CN107719369A - The longitudinally controlled method, apparatus of automatic Pilot and there is its automatic driving vehicle - Google Patents

The longitudinally controlled method, apparatus of automatic Pilot and there is its automatic driving vehicle Download PDF

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Publication number
CN107719369A
CN107719369A CN201710828439.7A CN201710828439A CN107719369A CN 107719369 A CN107719369 A CN 107719369A CN 201710828439 A CN201710828439 A CN 201710828439A CN 107719369 A CN107719369 A CN 107719369A
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mrow
msub
virtual target
car
speed
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CN107719369B (en
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王肖
颜波
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Beijing Idriverplus Technologies Co Ltd
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Beijing Idriverplus Technologies Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • B60W2720/106—Longitudinal acceleration

Abstract

The invention discloses a kind of longitudinally controlled method, apparatus of automatic Pilot and there is its automatic driving vehicle, methods described includes：S1, according to the expected path curvature information of this car, generate virtual target；S2, real goal is identified according to this car sensory perceptual system；S3, according to traffic scene, CAR SERVICE function and integration of user interaction functionality, it is converted into virtual target；S4, from each virtual target and real goal, filter out and follow target；S5, according to target is followed, plan the expectation acceleration of this car.The factor such as each path point and traffic scene on expected path, CAR SERVICE function and integration of user interaction functionality is conceptualized as virtual target by the present invention, the maximum conduct of the safety effects to this car is filtered out in virtual target and real goal again and uniquely follows target, the acceleration of this car is planned using unified control strategy, avoid in traditional control algorithm because caused by traffic scene changes during control mode switch caused by vehicle deceleration jump problem.

Description

The longitudinally controlled method, apparatus of automatic Pilot and there is its automatic driving vehicle
Technical field
The present invention relates to intelligent driving technical field, more particularly to a kind of longitudinally controlled method, apparatus of automatic Pilot and With its automatic driving vehicle.
Background technology
Intelligent driving technology is particularly important part in terms of intelligent transportation field, rapid with computing capability Lifting and the increasingly lifting of intelligent transportation automation demanding, increasing national research institution more close with incorporated business Note this field of intelligent driving.Intelligent driving technical research is related to mechano-electronic, pattern-recognition, artificial intelligence, control science And the interdiscipline such as soft project, the subject knowledge intersected more.And control section is divided into again as most basic and most traditional part Crosswise joint with it is longitudinally controlled.Wherein：Crosswise joint referred under conditions of known paths planned trajectory, major control vehicle Corner, to reach the purpose of trajectory track.It is longitudinally controlled be known to path planning track and path tracing it is preferable under the conditions of, it is main Car speed is controlled, for the purpose of the security, reasonability and comfortableness that reach speed planning.Under the traffic environment of complexity Automatically control vehicle gentle start, constant speed, with work(such as car, chance real goal ramp to stop, automatic emergency brake and stopping a train at a target point Energy；Instructed simultaneously according to decision rule layer and carry out speed control, coordinate crosswise joint to realize the behaviors such as the lane-change avoidance of vehicle.
The longitudinally controlled part as comparative basis in wagon control, utilization is comparatively ripe, and research method is less.Mesh Preceding disclosed pertinent literature, report mainly carry out the longitudinally controlled of vehicle using the judgement and matching of multiple patterns, realize It is relatively simple, the easily frequent switching between multiple patterns, but be the failure to consider that all kinds of real goals (such as traffic lights etc.) are right In longitudinally controlled influence.Traditional vehicle longitudinal control uses cruise CC, adaptive cruise ACC and automatic emergency brake The blending algorithm of AEB functions, this algorithm is feasible for simple road conditions, but for slightly complicated traffic environment, due to shadow It is many to ring the factor of automatic Pilot longitudinal direction behavior, including setting cruising speed, front particle distance and relative velocity and user Active behavior (such as requires to stop or start).It can be seen that longitudinal behavior influence factor is more, and intersected between these factors Influence, it is difficult to be described using consolidator framework, traditional vehicle longitudinal control method is cut because of state and the frequent of target Car speed jump problem can be caused by changing, and comfortableness is poor.In traditional ADAS (Advanced Driver Assistant Systems) in longitudinal research, and ACC (Adaptive Cruise Control)+CC (Cruising are generally classified as Control) three parts of+AEB (Autonomous Emergency Braking), but due to three parts boundary condition weight It is folded, it is necessary to a large amount of IF-THEN logics carry out scene switching, would potentially result in logical miss under real complex scene.Seriously When go out present condition control it is unreasonable, influence vehicle traveling security.
The content of the invention
It is an object of the invention to provide a kind of longitudinally controlled method, apparatus of automatic Pilot to overcome or at least mitigate existing It is at least one in the drawbacks described above of technology.
To achieve the above object, the present invention provides a kind of longitudinally controlled method of automatic Pilot, and the automatic Pilot is longitudinally controlled Method processed includes：
S1, according to the expected path curvature information of this car, generation characterizes the virtual target of path velocity and position attribution；
S2, the real goal with speed and position attribution is identified according to this car sensory perceptual system；
S3, according to traffic scene, CAR SERVICE function and integration of user interaction functionality, its feature is converted into several has speed The virtual target of degree and position attribution；
S4, from each virtual target and real goal in S1 and S3, filter out it is maximum to this car safety effects with With target；And
S5, target is followed to plan the expectation of this car relative to the speed and range information of this car according to what is filtered out in S4 Acceleration.
Further, in S1, virtual target relative to this car fore-and-aft distance dislonExpression formula is：dislon=THW × v +dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is the speed of this car.
Further, in S3, virtual target includes following one or more：
Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is： dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is Speed limit；
Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonTable It is up to formula：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The virtual target Speed for user psychology desired speed or be defaulted as road speed limit；
User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonIt is directly proportional to the speed of this car；The speed of the virtual target is 0km/h；
Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonFor its relative to The fore-and-aft distance of this car；The speed of the virtual target is 0km/h.
Further, in S4, filter out and follow mesh calibration method to specifically include this car safety effects maximum：
S41, according to following target selection model, the influence for calculating each virtual target and real goal in S1 and S3 is weighed Weight, the expression formula of target selection model are：
In formula：Score is the weighing factor of virtual target；
Factor of influence for virtual target or real goal relative to the lateral separation of this car；
Lateral prediction distance for virtual target or real goal relative to this car；
Factor of influence for virtual target or real goal relative to the fore-and-aft distance of this car；
DislonFore-and-aft distance for virtual target or real goal relative to this car；
FactorspeedSpeed factor of influence for virtual target or real goal relative to this car；
VrelSpeed for virtual target or real goal relative to this car；
S42, virtual target maximum the weighing factor Score being calculated via above-mentioned formula or real goal are to follow Target.
Further, in S5, the calculation formula of " expectation acceleration " is as follows：
In formula：atargetIt is expected deceleration；
Vre lTo follow speed of the target relative to this car；
EDis is the deviation for the actual range and desired distance for following target and this workshop；
KpsFor speed term proportional control factor；
TisFor speed term integration time constant；
TdsFor speed term derivative time constant；
KpdFor distance terms proportional control factor；
TidFor distance terms integration time constant；
TddFor distance terms derivative time constant.
The present invention also provides a kind of automatic Pilot longitudinally controlled device, and the longitudinally controlled device of automatic Pilot includes：
First virtual target generation module, it is used for the expected path curvature information according to this car, and generation characterizes path speed The virtual target of degree and position attribution；
Real goal generation module, it is used to being identified according to this car sensory perceptual system true with speed and position attribution Target；
Second virtual target generation module, it is used for according to traffic scene, CAR SERVICE function and integration of user interaction functionality, will Its feature, which is converted into several, has the virtual target of speed and position attribution；
Object filtering module, it is used for from the first virtual target generation module and the life of the second virtual target generation module Into each virtual target and the real goal generation module generation real goal in, filter out to this car safety effects most Big follows target；
It is expected acceleration planning module, it is used to follow target relative to this according to what the object filtering module filtered out The speed and range information of car, plan the expectation acceleration of this car.
Further, the virtual target of the first virtual target generation module generation relative to this car fore-and-aft distance dislonExpression formula is：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；It is described The speed of the virtual target of first virtual target generation module generation is the speed of this car；
The virtual target of the second virtual target generation module generation includes following one or more：
Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is： dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is Speed limit；
Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonTable It is up to formula：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The virtual target Speed for user psychology desired speed or be defaulted as road speed limit；
User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonIt is directly proportional to the speed of this car；The speed of the virtual target is 0km/h；
Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonFor its relative to The fore-and-aft distance of this car；The speed of the virtual target is 0km/h.
Further, the object filtering module specifically includes：
Weighing factor computing module, it is used for according to following target selection model, calculates the first virtual target generation The true mesh of each virtual target and real goal generation module generation of module and the generation of the second virtual target generation module Target weighing factor, the expression formula of target selection model are：
In formula：Score is the weighing factor of virtual target；
Factor of influence for virtual target or real goal relative to the lateral separation of this car；
Lateral prediction distance for virtual target or real goal relative to this car；
Factor of influence for virtual target or real goal relative to the fore-and-aft distance of this car；
DislonFore-and-aft distance for virtual target or real goal relative to this car；
FactorspeedSpeed factor of influence for virtual target or real goal relative to this car；
VrelSpeed for virtual target or real goal relative to this car；
Target determination module is followed, it is used for the weighing factor Score that the weighing factor computing module is calculated Maximum virtual target or real goal, which are used as, follows target.
Further, the calculation formula of " the expectation acceleration " for it is expected acceleration planning module is as follows：
In formula：atargetIt is expected deceleration；
Vre lTo follow speed of the target relative to this car；
EDis is the deviation for the actual range and desired distance for following target and this workshop；
KpsFor speed term proportional control factor；
TisFor speed term integration time constant；
TdsFor speed term derivative time constant；
KpdFor distance terms proportional control factor；
TidFor distance terms integration time constant；
TddFor distance terms derivative time constant.
The present invention also provides a kind of automatic driving vehicle, and the automatic driving vehicle is indulged including automatic Pilot as described above To control device.
The present invention is by each path point and traffic scene on expected path, CAR SERVICE function and integration of user interaction functionality etc. Factor is conceptualized as virtual target, then in virtual target and real goal it is maximum to the safety effects of this car at screening As uniquely target is followed, the acceleration of this car is planned using unified control strategy, greatly simplifies tradition The logic switch of longitudinally controlled middle cruise, adaptive learning algorithms and automatic emergency brake control, effectively prevent tradition In control algolithm because caused by traffic scene changes during control mode switch caused by vehicle deceleration jump problem, so as to Improve the stationarity and comfortableness of vehicle longitudinal control.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the longitudinally controlled embodiment of method one of automatic Pilot provided by the present invention.
Fig. 2 is the generation method schematic diagram of the virtual target in Fig. 1 method.
Fig. 3 is a variety of virtual targets in Fig. 1 method and the schematic diagram deposited.
Fig. 4 is the theory structure schematic diagram of the longitudinally controlled embodiment of device one of automatic Pilot provided by the present invention.
Fig. 5 is the structural representation of the embodiment of object filtering module one in Fig. 4.
Embodiment
In the accompanying drawings, represent same or similar element using same or similar label or there is same or like function Element.Embodiments of the invention are described in detail below in conjunction with the accompanying drawings.
In the description of the invention, term " " center ", " longitudinal direction ", " transverse direction ", "front", "rear", "left", "right", " vertical ", The orientation or position relationship of the instruction such as " level ", " top ", " bottom " " interior ", " outer " are to be closed based on orientation shown in the drawings or position System, it is for only for ease of and describes the present invention and simplify description, rather than indicates or imply that signified device or element must have Specific orientation, with specific azimuth configuration and operation, therefore it is not intended that limiting the scope of the invention.
As shown in figure 1, the longitudinally controlled method of automatic Pilot that the present embodiment is provided includes：
S1, according to the expected path curvature information of this car, generation characterizes the virtual target of path velocity and position attribution.Phase Routing information is hoped to be made up of multiple path points, each path point has corresponding coordinate value and course angle, expected path information It is that lane line or the expected path information that differential GPS devices provide are identified by camera.Utilize existing curve-fitting method Path point is sequentially connected in series by (either Bezier is fitted or used simple geometry teaching approximate calculation to least square method) Connection, and calculate the curvature information of each selected path point.Interval phase between two adjacent selected path points Together, the interval can according to the control accuracy requirement of automated driving system control module (such as：0.1m) determine.
Preferably, in S1, virtual target relative to this car fore-and-aft distance dislonExpression formula is：dislon=THW × v+ dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is the speed of this car.
S2, the real goal with speed and position attribution is identified according to this car sensory perceptual system.This car sensory perceptual system can With using radar, GPS, video camera etc., identification extraction such as vehicle, bicycle and pedestrian.Actual target information specifically includes： Position coordinates point, Speed attribute.The coordinate value of each point referred in the present invention can be seen as the coordinate value in vehicle axis system, Vehicle axis system can use known method to determine, will not be repeated here.Longitudinal direction refers to the fore-and-aft direction of vehicle, laterally refers to Be vehicle left and right directions.
S3, as shown in Figures 2 and 3, according to traffic scene, CAR SERVICE function and integration of user interaction functionality, its feature is turned Turning to several has the virtual target of speed and position attribution.
Preferably, in S3, according to traffic scene, CAR SERVICE function and integration of user interaction functionality, traffic scene ratio therein Such as road speed limit, board and traffic lights are travelled at a slow speed.Virtual target includes following one or more：
Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is： dislon=THW × v+dissafe, v represents the speed of this car, dissafeLongitudinal safe distance is represented, THW is usually arranged as 1.5- 2.3, according to driver's driving style (radical 1.5, general 1.8, guard 2.3) adjust.Virtual target corresponding to road speed limit Speed is speed limit.Virtual target corresponding to road speed limit is constant presence, and road speed limit refers to that institute's travel regulation permits Perhaps Maximum speed limit.
Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonTable It is up to formula：dislon=THW × v+dissafe, v represents the speed of this car, dissafeLongitudinal safe distance is represented, THW is generally set For 1.5-2.3, according to driver's driving style (radical 1.5, general 1.8, guard and 2.3) adjust.User psychology desired speed pair The speed for the virtual target answered is user psychology desired speed or is defaulted as road speed limit.It is empty corresponding to user psychology desired speed Plan target is constant presence.In actual applications, desired speed can be manually selected by user, or is defaulted as road speed limit.
User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonIt is directly proportional to the speed of this car；The speed of virtual target is 0km/h corresponding to the stopping of user's active control vehicle.User Virtual target corresponding to the stopping of active control vehicle is that condition is present.When user's initiative vehicle stops, now giving birth to immediately Into virtual target, the distance of the particle is directly proportional with car speed, i.e. the faster distance of speed is bigger, and this is the same as vehicle actual travel mistake Stopping distance needed for journey medium velocity is higher matches more greatly.After active is stopped, the virtual target will persistently be present, until user Initiative vehicle launch, then the particle disappear automatically.
Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonFor its relative to The fore-and-aft distance of this car.The speed of virtual target corresponding to stopping a train at a target point is 0km/h.Virtual target corresponding to stopping a train at a target point is bar Part is present.When vehicle meets certain stopping a train at a target point condition, such as crossroad red light before zebra stripes, it is necessary to stop, vehicle Returning storehouse needs in parking stall parking etc., at this moment needs to produce virtual target.
S4, from each virtual target and real goal in S1 and S3, filter out it is maximum to this car safety effects with With target.
Preferably, in S4, filter out and follow mesh calibration method to specifically include this car safety effects maximum：
S41, according to following target selection model, the influence for calculating each virtual target and real goal in S1 and S3 is weighed Weight, the expression formula of target selection model are：
In formula：Score is the weighing factor of virtual target；
Factor of influence for virtual target or real goal relative to the lateral separation of this car, the influence because Subrange 0~1, reduce with the increase of lateral prediction distance；
It is virtual target or real goal relative to the lateral prediction distance of this car, generally predicts true mesh The positional information being marked in the following 2s times；
For virtual target or real goal relative to the fore-and-aft distance of this car factor of influence (influence because 1.0) son is usually；
DislonFore-and-aft distance for virtual target or real goal relative to this car；
FactorspeedIt is virtual target or real goal relative to the speed factor of influence of this car, the speed factor of influence It is usually arranged as 2.5；
VrelSpeed for virtual target or real goal relative to this car；
S42, virtual target maximum the weighing factor Score being calculated via above-mentioned formula or real goal are to follow Target.
Target selection based on target selection model is that all targets of vehicle periphery are screened, and extracts and vehicle is transported Dynamic state influences maximum target.The target of vehicle periphery mainly include vehicle cut in advance with track real goal, neighbouring track/ Virtual target, the present embodiment such as the real goal and constant speed that cut out, speed limit signboard, crossroad are based on energy field thought, adopted Screening extraction is carried out to numerous targets with unified screening principle.
S5, target is followed to plan the expectation of this car relative to the speed and range information of this car according to what is filtered out in S4 Acceleration.
Preferably, in S5, the calculation formula of " expectation acceleration " is as follows：
In formula：atargetIt is expected deceleration；
Vre lTo follow speed of the target relative to this car；
EDis is the deviation for the actual range and desired distance for following target and this workshop；
KpsFor speed term proportional control factor, such as 0.5；
TisFor speed term integration time constant, such as 50；
TdsFor speed term derivative time constant, such as 0.02；
KpdFor distance terms proportional control factor, such as 0.1；
TidFor distance terms integration time constant, such as 200；
TddFor distance terms derivative time constant, such as 0.05.
The calculation formula of " expectation acceleration " in the present embodiment by constant-speed-cruise control of the tradition based on speed control and Adaptive learning algorithms and automatic emergency brake control algolithm based on distance controlling blend, and form unified longitudinal P ID controls Algorithm processed, because algorithm model is unified, it greatly simplify the longitudinally controlled middle cruise of tradition, adaptive learning algorithms and automatic Brake hard control logic switch, effectively prevent in traditional control algorithm because traffic scene change caused by control model cut Caused vehicle deceleration jump problem during changing, so as to improve the stationarity of vehicle longitudinal control and comfortableness.
As shown in figure 4, the present embodiment also provides a kind of automatic Pilot longitudinally controlled device, the longitudinally controlled dress of the automatic Pilot Put including the first virtual target generation module 1, real goal generation module 2, the second virtual target generation module 3, object filtering Module 4 and expectation acceleration planning module 5, wherein：
First virtual target generation module 1 is used for the expected path curvature information according to this car, and generation characterizes path velocity With the virtual target of position attribution.Expected path information is made up of multiple path points, and each path point has corresponding coordinate Value and course angle, expected path information are to identify lane line or the expected path information that differential GPS devices provide by camera. Utilize (the least square method either Bezier fitting or approximate using simple geometry teaching of existing curve-fitting method Calculate) path point is sequentially connected in series, and calculate the curvature information of each selected path point.Adjacent two are selected Path point between interval it is identical, the interval can according to the control accuracy requirement of automated driving system control module (such as 0.1m) determine.
Preferably, the virtual target that the first virtual target generation module 1 generates relative to this car fore-and-aft distance dislonExpression formula is：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；It is described The speed of the virtual target of first virtual target generation module generation is the speed of this car.
Real goal generation module 2 is used to identify the true mesh with speed and position attribution according to this car sensory perceptual system Mark.This car sensory perceptual system can use radar, GPS, video camera etc., collect many real goals around expected path Relevant information.Actual target information specifically includes：Position coordinates point, Speed attribute.
As shown in Figures 2 and 3, the second virtual target generation module 3 is used for according to traffic scene, CAR SERVICE function and use Family interactive function, its feature is converted into several has the virtual target of speed and position attribution.Traffic scene ratio therein Such as road speed limit, board and traffic lights are travelled at a slow speed.Virtual target includes following one or more：
Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is： dislon=THW × v+dissafe, v represents the speed of this car, dissafeLongitudinal safe distance is represented, THW is usually arranged as 1.5- 2.3, according to driver's driving style (radical 1.5, general 1.8, guard 2.3) adjust.Virtual target corresponding to road speed limit Speed is speed limit.Virtual target corresponding to road speed limit is constant presence, and road speed limit refers to that institute's travel regulation permits Perhaps Maximum speed limit.
Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonTable It is up to formula：dislon=THW × v+dissafe, v represents the speed of this car, dissafeLongitudinal safe distance is represented, THW is generally set For 1.5-2.3, according to driver's driving style (radical 1.5, general 1.8, guard and 2.3) adjust.User psychology desired speed pair The speed for the virtual target answered is user psychology desired speed or is defaulted as road speed limit.It is empty corresponding to user psychology desired speed Plan target is constant presence.In actual applications, desired speed can be manually selected by user, or is defaulted as road speed limit.
User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonIt is directly proportional to the speed of this car；The speed of virtual target is 0km/h corresponding to the stopping of user's active control vehicle.User Virtual target corresponding to the stopping of active control vehicle is that condition is present.When user's initiative vehicle stops, now giving birth to immediately Into virtual target, the distance of the particle is directly proportional with car speed, i.e. the faster distance of speed is bigger, and this is the same as vehicle actual travel mistake Stopping distance needed for journey medium velocity is higher matches more greatly.After active is stopped, the virtual target will persistently be present, until user Initiative vehicle launch, then the particle disappear automatically.
Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonFor its relative to The fore-and-aft distance of this car.The speed of virtual target corresponding to stopping a train at a target point is 0km/h.Virtual target corresponding to stopping a train at a target point is bar Part is present.When vehicle meets certain stopping a train at a target point condition, such as crossroad red light before zebra stripes, it is necessary to stop, vehicle Returning storehouse needs in parking stall parking etc., at this moment needs to produce virtual target.
Object filtering module 4 is used to give birth to from the first virtual target generation module 1 and the second virtual target generation module 3 Into each virtual target and the real goal generation module 2 generation real goal in, filter out to this car safety effects Maximum follows target.
It is expected that acceleration planning module 5 is used to follow target relative to this according to what the object filtering module 4 filtered out The speed and range information of car, plan the expectation acceleration of this car.
The virtual target that the second virtual target generation module 3 generates includes following one or more：
Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is： dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is Speed limit；
Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonTable It is up to formula：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The virtual target Speed for user psychology desired speed or be defaulted as road speed limit；
User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonIt is directly proportional to the speed of this car；The speed of the virtual target is 0km/h；
Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonFor its relative to The fore-and-aft distance of this car；The speed of the virtual target is 0km/h.
As shown in figure 5, in one embodiment, the object filtering module 4 specifically includes the He of weighing factor computing module 41 Target determination module 42 is followed, wherein：
Weighing factor computing module 41 is used for according to following target selection model, calculates the first virtual target generation mould The real goal of each virtual target and real goal generation module generation of block and the generation of the second virtual target generation module Weighing factor, the expression formula of target selection model is：
In formula：Score is the weighing factor of virtual target；
Factor of influence for virtual target or real goal relative to the lateral separation of this car, the influence because Subrange 0~1, reduce with the increase of lateral prediction distance；
It is virtual target or real goal relative to the lateral prediction distance of this car, generally predicts true mesh The positional information being marked in the following 2s times；
For virtual target or real goal relative to the fore-and-aft distance of this car factor of influence (influence because 1.0) son is usually；
DislonFore-and-aft distance for virtual target or real goal relative to this car；
FactorspeedIt is virtual target or real goal relative to the speed factor of influence of this car, the speed factor of influence It is usually arranged as 2.5；
VrelSpeed for virtual target or real goal relative to this car.
Target determination module 42 is followed to be used for the weighing factor Score that the weighing factor computing module 41 is calculated Maximum virtual target or real goal, which are used as, follows target.
Target selection based on target selection model is that all targets of vehicle periphery are screened, and extracts and vehicle is transported Dynamic state influences maximum target.The target of vehicle periphery mainly include vehicle cut in advance with track real goal, neighbouring track/ Virtual target, the present embodiment such as the real goal and constant speed that cut out, speed limit signboard, crossroad are based on energy field thought, adopted Screening extraction is carried out to numerous targets with unified screening principle.
In one embodiment, the calculation formula of " the expectation acceleration " for it is expected acceleration planning module 5 is as follows：
In formula：atargetIt is expected deceleration；
VrelTo follow speed of the target relative to this car；
EDis is the deviation for the actual range and desired distance for following target and this workshop；
KpsFor speed term proportional control factor, such as 0.5；
TisFor speed term integration time constant, such as 50；
TdsFor speed term derivative time constant, such as 0.02；
KpdFor distance terms proportional control factor, such as 0.1；
TidFor distance terms integration time constant, such as 200；
TddFor distance terms derivative time constant, such as 0.05.
The calculation formula of " expectation acceleration " in the present embodiment is by constant-speed-cruise control of the tradition based on speed control, base Blended in the adaptive learning algorithms and automatic emergency brake control algolithm of distance controlling, form unified longitudinal P ID controls Algorithm, because algorithm model is unified, it greatly simplify the longitudinally controlled middle cruise of tradition, adaptive learning algorithms and automatic tight The logic switch of anxious control for brake, effectively prevent in traditional control algorithm because traffic scene change caused by control mode switch During caused vehicle deceleration jump problem, so as to improve the stationarity of vehicle longitudinal control and comfortableness.
The present invention also provides a kind of automatic driving vehicle, and the automatic driving vehicle is included described in the various embodiments described above The longitudinally controlled device of automatic Pilot.The other parts of the automatic driving vehicle are prior art, herein not reinflated description.
The present invention is by each path point and traffic scene on expected path, CAR SERVICE function and integration of user interaction functionality etc. Factor is conceptualized as virtual target, then in virtual target and real goal it is maximum to the safety effects of this car at screening As uniquely target is followed, the acceleration of this car is planned using unified control strategy, greatly simplifies tradition The logic switch of longitudinally controlled middle cruise, adaptive cruise and automatic emergency brake control, effectively prevent Traditional control In algorithm because caused by traffic scene changes during control mode switch caused by vehicle deceleration jump problem, so as to improve The stationarity and comfortableness of vehicle longitudinal control.
It is last it is to be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.This The those of ordinary skill in field should be understood：Technical scheme described in foregoing embodiments can be modified, or it is right Which part technical characteristic carries out equivalent substitution；These modifications are replaced, and the essence of appropriate technical solution is departed from this Invent the spirit and scope of each embodiment technical scheme.

Claims (10)

  1. A kind of 1. longitudinally controlled method of automatic Pilot, it is characterised in that including：
    S1, according to the expected path curvature information of this car, generation characterizes the virtual target of path velocity and position attribution；
    S2, the real goal with speed and position attribution is identified according to this car sensory perceptual system；
    S3, according to traffic scene, CAR SERVICE function and integration of user interaction functionality, by its feature be converted into several have speed and The virtual target of position attribution；
    S4, from each virtual target and real goal in S1 and S3, filter out and mesh is followed to this car safety effects maximum Mark；And
    S5, target is followed relative to the speed and range information of this car according to what is filtered out in S4, plan that the expectation of this car accelerates Degree.
  2. 2. the longitudinally controlled method of automatic Pilot as claimed in claim 1, it is characterised in that in S1, virtual target is relative to this The fore-and-aft distance dis of carlonExpression formula is：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal direction Safe distance；The speed of the virtual target is the speed of this car.
  3. 3. the longitudinally controlled method of automatic Pilot as claimed in claim 1, it is characterised in that in S3, virtual target includes following One or more：
    Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is：dislon =THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is speed limit Value；
    Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonExpression formula For：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target Spend for user psychology desired speed or be defaulted as road speed limit；
    User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonWith The speed of this car is directly proportional；The speed of the virtual target is 0km/h；
    Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonIt is it relative to this car Fore-and-aft distance；The speed of the virtual target is 0km/h.
  4. 4. the longitudinally controlled method of automatic Pilot as claimed in claim 1, it is characterised in that in S4, filter out to this car safety Property influence maximum to follow mesh calibration method to specifically include：
    S41, according to following target selection model, calculate the weighing factor of each virtual target and real goal in S1 and S3, mesh Mark preference pattern expression formula be：
    <mrow> <mi>S</mi> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>e</mi> <mo>=</mo> <msub> <mi>Factor</mi> <mrow> <msub> <mi>dis</mi> <mrow> <mi>l</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> </mrow> </msub> <mo>*</mo> <msub> <mi>Dis</mi> <mrow> <msub> <mi>lat</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mi>i</mi> <mi>c</mi> <mi>t</mi> </mrow> </msub> </mrow> </msub> <mo>*</mo> <mrow> <mo>(</mo> <msub> <mi>Factor</mi> <mrow> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> <mo>*</mo> <msubsup> <mi>V</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> <mn>2</mn> </msubsup> <mo>+</mo> <msub> <mi>Factor</mi> <mrow> <msub> <mi>dis</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </msub> <mo>*</mo> <mfrac> <mn>1</mn> <mrow> <msub> <mi>Dis</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> </mrow>
    In formula：Score is the weighing factor of virtual target；
    Factor of influence for virtual target or real goal relative to the lateral separation of this car；
    Lateral prediction distance for virtual target or real goal relative to this car；
    Factor of influence for virtual target or real goal relative to the fore-and-aft distance of this car；
    DislonFore-and-aft distance for virtual target or real goal relative to this car；
    FactorspeedSpeed factor of influence for virtual target or real goal relative to this car；
    VrelSpeed for virtual target or real goal relative to this car；
    S42, virtual target maximum the weighing factor Score being calculated via above-mentioned formula or real goal are to follow mesh Mark.
  5. 5. the longitudinally controlled method of automatic Pilot as any one of Claims 1-4, it is characterised in that in S5, " it is expected The calculation formula of acceleration " is as follows：
    <mrow> <msub> <mi>a</mi> <mrow> <mi>t</mi> <mi>arg</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>K</mi> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>{</mo> <mrow> <msub> <mi>V</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>+</mo> <mfrac> <mn>1</mn> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </mfrac> <munderover> <mo>&amp;Integral;</mo> <mn>0</mn> <mi>t</mi> </munderover> <msub> <mi>V</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>T</mi> <mrow> <mi>d</mi> <mi>s</mi> </mrow> </msub> <mfrac> <mrow> <msub> <mi>dV</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow> <mrow> <mi>d</mi> <mi>t</mi> </mrow> </mfrac> </mrow> <mo>}</mo> </mrow> <mo>+</mo> <msub> <mi>K</mi> <mrow> <mi>p</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>{</mo> <mrow> <mi>e</mi> <mi>D</mi> <mi>i</mi> <mi>s</mi> <mo>+</mo> <mfrac> <mn>1</mn> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>d</mi> </mrow> </msub> </mfrac> <munderover> <mo>&amp;Integral;</mo> <mn>0</mn> <mi>t</mi> </munderover> <mi>e</mi> <mi>D</mi> <mi>i</mi> <mi>s</mi> <mo>+</mo> <msub> <mi>T</mi> <mrow> <mi>d</mi> <mi>d</mi> </mrow> </msub> <mfrac> <mrow> <mi>d</mi> <mi>e</mi> <mi>D</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>d</mi> <mi>t</mi> </mrow> </mfrac> </mrow> <mo>}</mo> </mrow> </mrow>
    In formula：atargetIt is expected deceleration；
    VrelTo follow speed of the target relative to this car；
    EDis is the deviation for the actual range and desired distance for following target and this workshop；
    KpsFor speed term proportional control factor；
    TisFor speed term integration time constant；
    TdsFor speed term derivative time constant；
    KpdFor distance terms proportional control factor；
    TidFor distance terms integration time constant；
    TddFor distance terms derivative time constant.
  6. A kind of 6. longitudinally controlled device of automatic Pilot, it is characterised in that including：
    First virtual target generation module, it is used for the expected path curvature information according to this car, generation characterize path velocity and The virtual target of position attribution；
    Real goal generation module, it is used to identify the true mesh with speed and position attribution according to this car sensory perceptual system Mark；
    Second virtual target generation module, it is used for according to traffic scene, CAR SERVICE function and integration of user interaction functionality, Jiang Qite Sign, which is converted into several, has the virtual target of speed and position attribution；
    Object filtering module, it is used for what is generated from the first virtual target generation module and the second virtual target generation module In each virtual target and the real goal of real goal generation module generation, filter out to this car safety effects maximum Follow target；
    It is expected acceleration planning module, it is used to follow target relative to this car according to what the object filtering module filtered out Speed and range information, plan the expectation acceleration of this car.
  7. 7. the longitudinally controlled device of automatic Pilot as claimed in claim 6, it is characterised in that first virtual target generates mould Block generation virtual target relative to this car fore-and-aft distance dislonExpression formula is：dislon=THW × v+dissafe, v expressions The speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target of the first virtual target generation module generation Spend the speed for this car；
    The virtual target of the second virtual target generation module generation includes following one or more：
    Virtual target corresponding to road speed limit, the virtual target relative to this car fore-and-aft distance dislonExpression formula is：dislon =THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target is speed limit Value；
    Virtual target corresponding to user psychology desired speed, the virtual target relative to this car fore-and-aft distance dislonExpression formula For：dislon=THW × v+dissafe, v represents the speed of this car, dissafeRepresent longitudinal safe distance；The speed of the virtual target Spend for user psychology desired speed or be defaulted as road speed limit；
    User's active control vehicle stop corresponding to virtual target, the virtual target relative to this car fore-and-aft distance dislonWith The speed of this car is directly proportional；The speed of the virtual target is 0km/h；
    Virtual target corresponding to stopping a train at a target point, the virtual target relative to this car fore-and-aft distance dislonIt is it relative to this car Fore-and-aft distance；The speed of the virtual target is 0km/h.
  8. 8. the longitudinally controlled device of automatic Pilot as claimed in claim 6, it is characterised in that the object filtering module is specifically wrapped Include：
    Weighing factor computing module, it is used to, according to following target selection model, calculate the first virtual target generation module The real goal generated with each virtual target of the second virtual target generation module generation and the real goal generation module Weighing factor, the expression formula of target selection model are：
    <mrow> <mi>S</mi> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>e</mi> <mo>=</mo> <msub> <mi>Factor</mi> <mrow> <msub> <mi>dis</mi> <mrow> <mi>l</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> </mrow> </msub> <mo>*</mo> <msub> <mi>Dis</mi> <mrow> <msub> <mi>lat</mi> <mrow> <mi>p</mi> <mi>r</mi> <mi>e</mi> <mi>d</mi> <mi>i</mi> <mi>c</mi> <mi>t</mi> </mrow> </msub> </mrow> </msub> <mo>*</mo> <mrow> <mo>(</mo> <msub> <mi>Factor</mi> <mrow> <mi>s</mi> <mi>p</mi> <mi>e</mi> <mi>e</mi> <mi>d</mi> </mrow> </msub> <mo>*</mo> <msubsup> <mi>V</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> <mn>2</mn> </msubsup> <mo>+</mo> <msub> <mi>Factor</mi> <mrow> <msub> <mi>dis</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </msub> <mo>*</mo> <mfrac> <mn>1</mn> <mrow> <msub> <mi>Dis</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> </mrow>
    In formula：Score is the weighing factor of virtual target；
    Factor of influence for virtual target or real goal relative to the lateral separation of this car；
    Lateral prediction distance for virtual target or real goal relative to this car；
    Factor of influence for virtual target or real goal relative to the fore-and-aft distance of this car；
    DislonFore-and-aft distance for virtual target or real goal relative to this car；
    FactorspeedSpeed factor of influence for virtual target or real goal relative to this car；
    VrelSpeed for virtual target or real goal relative to this car；
    Target determination module is followed, the weighing factor Score that it is used to the weighing factor computing module being calculated is maximum Virtual target or real goal as following target.
  9. 9. the longitudinally controlled device of automatic Pilot as claimed in claim 6, it is characterised in that the expectation acceleration planning module " expectation acceleration " calculation formula it is as follows：
    <mrow> <msub> <mi>a</mi> <mrow> <mi>t</mi> <mi>arg</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>K</mi> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>{</mo> <mrow> <msub> <mi>V</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>+</mo> <mfrac> <mn>1</mn> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </mfrac> <munderover> <mo>&amp;Integral;</mo> <mn>0</mn> <mi>t</mi> </munderover> <msub> <mi>V</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>T</mi> <mrow> <mi>d</mi> <mi>s</mi> </mrow> </msub> <mfrac> <mrow> <msub> <mi>dV</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> </mrow> <mrow> <mi>d</mi> <mi>t</mi> </mrow> </mfrac> </mrow> <mo>}</mo> </mrow> <mo>+</mo> <msub> <mi>K</mi> <mrow> <mi>p</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>{</mo> <mrow> <mi>e</mi> <mi>D</mi> <mi>i</mi> <mi>s</mi> <mo>+</mo> <mfrac> <mn>1</mn> <msub> <mi>T</mi> <mrow> <mi>i</mi> <mi>d</mi> </mrow> </msub> </mfrac> <munderover> <mo>&amp;Integral;</mo> <mn>0</mn> <mi>t</mi> </munderover> <mi>e</mi> <mi>D</mi> <mi>i</mi> <mi>s</mi> <mo>+</mo> <msub> <mi>T</mi> <mrow> <mi>d</mi> <mi>d</mi> </mrow> </msub> <mfrac> <mrow> <mi>d</mi> <mi>e</mi> <mi>D</mi> <mi>i</mi> <mi>s</mi> </mrow> <mrow> <mi>d</mi> <mi>t</mi> </mrow> </mfrac> </mrow> <mo>}</mo> </mrow> </mrow>
    In formula：atargetIt is expected deceleration；
    VrelTo follow speed of the target relative to this car；
    EDis is the deviation for the actual range and desired distance for following target and this workshop；
    KpsFor speed term proportional control factor；
    TisFor speed term integration time constant；
    TdsFor speed term derivative time constant；
    KpdFor distance terms proportional control factor；
    TidFor distance terms integration time constant；
    TddFor distance terms derivative time constant.
  10. 10. a kind of automatic driving vehicle, it is characterised in that indulged including the automatic Pilot as any one of claim 6 to 9 To control device.
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