CN109927719A - A kind of auxiliary driving method and system based on barrier trajectory predictions - Google Patents
A kind of auxiliary driving method and system based on barrier trajectory predictions Download PDFInfo
- Publication number
- CN109927719A CN109927719A CN201711351196.9A CN201711351196A CN109927719A CN 109927719 A CN109927719 A CN 109927719A CN 201711351196 A CN201711351196 A CN 201711351196A CN 109927719 A CN109927719 A CN 109927719A
- Authority
- CN
- China
- Prior art keywords
- barrier
- dynamic
- vehicle
- dynamic barrier
- driving trace
- 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.)
- Granted
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
The application provides a kind of auxiliary driving method and system based on barrier trajectory predictions, and the method includes obtaining the environmental data around collected vehicle of onboard sensor；Based on the environmental data, the travelable region of the dynamic barrier around this vehicle is determined；Historic state information and travelable region using dynamic barrier, predict the driving trace of dynamic barrier；Judge the risk status that the driving trace of dynamic barrier conflicts with this vehicle driving trace.The risk factor that barrier track Yu this wheel paths can be calculated, gives warning in advance.
Description
[technical field]
This application involves automation field more particularly to a kind of auxiliary driving method based on barrier trajectory predictions and
System.
[background technique]
It common are adaptive cruise control system (Adaptive Cruise in existing vehicle DAS (Driver Assistant System)
Control) and emergency automatic brake system (Autonomous Emergency Brake), system pass through travel road where detection
The barrier state on road just determines after certain conditions inspire and takes acceleration or brake deceleration strategy.
But in place of these existing vehicle DAS (Driver Assistant System) Shortcomings:
1, pre-warning time is shorter, due to using passive type detection technique, only when these conditions are triggered, such as works as obstacle
Object enters running region, just takes alarm or movement, and corresponding strategy can not be taken before barrier enters running region.Usually
When these conditions are triggered, vehicle has been in certain precarious position, keep for driver or DAS (Driver Assistant System) when
Between be often not sufficient to ensure that safety, it is even more impossible to guarantee comfort.
2, it is relatively narrow to be applicable in scene, only can be suitably used for the simple road scene of road scene, such as expressway, urban loop, very
Difficulty is applicable in common complicated urban road scene.
3, lack the ability to predict of active.
[summary of the invention]
The many aspects of the application provide a kind of auxiliary driving method and system based on barrier trajectory predictions, based on
The risk factor for calculating barrier track and this wheel paths, gives warning in advance.
The one side of the application provides a kind of auxiliary driving method based on barrier trajectory predictions, comprising:
Obtain the environmental data around collected vehicle of onboard sensor；
Based on the environmental data, the travelable region of the dynamic barrier around this vehicle is determined；
Historic state information and travelable region using dynamic barrier, predict the driving trace of dynamic barrier；
Judge the risk status that the driving trace of dynamic barrier conflicts with this vehicle driving trace.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the environment number
According to including: dynamic barrier, static-obstacle thing and traffic signals.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation is based on the ring
Border data determine that the travelable region of the dynamic barrier around this vehicle includes:
According to preset traffic rules, analyze dynamic barrier and dynamic barrier, dynamic barrier and static-obstacle thing,
Relationship between dynamic barrier and traffic signals extracts all travelable regions of dynamic barrier.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation is hindered using dynamic
Hinder object historic state information and travelable region, predict that the driving trace of dynamic barrier includes:
The historic state information of dynamic barrier and travelable region are inputted into barrier trajectory predictions model, prediction dynamic
The driving trace of barrier.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the barrier
Trajectory predictions model is deep neural network model.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation judges that dynamic hinders
The risk status for hindering the driving trace of object to conflict with this vehicle driving trace includes:
According to the time difference of the prediction locus of dynamic barrier and this vehicle driving trace same position；Or, same time point
Speed difference, range difference judge danger coefficient.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the traveling of this vehicle
Track is that the control instruction sent according to the current state information of this vehicle and this vehicle control is predicted.
The another aspect of the application provides a kind of DAS (Driver Assistant System) based on barrier trajectory predictions, comprising:
Module is obtained, for obtaining the environmental data around collected vehicle of onboard sensor；
It can travel area determination module, for being based on the environmental data, determine the dynamic barrier around this vehicle can
Running region；
Barrier trajectory prediction module, historic state information and travelable region for utilization dynamic barrier, prediction
The driving trace of dynamic barrier；
Judgment module, the risk status to conflict for judging the driving trace of dynamic barrier with this vehicle driving trace.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the environment number
According to including: dynamic barrier, static-obstacle thing and traffic signals.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, it is described to can travel
Area determination module is specifically used for:
According to preset traffic rules, analyze dynamic barrier and dynamic barrier, dynamic barrier and static-obstacle thing,
Relationship between dynamic barrier and traffic signals extracts all travelable regions of dynamic barrier.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the barrier
Trajectory prediction module is specifically used for:
The historic state information of dynamic barrier and travelable region are inputted into barrier trajectory predictions model, prediction dynamic
The driving trace of barrier.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the barrier
Trajectory predictions model is deep neural network model.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the judgement mould
Block is specifically used for:
According to the time difference of the prediction locus of dynamic barrier and this vehicle driving trace same position；Or, same time point
Speed difference, range difference judge danger coefficient.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the system is also
Including this wheel paths prediction module, the control instruction for being sent according to the current state information of this vehicle and this vehicle control is pre-
Survey this wheel paths.
Another aspect of the present invention, provides a kind of computer equipment, including memory, processor and is stored in the storage
On device and the computer program that can run on the processor, the processor are realized as previously discussed when executing described program
Method.
Another aspect of the present invention provides a kind of computer readable storage medium, is stored thereon with computer program, described
Method as described above is realized when program is executed by processor.
By the technical solution it is found that the embodiment of the present application can calculate the risk system of barrier track Yu this wheel paths
Number, gives warning in advance.
[Detailed description of the invention]
It in order to more clearly explain the technical solutions in the embodiments of the present application, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is some realities of the application
Example is applied, it for those of ordinary skill in the art, without any creative labor, can also be attached according to these
Figure obtains other attached drawings.
Fig. 1 is the process signal for the auxiliary driving method based on barrier trajectory predictions that one embodiment of the application provides
Figure；
Fig. 2 is a kind of schematic diagram of concrete scene in the embodiment of the present application；
Fig. 3 is the schematic diagram of another concrete scene in the embodiment of the present application；
Fig. 4 is the structural representation for the DAS (Driver Assistant System) based on barrier trajectory predictions that another embodiment of the application provides
Figure；
Fig. 5 shows the frame for being suitable for the exemplary computer system/server 012 for being used to realize embodiment of the present invention
Figure.
[specific embodiment]
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is
Some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art
Whole other embodiments obtained without creative efforts, shall fall in the protection scope of this application.
Fig. 1 is the schematic diagram for the auxiliary driving method based on barrier trajectory predictions that one embodiment of the application provides, such as
Shown in Fig. 1, comprising the following steps:
Step S11, the environmental data around collected vehicle of onboard sensor is obtained；
Step S12, it is based on the environmental data, determines the travelable region of the dynamic barrier around this vehicle；
Step S13, using the historic state information of dynamic barrier and travelable region, the traveling of dynamic barrier is predicted
Track；
Step S14, judge the risk status that the driving trace of dynamic barrier conflicts with this vehicle driving trace.
In a kind of preferred implementation of step S11,
Preferably, onboard sensor data collected are obtained, detect the dynamic disorder around this vehicle based on the data
Object, static-obstacle thing and traffic signals；And obtain the position of the dynamic barrier, static-obstacle thing and traffic signals, type
Information.
The onboard sensor includes: the phase of the upper end for being mounted on this Chinese herbaceous peony windshield, tailstock rear end, vehicle body two sides
Machine is mounted on the millimetre-wave radar of this Chinese herbaceous peony bumper center, front bumper two sides, rear bumper two sides, is mounted on this roof
Portion center, front end, left and right side laser radar, be mounted on the GPS-IMU integrated navigation module of top-support rear end
Deng.Form 360 degree of perception centered on this vehicle.
Preferably, the step includes following sub-step:
Sub-step S111, the Fast synchronization that multisensor is carried out by synchronous plate card, or, acquiring multiple biographies by timeline
The data information of sensor is realized multi-sensor data Collect jointly and is detected based on data collected.
Wherein, testing result includes: dynamic barrier, including but not limited to pedestrian, vehicle (motor vehicle and non-motor vehicle)
Deng；Static-obstacle thing, guardrail etc. including but not limited between roadblock facility, lane；Traffic signals, including but not limited to traffic signals
Lamp, traffic sign and traffic marking etc..
Preferably,
Control camera acquisition image is simultaneously detected；
Preferably, multiple cameras are controlled and acquire image from different orientation.It is acquired according to multiple cameras from different orientation
The image arrived；Carry out characteristic point acquisition and Feature Points Matching；Multiple two-dimensional plane coordinate institutes structure in all camera imaging faces
At a plurality of space different surface beeline, by three-dimensional coordinate location algorithm carry out three-dimensional point reconstruct, obtain obstacle article coordinate.
Preferably, image recognition, including identification type of vehicle, vehicle turn signal are carried out to image, identifies pedestrian, identification letter
Guardrail etc. between the position of signal lamp, type, color, and identification traffic sign, roadmarking, roadblock facility, lane.
In the present embodiment, it is shot with 10 frames speed per second.
Control the reflection signal that millimetre-wave radar obtains target, it is preferable that go to detect using FMCW continuously linear frequency-modulated wave
The range information of barrier receives the time delay i.e. phase difference of signal by multiple receiving antennas to detect the orientation of barrier letter
Breath.
The GPS signal and inertial navigation signal that this vehicle is obtained by GPS-IMU integrated navigation module, calculate the position of this vehicle
And posture information.
Preferably, laser radar acquisition laser point cloud data can also be controlled and detected, the supplement as camera.It is excellent
Selection of land, laser radar are constantly issued laser in this process and are collected reflection point with certain angular speed uniform rotation
Information, to obtain comprehensive environmental information.Laser radar can also be recorded simultaneously during collecting reflection point distance
The time and level angle that the point occurs, and each laser emitter has the vertical angle of number and fixation, according to these
Data can calculate the coordinate of all reflection points.Laser radar often rotates a circle the set of all reflection point coordinates being collected into
It is formed a cloud.The interference in laser point cloud, and the shape space position feature according to target are filtered out using filter, is passed through
The method of pattern clustering analysis carries out target detection, obtains obstacle identity；By adjusting the method for distance threshold, by cluster point
At subgroup reconsolidate, determine that new cluster centre realizes target positioning, obtain obstacle article coordinate.
Sub-step S112, selection reference frame, will test coordinate of the result in each sensor coordinate system and are transformed into ginseng
Examine coordinate system；
The sensor initial space configuration be it is previously known, can be according to multiple sensors on this vehicle car body
Measurement data obtains.
It, can be by barrier according to barrier and the relative positional relationship of this vehicle and the position and posture information of this vehicle
Location information be transformed into unified reference frame.The reference frame can be earth coordinates, to facilitate further
Processing.
Preferably, the location information of obtained each dynamic barrier in different time points is detected according to lasting, it is available
The status information of each dynamic barrier, including current state information and historic state information.The status information includes each dynamic
The position of state barrier, speed, directional information.
Preferably, the status information further includes the turn signal letter of vehicle in the dynamic barrier obtained by image recognition
Breath.
In a kind of preferred implementation of step S12,
Preferably, according to preset traffic rules library, analyze dynamic barrier and dynamic barrier, dynamic barrier with it is quiet
Relationship between state barrier, dynamic barrier and traffic signals extracts all travelable regions of dynamic barrier.
Preferably, aforesaid operations can be executed in the scene analysis module for the DAS (Driver Assistant System) that this vehicle is arranged, it can also be with
The position of dynamic barrier, static-obstacle thing and traffic signals that step S11 is obtained, type information are uploaded onto the server, by
Server executes aforesaid operations.
It preferably, may the area passed through of traveling under travelable Regional Representative dynamic barrier normally travel state
Domain is the region in roadmarking, and including but not limited to pedestrian jaywalks, vehicle straight trip, lane change, left-hand rotation, turns right, turns around
Act the region to be passed through.
The preset traffic rules library includes:
Dynamic barrier need to travel in corresponding road；
Dynamic barrier need to abide by traffic signals.
Preferably, when analyzing the relationship between dynamic barrier, such as vehicle and dynamic barrier, this vehicle is used as should
The corresponding dynamic barrier of vehicle is analyzed.
For example, as shown in Fig. 2, this vehicle 0 recognizes dynamic barrier vehicle 1, the travelable area of dynamic barrier vehicle 1
Domain 3 two kinds of possibility of lane-change for straight trip 2 and to the right.
As shown in Fig. 3, this vehicle 0 recognizes dynamic barrier vehicle 1, and the travelable region of dynamic barrier vehicle 1 is
Right-hand rotation 6, straight trip 7,8 three kinds of possibility of turning left；The travelable region of pedestrian 7 is to jaywalk 9 and 10 two kind of possibility.
In a kind of preferred implementation of step S13,
Preferably, the historic state information of dynamic barrier and travelable region are inputted into barrier trajectory predictions mould
Type generates the prediction locus of dynamic barrier；
For example, as shown in Fig. 2, generating the prediction locus 4 of dynamic barrier vehicle 1；As shown in Fig. 3, dynamic is generated
The prediction locus 12 of barrier vehicle 1, the prediction locus 11 of dynamic barrier pedestrian 5.
The barrier trajectory predictions model through the following steps that in advance training:
Acquire in the historic state information of dynamic barrier m seconds before each moment status informations；Wherein, data sampling is
Sampling in every 0.1 second is primary, meanwhile, analyze the travelable region at each moment.N seconds status informations are as output after taking.Its
Middle m, n are the integer more than or equal to 1；
According to the training set, using the travelable region at m seconds status informations before each moment and the moment as input,
N seconds status informations are as output, training barrier trajectory predictions model afterwards.
Preferably, the barrier trajectory predictions model is deep neural network model, and the deep neural network includes
Input layer, hidden layer and output layer, for receiving the travelable region of m seconds status informations and the moment before each moment, meter
Dynamic barrier traveling is calculated in the probability in each travelable region, is calculated and is exported pair in the highest travelable region of probability
The rear n seconds status informations answered.
It is adjusted using model parameter of the Back Propagation Algorithm to deep neural network.
Preferably, m=3, n=5 are taken.
In a kind of preferred implementation of step S14,
Preferably, in conjunction with the driving trace of the prediction locus of dynamic barrier and Ben Che, judge dynamic barrier and Ben Che
Between risk status, such as danger coefficient, comprising:
According to the time difference of same position, i.e., the difference of time that dynamic barrier and this garage sail at intersection of locus is sentenced
Disconnected danger coefficient；Or,
Danger coefficient is judged according to the speed difference of same time point, range difference, comprising: poor according to fore-and-aft distance, longitudinal speed
Degree difference judges danger coefficient, and, lateral velocity difference poor according to lateral distance judges danger coefficient.Wherein, the speed difference is
The closing speed of state barrier and this vehicle.
Wherein, the driving trace of this vehicle is the control instruction that the transmission of this vehicle control is received by DAS (Driver Assistant System), packet
Include: the instruction such as steering, acceleration, braking according to the current state information of this vehicle, is predicted.
Preferably, when the driving trace of the prediction locus of dynamic barrier and Ben Che are on same lane,
If fore-and-aft distance difference is greater than the brake safe distance under current longitudinal velocity difference, danger coefficient 0；
If fore-and-aft distance difference is less than the brake safe distance under current longitudinal velocity difference, danger coefficient 0.
For example, when the prediction locus of dynamic barrier and the driving trace of Ben Che are on adjacent lane,
If lateral distance difference is greater than the security reaction distance under current lateral velocity difference, danger coefficient 0；
If lateral distance difference is less than the security reaction distance under current lateral velocity difference, danger coefficient 1.
It preferably, can also be according to the danger coefficient between dynamic barrier and this vehicle, really to this vehicle driver early warning institute
Fixed possible collision accident.It is sounded an alarm according to the size of danger coefficient, passes through voice prompting, query by screen or any other
Output method come to driver carry out collision warning, make driver have one section can be with the distance of collision avoidance time, driver can at this time
Collision avoidance is carried out to make correct operation.
According to this embodiment, it can calculate the risk factor of barrier track Yu this wheel paths, give warning in advance.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the application is not limited by the described action sequence because
According to the application, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, related actions and modules not necessarily the application
It is necessary.
The introduction about embodiment of the method above, below by way of Installation practice, to scheme of the present invention carry out into
One step explanation.
In the described embodiment, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, reference can be made to the related descriptions of other embodiments.
Fig. 4 is the schematic diagram for the auxiliary driving method based on barrier trajectory predictions that one embodiment of the application provides, such as
Shown in Fig. 4, comprising:
Module 41 is obtained, for obtaining the environmental data around collected vehicle of onboard sensor；
It can travel area determination module 42, for being based on the environmental data, determine the dynamic barrier around this vehicle
It can travel region；
Barrier trajectory prediction module 43, historic state information and travelable region for utilization dynamic barrier, in advance
Survey the driving trace of dynamic barrier；
Judgment module 44, the risk status to conflict for judging the driving trace of dynamic barrier with this vehicle driving trace.
In a kind of preferred implementation for obtaining module 41,
Preferably, obtain onboard sensor data collected, detect based on the data dynamic barrier around this vehicle,
Static-obstacle thing and traffic signals；And obtain the position of the dynamic barrier, static-obstacle thing and traffic signals, type letter
Breath.
The onboard sensor includes: the phase of the upper end for being mounted on this Chinese herbaceous peony windshield, tailstock rear end, vehicle body two sides
Machine is mounted on the millimetre-wave radar of this Chinese herbaceous peony bumper center, front bumper two sides, rear bumper two sides, is mounted on this roof
Portion center, front end, left and right side laser radar, be mounted on the GPS-IMU integrated navigation module of top-support rear end
Deng.Form 360 degree of perception centered on this vehicle.
Preferably, the Fast synchronization that multisensor is carried out by synchronous plate card, or, acquiring multiple sensors by timeline
Data information, realize that multi-sensor data Collect jointly is simultaneously detected based on data collected.
Wherein, testing result includes: dynamic barrier, including but not limited to pedestrian, vehicle (motor vehicle and non-motor vehicle)
Deng；Static-obstacle thing, guardrail etc. including but not limited between roadblock facility, lane；Traffic signals, including but not limited to traffic signals
Lamp, traffic sign and traffic marking etc..
Preferably, multi-sensor data Collect jointly includes:
Control camera acquisition image is simultaneously detected；
Preferably, multiple cameras are controlled and acquire image from different orientation.It is acquired according to multiple cameras from different orientation
The image arrived；Carry out characteristic point acquisition and Feature Points Matching；Multiple two-dimensional plane coordinate institutes structure in all camera imaging faces
At a plurality of space different surface beeline, by three-dimensional coordinate location algorithm carry out three-dimensional point reconstruct, obtain obstacle article coordinate.
Preferably, image recognition, including identification type of vehicle, vehicle turn signal are carried out to image, identifies pedestrian, identification letter
Guardrail etc. between the position of signal lamp, type, color, and identification traffic sign, roadmarking, roadblock facility, lane.
In the present embodiment, it is shot with 10 frames speed per second.
Control the reflection signal that millimetre-wave radar obtains target, it is preferable that go to detect using FMCW continuously linear frequency-modulated wave
The range information of barrier receives the time delay i.e. phase difference of signal by multiple receiving antennas to detect the orientation of barrier letter
Breath.
The GPS signal and inertial navigation signal that this vehicle is obtained by GPS-IMU integrated navigation module, calculate the position of this vehicle
And posture information.
Preferably, laser radar acquisition laser point cloud data can also be controlled and detected, the supplement as camera.It is excellent
Selection of land, laser radar are constantly issued laser in this process and are collected reflection point with certain angular speed uniform rotation
Information, to obtain comprehensive environmental information.Laser radar can also be recorded simultaneously during collecting reflection point distance
The time and level angle that the point occurs, and each laser emitter has the vertical angle of number and fixation, according to these
Data can calculate the coordinate of all reflection points.Laser radar often rotates a circle the set of all reflection point coordinates being collected into
It is formed a cloud.The interference in laser point cloud, and the shape space position feature according to target are filtered out using filter, is passed through
The method of pattern clustering analysis carries out target detection, obtains obstacle identity；By adjusting the method for distance threshold, by cluster point
At subgroup reconsolidate, determine that new cluster centre realizes target positioning, obtain obstacle article coordinate.
Reference frame is selected, coordinate of the result in each sensor coordinate system is will test and is transformed into reference frame；
The sensor initial space configuration be it is previously known, can be according to multiple sensors on this vehicle car body
Measurement data obtains.
It, can be by barrier according to barrier and the relative positional relationship of this vehicle and the position and posture information of this vehicle
Location information be transformed into unified reference frame.The reference frame can be earth coordinates, to facilitate further
Processing.
Preferably, the location information of obtained each dynamic barrier in different time points is detected according to lasting, it is available
The status information of each dynamic barrier, including current state information and historic state information.The status information includes each dynamic
The position of state barrier, speed, directional information.
Preferably, the status information further includes the turn signal letter of vehicle in the dynamic barrier obtained by image recognition
Breath.
In a kind of preferred implementation that can travel area determination module 42,
Preferably, according to preset traffic rules library, analyze dynamic barrier and dynamic barrier, dynamic barrier with it is quiet
Relationship between state barrier, dynamic barrier and traffic signals extracts all travelable regions of dynamic barrier.
Preferably, aforesaid operations can be executed in the scene analysis module for the DAS (Driver Assistant System) that this vehicle is arranged, it can also be with
The position of dynamic barrier, static-obstacle thing and traffic signals that step S11 is obtained, type information are uploaded onto the server, by
Server executes aforesaid operations.
It preferably, may the area passed through of traveling under travelable Regional Representative dynamic barrier normally travel state
Domain is the region in roadmarking, and including but not limited to pedestrian jaywalks, vehicle straight trip, lane change, left-hand rotation, turns right, turns around
Act the region to be passed through.
The preset traffic rules library includes:
Dynamic barrier need to travel in corresponding road；
Dynamic barrier need to abide by traffic signals.
Preferably, when analyzing the relationship between dynamic barrier, such as vehicle and dynamic barrier, this vehicle is used as should
The corresponding dynamic barrier of vehicle is analyzed.
For example, as shown in Fig. 2, this vehicle 0 recognizes dynamic barrier vehicle 1, the travelable area of dynamic barrier vehicle 1
Domain 3 two kinds of possibility of lane-change for straight trip 2 and to the right.
As shown in Fig. 3, this vehicle 0 recognizes dynamic barrier vehicle 1, and the travelable region of dynamic barrier vehicle 1 is
Right-hand rotation 6, straight trip 7,8 three kinds of possibility of turning left；The travelable region of pedestrian 7 is to jaywalk 9 and 10 two kind of possibility.
In a kind of preferred implementation of barrier trajectory prediction module 43,
Preferably, the historic state information of dynamic barrier and travelable region are inputted into barrier trajectory predictions mould
Type generates the prediction locus of dynamic barrier；
For example, as shown in Fig. 2, generating the prediction locus 4 of dynamic barrier vehicle 1；As shown in Fig. 3, dynamic is generated
The prediction locus 12 of barrier vehicle 1, the prediction locus 11 of dynamic barrier pedestrian 5.
The barrier trajectory predictions model through the following steps that in advance training:
Acquire in the historic state information of dynamic barrier m seconds before each moment status informations；Wherein, data sampling is
Sampling in every 0.1 second is primary, meanwhile, analyze the travelable region at each moment.N seconds status informations are as output after taking.Its
Middle m, n are the integer more than or equal to 1；
According to the training set, using the travelable region at m seconds status informations before each moment and the moment as input,
N seconds status informations are as output, training barrier trajectory predictions model afterwards.
Preferably, the barrier trajectory predictions model is deep neural network model, and the deep neural network includes
Input layer, hidden layer and output layer, for receiving the travelable region of m seconds status informations and the moment before each moment, meter
Dynamic barrier traveling is calculated in the probability in each travelable region, is calculated and is exported pair in the highest travelable region of probability
The rear n seconds status informations answered.
It is adjusted using model parameter of the Back Propagation Algorithm to deep neural network.
Preferably, m=3, n=5 are taken.
In a kind of preferred implementation of step judgment module 44,
Preferably, in conjunction with the driving trace of the prediction locus of dynamic barrier and Ben Che, judge dynamic barrier and Ben Che
Between risk status, such as danger coefficient, comprising:
According to the time difference of same position, i.e., the difference of time that dynamic barrier and this garage sail at intersection of locus is sentenced
Disconnected danger coefficient；Or,
Danger coefficient is judged according to the speed difference of same time point, range difference, comprising: poor according to fore-and-aft distance, longitudinal speed
Degree difference judges danger coefficient, and, lateral velocity difference poor according to lateral distance judges danger coefficient.Wherein, the speed difference is
The closing speed of state barrier and this vehicle.
Wherein, the driving trace of this vehicle be by this wheel paths prediction module, for according to the current state information of this vehicle and
What the control instruction that this vehicle control is sent was predicted.
Preferably, when the driving trace of the prediction locus of dynamic barrier and Ben Che are on same lane,
If fore-and-aft distance difference is greater than the brake safe distance under current longitudinal velocity difference, danger coefficient 0；
If fore-and-aft distance difference is less than the brake safe distance under current longitudinal velocity difference, danger coefficient 0.
For example, when the prediction locus of dynamic barrier and the driving trace of Ben Che are on adjacent lane,
If lateral distance difference is greater than the security reaction distance under current lateral velocity difference, danger coefficient 0；
If lateral distance difference is less than the security reaction distance under current lateral velocity difference, danger coefficient 1.
Preferably, it is preferable that the system also includes cue modules, for according to the danger between dynamic barrier and this vehicle
Dangerous coefficient, to possible collision accident determined by the driver's early warning of this vehicle.It is sounded an alarm, is passed through according to the size of danger coefficient
Voice prompting, query by screen or any other output method to carry out collision warning to driver, make driver have one section can be with
The distance of collision avoidance time, driver can make correct operation and carry out collision avoidance at this time.
According to this embodiment, it can calculate the risk factor of barrier track Yu this wheel paths, give warning in advance.
In several embodiments provided herein, it should be understood that disclosed method and apparatus can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of the unit, only
Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.Another point, it is shown or discussed
Mutual coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or logical of device or unit
Letter connection can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.The integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
Fig. 5 shows the frame for being suitable for the exemplary computer system/server 012 for being used to realize embodiment of the present invention
Figure.The computer system/server 012 that Fig. 5 is shown is only an example, should not function and use to the embodiment of the present invention
Range band carrys out any restrictions.
As shown in figure 5, computer system/server 012 is showed in the form of universal computing device.Computer system/clothes
The component of business device 012 can include but is not limited to: one or more processor or processing unit 016, system storage
028, connect the bus 018 of different system components (including system storage 028 and processing unit 016).
Bus 018 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Computer system/server 012 typically comprises a variety of computer system readable media.These media, which can be, appoints
The usable medium what can be accessed by computer system/server 012, including volatile and non-volatile media, movably
With immovable medium.
System storage 028 may include the computer system readable media of form of volatile memory, such as deposit at random
Access to memory (RAM) 030 and/or cache memory 032.Computer system/server 012 may further include other
Removable/nonremovable, volatile/non-volatile computer system storage medium.Only as an example, storage system 034 can
For reading and writing immovable, non-volatile magnetic media (Fig. 5 do not show, commonly referred to as " hard disk drive ").Although in Fig. 5
It is not shown, the disc driver for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided, and to can
The CD drive of mobile anonvolatile optical disk (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these situations
Under, each driver can be connected by one or more data media interfaces with bus 018.Memory 028 may include
At least one program product, the program product have one group of (for example, at least one) program module, these program modules are configured
To execute the function of various embodiments of the present invention.
Program/utility 040 with one group of (at least one) program module 042, can store in such as memory
In 028, such program module 042 includes --- but being not limited to --- operating system, one or more application program, other
It may include the realization of network environment in program module and program data, each of these examples or certain combination.Journey
Sequence module 042 usually executes function and/or method in embodiment described in the invention.
Computer system/server 012 can also with one or more external equipments 014 (such as keyboard, sensing equipment,
Display 024 etc.) communication, in the present invention, computer system/server 012 is communicated with outside radar equipment, can also be with
One or more enable a user to the equipment interacted with the computer system/server 012 communication, and/or with make the meter
Any equipment (such as network interface card, the modulation that calculation machine systems/servers 012 can be communicated with one or more of the other calculating equipment
Demodulator etc.) communication.This communication can be carried out by input/output (I/O) interface 022.Also, computer system/clothes
Being engaged in device 012 can also be by network adapter 020 and one or more network (such as local area network (LAN), wide area network (WAN)
And/or public network, such as internet) communication.As shown in figure 5, network adapter 020 by bus 018 and computer system/
Other modules of server 012 communicate.It should be understood that computer system/server 012 can be combined although being not shown in Fig. 5
Using other hardware and/or software module, including but not limited to: microcode, device driver, redundant processing unit, external magnetic
Dish driving array, RAID system, tape drive and data backup storage system etc..
The program that processing unit 016 is stored in system storage 028 by operation, thereby executing described in the invention
Function and/or method in embodiment.
Above-mentioned computer program can be set in computer storage medium, i.e., the computer storage medium is encoded with
Computer program, the program by one or more computers when being executed, so that one or more computers execute in the present invention
State method flow shown in embodiment and/or device operation.
With time, the development of technology, medium meaning is more and more extensive, and the route of transmission of computer program is no longer limited by
Tangible medium, can also be directly from network downloading etc..It can be using any combination of one or more computer-readable media.
Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer-readable storage medium
Matter for example may be-but not limited to-system, device or the device of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or
Any above combination of person.The more preferably example (non exhaustive list) of computer readable storage medium includes: with one
Or the electrical connections of multiple conducting wires, portable computer diskette, hard disk, random access memory (RAM), read-only memory (ROM),
Erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light
Memory device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer readable storage medium can
With to be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
Person is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including --- but
It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be
Any computer-readable medium other than computer readable storage medium, which can send, propagate or
Transmission is for by the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In --- wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN) is connected to subscriber computer, or, it may be connected to outer computer (such as provided using Internet service
Quotient is connected by internet).
Finally, it should be noted that above embodiments are only to illustrate the technical solution of the application, rather than its limitations；Although
The application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (16)
1. a kind of auxiliary driving method based on barrier trajectory predictions characterized by comprising
Obtain the environmental data around collected vehicle of onboard sensor；
Based on the environmental data, the travelable region of the dynamic barrier around this vehicle is determined；
Historic state information and travelable region using dynamic barrier, predict the driving trace of dynamic barrier；
Judge the risk status that the driving trace of dynamic barrier conflicts with this vehicle driving trace.
2. the method according to claim 1, wherein
The environmental data includes: dynamic barrier, static-obstacle thing and traffic signals.
3. according to the method described in claim 2, it is characterized in that, determining the dynamic around this vehicle based on the environmental data
The travelable region of barrier includes:
According to preset traffic rules, dynamic barrier and dynamic barrier, dynamic barrier and static-obstacle thing, dynamic are analyzed
Relationship between barrier and traffic signals extracts all travelable regions of dynamic barrier.
4. the method according to claim 1, wherein using dynamic barrier historic state information and can travel
Region predicts that the driving trace of dynamic barrier includes:
The historic state information of dynamic barrier and travelable region are inputted into barrier trajectory predictions model, predict dynamic disorder
The driving trace of object.
5. according to the method described in claim 4, it is characterized in that,
The barrier trajectory predictions model is deep neural network model.
6. the method according to claim 1, wherein judging that the driving trace of dynamic barrier and this vehicle travel rail
The risk status of mark conflict includes:
According to the time difference of the prediction locus of dynamic barrier and this vehicle driving trace same position；Or, the speed of same time point
It spends poor, range difference and judges danger coefficient.
7. the method according to claim 1, wherein the driving trace of this vehicle is believed according to the current state of this vehicle
What the control instruction that breath and this vehicle control are sent was predicted.
8. a kind of DAS (Driver Assistant System) based on barrier trajectory predictions characterized by comprising
Module is obtained, for obtaining the environmental data around collected vehicle of onboard sensor；
It can travel area determination module, for being based on the environmental data, determine can travel for the dynamic barrier around this vehicle
Region；
Barrier trajectory prediction module, historic state information and travelable region for utilization dynamic barrier, prediction dynamic
The driving trace of barrier；
Judgment module, the risk status to conflict for judging the driving trace of dynamic barrier with this vehicle driving trace.
9. system according to claim 8, which is characterized in that
The environmental data includes: dynamic barrier, static-obstacle thing and traffic signals.
10. system according to claim 9, which is characterized in that the travelable area determination module is specifically used for:
According to preset traffic rules, dynamic barrier and dynamic barrier, dynamic barrier and static-obstacle thing, dynamic are analyzed
Relationship between barrier and traffic signals extracts all travelable regions of dynamic barrier.
11. system according to claim 8, which is characterized in that the barrier trajectory prediction module is specifically used for:
The historic state information of dynamic barrier and travelable region are inputted into barrier trajectory predictions model, predict dynamic disorder
The driving trace of object.
12. system according to claim 11, which is characterized in that
The barrier trajectory predictions model is deep neural network model.
13. system according to claim 8, which is characterized in that the judgment module is specifically used for:
According to the time difference of the prediction locus of dynamic barrier and this vehicle driving trace same position；Or, the speed of same time point
It spends poor, range difference and judges danger coefficient.
14. system according to claim 8, which is characterized in that the system also includes this wheel paths prediction modules, are used for
This wheel paths is predicted according to the control instruction that the current state information of this vehicle and this vehicle control are sent.
15. a kind of computer equipment, including memory, processor and it is stored on the memory and can be on the processor
The computer program of operation, which is characterized in that the processor is realized when executing described program as any in claim 1~7
Method described in.
16. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that described program is processed
Such as method according to any one of claims 1 to 7 is realized when device executes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711351196.9A CN109927719B (en) | 2017-12-15 | 2017-12-15 | Auxiliary driving method and system based on obstacle trajectory prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711351196.9A CN109927719B (en) | 2017-12-15 | 2017-12-15 | Auxiliary driving method and system based on obstacle trajectory prediction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109927719A true CN109927719A (en) | 2019-06-25 |
| CN109927719B CN109927719B (en) | 2022-03-25 |
Family
ID=66980214
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711351196.9A Active CN109927719B (en) | 2017-12-15 | 2017-12-15 | Auxiliary driving method and system based on obstacle trajectory prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109927719B (en) |
Cited By (63)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110371112A (en) * | 2019-07-06 | 2019-10-25 | 深圳数翔科技有限公司 | A kind of intelligent barrier avoiding system and method for automatic driving vehicle |
| CN110615001A (en) * | 2019-09-27 | 2019-12-27 | 汉纳森（厦门）数据股份有限公司 | Driving safety reminding method, device and medium based on CAN data |
| CN110654380A (en) * | 2019-10-09 | 2020-01-07 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN110654381A (en) * | 2019-10-09 | 2020-01-07 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN110703770A (en) * | 2019-11-12 | 2020-01-17 | 中科(徐州)人工智能研究院有限公司 | Method and device for controlling automatic running of track inspection vehicle |
| CN110758381A (en) * | 2019-09-18 | 2020-02-07 | 北京汽车集团有限公司 | Method and device for generating steering track, storage medium and electronic equipment |
| CN110834630A (en) * | 2019-10-22 | 2020-02-25 | 中国第一汽车股份有限公司 | Vehicle driving control method and device, vehicle and storage medium |
| CN110843776A (en) * | 2019-11-29 | 2020-02-28 | 深圳市元征科技股份有限公司 | Vehicle anti-collision method and device |
| CN111002980A (en) * | 2019-12-10 | 2020-04-14 | 苏州智加科技有限公司 | Road obstacle trajectory prediction method and system based on deep learning |
| CN111025297A (en) * | 2019-12-24 | 2020-04-17 | 京东数字科技控股有限公司 | Vehicle monitoring method and device, electronic equipment and storage medium |
| CN111027195A (en) * | 2019-12-03 | 2020-04-17 | 北京百度网讯科技有限公司 | Simulation scene generation method, device and equipment |
| CN111114554A (en) * | 2019-12-16 | 2020-05-08 | 苏州智加科技有限公司 | Method, device, terminal and storage medium for predicting travel track |
| CN111177869A (en) * | 2020-01-02 | 2020-05-19 | 北京百度网讯科技有限公司 | Method, device and equipment for determining sensor layout scheme |
| CN111301404A (en) * | 2020-02-06 | 2020-06-19 | 北京小马慧行科技有限公司 | Vehicle control method and device, storage medium and processor |
| CN111469836A (en) * | 2020-02-28 | 2020-07-31 | 广东中科臻恒信息技术有限公司 | Obstacle avoidance method and device based on vehicle-mounted unit and road side unit, and storage medium |
| CN111626097A (en) * | 2020-04-09 | 2020-09-04 | 吉利汽车研究院（宁波）有限公司 | Method and device for predicting future trajectory of obstacle, electronic equipment and storage medium |
| CN111710188A (en) * | 2020-05-29 | 2020-09-25 | 腾讯科技（深圳）有限公司 | Vehicle alarm prompting method and device, electronic equipment and storage medium |
| CN111829545A (en) * | 2020-09-16 | 2020-10-27 | 深圳裹动智驾科技有限公司 | Automatic driving vehicle and dynamic planning method and system for motion trail of automatic driving vehicle |
| CN111976726A (en) * | 2020-08-26 | 2020-11-24 | 中南大学 | Steering auxiliary system of intelligent rail vehicle and control method thereof |
| CN111982143A (en) * | 2020-08-11 | 2020-11-24 | 北京汽车研究总院有限公司 | Vehicle and vehicle path planning method and device |
| CN112046494A (en) * | 2020-09-11 | 2020-12-08 | 中国第一汽车股份有限公司 | Vehicle control method, device, equipment and storage medium |
| CN112092809A (en) * | 2020-09-15 | 2020-12-18 | 北京罗克维尔斯科技有限公司 | Auxiliary reversing method, device and system and vehicle |
| CN112363492A (en) * | 2019-07-25 | 2021-02-12 | 百度（美国）有限责任公司 | Computer-implemented method for operating an autonomous vehicle and data processing system |
| CN112519765A (en) * | 2019-09-03 | 2021-03-19 | 北京百度网讯科技有限公司 | Vehicle control method, apparatus, device, and medium |
| WO2021052383A1 (en) * | 2019-09-17 | 2021-03-25 | Huawei Technologies Co., Ltd. | Methods and systems for observation prediction in autonomous vehicles |
| CN112572451A (en) * | 2019-11-12 | 2021-03-30 | 百度（美国）有限责任公司 | Method and apparatus for autonomous driving vehicle execution |
| WO2021062593A1 (en) * | 2019-09-30 | 2021-04-08 | Beijing Voyager Technology Co., Ltd. | Systems and methods for predicting bicycle trajectory |
| CN112639821A (en) * | 2020-05-11 | 2021-04-09 | 华为技术有限公司 | Method and system for detecting vehicle travelable area and automatic driving vehicle adopting system |
| CN112660128A (en) * | 2019-10-15 | 2021-04-16 | 现代自动车株式会社 | Apparatus for determining lane change path of autonomous vehicle and method thereof |
| CN112784628A (en) * | 2019-11-06 | 2021-05-11 | 北京地平线机器人技术研发有限公司 | Trajectory prediction method, and neural network training method and device for trajectory prediction |
| CN112824832A (en) * | 2019-11-20 | 2021-05-21 | 炬星科技（深圳）有限公司 | Method, system, device and computer readable storage medium for predicting movement locus of obstacle |
| CN112977472A (en) * | 2019-12-12 | 2021-06-18 | 百度（美国）有限责任公司 | Method and system for predicting moving track of vehicle |
| CN113022593A (en) * | 2021-04-09 | 2021-06-25 | 长沙智能驾驶研究院有限公司 | Obstacle processing method and device and traveling equipment |
| CN113075668A (en) * | 2021-03-25 | 2021-07-06 | 广州小鹏自动驾驶科技有限公司 | Dynamic obstacle object identification method and device |
| CN113128326A (en) * | 2019-12-31 | 2021-07-16 | 百度（美国）有限责任公司 | Vehicle trajectory prediction model with semantic map and LSTM |
| CN113147794A (en) * | 2021-06-03 | 2021-07-23 | 北京百度网讯科技有限公司 | Method, device and equipment for generating automatic driving early warning information and automatic driving vehicle |
| CN113205088A (en) * | 2021-07-06 | 2021-08-03 | 禾多科技（北京）有限公司 | Obstacle image presentation method, electronic device, and computer-readable medium |
| CN113264066A (en) * | 2021-06-03 | 2021-08-17 | 阿波罗智能技术(北京)有限公司 | Obstacle trajectory prediction method and device, automatic driving vehicle and road side equipment |
| CN113424121A (en) * | 2019-12-31 | 2021-09-21 | 深圳元戎启行科技有限公司 | Vehicle speed control method and device based on automatic driving and computer equipment |
| CN113479217A (en) * | 2021-07-26 | 2021-10-08 | 惠州华阳通用电子有限公司 | Lane changing and obstacle avoiding method and system based on automatic driving |
| CN113537258A (en) * | 2020-04-16 | 2021-10-22 | 北京京东乾石科技有限公司 | Action track prediction method and device, computer readable medium and electronic equipment |
| CN113641734A (en) * | 2021-08-12 | 2021-11-12 | 驭势科技（北京）有限公司 | Data processing method, device, equipment and medium |
| CN113721598A (en) * | 2020-05-11 | 2021-11-30 | 北京京东乾石科技有限公司 | Obstacle trajectory prediction method, device, equipment and storage medium |
| CN113734190A (en) * | 2021-09-09 | 2021-12-03 | 北京百度网讯科技有限公司 | Vehicle information prompting method and device, electronic equipment, medium and vehicle |
| CN113753038A (en) * | 2021-03-16 | 2021-12-07 | 京东鲲鹏(江苏)科技有限公司 | Trajectory prediction method and apparatus, electronic device and storage medium |
| CN113811830A (en) * | 2019-12-30 | 2021-12-17 | 深圳元戎启行科技有限公司 | Trajectory prediction method, apparatus, computer device and storage medium |
| CN113819915A (en) * | 2021-03-03 | 2021-12-21 | 京东鲲鹏(江苏)科技有限公司 | Unmanned vehicle path planning method and related equipment |
| CN111409631B (en) * | 2020-04-10 | 2022-01-11 | 新石器慧通（北京）科技有限公司 | Vehicle running control method and device, vehicle and storage medium |
| CN114018265A (en) * | 2021-10-28 | 2022-02-08 | 山东新一代信息产业技术研究院有限公司 | Patrol robot driving track generation method, equipment and medium |
| CN114162133A (en) * | 2021-11-10 | 2022-03-11 | 上海人工智能创新中心 | Risk assessment method and device for driving scene and computer readable storage medium |
| CN114207541A (en) * | 2019-07-29 | 2022-03-18 | 本田技研工业株式会社 | Trajectory prediction |
| CN114446092A (en) * | 2022-01-19 | 2022-05-06 | 无锡学院 | S-shaped road simulated obstacle early warning method based on three-dimensional camera networking |
| CN114450703A (en) * | 2019-09-30 | 2022-05-06 | 北京航迹科技有限公司 | System and method for predicting moving track of pedestrian |
| CN114537474A (en) * | 2020-11-26 | 2022-05-27 | 银河水滴科技（北京）有限公司 | Protection method and device for train running safety |
| CN114596553A (en) * | 2022-03-11 | 2022-06-07 | 阿波罗智能技术(北京)有限公司 | Model training method, trajectory prediction method and device and automatic driving vehicle |
| CN114692289A (en) * | 2020-12-31 | 2022-07-01 | 华为技术有限公司 | Automatic driving algorithm testing method and related equipment |
| CN114771572A (en) * | 2022-05-10 | 2022-07-22 | 中国第一汽车股份有限公司 | Automatic driving track prediction method, device, equipment and storage medium |
| CN114863685A (en) * | 2022-07-06 | 2022-08-05 | 北京理工大学 | A method and system for predicting the trajectory of traffic participants based on risk acceptance |
| CN115214719A (en) * | 2022-05-30 | 2022-10-21 | 广州汽车集团股份有限公司 | Obstacle trajectory tracking method, device, intelligent driving device and storage medium |
| CN116615773A (en) * | 2020-12-04 | 2023-08-18 | 三菱电机株式会社 | Automatic driving system, server and dynamic map generation method |
| EP4239596A1 (en) * | 2022-03-02 | 2023-09-06 | Xiaomi EV Technology Co., Ltd. | Method and apparatus for detecting drivable area, mobile device and storage medium |
| WO2023173998A1 (en) * | 2022-03-18 | 2023-09-21 | 北京智行者科技股份有限公司 | Environmental modeling method and apparatus for slt spatial risk field of obstacle, and related product |
| CN118209127A (en) * | 2024-03-29 | 2024-06-18 | 重庆赛力斯凤凰智创科技有限公司 | Path optimization method, device, electronic device and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101681562A (en) * | 2007-06-20 | 2010-03-24 | 丰田自动车株式会社 | Vehicle travel track estimator |
| CN104614733A (en) * | 2015-01-30 | 2015-05-13 | 福州华鹰重工机械有限公司 | Dynamic obstacle detection method |
| CN105216792A (en) * | 2014-06-12 | 2016-01-06 | 株式会社日立制作所 | Obstacle target in surrounding environment is carried out to the method and apparatus of recognition and tracking |
| CN105549597A (en) * | 2016-02-04 | 2016-05-04 | 同济大学 | Unmanned vehicle dynamic path programming method based on environment uncertainty |
| CN106873580A (en) * | 2015-11-05 | 2017-06-20 | 福特全球技术公司 | Based on perception data autonomous driving at the intersection |
| US20170320500A1 (en) * | 2016-05-03 | 2017-11-09 | Hyundai Motor Company | Path planning apparatus and method for autonomous vehicle |
| CN107346611A (en) * | 2017-07-20 | 2017-11-14 | 北京纵目安驰智能科技有限公司 | A kind of barrier-avoiding method and obstacle avoidance system of the vehicle of autonomous driving |
-
2017
- 2017-12-15 CN CN201711351196.9A patent/CN109927719B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101681562A (en) * | 2007-06-20 | 2010-03-24 | 丰田自动车株式会社 | Vehicle travel track estimator |
| CN105216792A (en) * | 2014-06-12 | 2016-01-06 | 株式会社日立制作所 | Obstacle target in surrounding environment is carried out to the method and apparatus of recognition and tracking |
| CN104614733A (en) * | 2015-01-30 | 2015-05-13 | 福州华鹰重工机械有限公司 | Dynamic obstacle detection method |
| CN106873580A (en) * | 2015-11-05 | 2017-06-20 | 福特全球技术公司 | Based on perception data autonomous driving at the intersection |
| CN105549597A (en) * | 2016-02-04 | 2016-05-04 | 同济大学 | Unmanned vehicle dynamic path programming method based on environment uncertainty |
| US20170320500A1 (en) * | 2016-05-03 | 2017-11-09 | Hyundai Motor Company | Path planning apparatus and method for autonomous vehicle |
| CN107346611A (en) * | 2017-07-20 | 2017-11-14 | 北京纵目安驰智能科技有限公司 | A kind of barrier-avoiding method and obstacle avoidance system of the vehicle of autonomous driving |
Cited By (94)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110371112A (en) * | 2019-07-06 | 2019-10-25 | 深圳数翔科技有限公司 | A kind of intelligent barrier avoiding system and method for automatic driving vehicle |
| CN110371112B (en) * | 2019-07-06 | 2021-10-01 | 深圳数翔科技有限公司 | Intelligent obstacle avoidance system and method for automatic driving vehicle |
| CN112363492A (en) * | 2019-07-25 | 2021-02-12 | 百度（美国）有限责任公司 | Computer-implemented method for operating an autonomous vehicle and data processing system |
| CN114207541A (en) * | 2019-07-29 | 2022-03-18 | 本田技研工业株式会社 | Trajectory prediction |
| CN112519765A (en) * | 2019-09-03 | 2021-03-19 | 北京百度网讯科技有限公司 | Vehicle control method, apparatus, device, and medium |
| US12260300B2 (en) | 2019-09-17 | 2025-03-25 | Huawei Technologies Co., Ltd. | Methods and systems for observation prediction in autonomous vehicles |
| WO2021052383A1 (en) * | 2019-09-17 | 2021-03-25 | Huawei Technologies Co., Ltd. | Methods and systems for observation prediction in autonomous vehicles |
| CN110758381A (en) * | 2019-09-18 | 2020-02-07 | 北京汽车集团有限公司 | Method and device for generating steering track, storage medium and electronic equipment |
| CN110615001A (en) * | 2019-09-27 | 2019-12-27 | 汉纳森（厦门）数据股份有限公司 | Driving safety reminding method, device and medium based on CAN data |
| CN110615001B (en) * | 2019-09-27 | 2021-04-27 | 汉纳森（厦门）数据股份有限公司 | Driving safety reminding method, device and medium based on CAN data |
| CN114401876A (en) * | 2019-09-30 | 2022-04-26 | 北京航迹科技有限公司 | System and method for predicting bicycle trajectory |
| WO2021062593A1 (en) * | 2019-09-30 | 2021-04-08 | Beijing Voyager Technology Co., Ltd. | Systems and methods for predicting bicycle trajectory |
| CN114450703A (en) * | 2019-09-30 | 2022-05-06 | 北京航迹科技有限公司 | System and method for predicting moving track of pedestrian |
| CN114401876B (en) * | 2019-09-30 | 2024-08-16 | 北京航迹科技有限公司 | System and method for predicting bicycle track |
| CN110654381A (en) * | 2019-10-09 | 2020-01-07 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN110654380B (en) * | 2019-10-09 | 2023-12-15 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN110654380A (en) * | 2019-10-09 | 2020-01-07 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN112660128A (en) * | 2019-10-15 | 2021-04-16 | 现代自动车株式会社 | Apparatus for determining lane change path of autonomous vehicle and method thereof |
| CN112660128B (en) * | 2019-10-15 | 2024-04-16 | 现代自动车株式会社 | Apparatus for determining lane change path of autonomous vehicle and method thereof |
| CN110834630A (en) * | 2019-10-22 | 2020-02-25 | 中国第一汽车股份有限公司 | Vehicle driving control method and device, vehicle and storage medium |
| CN112784628A (en) * | 2019-11-06 | 2021-05-11 | 北京地平线机器人技术研发有限公司 | Trajectory prediction method, and neural network training method and device for trajectory prediction |
| CN112784628B (en) * | 2019-11-06 | 2024-03-19 | 北京地平线机器人技术研发有限公司 | Track prediction method, neural network training method and device for track prediction |
| CN112572451A (en) * | 2019-11-12 | 2021-03-30 | 百度（美国）有限责任公司 | Method and apparatus for autonomous driving vehicle execution |
| CN110703770A (en) * | 2019-11-12 | 2020-01-17 | 中科(徐州)人工智能研究院有限公司 | Method and device for controlling automatic running of track inspection vehicle |
| CN112824832B (en) * | 2019-11-20 | 2024-07-19 | 炬星科技（深圳）有限公司 | Obstacle movement track prediction method, system, equipment and computer readable storage medium |
| CN112824832A (en) * | 2019-11-20 | 2021-05-21 | 炬星科技（深圳）有限公司 | Method, system, device and computer readable storage medium for predicting movement locus of obstacle |
| CN110843776A (en) * | 2019-11-29 | 2020-02-28 | 深圳市元征科技股份有限公司 | Vehicle anti-collision method and device |
| CN111027195A (en) * | 2019-12-03 | 2020-04-17 | 北京百度网讯科技有限公司 | Simulation scene generation method, device and equipment |
| CN111027195B (en) * | 2019-12-03 | 2023-02-28 | 阿波罗智能技术(北京)有限公司 | Simulation scene generation method, device and equipment |
| CN111002980A (en) * | 2019-12-10 | 2020-04-14 | 苏州智加科技有限公司 | Road obstacle trajectory prediction method and system based on deep learning |
| CN112977472A (en) * | 2019-12-12 | 2021-06-18 | 百度（美国）有限责任公司 | Method and system for predicting moving track of vehicle |
| 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 |
| CN111114554A (en) * | 2019-12-16 | 2020-05-08 | 苏州智加科技有限公司 | Method, device, terminal and storage medium for predicting travel track |
| CN111025297A (en) * | 2019-12-24 | 2020-04-17 | 京东数字科技控股有限公司 | Vehicle monitoring method and device, electronic equipment and storage medium |
| CN113811830B (en) * | 2019-12-30 | 2022-05-10 | 深圳元戎启行科技有限公司 | Trajectory prediction method, apparatus, computer device and storage medium |
| CN113811830A (en) * | 2019-12-30 | 2021-12-17 | 深圳元戎启行科技有限公司 | Trajectory prediction method, apparatus, computer device and storage medium |
| CN113128326B (en) * | 2019-12-31 | 2024-04-09 | 百度（美国）有限责任公司 | Vehicle trajectory prediction model with semantic map and LSTM |
| CN113128326A (en) * | 2019-12-31 | 2021-07-16 | 百度（美国）有限责任公司 | Vehicle trajectory prediction model with semantic map and LSTM |
| CN113424121A (en) * | 2019-12-31 | 2021-09-21 | 深圳元戎启行科技有限公司 | Vehicle speed control method and device based on automatic driving and computer equipment |
| CN111177869A (en) * | 2020-01-02 | 2020-05-19 | 北京百度网讯科技有限公司 | Method, device and equipment for determining sensor layout scheme |
| CN111177869B (en) * | 2020-01-02 | 2023-09-01 | 北京百度网讯科技有限公司 | Method, device and equipment for determining sensor layout scheme |
| CN111301404A (en) * | 2020-02-06 | 2020-06-19 | 北京小马慧行科技有限公司 | Vehicle control method and device, storage medium and processor |
| CN111301404B (en) * | 2020-02-06 | 2022-02-18 | 北京小马慧行科技有限公司 | Vehicle control method and device, storage medium and processor |
| CN111469836A (en) * | 2020-02-28 | 2020-07-31 | 广东中科臻恒信息技术有限公司 | Obstacle avoidance method and device based on vehicle-mounted unit and road side unit, and storage medium |
| CN111626097A (en) * | 2020-04-09 | 2020-09-04 | 吉利汽车研究院（宁波）有限公司 | Method and device for predicting future trajectory of obstacle, electronic equipment and storage medium |
| CN111409631B (en) * | 2020-04-10 | 2022-01-11 | 新石器慧通（北京）科技有限公司 | Vehicle running control method and device, vehicle and storage medium |
| CN113537258A (en) * | 2020-04-16 | 2021-10-22 | 北京京东乾石科技有限公司 | Action track prediction method and device, computer readable medium and electronic equipment |
| CN113537258B (en) * | 2020-04-16 | 2024-04-05 | 北京京东乾石科技有限公司 | Action track prediction method and device, computer readable medium and electronic equipment |
| CN113721598A (en) * | 2020-05-11 | 2021-11-30 | 北京京东乾石科技有限公司 | Obstacle trajectory prediction method, device, equipment and storage medium |
| CN113721598B (en) * | 2020-05-11 | 2025-02-25 | 北京京东乾石科技有限公司 | Obstacle trajectory prediction method, device, equipment and storage medium |
| CN112639821A (en) * | 2020-05-11 | 2021-04-09 | 华为技术有限公司 | Method and system for detecting vehicle travelable area and automatic driving vehicle adopting system |
| CN112639821B (en) * | 2020-05-11 | 2021-12-28 | 华为技术有限公司 | A vehicle drivable area detection method, system and automatic driving vehicle using the system |
| CN111710188A (en) * | 2020-05-29 | 2020-09-25 | 腾讯科技（深圳）有限公司 | Vehicle alarm prompting method and device, electronic equipment and storage medium |
| CN111710188B (en) * | 2020-05-29 | 2024-03-29 | 腾讯科技（深圳）有限公司 | Vehicle alarm prompting method, device, electronic equipment and storage medium |
| CN111982143A (en) * | 2020-08-11 | 2020-11-24 | 北京汽车研究总院有限公司 | Vehicle and vehicle path planning method and device |
| CN111982143B (en) * | 2020-08-11 | 2024-01-19 | 北京汽车研究总院有限公司 | Vehicle and vehicle path planning method and device |
| CN111976726A (en) * | 2020-08-26 | 2020-11-24 | 中南大学 | Steering auxiliary system of intelligent rail vehicle and control method thereof |
| CN112046494B (en) * | 2020-09-11 | 2021-10-29 | 中国第一汽车股份有限公司 | Vehicle control method, device, equipment and storage medium |
| CN112046494A (en) * | 2020-09-11 | 2020-12-08 | 中国第一汽车股份有限公司 | Vehicle control method, device, equipment and storage medium |
| CN112092809A (en) * | 2020-09-15 | 2020-12-18 | 北京罗克维尔斯科技有限公司 | Auxiliary reversing method, device and system and vehicle |
| CN111829545A (en) * | 2020-09-16 | 2020-10-27 | 深圳裹动智驾科技有限公司 | Automatic driving vehicle and dynamic planning method and system for motion trail of automatic driving vehicle |
| CN114537474A (en) * | 2020-11-26 | 2022-05-27 | 银河水滴科技（北京）有限公司 | Protection method and device for train running safety |
| CN116615773A (en) * | 2020-12-04 | 2023-08-18 | 三菱电机株式会社 | Automatic driving system, server and dynamic map generation method |
| CN114692289A (en) * | 2020-12-31 | 2022-07-01 | 华为技术有限公司 | Automatic driving algorithm testing method and related equipment |
| CN113819915A (en) * | 2021-03-03 | 2021-12-21 | 京东鲲鹏(江苏)科技有限公司 | Unmanned vehicle path planning method and related equipment |
| CN113753038B (en) * | 2021-03-16 | 2023-09-01 | 京东鲲鹏(江苏)科技有限公司 | Track prediction method and device, electronic equipment and storage medium |
| CN113753038A (en) * | 2021-03-16 | 2021-12-07 | 京东鲲鹏(江苏)科技有限公司 | Trajectory prediction method and apparatus, electronic device and storage medium |
| CN113075668B (en) * | 2021-03-25 | 2024-03-08 | 广州小鹏自动驾驶科技有限公司 | Dynamic obstacle object identification method and device |
| CN113075668A (en) * | 2021-03-25 | 2021-07-06 | 广州小鹏自动驾驶科技有限公司 | Dynamic obstacle object identification method and device |
| CN113022593A (en) * | 2021-04-09 | 2021-06-25 | 长沙智能驾驶研究院有限公司 | Obstacle processing method and device and traveling equipment |
| CN113022593B (en) * | 2021-04-09 | 2022-10-14 | 长沙智能驾驶研究院有限公司 | Obstacle processing method and device and traveling equipment |
| CN113264066B (en) * | 2021-06-03 | 2023-05-23 | 阿波罗智能技术(北京)有限公司 | Obstacle track prediction method and device, automatic driving vehicle and road side equipment |
| CN113147794A (en) * | 2021-06-03 | 2021-07-23 | 北京百度网讯科技有限公司 | Method, device and equipment for generating automatic driving early warning information and automatic driving vehicle |
| CN113264066A (en) * | 2021-06-03 | 2021-08-17 | 阿波罗智能技术(北京)有限公司 | Obstacle trajectory prediction method and device, automatic driving vehicle and road side equipment |
| CN113205088A (en) * | 2021-07-06 | 2021-08-03 | 禾多科技（北京）有限公司 | Obstacle image presentation method, electronic device, and computer-readable medium |
| CN113479217A (en) * | 2021-07-26 | 2021-10-08 | 惠州华阳通用电子有限公司 | Lane changing and obstacle avoiding method and system based on automatic driving |
| CN113641734A (en) * | 2021-08-12 | 2021-11-12 | 驭势科技（北京）有限公司 | Data processing method, device, equipment and medium |
| CN113641734B (en) * | 2021-08-12 | 2024-04-05 | 驭势科技（北京）有限公司 | Data processing method, device, equipment and medium |
| CN113734190A (en) * | 2021-09-09 | 2021-12-03 | 北京百度网讯科技有限公司 | Vehicle information prompting method and device, electronic equipment, medium and vehicle |
| CN114018265A (en) * | 2021-10-28 | 2022-02-08 | 山东新一代信息产业技术研究院有限公司 | Patrol robot driving track generation method, equipment and medium |
| CN114018265B (en) * | 2021-10-28 | 2024-02-02 | 山东新一代信息产业技术研究院有限公司 | Method, equipment and medium for generating running track of inspection robot |
| CN114162133B (en) * | 2021-11-10 | 2024-06-04 | 上海人工智能创新中心 | Risk assessment method, risk assessment device and computer-readable storage medium for driving scene |
| CN114162133A (en) * | 2021-11-10 | 2022-03-11 | 上海人工智能创新中心 | Risk assessment method and device for driving scene and computer readable storage medium |
| CN114446092A (en) * | 2022-01-19 | 2022-05-06 | 无锡学院 | S-shaped road simulated obstacle early warning method based on three-dimensional camera networking |
| EP4239596A1 (en) * | 2022-03-02 | 2023-09-06 | Xiaomi EV Technology Co., Ltd. | Method and apparatus for detecting drivable area, mobile device and storage medium |
| CN114596553A (en) * | 2022-03-11 | 2022-06-07 | 阿波罗智能技术(北京)有限公司 | Model training method, trajectory prediction method and device and automatic driving vehicle |
| WO2023173998A1 (en) * | 2022-03-18 | 2023-09-21 | 北京智行者科技股份有限公司 | Environmental modeling method and apparatus for slt spatial risk field of obstacle, and related product |
| CN114771572A (en) * | 2022-05-10 | 2022-07-22 | 中国第一汽车股份有限公司 | Automatic driving track prediction method, device, equipment and storage medium |
| CN115214719B (en) * | 2022-05-30 | 2024-04-05 | 广州汽车集团股份有限公司 | Obstacle trajectory tracking method, device, intelligent driving equipment and storage medium |
| CN115214719A (en) * | 2022-05-30 | 2022-10-21 | 广州汽车集团股份有限公司 | Obstacle trajectory tracking method, device, intelligent driving device and storage medium |
| CN114863685A (en) * | 2022-07-06 | 2022-08-05 | 北京理工大学 | A method and system for predicting the trajectory of traffic participants based on risk acceptance |
| CN114863685B (en) * | 2022-07-06 | 2022-09-27 | 北京理工大学 | Traffic participant trajectory prediction method and system based on risk acceptance degree |
| CN118209127A (en) * | 2024-03-29 | 2024-06-18 | 重庆赛力斯凤凰智创科技有限公司 | Path optimization method, device, electronic device and storage medium |
| CN118209127B (en) * | 2024-03-29 | 2024-09-13 | 重庆赛力斯凤凰智创科技有限公司 | Path optimization method, path optimization device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109927719B (en) | 2022-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109927719A (en) | A kind of auxiliary driving method and system based on barrier trajectory predictions | |
| CN109817021A (en) | A kind of laser radar trackside blind area traffic participant preventing collision method and device | |
| CN109814112A (en) | A kind of ultrasonic radar and laser radar information fusion method and system | |
| CN103786729B (en) | lane recognition method and system | |
| EP3770549B1 (en) | Information processing device, movement device, method, and program | |
| CN109633688A (en) | A kind of laser radar obstacle recognition method and device | |
| US9569962B2 (en) | Method for identifying a vehicle detected by a sensor device | |
| CN114442101B (en) | Vehicle navigation method, device, equipment and medium based on imaging millimeter wave radar | |
| CN109572555A (en) | A kind of block information display methods and system applied to unmanned vehicle | |
| JP7537787B2 (en) | Collision prevention method, device, server and computer program | |
| CN112464229B (en) | Method and device for detecting spoofing attacks on an autopilot system | |
| CN111742235B (en) | Method and system for identifying an empty parking space suitable for a vehicle | |
| KR20190126024A (en) | Traffic Accident Handling Device and Traffic Accident Handling Method | |
| CN116872921A (en) | Method and system for avoiding risks of vehicle, vehicle and storage medium | |
| US20220253065A1 (en) | Information processing apparatus, information processing method, and information processing program | |
| CN109781129A (en) | A road safety detection system and method based on inter-vehicle communication | |
| CN108263376A (en) | Automated parking system and automatic parking method | |
| JP2022098397A (en) | Device and method for processing information, and program | |
| CN109901194A (en) | Onboard system, method, equipment and the storage medium of anticollision | |
| CN116438583A (en) | Available parking space recognition device, available parking space recognition method, and program | |
| US20230399008A1 (en) | Multistatic radar point cloud formation using a sensor waveform encoding schema | |
| EP4140815A1 (en) | Information processing device, information processing method, and program | |
| CN114013439A (en) | Active vehicle following method and system | |
| CN114049767A (en) | Edge calculation method and device and readable storage medium | |
| CN113591673A (en) | Method and device for recognizing traffic signs |
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 |