CN106843210B - One kind being based on bionic automatic driving vehicle progress control method - Google Patents
One kind being based on bionic automatic driving vehicle progress control method Download PDFInfo
- Publication number
- CN106843210B CN106843210B CN201710059510.XA CN201710059510A CN106843210B CN 106843210 B CN106843210 B CN 106843210B CN 201710059510 A CN201710059510 A CN 201710059510A CN 106843210 B CN106843210 B CN 106843210B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- decision
- lane change
- demand
- automatic driving
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 239000011664 nicotinic acid Substances 0.000 title claims abstract description 15
- 238000004891 communication Methods 0.000 claims abstract description 9
- 235000001968 nicotinic acid Nutrition 0.000 claims abstract description 9
- 230000002776 aggregation Effects 0.000 claims abstract description 5
- 238000004220 aggregation Methods 0.000 claims abstract description 5
- 230000008859 change Effects 0.000 claims description 48
- 230000001133 acceleration Effects 0.000 claims description 10
- 241000736199 Paeonia Species 0.000 claims description 9
- 235000006484 Paeonia officinalis Nutrition 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000006855 networking Effects 0.000 abstract description 9
- 238000013467 fragmentation Methods 0.000 abstract description 2
- 238000006062 fragmentation reaction Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 10
- 241001465754 Metazoa Species 0.000 description 5
- 230000004888 barrier function Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 241000251468 Actinopterygii Species 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 241000272814 Anser sp. Species 0.000 description 1
- 241000271566 Aves Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001680 brushing effect Effects 0.000 description 1
- 238000012508 change request Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0293—Convoy travelling
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The present invention relates to one kind to be based on bionic automatic driving vehicle progress control method, this method is based on bionics principle, Aggregation behaviour is analogous in vehicle operation control, comprising the following steps: 1) each individual vehicle is defined as decision vehicle, obtains the demand of decision vehicle itself；2) optimum individual is found in decision vehicle V2V communication, is defined as target vehicle, and the optimum individual is configured with the vehicle that current operation state is best in the vehicle cluster for having same requirements with the decision vehicle；3) operation reserve of decision vehicle is obtained according to the demand of the position and decision front side vehicle in target vehicle and decision workshop.Compared with prior art, the present invention can make vehicle be rapidly achieved requirement objective in 30 seconds, and be travelled in the form of stable fleet, reduce time-space distribution degree of fragmentation, the access capability for effectively improving road can enable automatic driving vehicle clustering efficient operation under car networking environment.
Description
Technical field
The present invention relates to road vehicle control technology research fields, are based on bionic automatic Pilot more particularly, to one kind
Vehicle progress control method.
Background technique
Current automatic driving vehicle technology has been able to by detecting and identifying etc. that technologies realize individual vehicle in road
On safety traffic.If patent application CN101380951A discloses a kind of Vehicular automatic driving identifying system, by brushing in road
The black and white in road center is oriented to graticule and bar code to indicate vehicle running state, and with the interim road conditions radio command of road and vehicle
Direction of travel obstacle distance, to guarantee the safety of automatic Pilot.Not with wireless communication technique and the intelligent vehicles technology
Disconnected development, car networking become the most active branch of Internet of Things, and vehicle is provided with information communication device, may be implemented between vehicle and vehicle
Information send and receive.Universal with automatic Pilot technology and car networking technology, people recognize to drive automatically gradually
Sailing vehicle can make traffic more safe and efficient in car networking environment downward driving.As patent application CN104391504A discloses one
The generation method of automatic Pilot control strategy of the kind based on car networking, according to the vehicle drive of current vehicle habit model, currently
The region driving habit model and road conditions model of vehicle region, generate the automatic Pilot control strategy of current vehicle.
And it to allow car networking vehicle to travel on road network and mainly need to solve the problems, such as of both section and node.Currently,
Research in terms of section is very more, drives from auxiliary, to using ACC and CACC as the semi-automatic driving of representative or even automatically
It drives, has all emerged a large amount of research achievement.But the current technology for traveling of the car networking on section still collects
In in the angle for guaranteeing vehicle driving safety, either auxiliary drive, semi-automatic driving or full-automatic driving control technology all
Many-sided demand of passenger and the system optimization for road traffic are not accounted for, is lacked to this completely new vehicles row
Effective management control method.
Therefore, need to propose that one kind can under car networking environment the today to reach its maturity in automatic driving vehicle
Guarantee safety, and is able to satisfy passenger's various aspects demand and takes into account the operation management method of overall efficiency.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of easy to control, guarantees
Vehicle operational safety, can meet passenger's various aspects demand based on bionic automatic driving vehicle progress control method, effectively
Improve road efficiency and access capability.
The purpose of the present invention can be achieved through the following technical solutions:
One kind being based on bionic automatic driving vehicle progress control method, and this method is based on bionics principle, by cluster
Behavior is analogous in vehicle operation control, comprising the following steps:
1) each individual vehicle is defined as decision vehicle, obtains the demand of decision vehicle itself, the demand is target vehicle speed；
2) optimum individual is found in decision vehicle V2V communication, is defined as target vehicle, the optimum individual is configured with
There is the vehicle that current operation state is best in the vehicle cluster of same requirements with the decision vehicle；
3) operation of decision vehicle is obtained according to the demand of the position and decision front side vehicle in target vehicle and decision workshop
Strategy:
If target vehicle is in decision front side and decision front side first car is the different vehicle of demand, then follow the steps
4)；
If decision vehicle itself is target vehicle and decision front side first car is the different vehicle of demand, then follow the steps
4)；
If target vehicle is in decision front side and decision vehicle between the target vehicle to, without the vehicle that demand is different, holding
Row step 5)；
If decision vehicle itself is target vehicle and decision the front side vehicle different without demand, then follow the steps 6)；
4) when the demand of the front first car is less than the demand of decision vehicle, judge on the left of decision vehicle with the presence or absence of barrier
Hinder, if so, decision vehicle keeps current state, first car sends the request of lane change to the right forwards simultaneously, if it is not, then decision vehicle
Lane change to the left；
5) decision vehicle pursues and attacks the target vehicle；
6) decision vehicle realizes the demand using peak acceleration.
The demand is selected by passenger, and the influence factor of selection includes quick, environmentally friendly and comfortable.
When decision vehicle searches out multiple vehicles for meeting optimum individual condition, optimum individual is selected by following principle:
4-1) orientation priority principle preferentially selects the vehicle positioned at decision front side as optimum individual；
4-2) distance priority principle is preferentially selected with decision vehicle apart from nearest vehicle as optimum individual.
In the step 4), judgement right side is with the presence or absence of barrier when front first car receives the request of lane change to the right
Hinder, if so, keeping current state to send feedback information to decision vehicle, if it is not, then front first car lane change to the right.
Judge when meeting following three conditions at the same time accessible:
5-1) road equipment judges: the outermost lane in the non-lane change direction of lane change vehicle traveling lane, and the lane change vehicle is certainly
Plan vehicle or front first car；
5-2) parking stall judges: lane change side is in lane change Chinese herbaceous peony bumper the first two parking stall to latter two parking stall of rear bumper
Apart from interior without other vehicles；
5-3) speed judges: the third parking stall after third parking stall and rear bumper before lane change side lane change Chinese herbaceous peony bumper
A virtual vehicle is respectively set, the virtual vehicle speed of third parking stall is v before definition front bumperBefore, third after rear bumper
The virtual vehicle speed of a parking stall is vAfterwards, decision vehicle current vehicle speed is v, and there are vBefore> v > vAfterwards。
Exist at third parking stall after third parking stall or/and rear bumper before lane change side lane change Chinese herbaceous peony bumper practical
When vehicle, then the current vehicle speed of actual vehicle is given to corresponding virtual vehicle, the third before lane change side lane change Chinese herbaceous peony bumper
When actual vehicle is not present behind parking stall or/and rear bumper at third parking stall, vBefore=200km/h, vAfterwards=0.
In the step 5), decision vehicle pursues and attacks model object pursuit vehicle by following:
vk=vkprev+kp·ek+kd·ek
Wherein, ek=xk-1-xk-thw·vk, xk-1For target vehicle current location, xkFor decision vehicle current location, vkFor certainly
Plan vehicle current vehicle speed, thwFor time headway, vkprevThe v obtained for preceding primary iterative calculationk, kpAnd kdFor correction factor.
In the step 6), the comfort level of passenger is considered when choosing peak acceleration.
Compared with prior art, the invention has the following advantages that
1, bionics is a not only ancient but also young subject.All the time, people study organism structure and function
Working principle, and new equipment and tool are invented according to these principles.In nature, the behavior of many animals is all cluster
Change, when moving, each individual finally shows individual aggregation by following simple rule to the animals such as birds, fish
Feature, the direction and speed of movement are with uniformity, for example, wild goose can change formation it is whole migrate, the shoal of fish can draw close to center
Collective looks for food, to achieve the purpose that global optimum, this behavior is referred to as animal Aggregation behaviour.Pass through this of study animal
Behavioural characteristic can derive many algorithms, with the operating capability of optimization system.The present invention has used bionics principle, analogy
The behavior of animal cluster so that each individual vehicle only need abide by several simple passing rules, according to self-demand from
Selection target vehicle in investigative range, and drawn close by way of lane change, follow the bus or acceleration to it, in without whole control
The form of fleet is ultimately present as in the case where the heart.
2, the present invention can make vehicle be rapidly achieved requirement objective in 30 seconds using bionics swarm algorithm, and with stabilization
Fleet form traveling, reduce time-space distribution degree of fragmentation, improve road traffic efficiency and road access capability.
3, only the research control method of car-following model is different from tradition, and the present invention not only guarantees that fleet drives safely, and
It is multiple target formation traveling, is a kind of control method for considering user demand.This method considers the demand of passenger, forms more mesh
More demand controls are marked, the individual with same target forms a cluster, more in view of meeting user while forming fleet
The demand of sample.
4, by the control of bionics principle, individual may eventually form cluster, and realize the control to cluster, in accordance with this
Several simple regular vehicles can be completed lane change as unit of cluster, overtake other vehicles, give way, and improve the traffic efficiency of road.
5, the present invention devises orientation priority principle and distance priority principle when select optimum individual, acquisition it is optimal a
Body can effectively improve control precision.
6, the present invention needs to judge by road equipment in lane change barrier judgment, parking stall judgement and speed judge three
Condition improves the safety of vehicle operation control.
Detailed description of the invention
Fig. 1 is the structural diagram of the present invention；
Fig. 2 is lane change flow chart of the invention；
Fig. 3 is that vehicle operation reserve of the invention judges figure；
Fig. 4 is that vehicle lane change obstacle of the invention judges figure.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
The present embodiment based on the assumption that
(1) embodiment is under car networking environment, and Che-vehicle communication and Che-Lu Tongxin, required letter may be implemented in all vehicles
Breath can be transmitted by Che-vehicle communication and Che-road communication；
(2) it is sufficiently fast with the speed of processing compare car speed for information transmission, and delay can be ignored in the process；
(3) influence of pedestrian and non motorized vehicle is not considered.
The application scenarios of the present embodiment are set as the major urban arterial highway of a two-way six-lane, but since counter flow will not
The formation of fleet is had an impact, while also only simulating wherein side to show the better effect of bionics fleet intuitively
Unidirectional three lanes.The wide 3.5m in every lane, driving vehicle are the minibus of width 1.5m, long 3.6m, and time headway is 0.6 second,
Target vehicle speed is divided into ecological, efficient, comfortable three kinds, respectively x km/h, y km/h and z km/h.
As shown in Figure 1, the present embodiment provides one kind to be based on bionic automatic driving vehicle progress control method, this method
Based on bionics principle, Aggregation behaviour is analogous in vehicle operation control, comprising the following steps:
1) each individual vehicle being defined as decision vehicle, obtains the demand of decision vehicle itself, the demand is selected by passenger,
The influence factor of selection is mainly reflected in target vehicle speed including quick, environmental protection and comfortably.
2) optimum individual is found in decision vehicle V2V communication, is defined as target vehicle, the optimum individual is configured with
There is the vehicle that current operation state is best in the vehicle cluster of same requirements with the decision vehicle.
When decision vehicle searches out multiple vehicles for meeting optimum individual condition, optimum individual is selected by following principle:
4-1) orientation priority principle preferentially selects the vehicle positioned at decision front side as optimum individual；
4-2) distance priority principle is preferentially selected with decision vehicle apart from nearest vehicle as optimum individual.
3) operation of decision vehicle is obtained according to the demand of the position and decision front side vehicle in target vehicle and decision workshop
Strategy:
If target vehicle is in decision front side and decision front side first car is the different vehicle of demand, such as the A1 of Fig. 3
It is shown, it thens follow the steps 4)；
If decision vehicle itself is target vehicle and decision front side first car is the different vehicle of demand, such as the A2 of Fig. 3
It is shown, it thens follow the steps 4)；
If target vehicle is in decision front side and decision vehicle between the target vehicle to, without the vehicle that demand is different, such as scheming
Shown in 3 B1 and B2, then follow the steps 5)；
If decision vehicle itself is target vehicle and decision the front side vehicle different without demand, as shown in the C1 and C2 of Fig. 3,
It thens follow the steps 6).
4) when the demand of the front first car is less than the demand of decision vehicle, judge on the left of decision vehicle with the presence or absence of barrier
Hinder, if so, decision vehicle keeps current state, first car sends the request of lane change to the right forwards simultaneously, if it is not, then decision vehicle
Lane change to the left, as shown in Figure 2.
As shown in figure 4, third parking stall is set after third parking stall and rear bumper before lane change side lane change Chinese herbaceous peony bumper
Set two virtual vehicles.Lane change vehicle is denoted as vehicle 4 (decision vehicle) to the left, and lane change vehicle is denoted as vehicle 3 to the right, and note left front is empty
Quasi- vehicle is 1, and left back virtual vehicle is 2, and right front virtual vehicle is denoted as 5, and right back virtual vehicle is denoted as 6.
To the left whether lane change makes the following judgment vehicle 4:
Condition 1:4 vehicle is not or not leftmost side lane；
Without vehicle in the vehicle left-hand lane front bumper the first two parking stall to the distance of latter two parking stall of rear bumper condition 2:4
?；
Condition 3: enabling the initial velocity v1=200km/h of virtual vehicle 1, the initial velocity v2=0 of virtual vehicle 2, if inspection
It measures corresponding positions and is equipped with actual vehicle, be then assigned to actual vehicle speed.Whether v1 > v4 > v2 is met.
The 4 vehicles lane change to the left if three conditions all meet, if being unsatisfactory for issuing lane change request to front truck.
Judgement right side is with the presence or absence of obstacle when front first car receives the request of lane change to the right, if so, keeping
Current state sends feedback information to decision vehicle, if it is not, then front first car lane change to the right.The whether to the right lane change of vehicle 3
It makes the following judgment:
Condition 1:3 vehicle is not or not rightmost side lane；
Without vehicle in the vehicle right-hand lane front bumper the first two parking stall to the distance of latter two parking stall of rear bumper condition 2:3
?；
Condition 3: enabling the initial velocity v5=200km/h of virtual vehicle 5, the initial velocity v6=0 of virtual vehicle 6, if inspection
It measures corresponding positions and is equipped with actual vehicle, be then assigned to actual vehicle speed.Whether v5 > v3 > v6 is met.
The 3 vehicles lane change to the right if three conditions all meet.
5) decision vehicle pursues and attacks the target vehicle.Decision vehicle pursues and attacks model object pursuit vehicle by following:
vk=vkprev+kp·ek+kd·ek
Wherein, ek=xk-1-xk-thw·vk, xk-1For target vehicle current location, xkFor decision vehicle current location, vkFor certainly
Plan vehicle current vehicle speed, thwFor time headway, vkprevThe v obtained for preceding primary iterative calculationk, kpAnd kdFor correction factor.
6) decision vehicle realizes the demand using peak acceleration.The comfort level of passenger is considered when choosing peak acceleration.
In the present embodiment, the peak acceleration of decision vehicle (4 vehicle) is chosen in the following manner:
If v4 < x, 4 vehicle acceleration a4 are set are as follows:
If v4 > x, 4 vehicle acceleration a4 are set are as follows:
Wherein, x is target vehicle speed.
7) it is back to step 3) and continues iteration.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (8)
1. one kind is based on bionic automatic driving vehicle progress control method, which is characterized in that it is former that this method is based on bionics
Aggregation behaviour is analogous in vehicle operation control by reason, comprising the following steps:
1) each individual vehicle is defined as decision vehicle, obtains the demand of decision vehicle itself, the demand is target vehicle speed；
2) optimum individual is found in decision vehicle V2V communication, is defined as target vehicle, the optimum individual is configured with and institute
State the decision vehicle vehicle best with current operation state in the vehicle cluster of same requirements；
3) operation reserve of decision vehicle is obtained according to the demand of the position and decision front side vehicle in target vehicle and decision workshop:
If target vehicle is in decision front side and decision front side first car is the different vehicle of demand, then follow the steps 4)；
If decision vehicle itself is target vehicle and decision front side first car is the different vehicle of demand, then follow the steps 4)；
If target vehicle is in decision front side and decision vehicle is to, without the vehicle that demand is different, executing step between the target vehicle
It is rapid 5)；
If decision vehicle itself is target vehicle and decision the front side vehicle different without demand, then follow the steps 6)；
4) when the demand of the front first car is less than the demand of decision vehicle, judge to whether there is obstacle on the left of decision vehicle,
If so, decision vehicle keeps current state, first car sends the request of lane change to the right forwards simultaneously, if it is not, then decision vehicle is to the left
Lane change；
5) decision vehicle pursues and attacks the target vehicle；
6) decision vehicle realizes the demand using peak acceleration.
2. according to claim 1 be based on bionic automatic driving vehicle progress control method, which is characterized in that described
Demand is selected by passenger, and the influence factor of selection includes quick, environmentally friendly and comfortable.
3. according to claim 1 be based on bionic automatic driving vehicle progress control method, which is characterized in that when certainly
When plan vehicle searches out multiple vehicles for meeting optimum individual condition, optimum individual is selected by following principle:
4-1) orientation priority principle preferentially selects the vehicle positioned at decision front side as optimum individual；
4-2) distance priority principle is preferentially selected with decision vehicle apart from nearest vehicle as optimum individual.
4. according to claim 1 be based on bionic automatic driving vehicle progress control method, which is characterized in that described
In step 4), judgement right side is with the presence or absence of obstacle when front first car receives the request of lane change to the right, if so, keeping
Current state sends feedback information to decision vehicle, if it is not, then front first car lane change to the right.
5. according to claim 1 or 4 be based on bionic automatic driving vehicle progress control method, which is characterized in that
Judge when meeting following three conditions at the same time accessible:
5-1) road equipment judges: the outermost lane in the non-lane change direction of lane change vehicle traveling lane, and the lane change vehicle is decision vehicle
Or front first car；
5-2) parking stall judges: lane change side is in the distance of lane change Chinese herbaceous peony bumper the first two parking stall to latter two parking stall of rear bumper
It is interior without other vehicles；
5-3) speed judges: the third parking stall difference after third parking stall and rear bumper before lane change side lane change Chinese herbaceous peony bumper
One virtual vehicle is set, and the virtual vehicle speed of third parking stall is v before definition front bumperBefore, third vehicle after rear bumper
The virtual vehicle speed of position is vAfterwards, decision vehicle current vehicle speed is v, and there are vBefore> v > vAfterwards。
6. according to claim 5 be based on bionic automatic driving vehicle progress control method, which is characterized in that becoming
Before road side lane change Chinese herbaceous peony bumper after third parking stall or/and rear bumper at third parking stall there are when actual vehicle, then will
The current vehicle speed of actual vehicle gives corresponding virtual vehicle, before lane change side lane change Chinese herbaceous peony bumper third parking stall or/and after
When actual vehicle is not present after bumper at third parking stall, vBefore=200km/h, vAfterwards=0.
7. according to claim 1 be based on bionic automatic driving vehicle progress control method, which is characterized in that described
In step 5), decision vehicle pursues and attacks model object pursuit vehicle by following:
vk=vkprev+kp·ek+kd·ek
Wherein, ek=xk-1-xk-thw·vk, xk-1For target vehicle current location, xkFor decision vehicle current location, vkFor decision vehicle
Current vehicle speed, thwFor time headway, vkprevThe v obtained for preceding primary iterative calculationk, kpAnd kdFor correction factor.
8. according to claim 1 be based on bionic automatic driving vehicle progress control method, which is characterized in that described
In step 6), the comfort level of passenger is considered when choosing peak acceleration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710059510.XA CN106843210B (en) | 2017-01-24 | 2017-01-24 | One kind being based on bionic automatic driving vehicle progress control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710059510.XA CN106843210B (en) | 2017-01-24 | 2017-01-24 | One kind being based on bionic automatic driving vehicle progress control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106843210A CN106843210A (en) | 2017-06-13 |
| CN106843210B true CN106843210B (en) | 2019-10-18 |
Family
ID=59122463
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710059510.XA Active CN106843210B (en) | 2017-01-24 | 2017-01-24 | One kind being based on bionic automatic driving vehicle progress control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106843210B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108216236B (en) * | 2017-12-25 | 2019-12-20 | 东软集团股份有限公司 | Vehicle control method, vehicle control device, vehicle and storage medium |
| CN109087503B (en) * | 2018-09-04 | 2021-04-20 | 惠州市德赛西威智能交通技术研究院有限公司 | Vehicle-vehicle cooperation system and method based on V2X |
| US20220105954A1 (en) * | 2019-03-12 | 2022-04-07 | Intel Corporation | Cascaded connection technology for vehicle platoons |
| KR20200111978A (en) * | 2019-03-20 | 2020-10-05 | 현대자동차주식회사 | Navigation system and route search method thereof |
| CN110920619B (en) * | 2019-11-25 | 2021-08-31 | 华为技术有限公司 | Vehicle regulation and control method and device and electronic equipment |
| CN112257486B (en) * | 2019-12-23 | 2023-12-29 | 北京国家新能源汽车技术创新中心有限公司 | Computing force distribution method, computing force distribution device, computer equipment and storage medium |
| CN113264049B (en) * | 2021-04-26 | 2022-07-05 | 同济大学 | Intelligent networking fleet cooperative lane change control method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103359013A (en) * | 2012-04-01 | 2013-10-23 | 同济大学 | Lane changing and overtaking assisting method and system based on inter-vehicle communication |
| CN103646298A (en) * | 2013-12-13 | 2014-03-19 | 中国科学院深圳先进技术研究院 | Automatic driving method and automatic driving system |
| CN103996287A (en) * | 2014-05-26 | 2014-08-20 | 江苏大学 | Vehicle forced lane changing decision-making method based on decision-making tree model |
| CN105329238A (en) * | 2015-12-04 | 2016-02-17 | 北京航空航天大学 | Self-driving car lane changing control method based on monocular vision |
| CN105730323A (en) * | 2016-02-18 | 2016-07-06 | 吉林大学 | Automatic control system and method for automobile safe lane change |
| CN105912814A (en) * | 2016-05-05 | 2016-08-31 | 苏州京坤达汽车电子科技有限公司 | Lane change decision model of intelligent drive vehicle |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3925474B2 (en) * | 2003-07-18 | 2007-06-06 | 日産自動車株式会社 | Lane change support device |
| US9369262B2 (en) * | 2014-08-28 | 2016-06-14 | Toyota Infotechnology Center Usa, Inc. | Full-duplex coordination system |
-
2017
- 2017-01-24 CN CN201710059510.XA patent/CN106843210B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103359013A (en) * | 2012-04-01 | 2013-10-23 | 同济大学 | Lane changing and overtaking assisting method and system based on inter-vehicle communication |
| CN103646298A (en) * | 2013-12-13 | 2014-03-19 | 中国科学院深圳先进技术研究院 | Automatic driving method and automatic driving system |
| CN103996287A (en) * | 2014-05-26 | 2014-08-20 | 江苏大学 | Vehicle forced lane changing decision-making method based on decision-making tree model |
| CN105329238A (en) * | 2015-12-04 | 2016-02-17 | 北京航空航天大学 | Self-driving car lane changing control method based on monocular vision |
| CN105730323A (en) * | 2016-02-18 | 2016-07-06 | 吉林大学 | Automatic control system and method for automobile safe lane change |
| CN105912814A (en) * | 2016-05-05 | 2016-08-31 | 苏州京坤达汽车电子科技有限公司 | Lane change decision model of intelligent drive vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 《复杂动态城市环境下无人驾驶车辆仿生换道决策模型研究》;田赓;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20161115;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106843210A (en) | 2017-06-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106843210B (en) | One kind being based on bionic automatic driving vehicle progress control method | |
| CN113781806B (en) | Mixed traffic flow passing method used in intelligent network connection environment | |
| CN103158704B (en) | Drive mode guide system and method thereof for vehicle | |
| CN110488802A (en) | A kind of automatic driving vehicle dynamic behaviour decision-making technique netted under connection environment | |
| CN111324130B (en) | Intelligent vehicle formation cooperative self-adaptive cruise control switching method imitating pigeon flock | |
| Zhang et al. | Multi-agent DRL-based lane change with right-of-way collaboration awareness | |
| CN112088117B (en) | Method for operating a motor vehicle, control system and motor vehicle | |
| CN108694841A (en) | A kind of intelligent vehicle passage crossroads traffic light method based on V2X technologies | |
| CN113867354A (en) | Regional traffic flow guiding method for intelligent cooperation of automatic driving of multiple vehicles | |
| CN112201070B (en) | Deep learning-based automatic driving expressway bottleneck section behavior decision method | |
| CN108827334A (en) | A kind of automatic Pilot method | |
| CN111081069A (en) | Vehicle track control method for bottleneck area of expressway | |
| CN115050183B (en) | Method for generating simulated traffic flow | |
| CN109901573A (en) | A kind of method and apparatus for realizing platooning | |
| Lu et al. | Decision-making method of autonomous vehicles in urban environments considering traffic laws | |
| CN110696836A (en) | Behavior decision method and device for intelligently driving vehicle | |
| Shen et al. | Coordination of connected autonomous and human-operated vehicles at the intersection | |
| CN116704793A (en) | Control method for improving traffic capacity of bottleneck section in expressway tunnel | |
| CN109059951A (en) | Vehicle energy management method, device, electronic equipment and computer storage system | |
| CN114783197A (en) | Multi-vehicle cooperative lane changing control method for signal-lamp-free crossroad among building groups | |
| CN114475607A (en) | Method and device for changing lane for automatic driving vehicle, vehicle and storage medium | |
| CN113548049A (en) | Intelligent vehicle driving behavior decision method and system based on finite-state machine | |
| CN115959129A (en) | Vehicle lane changing method, device and equipment | |
| CN116153065B (en) | Intersection traffic signal refined optimization method and device under vehicle-road cooperative environment | |
| Ye et al. | Efficient multi-maneuver platooning framework for autonomous vehicles on multi-lane highways |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |