CN110530384A - A kind of vehicle path planning method using mixing potential field ant group algorithm - Google Patents

A kind of vehicle path planning method using mixing potential field ant group algorithm Download PDF

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CN110530384A
CN110530384A CN201910643608.9A CN201910643608A CN110530384A CN 110530384 A CN110530384 A CN 110530384A CN 201910643608 A CN201910643608 A CN 201910643608A CN 110530384 A CN110530384 A CN 110530384A
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vehicle
potential field
path
speed
road
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CN110530384B (en
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袁朝春
魏悦
何友国
孙晓强
张厚忠
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Jiangsu University
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Jiangsu University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Abstract

The invention discloses a kind of using mixing potential field-ant group algorithm vehicle path planning method, step 1: obtaining vehicle itself and environmental information；Step 2: by analysis vehicle itself and environmental information, obtaining the current suffered virtual potential field power of vehicle, establish improved vehicle virtual potential field power model；Step 3: the relevant parameter of initialization with Ant colony algorithm；Step 4: path planning initial stage introduces improved virtual potential field resultant force in ant group algorithm and is used as heuristic information function；Introduce weight coefficient；Improve the transition function of ant group algorithm；Step 5: playing a role jointly with going deep into for obstacle-avoiding route planning, pheromone concentration and apart from inspiring；The effect for improving potential field resultant force is gradually decreased, carries out path planning using improved ant group algorithm；Step 6: being controlled by an electronic control unit speed and steering wheel angle according to the path that step 4 and 5 are planned.The present invention effectively avoids the stagnation of path search process, improves route searching efficiency.

Description

A kind of vehicle path planning method using mixing potential field ant group algorithm
Technical field
The present invention relates to path planning and actuating mechanism controls decision Integrated Algorithms under a kind of complicated dynamic operation condition, belong to Path Planning Technique field.
Background technique
With the continuous development of science and technology, the environment that Path Planning Technique is faced will be increasingly complex changeable.This will Ask path planning algorithm that there is the ability for responding rapidly to complex environment variation.Ant group algorithm uses concurrent operation and positive feedback machine System, it is the path planning algorithm of comparative maturity that search capability is strong, and robustness is good, but there are initial operating stage search randomness and Blindness, easy the disadvantages of stagnating, the present invention propose mixing potential field-ant group algorithm, virtual potential field are introduced on the basis of ant group algorithm Resultant force heuristic information function fully considers coefficient of road adhesion and car speed pair during ant group algorithm is used for path planning The influence of path planning plays the advantage of two kinds of algorithms, effectively avoids the randomness searched for, blindness and easily stagnates scarce Point.The characteristics of being constrained for path planning and control decision by intelligent vehicle dynamics, kinematics characteristic and traffic rules, the present invention Using path planning and control method as research object, by improve virtual potential field and the planning of hybrid ant colony realizing route and The unification of control decision.
Summary of the invention
The present invention introduces improved virtual potential field resultant force heuristic information on the basis of ant group algorithm is used for path planning Function；Weight coefficient is introduced, the influence of the coefficient of road adhesion and Vehicle Speed of planning path to Path selection is considered, subtracts The blindness and randomness at few path planning initial stage, effectively prevent the stagnation of path search process, improve route searching effect Rate；And it tracks in driving process on the control of speed, steering wheel angle in path so that vehicle is not influencing riding comfort Under the premise of be capable of the traveling of safety and stability.Concrete scheme is as follows:
It is a kind of to use mixing ant colony-improvement potential field algorithm vehicle path planning method, include the following steps:
Step 1: obtaining vehicle itself and environmental information；
Step 2: by analysis vehicle itself and environmental information, obtaining the current suffered virtual potential field power of vehicle, establish and improve Vehicle virtual potential field power model afterwards；
Step 3: the relevant parameter of initialization with Ant colony algorithm；
Step 4: path planning initial stage introduces in ant group algorithm at this time since the pheromone concentration of route searching is insufficient Improved virtual potential field resultant force is used as heuristic information function；Weight coefficient is introduced, considers the coefficient of road adhesion of planning path Influence with Vehicle Speed to avoidance Path selection improves the transition function of ant group algorithm, make it by distance and Improve the dual evocation of virtual potential field resultant force；Reduce the number of iterations of the improved ant group algorithm for route searching, In It improves and guarantees path planning direction accuracy under the action of virtual potential field resultant force, reduce the blindness at path planning initial stage and random Property.The stagnation for effectively preventing path search process improves avoidance route searching efficiency；
Step 5: playing a role jointly with going deep into for path planning, pheromone concentration and apart from inspiring；At this point, gradually dropping The low effect for improving potential field resultant force carries out path planning using improved ant group algorithm；
Step 6: being controlled by an electronic control unit speed and steering wheel angle according to the path that step 4 and 5 are planned；
Step 7: when vehicle along planning path close to barrier and when starting avoidance, speed is controlled by electronic control unit Within 40km/h, when far from barrier, speed restores normal；During path planning always by steering wheel angle control- In 23 °~+23 ° (being positive clockwise), so as to improve vehicle safety and riding comfort.
Step 8: after the unit time, repeating step 1 to 7 until vehicle reaches safety place.
Further, vehicle environmental information described in step 1 is obtained by millimetre-wave radar and CCD industrial camera, is obtained The information taken includes: current driving road front obstacle information；Current sensor check frequency information；Current traffic signal lamp Information；The lateral obstacle information of current driving road；Aiming spot information.The vehicle self information is sensed by vehicle body Device and GPS are obtained, and acquired information includes: car speed, steering wheel angle, current vehicle position.
Further, improved vehicle virtual potential field power model described in step 2 includes:
(1) the virtual repulsion model of front obstacle:
In formula, freject-frontIt is front vehicles to the repulsion coefficient of rear car；μroadIt is attached for road ahead-tire of identification Put forth effort characteristic information；viIt (t) is intelligent vehicle speed；β (t) is intelligent vehicle mass center deflection angle；For intelligent vehicle relative to The speed of front obstacle；Relative distance for intelligent vehicle relative to front obstacle is that barrier and vehicle theory are pacified Full distance, Ssafe-fronThe safe distance needed for colliding with front obstacle is avoided for intelligent vehicle.
(2) check frequency repulsion model:
In formula, freject-blindIt is check frequency to the repulsion coefficient of intelligent vehicle；It is intelligent vehicle relative to blind area Relative velocity；Sm-s-oIt is that other vehicles avoid safe distance needed for colliding with intelligent vehicle；It is intelligent vehicle relative to blind area side The relative distance on boundary；μroadFor road ahead-tire adhesion force characteristic information of identification；Ds-bIt is managed for intelligent vehicle and dead-zone boundary By safe distance.
(3) traffic lights signal-virtual repulsion model:
In formula, fr-lightIt is traffic lights to the repulsion coefficient of intelligent vehicle；MlightFor traffic lights information, Mlight =1 indicates that traffic lights are red light；Relative velocity for intelligent vehicle relative to traffic lights；SsafeFor intelligence The minimum range that vehicle can stop before stop line；Relative distance for intelligent vehicle relative to traffic lights； μroadFor road ahead-tire adhesion force characteristic information of identification.
(4) the lateral virtual repulsion model of intelligent vehicle:
In formula, freject-yIt is lateral barrier (guardrail, shrub, pedestrian etc.) to the repulsion coefficient of intelligent vehicle；For intelligence Relative velocity of the vehicle relative to lateral barrier；viThe component that sin β (t) is intelligent vehicle speed on lateral；θ (t) is intelligent vehicle Mass center deflection angle；It is the intelligent vehicle left and right sides at a distance from nearest object；μroadFor road ahead-tire attachment of identification Force characteristic information；SsafSafe distance required for colliding with lateral barrier is avoided for intelligent vehicle.
Further, step 3 initialization with Ant colony algorithm relevant parameter, Basic Ant Group of Algorithm transition function are as follows:
In formula:Indicate node j pheromone concentration；α is the pheromone concentration factor；β is the heuristic information factor； Allowed k (t) indicates to remove the node that can choose in next step after accessed node；Indicate node m to node n Apart from heuristic information function；dmnIndicate the distance between node m to node n.
Ant completes one cycle, and the pheromone concentration in each path is updated using following formula:
τmn(t+ δ)=(1- ρ) τmn(t)+Δτmn
In formula: ρ (0 < ρ < 1) indicates pheromone concentration volatility coefficient；ΔτmnIt indicates to believe on path (m, n) in this circulation Cease plain concentration increments；ΔτmnIndicate that kth ant stays in the pheromone concentration Q on path (m, n) in this circulation as pheromones The factor；LkFor the path length of kth Ant Search.
Further, the pheromone concentration in planning path is first determined whether in step 4, if concentration is low, will be introduced after improving Virtual potential field resultant force heuristic information function；Weight coefficient is introduced, considers the coefficient of road adhesion and vehicle driving of planning path Influence of the speed to Path selection.
From semiempirical road-tire mathematical model:
μ represents coefficient of road adhesion；S is slip rate；c1, c2, c3It is the constant obtained by experiment, see the table below:
Using wheel and road surface contact point as origin, the road surface on four direction (45 °, 135 °, 225 °, 315 °) is taken to adhere to system Number, estimates current road attachment coefficient peak value are as follows:
In formula, λ1、λ2、λ3、λ4Indicate weight coefficient,Indicate current road attachment coefficient, μ1max、μ2max、μ3max、μ4max Indicate the peak adhesion coefficient on the four direction of contact point between wheel and road surface contact point,It respectively indicates adjacent The attachment coefficient on road surface, the above parameter meet following relationship:Vehicle Speed is classified, Judge path planning process whether driving safety:
Wherein A: traveling is too fast；B: traveling is normal；C: low running speed is further The transition function of ant group algorithm is improved are as follows:
In formula, dmnShow node m to node n distance；dngShow node n to target point distance；It indicates from node m to node The variable quantity of n speed；It indicates apart from heuristic information function；It indicates to improve potential field resultant force heuristic information function；γ For the heuristic information factor；KmaxFor maximum number of iterations；K is current iteration number；A > 1 is constant；FtotalIndicate potential field resultant force； C1、C2Indicate weight coefficient；μmaxRepresent maximum coefficient of road adhesion；LvIndicate Vehicle Speed grade；θ indicates potential field resultant force With the angle in optional path direction, angle is smaller,Bigger, transition probability is bigger, easier to be mobile towards the path direction.
Further, go deep into step 5 with path planning, play pheromone concentration and distance, speed heuristic information Effect, gradually decreases the effect for improving virtual potential field resultant force, prevents intelligent vehicle from advancing all along potential field gradient direction.After improvement Ant colony transition function so that intelligent vehicle is advanced with maximum probability to target point, reduce the blindness of route searching and random Property, improve the efficiency of path planning.
Further, step 6 is controlled by an electronic control unit speed according to the path that step 4,5 are planned and steering wheel turns Angle.
Further, in step 7 when intelligent vehicle along planning path close to barrier and when starting avoidance, by electronic control unit By speed control within 30km/h, when far from barrier, speed restores normal；Steering wheel is turned always during path planning Angle control in -23 °~+23 ° (being positive) clockwise, so as to improve vehicle safety and riding comfort.The control of speed Simulation are as follows:
Do=Dse+Ds
In formula, λ is the constant coefficient less than 1, usually takes λ=0.4~0.6, DoFor automobile between barrier at a distance from；DseFor The distance that intelligent vehicle travels in this section of braking process from initial velocity to safe speed；DsFor reserved safe distance；vsFor avoidance Speed when braking starts；veSpeed when for end of braking；A (t) is braking deceleration, is the linear function of time；tdFor vapour Vehicle reaches the time used in maximum value from starting to brake to braking acceleration.
From rate control module as can be seen that automobile is after barriers to entry object coverage, speed can with obstacle The reduction of distance between object and reduce, when automobile cut-through object, speed increases with the increase of automobile and obstacle distance, When automobile is driven out to the coverage of barrier, speed will be no longer by the control of model.
And if cause automobile to lose stabilization since side acceleration is excessive during turning to avoidance when automobile, tune can be passed through Control of the steering wheel angle realization to body gesture is saved, guarantees safety and the comfort of vehicle driving.
Beneficial effects of the present invention:
(1) present invention introduces improvement potential field resultant forces to be used as route searching of the heuristic information factor for path planning initial stage, The improvement ant group algorithm of proposition can be reduced the blindness and randomness at path planning initial stage for vehicle path planning, effectively avoid The stagnation of path search process, improves route searching efficiency.
(2) present invention and consider that coefficient of road adhesion and speed change the influence to path planning, improve ant colony calculation The probability transfer function of method makes vehicle advance with maximum probability towards target point；
(3) control during vehicle control proposed by the present invention to Vehicle Speed and steering wheel angle, effectively mentions The safety and stability of vehicle driving during high path planning.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention:
Fig. 2 is the schematic diagram of virtual potential field power suffered by vehicle；
Fig. 3 is ant group algorithm schematic diagram；
Fig. 4 is potential field resultant force and optional path relation schematic diagram in improved ant group algorithm transition function；
Fig. 5 is vehicle driving-cycle schematic diagram；
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.
The present invention provides a kind of use and mixes ant colony-improvement potential field algorithm vehicle path planning method, as shown in Figure 1, Including the following steps:
Step 1: obtaining vehicle itself and environmental information.
Vehicle itself and environmental information are obtained by millimetre-wave radar and CCD industrial camera, and millimetre-wave radar is set as four A, wherein 1 splits in front of the car bumper bar middle position, other two is individually positioned between the front door of two sides and back door Middle position, the last one is placed on the tail portion of vehicle, for detecting obstacle information and transmission on vehicle four direction Electron controls list ECU.CCD industrial camera is mounted on front windshield of vehicle over top, for by the situation of vehicle front It is transferred to electronic control unit ECU.Acquired environmental information includes: current driving road front obstacle information；Work as forward pass Sensor check frequency information；Current traffic signal information；The lateral obstacle information of current driving road；Aiming spot letter Breath.The vehicle self information is obtained by vehicle body sensor and GPS, and acquired information includes: car speed, steering wheel turn Angle, current vehicle position.
Step 2: establishing fictitious force model suffered in vehicle travel process, as shown in Figure 2, comprising:
(1) the virtual repulsion model of front obstacle:
In formula, freject-fronIt is front vehicles to the repulsion coefficient of rear car；μroadFor road ahead-tire attachment of identification Force characteristic information；viIt (t) is intelligent vehicle speed；β (t) is intelligent vehicle mass center deflection angle；It is intelligent vehicle relative to preceding The speed of square barrier；Relative distance for intelligent vehicle relative to front obstacle is barrier and vehicle theory α coefficient Distance, Ssafe-fronThe safe distance needed for colliding with front obstacle is avoided for intelligent vehicle.
(2) check frequency repulsion model:
In formula, freject-blinIt is check frequency to the repulsion coefficient of intelligent vehicle；It is phase of the intelligent vehicle relative to blind area To speed；Sm-s-oIt is that other vehicles avoid safe distance needed for colliding with intelligent vehicle；It is intelligent vehicle relative to dead-zone boundary Relative distance；μroadFor road ahead-tire adhesion force characteristic information of identification；Ds-blindIt is managed for intelligent vehicle and dead-zone boundary By safe distance.
(3) traffic lights signal-virtual repulsion model:
In formula, fr-ligIt is traffic lights to the repulsion coefficient of intelligent vehicle；MlightFor traffic lights information, Mlight= 1 indicates that traffic lights are red light；Relative velocity for intelligent vehicle relative to traffic lights；SsafeFor intelligent vehicle energy Enough minimum ranges that can stop before stop line；Relative distance for intelligent vehicle relative to traffic lights；μroadFor The road ahead of identification-tire adhesion force characteristic information.
(4) the lateral virtual repulsion model of intelligent vehicle:
In formula, freject-yIt is lateral barrier (guardrail, shrub, pedestrian etc.) to the repulsion coefficient of intelligent vehicle；For intelligence Relative velocity of the vehicle relative to lateral barrier；ViThe component that sin β (t) is intelligent vehicle speed on lateral；θ (t) is intelligent vehicle Mass center deflection angle；It is the intelligent vehicle left and right sides at a distance from nearest object；μroadFor road ahead-tire attachment of identification Force characteristic information；Ssafe-Safe distance required for colliding with lateral barrier is avoided for intelligent vehicle.
Step 3: initialization improves potential field method and ant group algorithm relevant parameter, such as population quantity, cycle-index, heuristic information Parameter, pheromone concentration, route searching taboo list etc..
Step 4: as shown in figure 3, path planning initial stage, pheromone concentration is insufficient, introduces improved virtual potential field resultant force and makees For the heuristic information function of route searching, weight coefficient is introduced, considers the coefficient of road adhesion and vehicle row of avoidance planning path Influence of the speed to Path selection is sailed, the transition function of ant group algorithm is improved, make it by distance and improves virtual potential field The dual evocation of resultant force；The number of iterations of the improved ant group algorithm for route searching is reduced, in virtual potential field resultant force Under the action of guarantee path planning direction accuracy, reduce path planning initial stage blindness and randomness.Improved ant colony Potential field resultant force and the selection in path can be indicated with Fig. 4 in algorithm transition function.As seen from the figure, potential field resultant direction with can Routing diameter angular separation is smaller,Bigger, transition probability is bigger, i.e., easier to be mobile towards the direction of path 1..
Go deep into step 5 with path planning, plays the effect of pheromone concentration and distance, speed heuristic information, by The effect for gradually reducing virtual potential field resultant force prevents intelligent vehicle from advancing all along potential field gradient direction.
Step 6 is controlled by an electronic control unit speed and steering wheel angle according to the path that step 4,5 are planned.
Step 7: after the unit time, repeating step 1 to 6 until vehicle reaches safety place.
By taking the driving cycle in Fig. 5 as an example:
(1) vehicle itself and environmental information are obtained.
(2) by analysis vehicle itself and environmental information, virtual potential field power model is established, vehicle current driving environment is obtained Lower suffered fictitious force.
(3) initialization of virtual potential field method and ant group algorithm relevant parameter.
(4) path planning initial stage introduces virtual potential field resultant force and is used as road since the pheromone concentration in planning path is insufficient The heuristic information of path search improves the transition function of ant group algorithm, makes it by the dual of distance and virtual potential field resultant force Evocation；Guarantee path planning direction accuracy under the action of virtual potential field resultant force, reduces the blindness at route searching initial stage Property and randomness.
(5) virtual potential field power model is established, plans vehicle part driving path.
The virtual repulsion being subject in the process of moving from vehicle has: lateral barrier repulsion, lateral lane line repulsion, detection are blind Area (pedestrian, vehicle etc.) repulsion, front vehicles repulsion and traffic lights repulsion；Under the collective effect of these fictitious forces, vehicle The potential field established along fictitious force is moved from high potential energy point to low-potential energy point.
(6) whether setting speed threshold value and steering wheel angle threshold decision speed of operation and steering wheel angle are more than setting Threshold value, if being more than the threshold value of setting, the unsafe conditions such as sideslip, whipping can occur for vehicle；Therefore it is controlled by an electronic control unit vehicle In planning path driving process by speed and steering wheel angle control in the reasonable scope, guarantee the operation stabilization of vehicle Property, improve driving safety and comfort.
(7) after the unit time, step (1) to (6) are repeated until vehicle reaches safety place.
Mixing potential field-ant group algorithm vehicle local paths planning method proposed by the present invention, with the rule of fictitious force model Basis of the information as particle swarm algorithm optimizing is drawn, potential field resultant force is introduced and the part heuristic information of vehicle search path point is used as to close Reason rapidly cooks up local driving path, and the blindness for reducing ant group algorithm at route searching initial stage improves path with this Planning efficiency；The potential street accidents risks as brought by check frequency are reduced to improve the safety coefficient of driving；Introduce rule The speed control in driving process and steering wheel angle control are drawn, vehicle handling stability in driving process, safety are improved Property and comfort.
The series of detailed descriptions listed above only for feasible embodiment of the invention specifically Protection scope bright, that they are not intended to limit the invention, it is all without departing from equivalent implementations made by technical spirit of the present invention Or change should all be included in the protection scope of the present invention.

Claims (8)

1. a kind of using mixing potential field-ant group algorithm vehicle path planning method, which comprises the steps of:
Step 1: obtaining vehicle itself and environmental information；
Step 2: by analysis vehicle itself and environmental information, obtaining the current suffered virtual potential field power of vehicle, establish improved Vehicle virtual potential field power model；
Step 3: the parameter of initialization with Ant colony algorithm；
Step 4: path planning initial stage introduces improved virtual potential field resultant force in ant group algorithm and is used as heuristic information function； The influence of coefficient of road adhesion and Vehicle Speed to Path selection for planning path introduces weight coefficient, improves ant The transition function of group's algorithm makes it by distance and improves the dual evocation of virtual potential field resultant force；It reduces after improving Ant group algorithm be used for route searching the number of iterations；
Step 5: playing a role jointly with going deep into for path planning, pheromone concentration and apart from inspiring；Change at this point, gradually decreasing Into the effect of potential field resultant force, path planning is carried out using improved ant group algorithm；
Step 6: being controlled by an electronic control unit speed and steering wheel angle according to the path that step 4 and 5 are planned；
Step 7: when vehicle along planning path close to barrier and when starting avoidance, speed control is existed by electronic control unit Within 40km/h, when far from barrier, speed restores normal；
Step 8: after the unit time, repeating step 1 to 7 until vehicle reaches safety place.
2. according to claim 1 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In environmental information described in step 1 includes: current driving road front obstacle information；Current sensor check frequency letter Breath；Current traffic signal information；The lateral obstacle information of current driving road；Aiming spot information；The vehicle is certainly Body information includes: car speed, steering wheel angle, current vehicle position.
3. according to claim 1 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In improved vehicle virtual potential field power model described in step 2 includes:
(1) the virtual repulsion model of front obstacle:
In formula, freject-frontIt is front vehicles to the repulsion coefficient of rear car；μroadIt is special for road ahead-tire adhesion force of identification Property information；viIt (t) is intelligent vehicle speed；β (t) is intelligent vehicle mass center deflection angle；Hinder for intelligent vehicle relative to front Hinder the speed of object；Relative distance for intelligent vehicle relative to front obstacle is barrier and vehicle theory α coefficient away from From Ssafe-frontThe safe distance needed for colliding with front obstacle is avoided for intelligent vehicle；
(2) check frequency repulsion model:
In formula, freject-blinIt is check frequency to the repulsion coefficient of intelligent vehicle；It is intelligent vehicle relative to the relatively fast of blind area Degree；Sm-s-oIt is that other vehicles avoid safe distance needed for colliding with intelligent vehicle；It is phase of the intelligent vehicle relative to dead-zone boundary It adjusts the distance；μroadFor road ahead-tire adhesion force characteristic information of identification；Ds-blindPacify for intelligent vehicle and dead-zone boundary theory Full distance；
(3) traffic lights signal-virtual repulsion model:
In formula, fr-lighT is repulsion coefficient of the traffic lights to intelligent vehicle；MlightFor traffic lights information, Mlight=1 table Show that traffic lights are red light；Relative velocity for intelligent vehicle relative to traffic lights；SsafeIt can for intelligent vehicle The minimum range that can stop before stop line；Relative distance for intelligent vehicle relative to traffic lights；μroadTo distinguish The road ahead of knowledge-tire adhesion force characteristic information；
(4) the lateral virtual repulsion model of intelligent vehicle:
In formula, freject-yIt is lateral barrier to the repulsion coefficient of intelligent vehicle；Phase for intelligent vehicle relative to lateral barrier To speed；viThe component that sin β (t) is intelligent vehicle speed on lateral；β (t) is intelligent vehicle mass center deflection angle；For intelligence At left and right sides of vehicle at a distance from nearest object；μroadFor road ahead-tire adhesion force characteristic information of identification；SsafeFor intelligence Vehicle avoids safe distance required for colliding with lateral barrier.
4. according to claim 1 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In in step 3, initialization with Ant colony algorithm relevant parameter includes:
Basic Ant Group of Algorithm transition function are as follows:
In formula:Indicate node j pheromone concentration；α is the pheromone concentration factor；β is the heuristic information factor；allowed K (t) indicates to remove the node that can choose in next step after accessed node；Indicate that the distance of node m to node n open Photos and sending messages function；dmnIndicate the distance between node m to node n.
Ant completes one cycle, and the pheromone concentration in each path is updated using following formula:
τmn(t+ δ)=(1- ρ) τmn(t)+Δτmn
In formula: ρ (0 < ρ < 1) indicates pheromone concentration volatility coefficient；ΔτmnIndicate in this circulation pheromones on path (m, n) Concentration increments；ΔτmnIndicate that kth ant stays in the pheromone concentration Q on path (m, n) in this circulation as information prime factor； LkFor the path length of kth Ant Search.
5. according to claim 1 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In the concrete methods of realizing of step 4 includes:
It first determines whether the pheromone concentration in planning path, if concentration is low, introduces improved virtual potential field resultant force and inspire letter Cease function；Introduce weight coefficient, with consider planning path coefficient of road adhesion and Vehicle Speed to the shadow of Path selection It rings；
From semiempirical road-tire mathematical model:
μ represents coefficient of road adhesion；S is slip rate；c1, c2, c3It is the constant obtained by experiment.
Using wheel and road surface contact point as origin, the coefficient of road adhesion on four direction (45 °, 135 °, 225 °, 315 °) is taken, Estimate current road attachment coefficient peak value are as follows:
In formula, λ1、λ2、λ3、λ4Indicate weight coefficient,Indicate current road attachment coefficient, μ1max、μ2max、μ3max、μ4maxExpression connects Peak adhesion coefficient on the four direction of contact between wheel and road surface contact point,Respectively indicate adjacent road surface Attachment coefficient, the above parameter meet following relationship:
Vehicle Speed is classified, judge obstacle-avoiding route planning process whether driving safety:
Wherein
A: traveling is too fast；B: traveling is normal；C: low running speed.
6. according to claim 5 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In in the step 4, the transition function of ant group algorithm is improved are as follows:
In formula, dmnShow node m to node n distance；dngShow node n to target point distance；It indicates from node m to node n speed Variable quantity；It indicates apart from heuristic information function；It indicates to improve potential field resultant force heuristic information function；γ is to inspire The information factor；KmaxFor maximum number of iterations；K is current iteration number；A > 1 is constant；FtotalIndicate potential field resultant force；C1、C2 Indicate weight coefficient；μmaxRepresent maximum coefficient of road adhesion；LvIndicate Vehicle Speed grade；θ indicate potential field resultant force with can The angle of path direction is selected, angle is smaller,Bigger, transition probability is bigger, easier to be mobile towards the path direction.
7. according to claim 1 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In in the step 7, always by steering wheel angle control in -23 °~+23 ° during path planning.
8. according to claim 1 a kind of using mixing potential field-ant group algorithm vehicle path planning method, feature exists In, in the step 7, the Controlling model of speed are as follows:
Do=Dse+Ds
In formula, λ is the constant coefficient less than 1, usually takes λ=0.4~0.6, DoFor automobile between barrier at a distance from；DseFor from first The beginning speed distance that intelligent vehicle travels into this section of braking process of safe speed；DsFor reserved safe distance；vsFor avoidance braking Speed when beginning；veSpeed when for end of braking；A (t) is braking deceleration, is the linear function of time；tdFor automobile from Start braking and reaches the time used in maximum value to braking acceleration.
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