CN110610271B - Multi-vehicle track prediction method based on long and short memory network - Google Patents
Multi-vehicle track prediction method based on long and short memory network Download PDFInfo
- Publication number
- CN110610271B CN110610271B CN201910876478.3A CN201910876478A CN110610271B CN 110610271 B CN110610271 B CN 110610271B CN 201910876478 A CN201910876478 A CN 201910876478A CN 110610271 B CN110610271 B CN 110610271B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- network
- vehicles
- probability
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000009826 distribution Methods 0.000 claims abstract description 33
- 238000013528 artificial neural network Methods 0.000 claims abstract description 26
- 238000012549 training Methods 0.000 claims abstract description 22
- 230000006870 function Effects 0.000 claims abstract description 20
- 238000005070 sampling Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000000875 corresponding effect Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 230000002596 correlated effect Effects 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 3
- 238000011478 gradient descent method Methods 0.000 claims description 2
- 230000002123 temporal effect Effects 0.000 claims description 2
- 230000006399 behavior Effects 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a track prediction method of multiple vehicles based on a long and short memory network, which takes the characteristics of historical tracks of a main vehicle and adjacent vehicles as input, and fully considers the mutual influence of the positions of the vehicles and the driving behaviors; after the network coding and decoding, the output future track of the adjacent vehicle is further input into the mixed density network, the probability distribution of the vehicle position is estimated, the error of the mixed density network, the root mean square error of the track result and the parameter regularization term of the coder decoder network are combined in each training to form a loss function and guide the updating of the network parameters, so that the prediction accuracy of the applied network can be improved, the trained neural network can predict the position information with probability of the adjacent vehicle, the position information forms a continuous track according to the time sequence, and the main vehicle can be assisted to make decision and plan.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to a track prediction method for multiple vehicles based on a long and short memory network.
Background
Autopilot and related research has advanced greatly over the past few decades. However, achieving high levels of autopilot in complex urban environments such as highways still presents significant challenges. This is because in such an environment the driving behaviour of unmanned vehicles is in a highly dynamic variation and the driving strategies and the locations of the different road participants may be influenced by each other, resulting in driving behaviour that is too aggressive or conservative if the estimates of the locations of other vehicles in the surroundings are lost. For example, a vehicle in an adjacent lane suddenly changes lanes, which may cause a vehicle rear-end collision without predictive precautions.
Under the condition, the future tracks and speeds of other vehicles in the road need to be predicted, so that the predicted information and the state information of the main vehicle are combined, the decision and layout path planning of the unmanned vehicle are realized, and the unmanned vehicle is helped to efficiently avoid obstacles and drive more comfortably. In the field of trajectory prediction of unmanned vehicles, most of the existing methods randomly select a target vehicle from numerous data based on internet of vehicles information, and then predict the trajectory of a certain vehicle around the target vehicle. Such a system would lead to the following problems: 1. the condition that the sensor view angle of the unmanned vehicle is limited in the actual driving process is not met, and generally speaking, the main vehicle running on the road can only obtain the position information of the adjacent vehicles around the main vehicle through the sensor; 2. the primary prediction result only contains the future track of one vehicle, so that the efficiency is low; 3. the mutual influence among the driving behaviors of the vehicles in a certain range of vehicle clusters is ignored, so that the predicted result cannot be applied to the actual situation.
Disclosure of Invention
In view of this, the invention provides a method for predicting a track of multiple vehicles based on a long and short memory network, which can accurately predict the positions of adjacent vehicles.
A multiple vehicle trajectory prediction method includes the following steps:
by analogy, obtaining a plurality of pieces of training data;
Obtaining the probability of each sampling moment of all adjacent vehicles based on the probability result of each sampling moment of each vehicleSumming the values, calculating the probability sum of all sampling moments, and finally calculating the negative logarithm of the sum to obtain F2；
The regularization terms of the neural network parameters are summed by the squares to obtain F3；
The final loss function of the neural network is shown as follows:
and 6, updating each parameter by adopting an Adam gradient descent method based on the final loss function calculated in the step 5, namely: the loss function calculates gradient of each parameter distribution of the neural network, updates each parameter based on the gradient value, returns to the step 2 based on the updated parameter to continue iteration until the change rate of the loss function is lower than a set threshold value, and stops iteration;
and 7, predicting the position of the adjacent vehicle by using the neural network of the current updated parameters.
Preferably, the number of the adjacent vehicles is within 6.
Preferably, the neighboring position includes x-direction position information and y-direction speed information.
Preferably, the time period S1 is 50S; the time period S2 is less than 30S.
Preferably, the sampling frequency is 10 Hz.
The invention has the following beneficial effects:
the invention relates to a track prediction method of multiple vehicles based on a long and short memory network, which is based on a network architecture of a coder decoder taking the long and short memory network as a unit, and is based on the view angle of an unmanned vehicle, utilizes the historical tracks of surrounding vehicles recorded by a vehicle-mounted sensor, combines the characteristics of the position coordinate information of the vehicle, the lane where the vehicle is located, the anti-collision distance with the front vehicle and the like, and generates future track prediction of 6 vehicles at most at one time through a constructed neural network architecture; the neural network takes the characteristics of the history tracks and the like of the main vehicle and the adjacent vehicles as input, and fully considers the mutual influence of the positions of the vehicles and the driving behaviors; after the network coding and decoding, the output future track of the adjacent vehicle is further input into the mixed density network, the probability distribution of the vehicle position is estimated, the error of the mixed density network, the root mean square error of the track result and the parameter regularization term of the coder decoder network are combined in each training to form a loss function and guide the updating of the network parameters, so that the prediction accuracy of the applied network can be improved, the trained neural network can predict the position information with probability of the adjacent vehicle, the position information forms a continuous track according to the time sequence, and the main vehicle can be assisted to make decision and plan.
Drawings
FIG. 1 is a network framework diagram of the multiple vehicle trajectory prediction method based on long and short memory networks according to the present invention;
FIG. 2 is a schematic diagram of a coordinate system of data used in the present invention;
3-1, 3-2 and 3-3 are track prediction truth values and prediction value results of 6 adjacent vehicles in three different scenes in the embodiment respectively; wherein the scene is a random scene.
FIG. 4 shows the results of the prediction of the trajectory of three vehicles ahead of the host vehicle in the case of lane keeping driving, wherein:
the left diagram in fig. 4-1 is a schematic diagram of a driving scenario, and the right diagram is three predicted trajectories with probability bands.
FIG. 4-2 is a probability graph of spatiotemporal trajectories, with the x-axis, the y-axis representing actual position, and the z-axis representing the time axis, where three spatiotemporal trajectories with probability bands are observed.
Fig. 4-3 are cross-sectional views of spatio-temporal trajectory probability maps respectively intersecting along a plane defined parallel to the x-y axis at times when the z-axis t is 2s and t is 3s, where the instantaneous positions of the three vehicles a, B, and C with probability bands are observed, illustrating the probability distribution of the predicted vehicle positions in the neighborhood within the prediction time, which can be obtained by prediction of the present model.
Fig. 5 shows three predicted vehicles a, B and C ahead of the host vehicle in the case of lane change.
Fig. 5-1 includes a schematic diagram of a driving scenario and three predicted trajectories with probability bands.
FIG. 5-2 is a probability graph of spatiotemporal trajectories, with the x-axis, the y-axis representing actual position, and the z-axis representing the time axis, where three spatiotemporal trajectories with probability bands are observed.
Fig. 5-3 are cross-sectional views of the spatio-temporal trajectory probability map, each along a plane defined parallel to the x-y axis, at times when the z-axis t is 2s and t is 3s, and the instantaneous positions of the probability bands of three vehicles a, B, and C are observed when t is 2s, and the instantaneous positions of the probability bands of only two vehicles a and C are observed when t is 3 s.
Detailed Description
Aiming at the condition that the position and the speed of a vehicle are in a height change state in a high-speed environment and the condition that the vehicles between adjacent lanes influence each other, the invention utilizes a vehicle-mounted sensor laser radar to obtain the relative position information of the adjacent vehicles (or a visual sensor to obtain image data and a target tracking method to obtain the relative positions of surrounding vehicles) based on the visual angle of a main vehicle of an unmanned vehicle, records the track of the surrounding 6 vehicles which directly influence the main vehicle in the history of 5s, and outputs the track of a surrounding vehicle group in the future of 3s and the probability distribution of the track in one step by combining a mixed density network through the structure of a neural network of a coder decoder based on a long and short memory network, thereby assisting the main vehicle to make decision and plan. The specific process comprises the following steps:
specifically, in the above formula
Represent the velocity in the y-direction, not the distance. This is because during the course of the travel of the vehicle,the speed in the y direction is usually limited within a certain range, and is more suitable for being used as the input of a neural network. And integrating the predicted speed in the y direction to obtain the movement distance in the y direction.
And 2, sorting out the data preprocessed in the step 1 in a batch processing mode. Sequentially sending the data into an encoder which takes a deep-layer long and short memory network as a basic unit according to the time sequence. The encoder converts the input historical track data into a coded vector after encoding, wherein the vector contains high-dimensional features extracted from surrounding vehicle clusters. Then the coded vector is sent to a decoder which takes a deep long and short memory network as a basic unit, the coded vector is used as an initial hidden state of the decoder, and the last frame of data of 50 frames of historical data is used as the initial time input of the decoder, so that the predicted track and the historical track can be naturally connected. The decoder sequentially outputs the position information of 6 vehicles in the future within 3s according to the time sequence. In order to make the network pay more attention to the historical track information of the time adjacent to the current moment, an attention mechanism is used for giving different weights to the input historical track, so that the influence degree of positions at different historical times on future positions is displayed.
And 3, because the future tracks of the vehicles are in multi-modal distribution (the future tracks of the vehicles with the same historical track can be influenced by the driving habits of drivers and the like to generate different results), on the basis of the network architecture of the original coder decoder, a mixed density network is added to estimate the probability distribution of the future tracks, errors generated by the mixed density network are fed back to the original network, and the parameters of the network are guided to be optimized together by combining the original loss function. It is assumed that estimated values of x and y coordinates of the vehicle are respectively gaussian-distributed, and two variables are associated with each other. And (4) further mapping the output result in the step (2) through a full connection layer network to form characteristics. The data is fed into a mixed density network which is trained to output 6 sets of parameters with a Gaussian distribution, assuming the predicted vehicle at t, labeled (j), is considerediProbability of time position coordinates:
first, the mixed density network input is passed through the full linkThe prediction results of the layer processing are connected, and 6 groups of parameters describing Gaussian distribution are output
(because the input to the density network is mixed with ti-1The data at a time is correlated, so the lower corner of its output parameter representing time is denoted ti-1) Wherein the upper corner mark (j) represents the number of the vehicle, k represents the number of the gaussian distribution, and the lower corner mark represents the time. Therefore, the temperature of the molten metal is controlled,
coefficients for weighting among the parameters representing the k-th set of gaussian distributions,
the physical quantities describing the mean values of the x-direction and y-direction positions in the parameters respectively representing the k-th group of gaussian distributions,
representing the standard deviation in the x-direction and the y-direction,
and the correlation coefficients in the x direction and the y direction are correlated in the parameters representing the k-th group of Gaussian distribution. In order to make the parameters output by the mixed density network meet the requirement of the parameter range, the parameter values directly output by the mixed density network need to be further normalized:
wherein, the weight of the Gaussian distribution is subjected to normalization calculation after taking an index; the mean value is directly substituted without processing; taking an index calculation result of the standard deviation; the correlation coefficient is a value calculated by a hyperbolic tangent function.
Using a plurality of Gaussian distribution weights to fit the distribution result of the position coordinates of each vehicle in the x direction and the y direction at each moment, and calculating the position coordinates of the vehicle (j) at t according to a formulaiProbability of the location of the moment:
wherein,
is the historical track of 6 vehicles.
Representing the kth gaussian distribution used to fit the vehicle position, the probability density function of which can be calculated from the parameters output by the mixture density network:
in conclusion, the probability density functions of 6 Gaussian distributions are subjected to weighted fitting to obtain the t-th vehicle of the mark (j)iProbability of where the time of day is located.
And 4, constructing an error function to calculate a gradient, so as to update parameters of the neural network. On the basis of the loss function of the neural network of the original encoder and decoder, the error of the mixed density network is used as feedback to jointly build the error of the whole neural network, so that the parameter updating and convergence speed of the neural network are accelerated, and the probability distribution corresponding to the predicted track result is calculated. The loss function of the neural network is composed of three parts, namely:
(1) the difference between the predicted vehicle trajectory value and the true trajectory value, i.e., the root mean square error, output by the decoder;
(2) taking a negative logarithm form for the obtained probability result in the mixed density network;
(3) and training the regularization term of the updated network parameters.
The final loss function is shown as follows:
wherein T represents a time range and takes a value of 30(3s, 10 Hz); j represents the number of vehicles that fit within the calculation range, and is 6; n represents the number of parameter variables participating in the update in the network, K represents the number of gaussian distributions used to fit the mean and variance, and is 6; w1,W2And respectively representing weights for adjusting parameter regularization and adjusting mixed density network errors, and manually setting according to experience.
And 6, calculating the gradient by adopting an Adam gradient descent algorithm, and updating parameters of the neural network, wherein the specific updating process of the parameters is shown in the following table.
When the error convergence speed of the network is slowed down and the error change rate is lower than a certain threshold value delta, the network finishes the task learning, at the moment, the network model is saved, and the training process is terminated. And combining the positions of 6 vehicles in time sequence output by the encoder and the corresponding Gaussian distribution parameters output by the mixed density network to finally form a continuous predicted track with a probability band. And drawing a two-dimensional top view, a three-dimensional view and a cross-sectional view of all time of the track prediction result for the decision and planning of the unmanned vehicle.
Example (b):
in this embodiment, the specific hyper-parameters of the neural network are shown in the following table:
as shown in fig. 2, the figure includes an unmanned vehicle, i.e., a host vehicle (vehicle numbered (0)) and 6 surrounding vehicles (6 vehicles numbered (1) - (6)), the origin O of the coordinate system is at the geometric center of the host vehicle, the y-axis is along the tangential direction of the road advancing direction, the x-axis is perpendicular to the y-axis, and the left edge line of the road is directed to the right edge line.
As shown in FIG. 3, the method based on the encoder/decoder structure of the long and short memory network obtains the true value and the predicted value result of the trajectory prediction of the vehicle under 3 scenarios (FIG. 3-1, FIG. 3-2, and FIG. 3-3). Wherein, each test scene contains the test results of the vehicles with the numbers (1) to (6). In order to show the generalization capability of the network, the track graph corresponding to each label is actually formed by connecting the tracks of 16 groups of vehicles with the same label end to end, corresponding to 48s, and comprising 480 frames of data.
As shown in fig. 4, in order to keep the lane driving situation, only three predicted vehicles a, B, and C located in front of the host vehicle are considered in the figure for the sake of observation. Fig. 4-1 includes a schematic diagram of a driving scenario and three predicted trajectories with probability bands. FIG. 4-2 is a probability graph of spatiotemporal trajectories, with the x-axis, the y-axis representing actual position, and the z-axis representing the time axis, where three spatiotemporal trajectories with probability bands are observed. Fig. 4-3 are cross-sectional views of the spatio-temporal trajectory probability map, each along a plane defined parallel to the x-y axis, intersecting at times when the z-axis t is 2s and t is 3s, and the instantaneous positions of the three vehicles a, B, and C with the probability bands are observed when t is 2s and t is 3 s.
As shown in fig. 5, in the case where there is a lane change of the vehicle, only three predicted vehicles a, B, and C located in front of the host vehicle are considered in the figure for the sake of convenience of observation. Fig. 5-1 includes a schematic diagram of a driving scenario and three predicted trajectories with probability bands. FIG. 5-2 is a probability graph of spatiotemporal trajectories, with the x-axis, the y-axis representing actual position, and the z-axis representing the time axis, where three spatiotemporal trajectories with probability bands are observed. Fig. 5-3 are cross-sectional views of the spatio-temporal trajectory probability map, each along a plane defined parallel to the x-y axis, intersecting at the time when the z-axis t is 2s and t is 3s, wherein the instantaneous positions of the probability bands of three vehicles a, B, and C are observed when t is 2s, and the instantaneous positions of the probability bands of only two vehicles a and C are observed when t is 3 s.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A multiple vehicle trajectory prediction method is characterized by comprising the following steps:
step 0, sampling the position of the main vehicle, the lane where the main vehicle is located, the distance between the main vehicle and the front vehicle and the positions of adjacent vehicles around the main vehicle within a set time S1 to obtain historical data; sampling the positions of adjacent vehicles around the main vehicle in a set time period S2 after the time period S1 to obtain prediction data serving as a label of historical data during neural network training; the historical data and the corresponding labels form a piece of training data;
by parity of reasoning, a plurality of pieces of training data are obtained;
step 1, constructing a plurality of encoders taking a deep long and short memory network as a basic unit and a plurality of decoders taking the deep long and short memory network as a basic unit, thereby forming a neural network;
step 2, randomly selecting training data with a set number from the training data obtained in the step 0, and sequentially sending each piece of training data into an encoder according to the time sequence; the encoder converts input data into a coded vector after encoding, and then sends the coded vector into the decoder, wherein the coded vector is used as an initial hidden state of the decoder; the decoder outputs the predicted value of the position of the adjacent vehicle at each sampling moment in the time period S2;
step 3, decoding the decoder in the step 2The output predicted value is input into a full connection layer network and is mapped into characteristic data; the characteristic data is sent into a mixed density network, and 6 groups of description current time t are output through trainingiPrevious time t ofi-1Parameters of the temporal Gaussian distribution
Wherein, the upper corner mark (j) represents the mark number of the vehicle, k represents the serial number of the Gaussian distribution, and the lower corner mark represents the time;
coefficients for weighting among the parameters representing the k-th set of gaussian distributions,
the physical quantities describing the mean values of the x-direction and y-direction positions in the parameters respectively representing the k-th group of gaussian distributions,
representing the standard deviation in the x-direction and the y-direction,
correlation coefficients in x direction and y direction in parameters representing the kth group of Gaussian distribution are correlated;
step 4, performing normalization calculation after the weight output in the step 3 is indexed to obtain updated weight; taking an exponential calculation result of the standard deviation output in the step 3, taking a numerical value obtained by calculating a correlation number by a hyperbolic tangent function, and then substituting the numerical value into a formula (1) to calculate the t of the vehicle at the time of (j)iProbability of the location of the moment:
wherein,
representing a kth gaussian distribution for fitting the vehicle position;
step 5, calculating the root mean square error between the position predicted value output by the decoder and the label, and recording as F1；
Based on the probability result of each sampling time of each vehicle, obtaining the probability and value of each sampling time of all adjacent vehicles, then solving the probability sum of all sampling times, and finally solving the negative logarithm of the sum to obtain F2；
The regularization terms of the neural network parameters are summed by square to obtain F3；
The final loss function of the neural network is shown as follows:
and 6, updating each parameter by adopting an Adam gradient descent method based on the final loss function calculated in the step 5, namely: the loss function calculates gradient of each parameter distribution of the neural network, updates each parameter based on the gradient value, returns to the step 2 based on the updated parameter to continue iteration until the change rate of the loss function is lower than a set threshold value, and stops iteration;
and 7, predicting the position of the adjacent vehicle by using the neural network of the current updated parameters.
2. The method of claim 1, wherein the number of neighboring vehicles is less than 6.
3. The method of claim 1, wherein the neighboring vehicle position comprises x-direction position information and y-direction speed information.
4. The multiple vehicle trajectory prediction method according to claim 1, characterized in that the time period S1 is 50S; the time period S2 is less than 30S.
5. The method of claim 4, wherein the sampling frequency is 10 Hz.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910876478.3A CN110610271B (en) | 2019-09-17 | 2019-09-17 | Multi-vehicle track prediction method based on long and short memory network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910876478.3A CN110610271B (en) | 2019-09-17 | 2019-09-17 | Multi-vehicle track prediction method based on long and short memory network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110610271A CN110610271A (en) | 2019-12-24 |
| CN110610271B true CN110610271B (en) | 2022-05-13 |
Family
ID=68892800
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910876478.3A Active CN110610271B (en) | 2019-09-17 | 2019-09-17 | Multi-vehicle track prediction method based on long and short memory network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110610271B (en) |
Families Citing this family (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11900224B2 (en) * | 2019-12-26 | 2024-02-13 | Waymo Llc | Generating trajectory labels from short-term intention and long-term result |
| CN111161322B (en) * | 2019-12-31 | 2022-10-18 | 大连理工大学 | LSTM neural network pedestrian trajectory prediction method based on human-vehicle interaction |
| CN111340543B (en) * | 2020-02-25 | 2024-03-22 | 上海大学 | Fine granularity taxi taking demand prediction method based on attention mechanism |
| CN111401233A (en) * | 2020-03-13 | 2020-07-10 | 商汤集团有限公司 | Trajectory prediction method, apparatus, electronic device, and medium |
| CN111767475A (en) * | 2020-03-20 | 2020-10-13 | 吉利汽车研究院（宁波）有限公司 | Target pedestrian trajectory prediction model training method and device, electronic equipment and storage medium |
| CN111114543B (en) * | 2020-03-26 | 2020-07-03 | 北京三快在线科技有限公司 | Trajectory prediction method and device |
| CN113496268B (en) * | 2020-04-08 | 2024-07-05 | 北京图森智途科技有限公司 | Track prediction method and device |
| CN112703506B (en) * | 2020-04-22 | 2022-04-08 | 华为技术有限公司 | Lane line detection method and device |
| CN111583715B (en) * | 2020-04-29 | 2022-06-03 | 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 |
| CN114189449B (en) * | 2020-08-25 | 2023-03-24 | 同济大学 | Open type unmanned vehicle group model of expressway scene and vehicle group forming method |
| US11873006B2 (en) * | 2020-09-21 | 2024-01-16 | GM Global Technology Operations LLC | Virtual lane estimation using a recursive self-organizing map |
| CN112215487B (en) * | 2020-10-10 | 2023-05-23 | 吉林大学 | Vehicle running risk prediction method based on neural network model |
| CN114440908B (en) * | 2020-10-31 | 2023-07-28 | 华为技术有限公司 | Method and device for planning driving path of vehicle, intelligent vehicle and storage medium |
| CN114543800B (en) * | 2020-11-18 | 2024-08-30 | 蘑菇车联信息科技有限公司 | Processing method and related device for vehicle movement track estimation |
| CN112885079B (en) * | 2021-01-11 | 2022-11-29 | 成都语动未来科技有限公司 | Vehicle track prediction method based on global attention and state sharing |
| CN112906793B (en) * | 2021-02-22 | 2023-12-22 | 深圳市市政设计研究院有限公司 | Monitoring data repairing method and system for bridge health monitoring system |
| CN113076599A (en) * | 2021-04-15 | 2021-07-06 | 河南大学 | Multimode vehicle trajectory prediction method based on long-time and short-time memory network |
| CN113240115B (en) * | 2021-06-08 | 2023-06-06 | 深圳数联天下智能科技有限公司 | Training method for generating face change image model and related device |
| CN113435356B (en) * | 2021-06-30 | 2023-02-28 | 吉林大学 | Track prediction method for overcoming observation noise and perception uncertainty |
| CN113642682B (en) * | 2021-10-14 | 2021-12-31 | 北京理工大学 | Trajectory primitive extraction and analysis method and system under multi-vehicle interaction environment |
| CN114757355B (en) * | 2022-04-08 | 2024-04-02 | 中国科学技术大学 | Track data set variability measurement method, system, equipment and storage medium |
| CN115371678A (en) * | 2022-08-22 | 2022-11-22 | 广州足下科技有限公司 | Autonomous navigation method for mobile robot |
| CN118658304B (en) * | 2024-08-19 | 2024-10-25 | 青岛理工大学 | Speed field-based networked autonomous vehicle position prediction and risk quantification method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019023628A1 (en) * | 2017-07-27 | 2019-01-31 | Waymo Llc | Neural networks for vehicle trajectory planning |
| 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 |
| CN109910909A (en) * | 2019-02-25 | 2019-06-21 | 清华大学 | A kind of interactive prediction technique of vehicle track net connection of more vehicle motion states |
-
2019
- 2019-09-17 CN CN201910876478.3A patent/CN110610271B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019023628A1 (en) * | 2017-07-27 | 2019-01-31 | Waymo Llc | Neural networks for vehicle trajectory planning |
| 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 |
| CN109910909A (en) * | 2019-02-25 | 2019-06-21 | 清华大学 | A kind of interactive prediction technique of vehicle track net connection of more vehicle motion states |
Non-Patent Citations (1)
| Title |
|---|
| Mengyin Fu ; Ting Zhang ; Wenjie Song ; Yi Yang ; Meiling Wang."Trajectory Prediction-Based Local Spatio-Temporal Navigation Map for Autonomous Driving in Dynamic Highway Environments".《IEEE Transactions on Intelligent Transportation Systems》.2021, * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110610271A (en) | 2019-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110610271B (en) | Multi-vehicle track prediction method based on long and short memory network | |
| Huang et al. | Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving | |
| CN114384920A (en) | Dynamic obstacle avoidance method based on real-time construction of local grid map | |
| Grigorescu et al. | Neurotrajectory: A neuroevolutionary approach to local state trajectory learning for autonomous vehicles | |
| CN112734808B (en) | Trajectory prediction method for vulnerable road users in vehicle driving environment | |
| CN113044064B (en) | Vehicle self-adaptive automatic driving decision method and system based on meta reinforcement learning | |
| CN112651374B (en) | Future trajectory prediction method based on social information and automatic driving system | |
| CN114005280A (en) | Vehicle track prediction method based on uncertainty estimation | |
| CN114399743B (en) | Method for generating future track of obstacle | |
| Huang et al. | Deductive reinforcement learning for visual autonomous urban driving navigation | |
| CN115147790B (en) | Future track prediction method of vehicle based on graph neural network | |
| Cho et al. | Deep predictive autonomous driving using multi-agent joint trajectory prediction and traffic rules | |
| CN110986945B (en) | Local navigation method and system based on semantic altitude map | |
| CN114185339A (en) | Mobile robot path planning method in dynamic environment | |
| CN115547040A (en) | Driving behavior prediction method based on inner neural network under safety potential field | |
| CN111695737A (en) | Group target advancing trend prediction method based on LSTM neural network | |
| CN115062202A (en) | Method, device, equipment and storage medium for predicting driving behavior intention and track | |
| WO2023242223A1 (en) | Motion prediction for mobile agents | |
| CN116679711A (en) | Robot obstacle avoidance method based on model-based reinforcement learning and model-free reinforcement learning | |
| Ginerica et al. | Observenet control: A vision-dynamics learning approach to predictive control in autonomous vehicles | |
| Yoon et al. | Trajectory Prediction Using Graph-Based Deep Learning for Longitudinal Control of Autonomous Vehicles: A Proactive Approach for Autonomous Driving in Urban Dynamic Traffic Environments | |
| CN117408406A (en) | Planning coupling multi-target vehicle track prediction method and system | |
| Ye et al. | GSAN: Graph self-attention network for interaction measurement in autonomous driving | |
| CN115700509A (en) | Guideline generation method and system based on simulation feedback data | |
| Yang et al. | Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |