CN110083163A - A kind of 5G C-V2X bus or train route cloud cooperation perceptive method and system for autonomous driving vehicle - Google Patents
A kind of 5G C-V2X bus or train route cloud cooperation perceptive method and system for autonomous driving vehicle Download PDFInfo
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Abstract
The present invention provides a kind of 5G C-V2X bus or train route cloud cooperation perceptive method and system for autonomous driving vehicle, the Quick Acquisition to information various on road and transmission are realized by 5G technology and car networking technology, and according to the information architecture dynamic scene of acquisition, operating path of the simulated target vehicle in dynamic scene, therefrom select optimal path, to which target vehicle can realize automatic Pilot under the auxiliary of optimal path, since information collected may include the barrier in roadside, traffic lights, the information such as the vehicle of pedestrian and traveling, and the shared of data is carried out by 5G technology, to provide enough information for automatic Pilot, guarantee the rapidity and correctness of autonomous driving vehicle decision.
Description
Technical field
The present invention relates to automatic Pilot technical field, in particular to a kind of 5G C-V2X bus or train route for autonomous driving vehicle
Cloud cooperation perceptive method and system.
Background technique
Vehicle and vehicle, Che Yuren, vehicle and trackside and vehicle may be implemented in V2X (vehicle to everything) car networking
Sharing between information is realized in communication between cloud platform, and automatic Pilot is made to take leave of " bicycle intelligence " epoch.
Currently, typical automatic Pilot intelligent vehicle system uses onboard sensor generally to obtain road scene, vehicle appearance
The information such as state, then passage path planning and driving behavior decision-making mechanism determine car body control instruction (such as power, direction, brake
Deng), the real time information that sensor obtains finally is relied in the process of moving, in conjunction with feedback mechanism, realizes adaptive traveling control
System and driving safety carry out the acquisition of information no doubt unknown message on the available vehicle whole body and progress mesh using onboard sensor
Mark Fen Lei and not handled, but only lean on monomer vehicle sensors, and sensing capability and processing speed are limited, and every kind of sensing
Device has a short slab of oneself, and no matter which kind of sensor or fusion perception are unable to realize and cross barrier current automatic driving vehicle
Object and space-time is hindered to limit the row that do not can solve in field obscuration situation and perception blind area to realize the complete perception to surrounding enviroment
Safety problem, etc. is sailed, these all constitute the bottleneck problem for restricting automatic driving vehicle development.
Summary of the invention
With this, the present invention proposes a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle and is mirror
System can help automobile to realize the perception of richer scene, enhance the ability to predict to anomalous event, increase Intelligent treatment when
Between redundancy, the give way various things of road of the angle cooperateed with from bus or train route are that vehicle provides enough information, guarantee automatic Pilot vapour
The rapidity and correctness of vehicle decision.
The technical scheme of the present invention is realized as follows:
A kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle, comprising the following steps:
S1, it detects barrier and obtains obstacle information, the barrier includes static-obstacle thing and dynamic barrier；
Running environment information around S2, target vehicle detection；
S3, Weather information is obtained；
S4, dynamic scene construction is carried out according to obstacle information, surrounding running environment information and Weather information；
S5, optimum path planning is carried out in dynamic scene；
S6, obstacle information and optimal path are sent to target vehicle；
S7, target vehicle carry out automatic Pilot.
Preferably, further comprising the steps of between the step S2 and step S3:
S21, the input of road shape, boundary, lane quantity and position, lane width as dynamic scene construction is obtained
Amount.
Preferably, the specific steps of dynamic scene construction include: in the step S4
S41, high-precision map structuring spatial scene is obtained；
S42, static-obstacle thing, dynamic barrier and Weather information are building up in spatial scene；
S43, behavior prediction is carried out to dynamic barrier；
S44, the location information for positioning target vehicle, and be mapped in spatial scene.
Preferably, further comprising the steps of before the step S5:
S45, establish vehicle-state model, the input parameter of the vehicle-state model include speed of operation, light information,
Prestissimo and peak acceleration when controlling delay, minimum turning radius, turning.
Preferably, the specific steps of the step S5 are as follows:
S51, the point of destination information for obtaining target vehicle, and be mapped in dynamic scene；
S52, the mulitpath for driving to point of destination in dynamic scene according to vehicle-state modeling target vehicle；
S53, processing is optimized to mulitpath obtain optimal path.
Preferably, the specific steps of the step S52 are as follows: according to the virtual lane constructed in dynamic scene, using being based on
The dynamic virtual lane local path generating algorithm of Study on Trend, centered on vehicle-state model, according to dynamic barrier with
And a plurality of path arrived at the destination is cooked up in position of the static-obstacle thing in dynamic scene.
A kind of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle, comprising:
Roadside unit, for acquiring obstacle information and road information；
Onboard sensor, for acquiring the object information of vehicle periphery；
Cloud platform constructs dynamic scene and plans optimal path；
Auto-pilot controller controls Vehicular automatic driving；
5G transmission module, for carrying out data transmission；
Weather information module, for obtaining Weather information；
The 5G transmission module respectively with roadside unit, onboard sensor, cloud platform, Weather information module and automatically drive
Controller data connection is sailed, the roadside unit, onboard sensor and Weather information module pass the information of acquisition by 5G
Defeated module transfer is to cloud platform；The cloud platform includes information processing layer, map structuring layer and path planning layer, and the 5G is passed
Defeated module is connect with information processing layer, path planning layer data respectively, and the information processing layer handles roadside unit, vehicle-mounted sensing
Map structuring layer is transferred to after device and the information of Weather information module acquisition, map structuring layer information structure based on the received
It builds dynamic scene and dynamic scene information is output in path planning layer, the path planning layer plans mesh in dynamic scene
The optimal path of vehicle drive is marked, the optimal path of planning is sent to by the path planning layer by 5G transmission module to be driven automatically
Sail controller.
Preferably, the roadside unit includes that first acquisition unit in roadside is arranged in and is arranged in traffic lights one
Second acquisition unit of side, the 5G transmission module respectively with the first acquisition unit, the second acquisition unit data connection.
Preferably, the onboard sensor includes laser radar, millimetre-wave radar, stereoscopic camera, infrared camera, ultrasound
Wave rangefinder.
It preferably, further include vehicle localization module, the vehicle localization module and 5G transmission module data connection.
Compared with prior art, the beneficial effects of the present invention are:
The present invention provides a kind of 5G C-V2X bus or train route cloud cooperation perceptive methods and system for autonomous driving vehicle, can
To acquire the barrier on road, the information around target vehicle traveling and neighbouring Weather information, and pass through building dynamic
Scene, operating path of the simulated target vehicle in dynamic scene, to select optimal path, so that target vehicle can be most
Automatic Pilot is realized under the auxiliary of shortest path, since information collected includes the obstacle information of road, the letter of vehicle periphery
The information such as breath and weather, and the shared of data is carried out by 5G technology, to provide information abundant for automatic Pilot, guarantee
The rapidity and correctness of autonomous driving vehicle decision.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, the accompanying drawings in the following description is only the preferred embodiment of the present invention, for
For those of ordinary skill in the art, without any creative labor, it can also be obtained according to these attached drawings
His attached drawing.
Fig. 1 is an a kind of reality of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle of the invention
Apply the schematic diagram of example；
Fig. 2 is an a kind of reality of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle of the invention
Apply the schematic diagram of the building dynamic scene of example；
Fig. 3 is an a kind of reality of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle of the invention
Apply the flow chart of example；
Fig. 4 is an a kind of reality of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle of the invention
Apply the functional hierarchy figure of example；
In figure, 1 is roadside unit, and 11 be the first acquisition unit, and 12 be the second acquisition unit, and 2 be onboard sensor, and 3 are
Cloud platform, 4 be auto-pilot controller, and 5 be 5G transmission module, and 6 be vehicle localization module, and 7 be Weather information module.
Specific embodiment
In order to be best understood from the technology of the present invention content, a specific embodiment is provided below, and do to the present invention in conjunction with attached drawing
Further instruction.
Referring to Fig. 1, a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle provided by the invention,
The following steps are included:
S1, it detects barrier and obtains obstacle information, the barrier includes static-obstacle thing and dynamic barrier；
Running environment information around S2, target vehicle detection；
S3, Weather information is obtained；
S4, dynamic scene construction is carried out according to obstacle information, surrounding running environment information and Weather information；
S5, optimum path planning is carried out in dynamic scene；
S6, obstacle information and optimal path are sent to target vehicle；
S7, target vehicle carry out automatic Pilot.
In the present embodiment, it in order to guarantee the safety and reliability of automatic Pilot, needs to travel automatic driving vehicle
The various factors that can be encountered in the process all considers, for example, the case where road, traffic lights the case where, vehicle flowrate, flow of the people, road road sign
Will, road surface evenness degree and weather condition etc. require to consider, and current automatic driving vehicle is only merely to itself vehicle
Ambient enviroment detected, automatic Pilot is carried out with this, does not ensure that the safety of automatic driving vehicle, therefore this hair
It is bright road information, pedestrian information, traffic lights information, driving vehicle information and Weather information to be acquired, then will
The information of acquisition carries out shared processing, and constructs the dynamic scene of a simulation, constantly updates dynamic according to the information of acquisition
Scene information, while target vehicle can also be placed in dynamic scene, when target vehicle needs to go to destination, according to
Each object of reference of the target vehicle in the position and dynamic scene in dynamic scene, the planning of Lai Jinhang optimal path, finally
Obstacle information, road information and optimal path etc. are sent to target vehicle, target vehicle then carries out the control of automatic Pilot.
The present invention comes to carry out data sharing to trackside facility, pedestrian, traffic lights information by using V2X car networking technology,
The acquisition of information can also be carried out between vehicle and vehicle simultaneously, the information of acquisition is uploaded in public cloud platform 3 and is total to
It enjoys and handles, be automatic driving vehicle so as to realize the information exchange between vehicle and people, Che Yulu, vehicle and cloud platform 3
Sufficient Informational support is provided, thereby may be ensured that safety that automatic driving vehicle travels in a certain range and reliable
Property.
Although the case where road itself will not change, vehicle flowrate and flow of the people on road are all continuous
Variation, if autonomous driving vehicle is only to carry out automatic Pilot according to fixed scene, it not can guarantee automatic Pilot
Carry out, the present invention can continual acquisition dealing information of vehicles and pedestrian information, and information of vehicles and pedestrian are believed
Breath, which uploads, to be shared, and dynamic scene simulation can be with real-time update, so as to provide for the automatic Pilot of autonomous driving vehicle
Real-time information state guarantees the safety of automatic Pilot.
Specifically, Weather information is added in automatic Pilot by the invention, autonomous driving vehicle is being carried out certainly
When dynamic driving, the adjustment that can be travelled according to weather conditions obtains current location by being linked into weather forecast system
Real-time weather information, or current Weather information is obtained by the way that multiple sensor groups are arranged, at Weather information
Reason, so that automatic Pilot is assisted, for example, automatic driving vehicle speed cannot be excessively high, and should open when weather is rain and fog weather
Open indicator light, prevent from colliding with other vehicles, a complete network is trained by deep learning algorithm, when input not
With Weather information when, can the corresponding car speed of corresponding output, indicator light switch information etc., car speed can be used for
The traveling of simulating vehicle in subsequent progress optimum path planning, and indicator light switch information can be exported makes it to target vehicle
Automatic running is carried out under special circumstances, to other the non-automatic driving vehicles and pedestrian travelled on road with alarm function,
The Weather information that auxiliary is provided, and is obtained for automatic Pilot also can synchronization map arrive dynamic scene, for constructing and reality ring
The almost the same simulated scenario in border.
Wherein the detection of static-obstacle thing includes building, trees, bridge, traffic mark and the traffic around detection road
Lamp information etc.；The detection of the dynamic barrier includes the mobile vehicle of detection and pedestrian, and the detection of static-obstacle thing can be with
Using the method based on single image, the method based on light stream and the method based on stereoscopic vision, the method based on single image
Only it is used to detect specific barrier, such as the vehicle of surrounding, and optical flow method uses one group of image sequence from same video camera,
Light stream in usual image is consistent with sports ground, therefore can be used to detect the barrier of movement, and the method for stereoscopic vision can
To use multiple video cameras from different perspectives in the multiple image of synchronization, great advantage is opposite between video camera
Relationship determines that there is no need to the dominant motion estimation of similar optical flow approach, the methods that the present embodiment then uses stereoscopic vision, in trackside
Multiple video cameras are set up, the acquisition of the information of multiple and different angles is carried out for the static-obstacle thing to roadside.
Dynamic barrier includes mobile vehicle and pedestrian, can be to mobile vehicle by the sensor for being arranged onboard
Information collection is carried out, so that the building for dynamic scene provides complete input, while roadside unit 1 also can detecte distant place
Mobile vehicle and pedestrian, and be building up in dynamic scene, then pass through vehicle behavior and row different in each path
People's behavior to carry out optimum path planning to target vehicle.
Preferably, further comprising the steps of between the step S2 and step S3:
S21, the input of road shape, boundary, lane quantity and position, lane width as dynamic scene construction is obtained
Amount.
It when dynamic scene construction, needs to fully take into account the actual conditions of road, optimal road could be made to target vehicle
The planning of diameter, therefore the information such as the boundary of road, lane quantity, width are required carry out with the acquisition of information, this type of information
It is generally no variation in, the photographic device etc. by the way that roadside is arranged in may be implemented to acquire.
Preferably, the specific steps of dynamic scene construction include: in the step S4
S41, high-precision map structuring spatial scene is obtained；
S42, static-obstacle thing, dynamic barrier and Weather information are building up in spatial scene；
S43, behavior prediction is carried out to dynamic barrier；
S44, the location information for positioning target vehicle, and be mapped in spatial scene.
By construct dynamic scene, can construct one simulation space, and simultaneously by the building of surrounding, road and
Weather information is configured in simulation space, is carried out dynamic prediction to mobile vehicle and pedestrian in combination with spatio temporal reasoning, is made
Entire dynamic scene close to real space, after target vehicle is then mapped to dynamic scene, can according to destination come
Carry out the planning of optimal path.
The building of dynamic scene relates generally to 3 vehicle location, space-time modeling and spatio temporal reasoning aspects, and wherein vehicle is fixed
Position, which use, realizes that space-time modeling is to pass through acquisition for GPS supplemented by main GPS with BDS-3 (Beidou three)
High-precision map, such as be connected with existing navigation map server to obtain high-precision map, then according to accurately
Figure is mapped in spatial scene to construct spatial scene, then by the static-obstacle thing acquired in advance and Weather information, real
The Primary Construction of existing spatial scene；Finally again by spatio temporal reasoning to switching of mobile vehicle, pedestrian and traffic lights etc. into
The prediction of Mobile state barrier, and dynamic barrier is mapped in spatial scene simultaneously, thus realize the building of dynamic scene,
It is illustrated in figure 2 the schematic diagram of building dynamic scene, input needed for constructing dynamic scene includes road information, dynamic barrier
Information, static-obstacle thing information and traffic mark etc. pass through the road information in the road information and high-precision map of acquisition
Virtual road is simulated after carrying out information registration and integrated treatment, while road information and road signs information can be combined
Behavior prediction is carried out to dynamic barrier, specific behavior prediction includes: that object run trend analysis, mutual alignment relation push away
Lead, environment dynamic modeling analysis and multiple target state of motion analysis etc., and the behavior prediction of dynamic barrier use based on view
The method of feel, such as direct matching method, Bayesian method.
Preferably, further comprising the steps of before the step S5:
S45, establish vehicle-state model, the input parameter of the vehicle-state model include speed of operation, light information,
Prestissimo and peak acceleration when controlling delay, minimum turning radius, turning.
Vehicle-state model includes the function of series of parameters, and most important parameter includes speed of operation, lamp among these
Prestissimo and peak acceleration etc. when optical information, control delay, minimum turning radius, turning, by establishing vehicle-state
Model, to obtain the mobile message of vehicle, thus can be using vehicle-state model as simulating vehicle when carrying out path planning
To carry out simulation movement.
For the safety for guaranteeing special weather automatic Pilot, therefore, to assure that the speed of automatic Pilot cannot be too fast, and answers
Corresponding indicator light is opened, after collecting Weather information, phase is obtained according to Weather information and trained deep learning network
The vehicle speed information and indicator light information answered, vehicle speed information is for establishing vehicle-state model, so that vehicle-state model can be
The planning of optimal path is carried out in subsequent path planning according to corresponding speed.
Preferably, the specific steps of the step S5 are as follows:
S51, the point of destination information for obtaining target vehicle, and be mapped in dynamic scene；
S52, the mulitpath for driving to point of destination in dynamic scene according to vehicle-state modeling target vehicle；
S53, processing is optimized to mulitpath obtain optimal path.
After constructing dynamic scene, need to obtain the optimal path that target vehicle arrives at the destination, therefore, it is necessary to
On dynamic scene obtain point of destination position can just be simulated, due in dynamic scene include actual scene in stationary body,
Dynamic vehicles or pedestrians and corresponding Weather information etc., therefore can be arrived at the destination according to vehicle-state model to simulate
Mulitpath, then carry out comprehensive analysis from time, safety and road safety state etc., finally obtain optimal
Then optimal path, road information, obstacle information etc. are sent to automatic driving vehicle by path, automatic so as to carry out
It drives.
Preferably, the specific steps of the step S52 are as follows: according to the virtual lane constructed in dynamic scene, using being based on
The dynamic virtual lane local path generating algorithm of Study on Trend, centered on vehicle-state model, according to dynamic barrier with
And a plurality of path arrived at the destination is cooked up in position of the static-obstacle thing in dynamic scene.
Using the dynamic virtual lane local path generating algorithm based on Study on Trend, automatic driving vehicle is being fully considered
The flexibility that vehicle behavior is improved while safety, by calculating automatic driving vehicle and surrounding dynamic barrier in real time
State, dynamic establishes the virtual lane of optimization, since virtual lane is overlapped on true lane, according to accurate auto model
With the estimation to vehicle, centered on vehicle and laterally generate certain extension (greater than automatic driving vehicle width but
Do not exceed the width in a lane) connection automatic driving vehicle initial position and destination locations smooth virtual quadrangle.
Battle field situation then fully considers influence (for example whether for one-way road, if speed limit etc.) of the traffic information to Route Generation, relies on
Information (such as traffic sign, all types of barrier lists, the lane information, weather road information that sensor and 5G car networking obtain
Deng), so that automatic driving vehicle is judged the scene locating for itself using spatio temporal reasoning and makes suitable action selection.
Reference Fig. 3, a kind of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle provided by the invention,
Include:
Roadside unit 1, for acquiring obstacle information and road information；
Onboard sensor 2, for acquiring the object information of vehicle periphery；
Cloud platform 3 constructs dynamic scene and plans optimal path；
Auto-pilot controller 4 controls Vehicular automatic driving；
5G transmission module 5, for carrying out data transmission；
Weather information module 7, for obtaining Weather information；
The 5G transmission module 5 respectively with roadside unit 1, onboard sensor 2, cloud platform 3, Weather information module 7 and
4 data connection of auto-pilot controller, the roadside unit 1, onboard sensor 2 and Weather information module 7 are by the letter of acquisition
Breath is transferred to cloud platform 3 by 5G transmission module 5；The cloud platform 3 includes information processing layer, map structuring layer and path rule
Layer is drawn, the 5G transmission module 5 is connect with information processing layer, path planning layer data respectively, and the information processing floor handles road
Map structuring layer, the map structuring are transferred to after the information that side unit 1, onboard sensor 2 and Weather information module 7 acquire
Dynamic scene information information architecture dynamic scene and is output in path planning layer, the path planning layer by layer based on the received
The optimal path of object of planning vehicle drive in dynamic scene, the path planning layer pass the optimal path of planning by 5G
Defeated module 5 is sent to auto-pilot controller 4.
In the present embodiment, realized by the roadside unit of setting 1, onboard sensor 2 and cloud platform 3 vehicle and people,
Information sharing between vehicle and vehicle, Che Yulu, vehicle and cloud platform 3, and realized between information by setting 5G transmission module 5
Quick transmission, for previous 4G transmission, the transmitting of information more quickly, it is accurate, can be directed to continually changing
Road is updated in real time, to provide complete information for automatic Pilot.
Specifically, on the one hand the information exchange of V2X car networking is to be realized by roadside unit 1, it is on the other hand logical
Onboard sensor 2 is crossed to realize, roadside unit 1 can acquire the information of pedestrian, the information of road, the information of vehicles of traveling, friendship
Logical lamp information, road signs information etc., at the same onboard sensor 2 can also acquire the pedestrian information of vehicle in the process of moving with
And the vehicle of traveling, can be to avoid this in subsequent path planning so as to know flow of the people or the biggish place of vehicle flowrate
Paths.
After acquiring relevant information by car networking system, relevant processing is carried out to information by cloud platform 3, is specifically included
The building of dynamic scene and the planning of optimal path construct the spatial field of entire vehicle driving by downloading high-precision map
Then scape will collect information and be mapped in spatial scene, and the dynamic scene of vehicle driving be simulated, according to target vehicle
After selecting an optimal path in the mulitpath arrived at the destination, autonomous driving vehicle can carry out certainly according to optimal path
It is dynamic to drive.
It include that Weather information handles part in the information processing layer of cloud platform 3, which includes trained deep learning
Network, the input of the deep learning network are Weather information, are exported as the various parameters requirement of running car, including Travel vehicle
Speed, indicator light information etc. are handled, are obtained under current weather condition needed for running car by inputting current weather information
Speed, indicator light unlatching situation for wanting etc., such as in rain and fog weather, should ensure that automobile is in low-speed running state, and answer
Open signal lamp etc., Weather information module 7 should be mounted on autonomous driving vehicle, for obtaining autonomous driving vehicle current location
Weather information, and cloud platform 3 is transferred to by 5G transmission module 5, to obtain what the autonomous driving vehicle under corresponding weather travelled
Parameter request, in the automatic Pilot control for combining the carry out such as road information, information of vehicles and pedestrian information final, Weather information
Module 7, which can be linked into weather forecast system, carries out real-time update, and/or by being arranged in wind sensor, humidity sensor
The sensors such as device, Raindrop sensor group carries out the acquisition of information, and judges current weather according to information, to carry out subsequent
Processing.
Auto-pilot controller 4 of the present invention reaches SoC TX2 controller TITAN, 5G transmission module 5 using tall and handsome
Using 5G multimode terminal chip --- Ba Long 5000, Ba Long 5000 is the first multimode chip for supporting V2X in the whole world, and letter may be implemented
Quick, the accurate delivery of breath.
Preferably, the roadside unit 1 includes that first acquisition unit 11 in roadside is arranged in and is arranged in traffic signals
Second acquisition unit 12 of lamp side, the 5G transmission module 5 respectively with the first acquisition unit 11,12 data of the second acquisition unit
Connection.
Since roadside unit 1 needs to acquire the various static or mobile object information on road, traffic lights is also acquired
Change information, therefore roadside unit 1 is divided for the first acquisition unit 11 and the second acquisition unit 12, by the first acquisition unit 11
Second acquisition unit 12 is arranged in traffic lights side in roadside, realizes the acquisition of information by setting.
The information that roadside unit 1 acquires is substantially pictorial information, therefore for the first acquisition unit 11 and the second acquisition
For unit 12, it can be realized using video camera.
Preferably, the onboard sensor 2 includes laser radar, millimetre-wave radar, stereoscopic camera, infrared camera, surpasses
Sound-ranging equipment.
Specifically, onboard sensor 2 can be used for acquiring other object informations in vehicle travel process, while can also be with
Auxiliary is provided when carrying out automatic Pilot, wherein laser radar can establish the 3D model of surrounding enviroment by cloud, can be with
It detects to include the details such as vehicle, pedestrian, trees, curb, millimetre-wave radar can accurately detect the distance and speed of front vehicles
Degree, has the ability of stronger penetrating fog, cigarette, dust, camera vision system can obtain the targets such as lane line, traffic signals
The details such as color and shape, to carry out depth recognition, infrared camera can incude human body heat source, and ultrasonic distance measurement then can be with
Vehicle is detected at a distance from surrounding objects, the information that the sensor can will test is transferred in cloud platform 3 and is handled,
Simultaneously when vehicle carries out automatic Pilot, aid decision can be provided.
It preferably, further include vehicle localization module 6, the vehicle localization module 6 and 5 data connection of 5G transmission module.
In the planning of the building and optimal path that carry out dynamic scene, need to know the geographical location of target vehicle
It can accurately be calculated, therefore the present invention should also have vehicle localization module 6, vehicle localization module 6 constantly will positioning
Information is transferred to cloud platform 3 by 5G transmission module 5, the position of target vehicle is updated by cloud platform 3, and carrying out optimal path
It can know the starting point of target vehicle when calculating, it is supplemented by main GPS that vehicle localization module 6, which is used with BDS-3 (Beidou three),
GPS.
The present invention can also include identifier, be arranged on autonomous driving vehicle, be mainly applied to unmanned taxi and lead
Domain obtains identification badge, when autonomous driving vehicle drives to after passenger passes through smart machine chauffeur on intelligent devices
Behind designated position, the identification badge of passenger is identified by identifier, after only identification passes through, autonomous driving vehicle is
It can star road, and optimal path automatically selected according to destination, passenger is sent to destination, identifier can be using two dimension
Code identifier or identity recognizer etc., passenger can scan the two dimensional code on smart machine by identifier or put identity card
It sets and is identified on identifier.
Specifically, a kind of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle of the invention is from function
Include six levels if being decomposed on level, is traffic environment layer, information collection layer, information processing layer, map structure respectively
Build-up layers, path planning layer, real-time control layer, as shown in figure 4, six functional layers constitute complete automatic driving control system, with
This automatic Pilot to realize vehicle.
Traffic environment layer: the layer mainly using fine road network and real-time map obtain road boundary, lane number of locations,
Isolation strip, lane width and lane markings information track the obstacle described within the scope of road using dynamic and static barrier table
Principle condition, for moving targets such as nearby vehicles, design expresses its movement within the possible range with different parameters weightings and becomes
Gesture expresses the complex situations of traffic intersection using virtual lane, realizes the abstract expression to entire running environment traffic information.
Information collection layer: the layer mainly utilizes onboard sensor 2 (such as laser radar, millimetre-wave radar, vehicle-mounted stereoscopic camera
Deng) and various location navigation means (such as BDS/GPS/INS) to obtain, site of road parameter and detection be horizontal and vertical range
Upper various targets, then by combining 5G technology, car networking technology and roadside unit 1, nearby vehicle and cloud platform 3 etc. to carry out letter
Breath exchange, obtains periphery traffic information, information of vehicles and universe traffic state information etc. in real time.
Information processing layer: this layer is mainly to the traffic information of acquisition, intersection information, information of vehicles etc. and onboard sensor 2
Acquired various heat transfer agent itself is merged, and unified data representation format is formed, on this basis, according to true road
Identification work is divided into road range detection, static-obstacle thing analysis, dynamic barrier tracking by the abstract model of road running environment
Etc. tasks, and solve the difficulties of dynamic barrier movement tendency reliable analysis.
Map structuring layer: the layer is mainly studied as realized comprehensive dynamic map visualization, base centered on how driving vehicle
In the recallable amounts method of dynamic object, and the movement tendency of vehicle-to-target is combined, studies vehicle and multiple target in scene
In mutual alignment relation and its differentiation, realize the locally traveling scene dynamics building of Multi hiberarchy and multi scale centered on vehicle.
Map structuring layer includes perception data synthesis display, identification information is comprehensive, dynamic scene models and multiple target
State of motion modeling, wherein perception data synthesis display and identification information synthesis adopt onboard sensor 2 and roadside unit 1
The information of collection carries out display and integrated treatment, then carries out dynamic scene modeling, carries out in dynamic scene to multiple targets
Situation modeling, situation modeling uses the dynamic virtual lane local path generating algorithm based on Study on Trend, to automatic Pilot vehicle
And road on other vehicles for travelling carry out Study on Trend, on the one hand guidance can be provided for automatic driving vehicle, it is another
Aspect can be used for predicting the movement tendency of other vehicles.
Virtual lane is generated by the battle field situation to intersection, and actively generates path, determines automatic driving vehicle
It is to continue with and is still waited for parking by intersection or accelerate to pass through, can not only be applied to the path rule of automatic driving vehicle
It draws in guidance, the behavior prediction of other vehicles can also be applied to, so that the planning for optimal path provides guidance.
Path planning layer: the reliable Route Generation technology of the main research trends of the layer is based on multilane traffic intersection dynamic office
Gesture is estimated in the imperfect situation of external information, to utilize virtual lane path generation technique to realize high-rise driving behavior decision
Realize the advanced automatic Pilot behavior that personalizes of local scene.
Path planning layer includes behaviour decision making, behaviour decision making by rule-based automatic Pilot strategy and end to end from
Dynamic driving strategy is combined together, and forms the automatic Pilot control decision that car-mounted terminal cooperates with processing with cloud, and car-mounted terminal is adopted
The end-to-end target detection and semantic segmentation method combined with deep learning with probability graph model, first by vehicle in sensor
2 equal acquisition external informations simultaneously carry out registration fusion, recycle vehicle-mounted GPU unit using deep learning method that sensing data is advanced
Full connection convolutional network FCN (Fully Convolutional Networks) of row extracts preliminary feature, then uses probability graph
Model such as Markov random field MRF (Markov Random Fields), condition random field CRF (Conditional
Random Fields) etc. optimization front end output, Pixel-level Tag Estimation is improved by way of reasoning, with generate clearly
Boundary and fine-grained segmentation, so that the shortcomings that overcoming CNN Pixel-level to mark task, makes up detailed information during down-sampling
Loss finally obtains with clearly defined objective semantic segmentation figure and carries out target classification and behavior perception.
Real-time control layer: the layer mainly improves existing vehicle control system, realizes that the autonomous driving instruction of vehicle is (such as right
The operation of power, brake, steering wheel etc. and corresponding control parameter) real-time generation.
In conclusion the present invention realizes the reality of traffic environment information on the basis of using 5G technology and car networking technology
When it is shared and update, help automatic driving vehicle to understand real-time traffic environment, so as to provide for automatic driving vehicle
Correctly, it quickly navigates, guarantees the stability and reliability of automatic Pilot.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle, which is characterized in that including following step
It is rapid:
S1, it detects barrier and obtains obstacle information, the barrier includes static-obstacle thing and dynamic barrier；
Running environment information around S2, target vehicle detection；
S3, Weather information is obtained；
S4, dynamic scene construction is carried out according to obstacle information, surrounding running environment information and Weather information；
S5, optimum path planning is carried out in dynamic scene；
S6, obstacle information and optimal path are sent to target vehicle；
S7, target vehicle carry out automatic Pilot.
2. a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle according to claim 1,
It is characterized in that, further comprising the steps of between the step S2 and step S3:
S21, the input quantity of road shape, boundary, lane quantity and position, lane width as dynamic scene construction is obtained.
3. a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle according to claim 1,
It is characterized in that, the specific steps of dynamic scene construction include: in the step S4
S41, high-precision map structuring spatial scene is obtained；
S42, static-obstacle thing, dynamic barrier and Weather information are building up in spatial scene；
S43, behavior prediction is carried out to dynamic barrier；
S44, the location information for positioning target vehicle, and be mapped in spatial scene.
4. a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle according to claim 1,
It is characterized in that, further comprising the steps of before the step S5:
S45, vehicle-state model is established, the input parameter of the vehicle-state model includes speed of operation, light information, control
Prestissimo and peak acceleration when delay, minimum turning radius, turning.
5. a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle according to claim 4,
It is characterized in that, the specific steps of the step S5 are as follows:
S51, the point of destination information for obtaining target vehicle, and be mapped in dynamic scene；
S52, the mulitpath for driving to point of destination in dynamic scene according to vehicle-state modeling target vehicle；
S53, processing is optimized to mulitpath obtain optimal path.
6. a kind of 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle according to claim 5,
It is characterized in that, the specific steps of the step S52 are as follows: according to the virtual lane constructed in dynamic scene, using based on situation point
The dynamic virtual lane local path generating algorithm of analysis, centered on vehicle-state model, according to dynamic barrier and static state
Cook up a plurality of path arrived at the destination in position of the barrier in dynamic scene.
7. the one of any 5G C-V2X bus or train route cloud cooperation perceptive method for autonomous driving vehicle of application claim 1-6
Kind is used for the 5G C-V2X bus or train route cloud collaborative perception system of autonomous driving vehicle characterized by comprising
Roadside unit, for acquiring obstacle information and road information；
Onboard sensor, for acquiring the object information of vehicle periphery；
Cloud platform constructs dynamic scene and plans optimal path；
Auto-pilot controller controls Vehicular automatic driving；
5G transmission module, for carrying out data transmission；
Weather information module, for obtaining Weather information；
The 5G transmission module respectively with roadside unit, onboard sensor, cloud platform, Weather information module and automatic Pilot control
The information of acquisition is transmitted mould by 5G by device data connection processed, the roadside unit, onboard sensor and Weather information module
Block is transferred to cloud platform；The cloud platform includes information processing layer, map structuring layer and path planning layer, and the 5G transmits mould
Block is connect with information processing layer, path planning layer data respectively, information processing layer processing roadside unit, onboard sensor with
And map structuring layer is transferred to after the information of Weather information module acquisition, information architecture is dynamic based on the received for the map structuring layer
Dynamic scene information is simultaneously output in path planning layer by state scene, path planning layer object of planning vehicle in dynamic scene
The optimal path driven, the optimal path of planning is sent to automatic Pilot control by 5G transmission module by the path planning layer
Device processed.
8. a kind of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle according to claim 7,
It is characterized in that, the roadside unit includes that first acquisition unit in roadside is arranged in and is arranged in the of traffic lights side
Two acquisition units, the 5G transmission module respectively with the first acquisition unit, the second acquisition unit data connection.
9. a kind of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle according to claim 7,
It is characterized in that, the onboard sensor includes laser radar, millimetre-wave radar, stereoscopic camera, infrared camera, ultrasonic distance measurement
Instrument.
10. a kind of 5G C-V2X bus or train route cloud collaborative perception system for autonomous driving vehicle according to claim 7,
It is characterized in that, further includes vehicle localization module, the vehicle localization module and 5G transmission module data connection.
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