CN110400490A - Trajectory predictions method and apparatus - Google Patents
Trajectory predictions method and apparatus Download PDFInfo
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- CN110400490A CN110400490A CN201910729763.2A CN201910729763A CN110400490A CN 110400490 A CN110400490 A CN 110400490A CN 201910729763 A CN201910729763 A CN 201910729763A CN 110400490 A CN110400490 A CN 110400490A
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Abstract
The embodiment of the invention discloses a kind of trajectory predictions method and apparatus;The available cartographic information of the embodiment of the present invention, the driving information of target vehicle, lane prediction model, motion prediction model;Driving information based on cartographic information and target vehicle determines the driving feature of the association lane and target vehicle of target vehicle relative to association lane;Using lane prediction model, according to the driving feature target lane that prediction target vehicle will drive into association lane;Using motion prediction model, according to motion information of the driving feature prediction target vehicle on target lane;Based on driving information, target lane and motion information, the motion profile of target vehicle is calculated.The motion information of the target lane and vehicle of vehicle on target lane is predicted by different prediction models in embodiments of the present invention, to carry out trajectory calculation.The program can promote the precision of prediction of trajectory predictions as a result,.
Description
Technical field
The present invention relates to computer fields, and in particular to a kind of trajectory predictions method and apparatus.
Background technique
It is universal with Internet of Things, vehicle can by the auxiliary of computer technology come programme path, evade collision accident,
Driving efficiency is improved simultaneously.It, can be with for example, car-mounted computer be in addition to that can tell the static-obstacle thing around vehicle
The driving trace of other vehicles around prediction, and judge surrounding vehicles whether to itself vehicle according to the driving trace of surrounding vehicles
Constitute potential security threat.
However, the prediction accuracy of the method for trajectory predictions is lower at present.
Summary of the invention
The embodiment of the present invention provides a kind of trajectory predictions method and apparatus, can promote the precision of prediction of trajectory predictions.
The embodiment of the present invention provides a kind of trajectory predictions method, comprising:
Obtain cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model, wherein the vehicle
Road prediction model, motion prediction model are formed by training sample training;
Driving information based on the cartographic information and target vehicle determines association lane and the mesh of target vehicle
Mark driving feature of the vehicle relative to the association lane;
Using lane prediction model, predict that target vehicle will drive into the association lane according to the driving feature
Target lane;
Using motion prediction model, believed according to movement of the driving feature prediction target vehicle on the target lane
Breath;
Based on the driving information, target lane and motion information, the motion profile of target vehicle is calculated.
In some embodiments, the motion prediction model includes the first motion prediction model, the second motion prediction model,
The motion information on the target lane includes the target speed information in predetermined time relative to the target lane
With target range information, the target lane includes target lane center;
It is described to use motion prediction model, according to fortune of the driving feature prediction target vehicle on the target lane
Dynamic information, comprising:
Using the first motion prediction model, predict target vehicle in predetermined time relative to described according to the driving feature
The target speed information in target lane;
Using the second motion prediction model, predict target vehicle in predetermined time relative to described according to the driving feature
The target range information of target lane center;
It is described to be based on the driving information, target lane and motion information, calculate the motion profile of target vehicle, comprising:
Based on the driving information, target lane, target speed information and target range information, the fortune of target vehicle is calculated
Dynamic rail mark.
In some embodiments, the driving information includes initial position message, initial velocity information;
Based on the driving information, target lane, target speed information and target range information, the fortune of target vehicle is calculated
Dynamic rail mark, comprising:
According to the target lane and target range information, determine target vehicle in the target position information of predetermined time;
Target carriage is calculated based on the initial position message, target position information, initial velocity information, target speed information
Motion profile.
In some embodiments, described to use lane prediction model, according to the driving feature in the association lane
The target lane that prediction target vehicle will drive into, comprising:
Using lane prediction model, the association vehicle is driven into predetermined time according to the driving feature calculation target vehicle
Road drives into probability;
Probability is driven into according to described, target lane is determined from the association lane.
In some embodiments, the lane prediction model includes multiple lane prediction submodels, predicts mould using lane
Type drives into the association lane in predetermined time according to the driving feature calculation target vehicle and drives into probability, comprising:
Submodel is predicted using lane, the association is driven into predetermined time according to the driving feature calculation target vehicle
Sub- probability is driven into lane;
It drives into sub- probability to described and is weighted summation, obtain target vehicle in predetermined time and drive into the association lane
Drive into probability.
In some embodiments, cartographic information, the driving information of target vehicle, lane prediction model, motion prediction are obtained
Model, before, further includes:
Obtain training sample and initial predicted model, the corresponding multiple sample marks of each of described training sample;
Rejecting processing is carried out to the training sample corresponding sample mark, obtains lane training sample and training
Sample;
Initial predicted model is trained using the lane training sample, obtains lane prediction model;
Initial predicted model is trained using the training sample, obtains motion prediction model.
In some embodiments, the sample mark includes lane mark, distance mark, speed mark, the movement instruction
Practicing sample includes apart from training sample and speed training sample;
Rejecting processing is carried out to the training sample corresponding sample mark, obtains lane training sample and training
Sample, comprising:
The distance mark and speed mark for abandoning the training sample are only remained the lane training sample of lane mark
This;
It abandons the lane mark of the training sample and apart from mark, is only remained the speed training sample of speed mark
This;
The lane mark and speed mark for abandoning the training sample are only remained the distance training sample of distance mark
This.
In some embodiments, the training sample includes speed training sample, apart from training sample, the speed
Training sample includes speed training subsample, described to include distance training subsample, the motion prediction mould apart from training sample
Type includes speed prediction model, range prediction model;
Initial predicted model is trained using the training sample, obtains motion prediction model, comprising:
Initial predicted model is trained using the speed training subsample, obtains speed prediction model;
Initial predicted model is trained using distance training subsample, obtains range prediction model.
In some embodiments, the driving information based on the cartographic information and target vehicle, determines target vehicle
It is associated with the driving feature of lane and target vehicle relative to the association lane, comprising:
According to the cartographic information and the driving information of target vehicle, the current lane of target vehicle is determined;
Topological analysis is carried out according to current lane of the cartographic information to target vehicle, is obtained and the current lane phase
Associated association lane;
Driving information based on the association lane and target vehicle calculates target vehicle relative to the association lane
Driving feature.
The embodiment of the present invention also provides a kind of trajectory predictions device, comprising:
Acquiring unit, for obtaining the driving information, lane prediction model, motion prediction mould of cartographic information, target vehicle
Type, wherein the lane prediction model, motion prediction model are formed by training sample training;
Associative cell determines the pass of target vehicle for the driving information based on the cartographic information and target vehicle
Join the driving feature of lane and target vehicle relative to the association lane;
Lane unit predicts mesh in the association lane according to the driving feature for using lane prediction model
The target lane that mark vehicle will drive into;
Moving cell predicts target vehicle in the target for using motion prediction model according to the driving feature
Motion information on lane;
Trajectory unit calculates the movement rail of target vehicle for being based on the driving information, target lane and motion information
Mark.
The available cartographic information of the embodiment of the present invention, the driving information of target vehicle, lane prediction model, motion prediction
Model, wherein lane prediction model, motion prediction model are formed by training sample training;Based on cartographic information and target carriage
Driving information, determine target vehicle association lane and target vehicle relative to association lane driving feature;Using
Lane prediction model, according to the driving feature target lane that prediction target vehicle will drive into association lane;Using movement
Prediction model, according to motion information of the driving feature prediction target vehicle on target lane;Based on driving information, target lane
And motion information, calculate the motion profile of target vehicle.
The target lane for predicting vehicle by different prediction models in embodiments of the present invention and vehicle are in target
Motion information on lane, to carry out trajectory calculation.The program can promote the precision of prediction of trajectory predictions as a result,.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 a is the schematic diagram of a scenario of trajectory predictions method provided in an embodiment of the present invention;
Fig. 1 b is the flow diagram of trajectory predictions method provided in an embodiment of the present invention;
Fig. 1 c is the map schematic diagram of a layer structure of high-precision map provided in an embodiment of the present invention;
Fig. 1 d is the relation schematic diagram of target vehicle and current lane provided in an embodiment of the present invention;
Fig. 1 e is the structural schematic diagram of Random Forest model provided in an embodiment of the present invention;
Fig. 1 f is the schematic illustration of moving track calculation provided in an embodiment of the present invention;
Fig. 2 is that the embodiment of the present invention provides the trajectory predictions method flow schematic diagram including model predictive process;
Fig. 3 a is the first structural schematic diagram of trajectory predictions device provided in an embodiment of the present invention;
Fig. 3 b is second of structural schematic diagram of trajectory predictions device provided in an embodiment of the present invention;
Fig. 3 c is the third structural schematic diagram of trajectory predictions device provided in an embodiment of the present invention;
Fig. 3 d is the 4th kind of structural schematic diagram of trajectory predictions device provided in an embodiment of the present invention;
Fig. 3 e is the 5th kind of structural schematic diagram of trajectory predictions device provided in an embodiment of the present invention;
Fig. 4 is the structural schematic diagram of the network equipment provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those skilled in the art's every other implementation obtained without creative efforts
Example, shall fall within the protection scope of the present invention.
The embodiment of the present invention provides a kind of trajectory predictions method and apparatus.
Wherein, which specifically can integrate in the electronic device, which can be terminal, service
The equipment such as device.Wherein, terminal can for automatic pilot, smart phone, tablet computer, smart bluetooth equipment, laptop,
The equipment such as PC (Personal Computer, PC);Server can be single server, be also possible to by multiple clothes
The server cluster of business device composition.
In some embodiments, which can also be integrated in multiple electronic equipments, for example, trajectory predictions
Device can integrate in multiple servers, and trajectory predictions method of the invention is realized by multiple servers.
For example, the electronic equipment can be carried by automatic pilot and by vehicle, automatic pilot can be logical in network
Letter, and the sensor by carrying on vehicle obtain the driving information of surrounding vehicles.For example, with reference to Fig. 1 a, trajectory predictions dress
It sets and is integrated in automatic pilot, the automatic pilot that this vehicle is equipped with can obtain this vehicle week by the sensor of this vehicle
Enclose the driving information of other vehicles.For example, cartographic information, lane prediction model, motion prediction model can be obtained by network,
And the driving information of other vehicles of surrounding is obtained (for example, the traveling speed of other vehicles of surrounding by the sensing system of vehicle
The distance between degree, type of vehicle and this vehicle, direction of traffic, etc.);Then, automatic pilot can based on cartographic information with
And the driving information of surrounding other vehicles, come the current association lane of other vehicles around determining, and surrounding other vehicle phases
For being associated with the driving feature in lane;Again use lane prediction model, according to driving feature association lane in prediction around its
The target lane that his vehicle will drive into, using motion prediction model, according to other vehicles around driving feature prediction in target
Motion information on lane;It is finally based on driving information, target lane and motion information, calculates the movement rail of other vehicles of surrounding
Mark.
It is described in detail separately below.It should be noted that the serial number of following embodiment is not as preferably suitable to embodiment
The restriction of sequence.
In the present embodiment, a kind of trajectory predictions method is provided, as shown in Figure 1 b, the specific stream of the trajectory predictions method
Journey can be such that
101, cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model are obtained.
Wherein, cartographic information, which can be, depicts the spatial informations such as road in real world, traffic condition, administrative region
Image information is also possible to the map datum of customized virtual world.Cartographic information can be used for ground traffic control, vehicle
Navigation, vehicle driving route planning etc..
In some embodiments, cartographic information may include high-precision map, which may include static height
Smart map layer and the high-precision map layer of dynamic.
Wherein, may include lane layer, road component layer, road attribute layer etc. in static high-precision map layer includes static letter
The map layer of breath.Specifically, lane layer in may include lane specific information, as lane line, lane center, lane width,
The information such as curvature, the gradient, course, lane rule.It may include the roads such as traffic mark board, pavement marker portion in road component layer
Part, for example, recording traffic signal lamp exact position and height etc..
Wherein, the high-precision map layer of dynamic may include congestion in road layer, condition of construction layer, traffic accident layer, traffic control
Layer, day gas-bearing formation etc. include the map layer of dynamic information.For example, being may include in condition of construction layer such as trimming, road markings
The information such as line wears and repaints, traffic marking changes.
For example, as illustrated in figure 1 c, providing a kind of map schematic diagram of a layer structure of high-precision map, including static state is high-precisionly
Figure layer and the high-precision map layer of dynamic, wherein including lane layer and road component layer, the high-precision map of dynamic in static high-precision map layer
Layer includes a day gas-bearing formation.
In embodiments of the present invention, it can will be equipped with trajectory predictions arrangement vehicle and be known as this vehicle, target vehicle is except this
Other vehicles other than vehicle, around this vehicle in certain distance.The distance can also be set by user setting by technical staff
It sets, it can also be with this vehicle sensor sensing distance dependent.
The driving information of target vehicle refer to target vehicle in the process of moving can by the information detected by this vehicle, than
Such as position location, travel speed, driving direction, vehicle license, type of vehicle.
Lane prediction model can be a kind of for predicting target vehicle in lane where future time instance (for example, after 3 seconds)
Mathematical model, similar, motion prediction model is a kind of for predicting target vehicle in the prediction of future time instance motion information
Mathematical model.
Wherein, the mode of cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model is obtained
Multiplicity, cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model can obtain in the same way
It takes, can also obtain in different ways.
For example, cartographic information, lane prediction model, motion prediction model can be read from local memory, and pass through biography
The driving information of sensor system acquisition target vehicle.
For another example, lane prediction model and motion prediction model can be obtained by network, then is read from local memory
Cartographic information, and pass through the driving information, etc. of sensing system acquisition target vehicle.
102, the driving information based on cartographic information and target vehicle determines association lane and the mesh of target vehicle
Mark driving feature of the vehicle relative to association lane.
Lane as where vehicle is possible to keep the current time vehicle in future time instance, it is also possible to lane change to the left,
Lane change to the right, or even connect change twice, therefore, before carrying out trajectory predictions, need the row based on cartographic information and target vehicle
It sails information sifting and goes out the vehicle and be possible to the lane driven into future time instance, these lanes are denoted as association lane.
Association lane, which refers to, is currently located the relevant lane in lane to target vehicle.Specifically, association lane refers to target
The lane that vehicle can drive into from current lane.
It should be noted that association lane can be target vehicle and be currently located lane.
In some embodiments, step 102 can specifically include following steps:
(1) driving information of information and target vehicle according to the map, determines the current lane of target vehicle.
Wherein, the driving information of target vehicle be this vehicle by collected vehicle-to-target vehicle of sensing system it
Between relative positional relationship information target vehicle can be derived in height by combining position of this vehicle on high-precision map
Exact position on precision map determines target vehicle in high-precision according to exact position of the target vehicle on high-precision map
Locating current lane on map.
For example, in the present embodiment, with reference to shown in Fig. 1 d, it is locating current on high-precision map to provide target vehicle
The schematic illustration in lane, it is known that position coordinates of this vehicle on high-precision map are (x=0, y=0), collect target vehicle
The distance between this vehicle is d=3 meters, and the position angle between target vehicle and this vehicle is θ=60 °, then can calculate mesh
Vehicle and relative distance of this vehicle in controlled map reference axis are marked, i.e. target vehicle is d* at a distance from this vehicle is between X-axis
Sin θ, the distance between Y-axis are d*cos θ, then position coordinates of the target vehicle on high-precision map are (x=1.5, y=-
2.6), it is known that, which falls into the regional scope (1 < x < 2, -20 < y < 20) in lane 2, current where judgement target vehicle
Lane is lane 2.
(2) information carries out topological analysis to the current lane of target vehicle according to the map, obtains associated with current lane
Association lane.
After determining the lane that target vehicle is currently located, topological analysis can be carried out to the current lane of target vehicle,
Association associated with current lane lane is obtained, specifically, the lane topological relation being currently located by obtaining target vehicle,
It is available that the associated association lane in lane is currently located with target vehicle according to the topological relation.
Wherein, which may include the relationships, in some embodiments, map such as abutting, be associated with, include and being connected to
Information may include the topological relation between all roads.
(3) driving information based on association lane and target vehicle calculates row of the target vehicle relative to association lane
Vehicle feature.
In above-mentioned steps, the corresponding one or more associations lane of each target vehicle can be obtained, target carriage is passed through
Driving information can calculate target vehicle relative to association lane driving feature.
Wherein, feature of driving a vehicle refers to physical features when vehicle driving, for example, row of the target vehicle relative to association lane
Vehicle feature can be target vehicle be associated with the distance between lane, target vehicle and the relative velocity, the target carriage that are associated with lane
With the relative distance etc. that is associated on lane between barrier.
For example, target vehicle may include target vehicle and be associated with obstacle on lane relative to the driving feature in association lane
Relative velocity between object, wherein barrier refers to may cause the object hindered, the barrier on association lane to target vehicle
Hinder object to can be stationary body, be also possible to dynamic object, for example, barrier can be traffic lights, motor vehicle, non-maneuver
Vehicle, greenbelt, etc..
Specifically, target vehicle may include being associated with the land occupation of barrier in lane relative to the driving feature in association lane
Relative velocity between barrier and target vehicle in area, size, type, motion state, and association lane, it is opposite away from
From, etc..
103, using lane prediction model, according to the driving feature mesh that prediction target vehicle will drive into association lane
Mark lane.
Wherein, the type of lane prediction model has a variety of, for example, lane prediction model can be neural network model,
Such as convolutional neural networks model (Convolutional Neural Networks, CNN), deep neural network model (Deep
Neural Networks, DNN), Recognition with Recurrent Neural Network model (Recurrent Neural Networks, CNN), etc..
For example, in some embodiments, lane prediction model can be the deep neural network based on random forests algorithm
Model.
Wherein, random forests algorithm refers to using more decision trees a kind of algorithm for being trained and predicting to sample.
It may include multiple decision trees in Random Forest model, for example, the Random Forest model with reference to shown in Fig. 1 e, including two
The classification that can be exported by partial decision tree-model of classification of decision tree, Random Forest model output determines, numerical value are as follows:
Wherein, P1(c | f) is the output of left side decision tree in Fig. 1 e, Pn(c | f) is the output of right side decision tree in Fig. 1 e, P
(c | f) it is Random Forest model output.
Specifically, in some embodiments, step 103 may include steps of:
(1) lane prediction model is used, association lane is driven into predetermined time according to driving feature calculation target vehicle
Drive into probability.
For example, in some embodiments, in order to reduce over-fitting, the efficiency for improving processing high dimensional feature, adapt to it is a large amount of more
The prediction data of sample, lane prediction model can be the deep neural network model based on random forests algorithm.At this point, using should
Lane prediction model handles driving feature, and available target vehicle drives into the more of an association lane in predetermined time
It is a to drive into sub- probability, it sub- probability is driven into these is weighted summation and can find out target vehicle and drive into association vehicle in predetermined time
Road drives into probability.
(2) according to probability is driven into, target lane is determined from association lane.
For example, in some embodiments, lane prediction model may include multiple lanes prediction submodels (such as decision tree
Model), using lane prediction model, driving into generally for association lane is driven into predetermined time according to driving feature calculation target vehicle
Rate can specifically include following steps:
(a) submodel is predicted using lane, association lane is driven into predetermined time according to driving feature calculation target vehicle
Drive into sub- probability;
(b) summation is weighted to driving into sub- probability, obtain target vehicle in predetermined time and drive into driving into for association lane
Probability.
Association lane is driven into according to target vehicle and drives into probability, and one or more target carriages can be determined from association lane
Road.
Wherein, to determine that the method in one or more target lanes has from association lane a variety of, for example, in some implementations
In example, to the descending sequence of probability is driven into, the association lane corresponding to probability of driving into of preceding preset quantity is denoted as target
Lane;For example, preset quantity is 3, to driving into the descending sequence of probability, association vehicle corresponding to probability is driven by first 3
Road is denoted as target lane.
In some embodiments, it can also will drive into probability to be compared with predetermined probabilities range, predetermined probabilities will be belonged to
The association lane corresponding to probability of driving into of range is denoted as target lane.For example, predetermined probabilities range is [0.8,1], then it will be general
Drive into probability corresponding to institute relevant lane of the rate score more than or equal to 0.8 is denoted as target lane.
Wherein, preset quantity and predetermined probabilities range can be obtained by reading local memory, can also pass through network
It is obtained from server, it can also be by user setting, etc..
104, for using motion prediction model, believed according to movement of the driving feature prediction target vehicle on target lane
Breath.
Wherein, motion information may include travel speed, driving direction, phase of the target vehicle when driving on target lane
It adjusts the distance, the information such as position location.
Wherein, the type of motion prediction model has a variety of, for example, motion prediction model can be also possible to neural network
Model, such as convolutional neural networks model, deep neural network model, Recognition with Recurrent Neural Network model, etc..
Similarly, motion prediction model is also possible to the deep neural network model based on random forests algorithm.
In some embodiments, motion prediction model includes the first motion prediction model, the second motion prediction model, movement
Information includes target speed information and target range information, and target lane may include target lane center, and target vehicle exists
Motion information on target lane can refer to target speed information and mesh of the target vehicle in predetermined time relative to target lane
Subject distance information, step 104 may include steps of:
(1) the first motion prediction model is used, according to driving feature prediction target vehicle in predetermined time relative to target
The target speed information in lane;
(2) the second motion prediction model is used, according to driving feature prediction target vehicle in predetermined time relative to target
The target range information of lane center.
Wherein, target lane center refers to the lane center from target lane.Specifically, lane center refer to from
The origin-to-destination in lane, the line being connected in sequence by the central point between the shoulder of lane.It can be in high-precision map
Store the lane center information in each lane.
105, it is based on driving information, target lane and motion information, calculates the motion profile of target vehicle.
In some embodiments, obtain target vehicle predetermined time relative to target lane target speed information,
After predetermined time is relative to the target range information in target lane, step 105 can specifically can be based on row target vehicle
Information, target lane, target speed information and target range information are sailed, the motion profile of target vehicle is calculated.
In some embodiments, the driving information of target vehicle includes initial position message, initial velocity information, wherein
Initial position message can be currently located the distance between the lane middle line in lane, initial velocity with feeling the pulse with the finger-tip mark vehicle-to-target vehicle
Information can refer to that the current speed of target vehicle, step 105 can specifically include following steps:
(1) according to target lane and target range information, determine target vehicle in the target position information of predetermined time;
(2) target carriage is calculated based on initial position message, target position information, initial velocity information, target speed information
Motion profile.
Target vehicle is calculated from currently to the method for the motion profile future time instance with a variety of.For example, specifically may be used
To carry out trajectory calculation, etc. using cubic polynomial curve, quintic algebra curve curve, S curve, step curve etc..
For example, calculation can refer to Fig. 1 f, V1 is the present speed of target vehicle, and V2 is the target carriage that prediction obtains
Future time instance (for example, after 3 seconds) target velocity;D1 is that target vehicle is currently currently located between lane center with it
Distance, d2 be target vehicle at a distance from where future time instance with its future time instance between target lane center, d is target
Vehicle is currently located the distance between lane and target lane.
Wherein, lane center can be obtained from high-precision map, between target vehicle and Future targets vehicle away from
It is as follows from the calculation formula of D:
D=d1+d2+d
At this point, by distance D and target vehicle present speed V1 and target vehicle future time instance target velocity
Target vehicle can be calculated from currently to the motion profile future time instance in V2.
Specifically, the method that the calculating of motion profile Q is carried out using quintic algebra curve curve is as follows:
Q(tstart,tend)=q (tstart)+q(tstart+1)+q(tstart+2)+...q(tend)=∑ q (ti)
Wherein, location point when q is certain moment where target vehicle;Q is the line being linked to be by multiple location points, that is, is moved
Track;tstartFor initial time;tendFor future time instance;tiSampling instant between initial time and future time instance, the sampling
Moment can be configured by technical staff, can also be set by the user.
Target vehicle is in the location of sampling instant point q (ti) can be found out by following formula:
q(ti)=q (tsatrt)+w1(ti-tstrat)+w2(ti-tstrat)2+w3(ti-tstrat)3+w4(ti-tstrat)4+w5(ti-
tstrat)5
Wherein, w1、w2、w3、w4、w5For the coefficient of the quintic algebra curve.
Known physical equation is as follows, in tiMoment, speed v can ask first derivative to obtain by location point q, acceleration of motion
A can ask second dervative to obtain by location point q:
q′(ti)=v (ti), q " (ti)=a (ti)
It can be concluded that coefficient w1、w2、w3、w4、w5Calculation formula it is as follows:
w1=vstart
Wherein, h=q (ti)-q(tstart)。
From the foregoing, it will be observed that the available cartographic information of the embodiment of the present invention, the driving information of target vehicle, lane prediction mould
Type, motion prediction model, wherein lane prediction model, motion prediction model are formed by training sample training;Based on cartographic information
And the driving information of target vehicle, determine the row of the association lane and target vehicle of target vehicle relative to association lane
Vehicle feature;Using lane prediction model, according to the driving feature target carriage that prediction target vehicle will drive into association lane
Road;Using motion prediction model, according to motion information of the driving feature prediction target vehicle on target lane;Believed based on traveling
Breath, target lane and motion information, calculate the motion profile of target vehicle.
Thus this programme can predict vehicle by different prediction models target lane and vehicle are in target carriage
Motion information on road, to carry out trajectory calculation.The program can promote the precision of prediction of trajectory predictions as a result,.
The method according to described in above-described embodiment, will now be described in further detail below.
Trajectory predictions scheme provided in an embodiment of the present invention can be applied in various traffic scenes, for each rank
Automatic driving vehicle system, to realize that automatic driving vehicle predicts the motion profile of surrounding vehicles.
In the present embodiment, will be with server training lane prediction model and motion prediction model, automatic driving vehicle is logical
It crosses for the obtained model of these servers training predicts the motion profile in surrounding vehicles 3 seconds, with reference to Fig. 2, to this
The method of inventive embodiments is described in detail, and detailed process is as follows:
201, server obtains training sample and initial predicted model, and each training sample corresponds to multiple sample marks.
Wherein, the mode of server acquisition training sample and initial predicted model has a variety of, for example, server passes through
Network obtains training sample, server directly reads initial predicted model in its local memory, server is obtained by network
Training data, and training data is labeled, obtain available training sample, etc..
The training data can be by being equipped with the sensors such as laser radar, camera, High Accuracy Inertial and corresponding perception
The acquisition vehicle of algorithm acquires.Wherein, acquisition vehicle can be the vehicle for being exclusively used in collecting training data, be also possible to be equipped with
The vehicle of automated driving system.
For example, acquisition vehicle can acquire surrounding traffic condition, and traffic condition institute is recorded according to accurately seal
The place of generation.For example, acquisition vehicle can acquire the picture of its peripheral obstacle, the lane at place, the position at place, speed
Degree, the direction of motion, acceleration, angular speed and number of barrier, etc..Wherein, barrier may include vehicle, traffic
Indicator light, pedestrian etc. can cause the people hindered and object to vehicle driving.
For example, acquisition vehicle can acquire travel speed, the driving direction in 15 meters in other vehicles 3 seconds around, and
Initial position, final position in this 3 seconds, thus obtain around in 15 meters lane sums where other vehicle initial times 3
Target lane where after second.
In some embodiments, server can be labeled training data, obtain available training sample, specific to walk
It is rapid as follows:
(1) the association lane and target lane of barrier are determined.
Wherein, target lane is barrier in the lane where future time instance (such as after 3 seconds), which can
To be included in training data.
Wherein, the association lane of barrier, which refers to, is currently located the associated lane in lane with barrier, specific to determine barrier
The mode in the association lane of object is hindered to have a variety of, such as shown in following steps:
A. according to the position location of high-precision map and barrier, the current lane of barrier is determined;
B. topological analysis is carried out according to current lane of the high-precision map to barrier, obtains the association lane of barrier.
Wherein, the position location of barrier can collect its relative position between barrier by acquisition vehicle
Relationship is derived with position of the acquisition vehicle on high-precision map to combine.
Wherein, topological relation can be obtained from high-precision map, which may include abutting, being associated with, including
With the relationships such as be connected to.
(2) according to training data, determine barrier for the driving feature in association lane.
Wherein, barrier for be associated with lane driving feature may include association lane in barrier occupied area,
Relative velocity, relative distance in size, type, motion state, and association lane between barrier and target vehicle, etc.
Deng.
For example, shown in reference table 1 driving feature list it is found that barrier for be associated with lane driving feature have it is more
Kind:
Table 1
(3) training data is labeled according to target lane.
Wherein, each training sample can correspond to the sample mark of multiple types, for example, in some embodiments, each
Training sample can correspond to three sample marks, respectively lane mark, distance mark, speed mark, etc..
In the present embodiment, the association lane in training data can be labeled according to target lane.For example, working as
When target lane is identical as association lane, generates the lane that the association lane corresponds to training data and be labeled as positive sample mark, when
When target lane is with lane difference is associated with, generates the lane that the association lane corresponds to training data and be labeled as negative sample mark.
For example, the association lane of barrier is lane A, lane B, lane C, then can generate characteristic set according to lane
[A]、[B]、[C]。
For example, lane of the barrier where after 3 seconds is lane B, then the target lane of barrier is lane B, it is known that barrier
Hinder target lane and the lane A of object different, identical as lane B, different with lane C, then characteristic set [A] is used as negative sample at this time
This, is labeled as [A, 0];Characteristic set [B] is used as positive sample, is labeled as [B, 1];Characteristic set [C] is used as negative sample, mark
Note is [C, 0].
In some embodiments, characteristic set can also be labeled according to travel speed of the barrier after 3 seconds,
In, the data of negative sample are noted as without marking travel speed, for example, travel speed of the barrier after 3 seconds is v, then will
Negative sample [A, 0] is labeled as [A, 0,0];Positive sample [B, 1] is labeled as [B, 1, v];Negative sample [C, 0] is labeled as [C, 0,
0]。
It in some embodiments, can also be according to distance of the barrier after 3 seconds relative to target lane to characteristic set
Be labeled, wherein be noted as the data of negative sample without marking distance, for example, barrier after 3 seconds with target lane
Distance is D, then negative sample [A, 0,0] is labeled as [A, 0,0,0], positive sample [B, 1, v] is labeled as [B, 1, v, D], will be born
Sample [C, 0,0] is labeled as [C, 0,0,0].
202, server carries out rejecting processing to training sample corresponding sample mark, obtains lane training sample and fortune
Dynamic training sample.
After different types of sample mark corresponding to training sample carries out rejecting processing, training sample can retain part
The sample of type marks.For example, in some embodiments, sample mark includes lane mark, distance mark, speed mark, fortune
Dynamic training sample includes carrying out at rejecting apart from training sample and speed training sample to training sample corresponding sample mark
Reason, obtains lane training sample and training sample can specifically include following steps:
(a) the distance mark and speed mark for abandoning training sample are only remained the lane training sample of lane mark
This;
(b) it abandons the lane mark of training sample and apart from mark, is only remained the speed training sample of speed mark
This;
(c) the lane mark and speed mark for abandoning training sample are only remained the distance training sample of distance mark
This.
For example, when training sample be negative sample [A, 0,0,0], positive sample [B, 1, v, D], negative sample [C, 0,0,0],
In, the first item of training sample refers to candidate lane, and Section 2 refers to sample type (such as the positive sample type, negative sample of training sample
Type), Section 3 refers to travel speed of the barrier on target lane, Section 4 refer between barrier and target lane away from
From.
For example, executing step a, i.e., the speed mark and Section 4 of discardable training sample three to above-mentioned training sample
Distance mark, then obtain lane training sample [A, 0], [B, 1], [C, 0].
For example, executing step b, i.e., the lane mark and Section 4 of discardable training sample first item to above-mentioned training sample
Distance mark, then obtain speed training sample [0,0], [1, v], [0,0].
For example, executing step c, i.e., the lane mark and Section 3 of discardable training sample first item to above-mentioned training sample
Speed mark, then obtain apart from training sample [0,0], [1, D], [0,0].
203, server is trained initial predicted model using lane training sample, obtains lane prediction model.
For example, server is trained initial predicted model using lane training sample [A, 0], [B, 1], [C, 0], directly
To convergence, lane prediction model is obtained.
204, server is trained initial predicted model using training sample, obtains motion prediction model.
In some embodiments, training sample includes speed training sample, apart from training sample, speed training sample
It include distance training subsample apart from training sample, motion prediction model includes prediction of speed mould including speed training positive sample
Type, range prediction model are trained initial predicted model using training sample, obtain motion prediction model and specifically may be used
With the following steps are included:
(a) initial predicted model is trained using speed training subsample, obtains speed prediction model;
(b) initial predicted model is trained using distance training subsample, obtains range prediction model.
For example, speed training sample [0,0], [1, v], first item is sample type in [0,0], wherein 0 represents negative sample
This, 1 represents positive sample, then speed training sample [0,0], [1, v], include two negative samples [0,0] and one in [0,0]
Positive sample [1, v].
205, automatic driving vehicle obtains cartographic information, lane prediction model, motion prediction model from server, and passes through
The driving information of sensor acquisition target vehicle.
Wherein, automatic driving vehicle can obtain cartographic information, lane prediction model, movement in advance from server by network
Model is surveyed, cartographic information, lane prediction model, motion prediction model, etc. can also be imported from server by storage equipment.
206, driving information of the automatic driving vehicle based on cartographic information, target vehicle, using lane prediction model and fortune
The motion profile of dynamic prediction model prediction target vehicle.
Step (6) can refer to above-mentioned steps 102,103,104,105, and this will not be repeated here.
From the foregoing, it will be observed that in embodiments of the present invention, server, which passes through, obtains training sample and initial predicted model, and right
The corresponding sample mark of training sample carries out rejecting processing, obtains lane training sample and training sample, server can
To be trained using lane training sample to initial predicted model, lane prediction model is obtained, using training sample pair
Initial predicted model is trained, and obtains motion prediction model.Automatic driving vehicle is pre- from server acquisition cartographic information, lane
Model, motion prediction model are surveyed, and acquires the driving information of target vehicle by sensor;Automatic driving vehicle is obtained from server
Cartographic information, lane prediction model, motion prediction model are taken, and acquires the driving information of target vehicle by sensor.
The target lane and vehicle for predicting vehicle by different prediction models in embodiments of the present invention as a result, exist
Motion information on target lane, to carry out trajectory calculation.The efficiency of trajectory predictions can be improved in the program as a result, simultaneously
Promote the precision of prediction of trajectory predictions.
In order to better implement above method, the embodiment of the present invention also provides a kind of trajectory predictions device, the trajectory predictions
Device specifically can integrate in the electronic device, which can be the equipment such as terminal, server.Wherein, which sets
It is standby to be specifically as follows automatic pilot, server, etc..
For example, in the present embodiment, it will be by taking trajectory predictions device be integrated in the server as an example, to the embodiment of the present invention
Method is described in detail.
For example, as shown in Figure 3a, which may include acquiring unit 301, associative cell 302, lane list
Member 303, moving cell 304 and trajectory unit 305 are as follows:
(1) acquiring unit 301:
Acquiring unit 301, for obtaining driving information, the lane prediction model, motion prediction of cartographic information, target vehicle
Model, wherein lane prediction model, motion prediction model are formed by training sample training.
With reference to Fig. 3 b, in some embodiments, acquiring unit 301 can also include obtaining subelement 3011, rejecting son list
Member 3012, lane model subelement 3013 and motion model subelement 3014, as follows:
(1) subelement 3011 is obtained:
Subelement is obtained, for obtaining training sample and initial predicted model, each training sample corresponds to multiple samples
Mark;
(2) subelement 3012 is rejected:
Subelement is rejected, for carrying out rejecting processing to training sample corresponding sample mark, obtains lane training sample
And training sample;
(3) lane model subelement 3013:
It is pre- to obtain lane for being trained using lane training sample to initial predicted model for lane model subelement
Survey model;
(4) motion model subelement 3014:
It is pre- to obtain movement for being trained using training sample to initial predicted model for motion model subelement
Survey model.
In some embodiments, sample mark includes lane mark, distance mark, speed mark, training sample packet
It includes apart from training sample and speed training sample, rejecting subelement 3012 specifically can be used for:
The distance mark and speed mark for abandoning training sample, are only remained the lane training sample of lane mark;
It abandons the lane mark of training sample and apart from mark, is only remained the speed training sample of speed mark;
The lane mark and speed mark for abandoning training sample, are only remained apart from mark apart from training sample.
In some embodiments, training sample includes speed training sample, apart from training sample, speed training sample
It include distance training subsample apart from training sample, motion prediction model includes prediction of speed mould including speed training positive sample
Type, range prediction model, motion model subelement 3014 specifically can be used for:
Initial predicted model is trained using speed training subsample, obtains speed prediction model;
Initial predicted model is trained using distance training subsample, obtains range prediction model.
(2) associative cell 302:
Associative cell 302 determines the association of target vehicle for the driving information based on cartographic information and target vehicle
The driving feature of lane and target vehicle relative to association lane.
In some embodiments, associative cell 302 specifically can be used for:
The driving information of information and target vehicle according to the map, determines the current lane of target vehicle;
Information carries out topological analysis to the current lane of target vehicle according to the map, obtains pass associated with current lane
Join lane;
Driving information based on association lane and target vehicle, the driving for calculating target vehicle relative to association lane are special
Sign.
(3) lane unit 303:
Lane unit 303 predicts target vehicle in association lane according to driving feature for using lane prediction model
The target lane that will be driven into.
In some embodiments, with reference to Fig. 3 c, lane unit 303 may include probability subelement 3031 and lane subelement
3032, as follows:
(1) probability subelement 3031:
Probability subelement is sailed according to driving feature calculation target vehicle in predetermined time for using lane prediction model
Enter to be associated with lane drives into probability;
(2) lane subelement 3032:
Lane subelement, for determining target lane from association lane according to probability is driven into.
In some embodiments, lane prediction model includes multiple lane prediction submodels, and probability subelement 3031 is specific
It can be used for:
Submodel is predicted using lane, sailing for association lane is driven into predetermined time according to driving feature calculation target vehicle
Enter sub- probability;
It is weighted summation to sub- probability is driven into, target vehicle is obtained in predetermined time and drives into driving into generally for association lane
Rate.
(4) moving cell 304:
Moving cell 304, for using motion prediction model, according to driving feature prediction target vehicle on target lane
Motion information.
In some embodiments, motion prediction model includes the first motion prediction model, the second motion prediction model, movement
Information includes target speed information and target range information, and with reference to Fig. 3 d, moving cell 304 may include the first movement subelement
3041 and second move subelement 3042, as follows:
(1) first moving cell 3041:
First moving cell, for using the first motion prediction model, according to driving feature prediction target vehicle default
Target speed information of the moment relative to target lane;
(2) second moving cells 3042:
Second moving cell, for using the second motion prediction model, according to driving feature prediction target vehicle default
Target range information of the moment relative to target lane.
(5) trajectory unit 305:
Trajectory unit 305 calculates the movement rail of target vehicle for being based on driving information, target lane and motion information
Mark.
In some embodiments, trajectory unit 305 specifically can be used for based on driving information, target lane, target velocity
Information and target range information, calculate the motion profile of target vehicle.
In some embodiments, driving information includes initial position message, initial velocity information;With reference to Fig. 3 e, track is single
Member 305 may include location subunit 3051 and track subelement 3052, as follows:
(1) location subunit 3051:
Location subunit, for determining target vehicle in the mesh of predetermined time according to target lane and target range information
Cursor position information;
(2) track subelement 3052:
Track subelement, for based on initial position message, target position information, initial velocity information, target velocity letter
Breath calculates the motion profile of target vehicle.
When it is implemented, above each unit can be used as independent entity to realize, any combination can also be carried out, is made
It is realized for same or several entities, the specific implementation of above each unit can be found in the embodiment of the method for front, herein not
It repeats again.
From the foregoing, it will be observed that the trajectory predictions device of the present embodiment is obtained the traveling of cartographic information, target vehicle by acquiring unit
Information, lane prediction model, motion prediction model;Driving information by associative cell based on cartographic information and target vehicle,
Determine the driving feature of the association lane and target vehicle of target vehicle relative to association lane;Vehicle is used by lane unit
Road prediction model, according to the driving feature target lane that prediction target vehicle will drive into association lane;By moving cell
Using motion prediction model, according to motion information of the driving feature prediction target vehicle on target lane;By trajectory unit base
In driving information, target lane and motion information, the motion profile of target vehicle is calculated.
The target lane that vehicle can be predicted by different prediction models due to the program and vehicle are in target carriage
Motion information on road, to carry out trajectory calculation.The program can promote the precision of prediction of trajectory predictions as a result,.
The embodiment of the present invention also provides a kind of electronic equipment, which can be smart phone, smartwatch, plate
Computer, microcomputer, automatic pilot, server etc..As shown in figure 4, it illustrates involved in the embodiment of the present invention
The structural schematic diagram of electronic equipment, specifically:
The electronic equipment may include one or more than one processing core processor 401, one or more
Memory 402, power supply 403, the input unit 404 of computer readable storage medium, in addition to this it is possible to include sensor system
The components such as system 405, positioning system 406.It will be understood by those skilled in the art that the not structure of electronic devices structure shown in Fig. 4
The restriction of paired electrons equipment may include perhaps combining certain components or different than illustrating more or fewer components
Component layout.Wherein:
Processor 401 is the control centre of the electronic equipment, utilizes various interfaces and the entire electronic equipment of connection
Various pieces by travelling or execute the software program and/or module that are stored in memory 402, and are called and are stored in
Data in reservoir 402 execute the various functions and processing data of electronic equipment, to carry out integral monitoring to electronic equipment.
In some embodiments, processor 401 may include one or more processing cores;In some embodiments, processor 401 can collect
At application processor and modem processor, wherein the main processing operation system of application processor, user interface and apply journey
Sequence etc., modem processor mainly handle wireless communication.It is understood that above-mentioned modem processor can not also collect
At in processor 401.
Memory 402 can be used for storing software program and module, and processor 401 is stored in memory 402 by traveling
Software program and module, thereby executing various function application and data processing.Memory 402 can mainly include storage journey
Sequence area and storage data area, wherein storing program area can application program needed for storage program area, at least one function etc.;
Storage data area, which can be stored, uses created data etc. according to electronic equipment.In addition, memory 402 may include high speed with
Machine access memory, can also include nonvolatile memory, a for example, at least disk memory, flush memory device or its
His volatile solid-state part.Correspondingly, memory 402 can also include Memory Controller, right to provide processor 401
The access of memory 402.
Electronic equipment further includes the power supply 403 powered to all parts, and in some embodiments, power supply 403 can pass through
Power-supply management system and processor 401 are logically contiguous, to realize management charging, electric discharge, Yi Jigong by power-supply management system
The functions such as consumption management.Power supply 403 can also include one or more direct current or AC power source, recharging system, power supply
The random components such as fault detection circuit, power adapter or inverter, power supply status indicator.
The electronic equipment may also include input unit 404, the input unit 404 can be used for receiving the number of input, character,
Image, location information etc., and generate key related with user setting and function control, dummy keyboard, steering wheel, behaviour
Make the signals such as bar, sensor input, for example, input unit can receive sensing system and positioning system input image,
Manage position and driving information etc..
The electronic equipment may also include sensing system 405, which may include multiple sensors, than
Such as, radar, camera, infrared sensor, etc..The structure of sensing system 405 can for centralization, distribution, stagewise,
Hybrid and multi-stag etc., multiple sensors therein may include sensing element, conversion original part, accessory power supply and transformation
The components such as circuit, sensing element can be with direct feeling, measurement, and exports and be measured the physical quantity signal for having determining relationship;Turn
It changes element and the physical quantity signal that sensing element exports is converted into electric signal;Translation circuit is responsible for the telecommunications exported to conversion element
Number amplify modulation;Conversion element and translation circuit generally also need accessory power supply to power.
The electronic equipment may also include positioning system 406, which can receive, tracks, converts and measure position
Confidence number provides the position and speed of carrier in real time.Positioning system 406 can be by antenna element, receiver main computer unit and electricity
Three, source composition, the location navigation signal that can be will acquire of antenna element is converted into electric current, and carries out to this signal code
Amplification and frequency-conversion processing;Receiver unit can be tracked, handled and be surveyed to the signal power source by amplification and frequency-conversion processing
Amount.
Although being not shown, electronic equipment can also include display unit, communication unit etc., and details are not described herein.Specifically exist
In the present embodiment, the processor 401 in electronic equipment can be according to following instruction, by one or more application program
The corresponding executable file of process is loaded into memory 402, and is travelled and be stored in memory 402 by processor 401
Application program, thus realize various functions, it is as follows:
Obtain cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model, wherein lane is pre-
Survey model, motion prediction model is formed by training sample training;
Driving information based on cartographic information and target vehicle determines association lane and the target carriage of target vehicle
Relative to association lane driving feature;
Using lane prediction model, according to the driving feature target carriage that prediction target vehicle will drive into association lane
Road;
Using motion prediction model, according to motion information of the driving feature prediction target vehicle on target lane;
Based on driving information, target lane and motion information, the motion profile of target vehicle is calculated.
The specific implementation of above each operation can be found in the embodiment of front, and details are not described herein.
From the foregoing, it will be observed that in embodiments of the present invention, the traveling letter of the available cartographic information of electronic equipment, target vehicle
Breath, lane prediction model, motion prediction model;Driving information based on cartographic information and target vehicle, determines target vehicle
Association lane and target vehicle relative to association lane driving feature;Using lane prediction model, according to driving feature
The target lane that prediction target vehicle will drive into association lane;Using motion prediction model, according to driving feature prediction
Motion information of the target vehicle on target lane;Based on driving information, target lane and motion information, target vehicle is calculated
Motion profile.Thus the program can predict vehicle by different prediction models target lane and vehicle are in target carriage
Motion information on road, to carry out trajectory calculation.The program can promote the precision of prediction of trajectory predictions as a result,.
It will appreciated by the skilled person that all or part of the steps in the various methods of above-described embodiment can be with
It is completed by instructing, or relevant hardware is controlled by instruction to complete, which can store computer-readable deposits in one
In storage media, and is loaded and executed by processor.
For this purpose, the embodiment of the present invention provides a kind of storage medium, wherein being stored with a plurality of instruction, which can be processed
Device is loaded, to execute the step in any trajectory predictions method provided by the embodiment of the present invention.For example, the instruction can
To execute following steps:
Obtain cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model, wherein lane is pre-
Survey model, motion prediction model is formed by training sample training;
Driving information based on cartographic information and target vehicle determines association lane and the target carriage of target vehicle
Relative to association lane driving feature;
Using lane prediction model, according to the driving feature target carriage that prediction target vehicle will drive into association lane
Road;
Using motion prediction model, according to motion information of the driving feature prediction target vehicle on target lane;
Based on driving information, target lane and motion information, the motion profile of target vehicle is calculated.
Wherein, which may include: read-only memory (ROM, Read Only Memory), random access memory
Body (RAM, Random Access Memory), disk or CD etc..
By the instruction stored in the storage medium, it is pre- that any track provided by the embodiment of the present invention can be executed
Step in survey method, it is thereby achieved that achieved by any trajectory predictions method provided by the embodiment of the present invention
Beneficial effect is detailed in the embodiment of front, and details are not described herein.
It is provided for the embodiments of the invention a kind of trajectory predictions method and apparatus above to be described in detail, herein
Apply that a specific example illustrates the principle and implementation of the invention, the explanation of above example is only intended to help
Understand method and its core concept of the invention;Meanwhile for those skilled in the art, according to the thought of the present invention, having
There will be changes in body embodiment and application range, in conclusion the content of the present specification should not be construed as to the present invention
Limitation.
Claims (10)
1. a kind of trajectory predictions method characterized by comprising
Obtain cartographic information, the driving information of target vehicle, lane prediction model, motion prediction model, wherein the lane is pre-
Survey model, motion prediction model is formed by training sample training;
Driving information based on the cartographic information and target vehicle determines association lane and the target carriage of target vehicle
Relative to it is described association lane driving feature;
Using lane prediction model, according to the driving feature mesh that prediction target vehicle will drive into the association lane
Mark lane;
Using motion prediction model, according to motion information of the driving feature prediction target vehicle on the target lane;
Based on the target lane, motion information and driving information, the motion profile of target vehicle is calculated.
2. trajectory predictions method as described in claim 1, which is characterized in that the motion prediction model includes that the first movement is pre-
Model, the second motion prediction model are surveyed, the motion information on the target lane is included in predetermined time relative to institute
The target speed information and target range information in target lane are stated, the target lane includes target lane center;
It is described to use motion prediction model, believed according to movement of the driving feature prediction target vehicle on the target lane
Breath, comprising:
Using the first motion prediction model, predict target vehicle in predetermined time relative to the target according to the driving feature
The target speed information in lane;
Using the second motion prediction model, predict target vehicle in predetermined time relative to the target according to the driving feature
The target range information of lane center;
It is described to be based on the driving information, target lane and motion information, calculate the motion profile of target vehicle, comprising:
Based on the driving information, target lane, target speed information and target range information, the movement rail of target vehicle is calculated
Mark.
3. trajectory predictions method as claimed in claim 2, which is characterized in that the driving information include initial position message,
Initial velocity information;
Based on the driving information, target lane, target speed information and target range information, the movement rail of target vehicle is calculated
Mark, comprising:
According to the target lane and target range information, determine target vehicle in the target position information of predetermined time;
Target vehicle is calculated based on the initial position message, target position information, initial velocity information, target speed information
Motion profile.
4. trajectory predictions method as described in claim 1, which is characterized in that it is described to use lane prediction model, according to described
The driving feature target lane that prediction target vehicle will drive into the association lane, comprising:
Using lane prediction model, the association lane is driven into predetermined time according to the driving feature calculation target vehicle
Drive into probability;
Probability is driven into according to described, target lane is determined from the association lane.
5. trajectory predictions method as claimed in claim 4, which is characterized in that the lane prediction model includes that multiple lanes are pre-
It surveys submodel and the association is driven into predetermined time according to the driving feature calculation target vehicle using lane prediction model
Probability is driven into lane, comprising:
Submodel is predicted using lane, the association lane is driven into predetermined time according to the driving feature calculation target vehicle
Drive into sub- probability;
It drives into sub- probability to described and is weighted summation, obtain target vehicle in predetermined time and drive into driving into for the association lane
Probability.
6. trajectory predictions method as described in claim 1, which is characterized in that obtain the traveling letter of cartographic information, target vehicle
Breath, lane prediction model, motion prediction model, before, further includes:
Obtain training sample and initial predicted model, the corresponding multiple sample marks of each of described training sample;
Rejecting processing is carried out to the training sample corresponding sample mark, obtains lane training sample and training sample
This;
Initial predicted model is trained using the lane training sample, obtains lane prediction model;
Initial predicted model is trained using the training sample, obtains motion prediction model.
7. trajectory predictions method as claimed in claim 6, which is characterized in that the sample mark includes lane mark, distance
Mark, speed mark, the training sample includes apart from training sample and speed training sample;
Rejecting processing is carried out to the training sample corresponding sample mark, obtains lane training sample and training sample
This, comprising:
The distance mark and speed mark for abandoning the training sample, are only remained the lane training sample of lane mark;
It abandons the lane mark of the training sample and apart from mark, is only remained the speed training sample of speed mark;
The lane mark and speed mark for abandoning the training sample, are only remained apart from mark apart from training sample.
8. trajectory predictions method as claimed in claim 6, which is characterized in that the training sample includes speed training sample
Originally, apart from training sample, the speed training sample includes speed training subsample, described to include distance instruction apart from training sample
Practice subsample, the motion prediction model includes speed prediction model, range prediction model;
Initial predicted model is trained using the training sample, obtains motion prediction model, comprising:
Initial predicted model is trained using the speed training subsample, obtains speed prediction model;
Initial predicted model is trained using distance training subsample, obtains range prediction model.
9. trajectory predictions method as described in claim 1, which is characterized in that based on the cartographic information and target vehicle
Driving information determines the driving feature of the association lane and target vehicle of target vehicle relative to the association lane, packet
It includes:
According to the cartographic information and the driving information of target vehicle, the current lane of target vehicle is determined;
Topological analysis is carried out according to current lane of the cartographic information to target vehicle, is obtained associated with the current lane
Association lane;
Driving information based on the association lane and target vehicle calculates row of the target vehicle relative to the association lane
Vehicle feature.
10. a kind of trajectory predictions device characterized by comprising
Acquiring unit, for obtaining the driving information, lane prediction model, motion prediction model of cartographic information, target vehicle,
In, the lane prediction model, motion prediction model are formed by training sample training;
Associative cell determines the association vehicle of target vehicle for the driving information based on the cartographic information and target vehicle
The driving feature of road and target vehicle relative to the association lane;
Lane unit predicts target carriage in the association lane according to the driving feature for using lane prediction model
The target lane that will be driven into;
Moving cell predicts target vehicle in the target lane for using motion prediction model according to the driving feature
On motion information;
Trajectory unit calculates the motion profile of target vehicle for being based on the driving information, target lane and motion information.
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WO2021174445A1 (en) * | 2020-03-04 | 2021-09-10 | 华为技术有限公司 | Method and device for predicting exit for vehicle |
CN114005280A (en) * | 2021-11-17 | 2022-02-01 | 同济大学 | Vehicle track prediction method based on uncertainty estimation |
CN113997954A (en) * | 2021-11-29 | 2022-02-01 | 广州文远知行科技有限公司 | Vehicle driving intention prediction method, device and equipment and readable storage medium |
WO2022022384A1 (en) * | 2020-07-31 | 2022-02-03 | 华为技术有限公司 | Method and device for recognizing vehicle motion state |
CN114283576A (en) * | 2020-09-28 | 2022-04-05 | 华为技术有限公司 | Vehicle intention prediction method and related device |
WO2022178858A1 (en) * | 2021-02-26 | 2022-09-01 | 华为技术有限公司 | Vehicle driving intention prediction method and apparatus, terminal and storage medium |
CN115123252A (en) * | 2022-07-05 | 2022-09-30 | 小米汽车科技有限公司 | Vehicle control method, vehicle control device, vehicle and storage medium |
CN116153084A (en) * | 2023-04-20 | 2023-05-23 | 智慧互通科技股份有限公司 | Vehicle flow direction prediction method, prediction system and urban traffic signal control method |
WO2023123456A1 (en) * | 2021-12-31 | 2023-07-06 | 深圳市大疆创新科技有限公司 | Vehicle location prediction method and apparatus, and vehicle and storage medium |
WO2023159915A1 (en) * | 2022-02-23 | 2023-08-31 | 中国第一汽车股份有限公司 | Vehicle trajectory prediction method and device |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140032108A1 (en) * | 2012-07-30 | 2014-01-30 | GM Global Technology Operations LLC | Anchor lane selection method using navigation input in road change scenarios |
CN103809593A (en) * | 2012-11-06 | 2014-05-21 | 现代摩比斯株式会社 | Control apparatus of vehicle for changing lane and control method of the same |
EP2950294A1 (en) * | 2014-05-30 | 2015-12-02 | Honda Research Institute Europe GmbH | Method and vehicle with an advanced driver assistance system for risk-based traffic scene analysis |
CN107919027A (en) * | 2017-10-24 | 2018-04-17 | 北京汽车集团有限公司 | Predict the methods, devices and systems of vehicle lane change |
CN108981729A (en) * | 2017-06-02 | 2018-12-11 | 腾讯科技(深圳)有限公司 | Vehicle positioning method and device |
CN109583151A (en) * | 2019-02-20 | 2019-04-05 | 百度在线网络技术(北京)有限公司 | The driving trace prediction technique and device of vehicle |
CN109885066A (en) * | 2019-03-26 | 2019-06-14 | 北京经纬恒润科技有限公司 | A kind of motion profile prediction technique and device |
CN110020748A (en) * | 2019-03-18 | 2019-07-16 | 杭州飞步科技有限公司 | Trajectory predictions method, apparatus, equipment and storage medium |
-
2019
- 2019-08-08 CN CN201910729763.2A patent/CN110400490B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140032108A1 (en) * | 2012-07-30 | 2014-01-30 | GM Global Technology Operations LLC | Anchor lane selection method using navigation input in road change scenarios |
CN103809593A (en) * | 2012-11-06 | 2014-05-21 | 现代摩比斯株式会社 | Control apparatus of vehicle for changing lane and control method of the same |
EP2950294A1 (en) * | 2014-05-30 | 2015-12-02 | Honda Research Institute Europe GmbH | Method and vehicle with an advanced driver assistance system for risk-based traffic scene analysis |
CN108981729A (en) * | 2017-06-02 | 2018-12-11 | 腾讯科技(深圳)有限公司 | Vehicle positioning method and device |
CN107919027A (en) * | 2017-10-24 | 2018-04-17 | 北京汽车集团有限公司 | Predict the methods, devices and systems of vehicle lane change |
CN109583151A (en) * | 2019-02-20 | 2019-04-05 | 百度在线网络技术(北京)有限公司 | The driving trace prediction technique and device of vehicle |
CN110020748A (en) * | 2019-03-18 | 2019-07-16 | 杭州飞步科技有限公司 | Trajectory predictions method, apparatus, equipment and storage medium |
CN109885066A (en) * | 2019-03-26 | 2019-06-14 | 北京经纬恒润科技有限公司 | A kind of motion profile prediction technique and device |
Cited By (27)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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CN111114554B (en) * | 2019-12-16 | 2021-06-11 | 苏州智加科技有限公司 | Method, device, terminal and storage medium for predicting travel track |
CN111340880A (en) * | 2020-02-17 | 2020-06-26 | 北京百度网讯科技有限公司 | Method and apparatus for generating a predictive model |
CN111340880B (en) * | 2020-02-17 | 2023-08-04 | 北京百度网讯科技有限公司 | Method and apparatus for generating predictive model |
WO2021174445A1 (en) * | 2020-03-04 | 2021-09-10 | 华为技术有限公司 | Method and device for predicting exit for vehicle |
CN111752272A (en) * | 2020-04-16 | 2020-10-09 | 北京京东乾石科技有限公司 | Trajectory prediction method, apparatus, device and storage medium |
CN111428943B (en) * | 2020-04-23 | 2021-08-03 | 福瑞泰克智能***有限公司 | Method, device and computer device for predicting obstacle vehicle track |
CN111428943A (en) * | 2020-04-23 | 2020-07-17 | 福瑞泰克智能***有限公司 | Method, device and computer device for predicting obstacle vehicle track |
CN111583715A (en) * | 2020-04-29 | 2020-08-25 | 宁波吉利汽车研究开发有限公司 | Vehicle track prediction method, vehicle collision early warning method, device and storage medium |
CN111824157A (en) * | 2020-07-14 | 2020-10-27 | 广州小鹏车联网科技有限公司 | Automatic driving method and device |
WO2022022384A1 (en) * | 2020-07-31 | 2022-02-03 | 华为技术有限公司 | Method and device for recognizing vehicle motion state |
CN114056347A (en) * | 2020-07-31 | 2022-02-18 | 华为技术有限公司 | Vehicle motion state identification method and device |
CN114283576A (en) * | 2020-09-28 | 2022-04-05 | 华为技术有限公司 | Vehicle intention prediction method and related device |
CN112562328A (en) * | 2020-11-27 | 2021-03-26 | 腾讯科技(深圳)有限公司 | Vehicle behavior prediction method and device |
CN112833903A (en) * | 2020-12-31 | 2021-05-25 | 广州文远知行科技有限公司 | Trajectory prediction method, apparatus, device and computer readable storage medium |
CN112833903B (en) * | 2020-12-31 | 2024-04-23 | 广州文远知行科技有限公司 | Track prediction method, device, equipment and computer readable storage medium |
EP4286972A4 (en) * | 2021-02-26 | 2024-03-27 | Huawei Technologies Co., Ltd. | Vehicle driving intention prediction method and apparatus, terminal and storage medium |
WO2022178858A1 (en) * | 2021-02-26 | 2022-09-01 | 华为技术有限公司 | Vehicle driving intention prediction method and apparatus, terminal and storage medium |
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