,λ2Is a weight coefficient, and eta is a track point on the comprehensive prediction track;
6.2 establishing an objective function, specifically:
Figure FDA0002916403730000041
Figure FDA0002916403730000042
6.3 Nash-optimization method for lambda1,λ2Optimizing:
Figure FDA0002916403730000043
6.4 obtaining the optimized comprehensive predicted track T (t):
Figure FDA0002916403730000044
Figure FDA0002916403730000045
CN202110102245.5A 2021-01-26 2021-01-26 Internet-of-wire control chassis vehicle comprehensive motion prediction method Pending CN112800939A (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200172093A1 (en) * 2018-11-29 2020-06-04 291, Daehak-ro Lane-based probabilistic motion prediction of surrounding vehicles and predictive longitudinal control method and apparatus
CN111267846A (en) * 2020-02-11 2020-06-12 南京航空航天大学 Game theory-based peripheral vehicle interaction behavior prediction method
CN112116100A (en) * 2020-09-08 2020-12-22 南京航空航天大学 Game theory decision method considering driver types

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200172093A1 (en) * 2018-11-29 2020-06-04 291, Daehak-ro Lane-based probabilistic motion prediction of surrounding vehicles and predictive longitudinal control method and apparatus
CN111267846A (en) * 2020-02-11 2020-06-12 南京航空航天大学 Game theory-based peripheral vehicle interaction behavior prediction method
CN112116100A (en) * 2020-09-08 2020-12-22 南京航空航天大学 Game theory decision method considering driver types

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