CN108052100A - A kind of intelligent network connection control system of electric automobile and its control method - Google Patents
A kind of intelligent network connection control system of electric automobile and its control method Download PDFInfo
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- CN108052100A CN108052100A CN201711178488.7A CN201711178488A CN108052100A CN 108052100 A CN108052100 A CN 108052100A CN 201711178488 A CN201711178488 A CN 201711178488A CN 108052100 A CN108052100 A CN 108052100A
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- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000033001 locomotion Effects 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000007613 environmental effect Effects 0.000 claims abstract description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000002922 simulated annealing Methods 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 4
- 230000002153 concerted effect Effects 0.000 claims description 3
- 230000004888 barrier function Effects 0.000 claims description 2
- 238000004088 simulation Methods 0.000 claims description 2
- 206010039203 Road traffic accident Diseases 0.000 abstract description 4
- 238000001514 detection method Methods 0.000 abstract description 2
- 239000007787 solid Substances 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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 or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- 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
Abstract
The invention discloses a kind of intelligent network connection control system of electric automobile and its control methods, it is characterized in that, the system comprises main control-processing unit, V2X information exchanges unit, environment and vehicle-states to perceive unit, vehicle CAN network, state of motion of vehicle control unit and driving execution unit.The present invention receives environmental informations and other car status informations such as road by V2X information exchanges unit；Environment and vehicle-state perceive unit detection vehicle-periphery and vehicle body status information；All information are supplied to main control-processing unit by vehicle CAN network；State of motion of vehicle control unit is by controlling driving execution unit to control the state of vehicle；It is relatively low to solve intelligent vehicle control versatility, and the problem of cost is higher, to improving intelligent vehicle traffic efficiency, slows down congestion, reducing traffic accident incidence has important practical significance.
Description
Technical field
The present invention relates to a kind of intelligent network connection control system of electric automobile and its control methods, belong to automobile control technology neck
Domain.
Background technology
A part of the intelligent vehicle as intelligent transportation system, there is very extensive application prospect.Intelligent vehicle refers to
It can independently perceive, make decisions on one's own, carry out trajectory planning and a kind of vehicle tracked.Trajectory Tracking Control is intelligent vehicle research
Basic problem and necessary condition.To improve active safety performance, reduction rear-end impact thing of the automobile during running at high speed
Therefore incidence, research and develop high performance Automotive active anti-collision system become there is an urgent need to.Automotive active anti-collision system is believed using modern
Breath technology and sensing technology obtain external information, comprehensive road condition and situation of remote, recognize whether there are security risk, and urgent
In the case of can take measures automatically, make automobile actively avert danger, ensure vehicle safety travel.
The content of the invention
To solve the deficiencies in the prior art, it is an object of the invention to provide a kind of intelligent networks to join control system of electric automobile
And its control method, intelligent vehicle traffic efficiency is improved, slows down congestion, reduces traffic accident incidence, ensures traffic safety, and solves
The problem of certainly control system versatility is relatively low.
In order to realize above-mentioned target, the present invention adopts the following technical scheme that：
A kind of intelligent network joins control system of electric automobile, it is characterized in that, including main control-processing unit, V2X information exchanges
Unit, environment and vehicle-state perceive unit, vehicle CAN network, state of motion of vehicle control unit and driving execution unit：Institute
Main control-processing unit, state of motion of vehicle control unit and driving execution unit is stated to be sequentially connected；
The V2X information exchanges unit is used to receive environmental information, road information and other car status informations；It is described
Environment and vehicle-state perceive unit for detecting vehicle-periphery and vehicle body status information；The vehicle CAN network is used for
The required information of vehicle safe driving is provided to main control-processing unit；
The state of motion of vehicle control unit is used for by controlling state of the driving execution unit to the vehicle
It is controlled；
The main control-processing unit by with CAN bus respectively with the V2X information exchanges unit, the environment and institute
It states vehicle-state and perceives unit, state of motion of vehicle control unit connection, for according to road environment information, transport condition
Information controls the state of the vehicle by the state of motion of vehicle control unit.
A kind of foregoing intelligent network connection control system of electric automobile, it is characterized in that, the vehicle-state control unit includes
Engine management system EMS control units, electronic stability ESC control units, electric power steering EPS control units：
The engine management system EMS control units are used for by the way that the engine is controlled to accelerate the vehicle
Control；The electronic stability ESC control units are used for by the way that the brake unit is controlled to carry out control for brake to the vehicle；
The electric power steering EPS control units are used for by the way that the steering unit is controlled to carry out course changing control to the vehicle.
A kind of foregoing intelligent network connection control system of electric automobile, it is characterized in that, the driving execution unit includes starting
Machine, brake unit and steering unit.
A kind of foregoing intelligent network connection control system of electric automobile, it is characterized in that, the road environment information includes vehicle
Around obstacle information, the traffic information of vehicle front, the state letter of the light information of current driving environment and other vehicles
Breath；The speed of the vehicle body status information and other car status informations including vehicle, wheel speed, transverse acceleration, longitudinal direction accelerate
Degree and yaw velocity.
A kind of foregoing intelligent network connection control system of electric automobile, it is characterized in that, the environment and vehicle-state perceive list
Member includes radar sensor and visual sensor；The radar sensor is used to detect the state of the barrier of vehicle periphery；Institute
Visual sensor is stated for perceiving the light of the road conditions of the vehicle front and current driving environment.
A kind of foregoing intelligent network connection control system of electric automobile, it is characterized in that, the radar sensor uses millimeter wave
Radar sensor.
A kind of control method of intelligent network connection control system of electric automobile, it is characterized in that, include the following steps：
1) environmental information of vehicle is detected, obtains vehicle and the running condition information of other vehicles；Obtain road environment
Information；
2) vehicle transverse and longitudinal kinetic model is established：
Longitudinal dynamics formula is as follows：
Transverse state equation such as following formula：
Wherein,For act on the external force on vehicle along the y-axis direction make a concerted effort,For vehicle around barycenter each moment it
With,It is front tyre side drift angle,It is rear tyre side drift angle, m is the complete vehicle quality of vehicle, IZFor the rotary inertia of vehicle,
It is the yaw velocity of vehicle,For the yaw angular acceleration of vehicle, k1For the cornering stiffness of automobile front-axle, k2For vehicle rear axle
Cornering stiffness；uiIt is the longitudinal velocity at the barycenter of vehicle, βiIt is the side slip angle of vehicle,It is the barycenter lateral deviation of vehicle
Angular speed, δiFront wheel angle, a is the barycenter of vehicle to the distance of automobile front-axle, b be vehicle barycenter to vehicle rear axle away from
From；
Under inertial coodinate system oxy, the equation of motion of vehicle centroid in the Y direction：Wherein,For the longitudinal velocity of vehicle centroid under inertial coodinate system,It is the yaw angle of vehicle, viIt is the laterally speed at vehicle centroid
Degree；
3) binding model establishes state equation x (k+1)=Ax (k)+Bu (k)+d (k),
Wherein
The wherein T simulation step lengths function of time, x (k) are the quantity of state of moment k (k=1,2,3,4...), and u (k) is moment k
Controlled quentity controlled variable, d (k) be moment k discretization difference functions；
4) object function is established
Wherein,Represent the predicted value of the output quantity at k+i moment in future, yrefWhen (k+i | k) represents future k+i
The reference value of the output quantity at quarter, Δ u (k+i | k) are following controlled quentity controlled variable sequence, NpFor predicted quantity, NcQuantity in order to control, ρ are
Weight coefficient, ε are relaxation factor, and Q and R are respectively weight coefficient,The formula of respectively weighted quadratic summation represents shape
Formula；
In real vehicle, the front wheel angle of vehicle has certain angle range, and the front wheel angle of vehicle is subject to about
Beam forms the constraint function of object function：Umin(k)≤U(k)≤Umax(k), in formula, Umin(k) it is controlled quentity controlled variable minimum value, Umax
(k) it is controlled quentity controlled variable maximum；
5) simulated annealing method using Metropolis as criterion is used to the object function in step 4) solve
To optimum speed and steering wheel angle, and pass through CAN bus network transmission and give vehicle motion control unit.
A kind of foregoing intelligent network connection Control of Electric Vehicles method, it is characterized in that, step is specifically solved in the step 5)
Suddenly it is：
51) initial temperature T=T is given0And initial solutionCalculating target function value
52) a new explanation is generated to currently solving random perturbationCalculating target function value
53) increment is calculated
54) increment Delta J is judged, decision condition is Δ J ＜ 0 or exp (- Δ J/T) ＞ rand (0,1)；If the formula
It sets up, receivesFor new explanation, i.e.,Otherwise carry out in next step；
55) determine whether to reach iterations upper limit value, be carried out if reaching in next step, otherwise return to step 52)
56) determine whether to meet end condition, if meet if computing terminate, export optimal solution；Otherwise Current Temperatures are reduced
T, and return to step 52).
The advantageous effect that the present invention is reached：The present invention by V2X information exchanges unit receive the environmental informations such as road with
And other car status informations；Environment and vehicle-state perceive unit detection vehicle-periphery and vehicle body status information；It is all
Information is supplied to main control-processing unit by vehicle CAN network；State of motion of vehicle control unit is by controlling driving to perform
Unit controls the state of vehicle；It is relatively low to solve intelligent vehicle control versatility, and the problem of cost is higher, to carrying
High intelligent vehicle traffic efficiency slows down congestion, and reducing traffic accident incidence has important practical significance.
Description of the drawings
Fig. 1 is the system structure diagram of the present invention；
Fig. 2 is simulated annealing flow chart.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and be not intended to limit the protection scope of the present invention and limit the scope of the invention.
The kinetic control system and method for intelligent network connection electric vehicle provided by the invention, system structure is as shown in Figure 1, main
Control process unit, V2X information exchanges unit, environment and vehicle-state perceive unit, vehicle CAN network, state of motion of vehicle
Control unit and driving execution unit.
Main control-processing unit following manner controls system in this programme：
1) environmental information of vehicle is detected, obtains vehicle and the running condition information of other vehicles；Obtain road environment
Information；
2) vehicle transverse and longitudinal kinetic model is established：
Longitudinal dynamics formula is as follows：
Transverse state equation such as following formula：
Wherein,For act on the external force on vehicle along the y-axis direction make a concerted effort,It is vehicle around the sum of each moment of barycenter,It is
Front tyre side drift angle,It is rear tyre side drift angle, m is the complete vehicle quality of vehicle, IZFor the rotary inertia of vehicle,It is vehicle
Yaw velocity,For the yaw angular acceleration of vehicle, k1For the cornering stiffness of automobile front-axle, k2It is firm for the lateral deviation of vehicle rear axle
Degree；uiIt is the longitudinal velocity at the barycenter of vehicle, βiIt is the side slip angle of vehicle,It is the side slip angle speed of vehicle, δi
It is front wheel angle, a is the barycenter of vehicle to the distance of automobile front-axle, and b is the barycenter of vehicle to the distance of vehicle rear axle；
One of track following problem needed to be considered of vehicle is between the real time position of vehicle and the desired locations of vehicle
Relation, and deviation between the two is gone to zero.Therefore the real-time position information of vehicle is the key that carry out crosswise joint.
Under inertial coodinate system oxy, the equation of motion of vehicle centroid in the Y direction：Wherein,For the yaw angle of vehicle,For the longitudinal velocity of vehicle centroid under inertial coodinate system；
3) since the Trajectory Tracking Control under fair speed is higher to requirement of real-time, herein using Linear Model for Prediction control
Method planned course tracking control unit processed.
It is linearized and pays attention to being adjusted matrix dimensionality according to the increase of car status information：Binding model
State equation x (k+1)=Ax (k)+Bu (k)+d (k) is established, wherein
4) in order to ensure tracking accuracy of the vehicle during track following, longitudinal direction of car position is considered in object function
Deviation and slogan banner angular speed deviation simultaneously add in relaxation factor in object function, establish object functionEnsure that object function can acquire feasible solution.
The prediction yaw velocity of vehicle and the deviation of desired value are considered in this object function respectively；The prediction of vehicle is indulged
To position and it is expected the deviation of lengthwise position, characterize the precision of Vehicular system track reference track；The variation of controlled quentity controlled variable, to protect
It is steady to demonstrate,prove controlled quentity controlled variable variation, avoids generating mutation, influences Vehicular system normal work.
In real vehicle, the front wheel angle of vehicle has certain angle range, and the front wheel angle of vehicle is subject to about
Beam forms the constraint function of object function：Umin(k)≤U(k)≤Umax(k), in formula, Umin(k) it is controlled quentity controlled variable minimum value, Umax
(k) it is controlled quentity controlled variable maximum；
5) simulated annealing method using Metropolis as criterion is used to the object function in step 4) solve
To optimum speed and steering wheel angle, and pass through CAN bus network transmission and give vehicle motion control unit.
Metropolis criterions are also referred to as the acceptance criterion of new state, and the particular content of criterion is：First, assigned to solid
Original state i is given, using this state state current as solid, and sets the energy of state i as Ei；
Particle is randomly selected using perturbation method, the displacement of chosen particle is made to randomly generate a small change
It is dynamic；And the energy of state j is set as Ej。
If Ej＜ Ei, then new state is just as important state；If Ej＞ Ei, then for new state whether as important
State will consider the effect of warm-up movement.
Solid is in state i and solid is in ratio phase of the ratio between the probability of state j with the corresponding Boltzmann factors
Deng that is,In formula, r is the number less than 1；K is Boltzmann constant；T is thermodynamic temperature.
A random number ξ being located inside [0,1] closed interval is generated with random device, it is judged：If meet r ＞
ξ then receives new state j as important state, otherwise, is cast out；
Simulated annealing method can solve different nonlinear problems, there is stronger robustness to initial value, simple general-purpose, and
And be easily achieved, it is a kind of global optimization approach.
Intelligent algorithm calculates the optimum speed and steering wheel angle of intelligent vehicle, and pass through CAN bus network transmission to
Vehicle motion control unit, according to road environment information, running condition information and other car status informations to the state of vehicle
It is controlled.The object function of simulated annealing insertion intelligent algorithm bottom is acquired into optimization solution in the present invention, is solved
The problem of intelligent vehicle control versatility is relatively low, and cost is higher to improving intelligent vehicle traffic efficiency, slows down congestion, reduces
Traffic accident incidence has important practical significance.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformation can also be made, these are improved and deformation
Also it should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of intelligent network joins control system of electric automobile, it is characterized in that, including main control-processing unit, V2X information exchange lists
Member, environment and vehicle-state perceive unit, vehicle CAN network, state of motion of vehicle control unit and driving execution unit：It is described
Main control-processing unit, state of motion of vehicle control unit and driving execution unit are sequentially connected；
The V2X information exchanges unit is used to receive road environmental information and other car status informations；The environment and vehicle
State aware unit is for detecting vehicle-periphery and vehicle body status information；The vehicle CAN network is used for main control
Processing unit provides the required information of vehicle safe driving；
The state of motion of vehicle control unit is used for by the way that the driving execution unit is controlled to carry out the state of the vehicle
Control；
The main control-processing unit by with CAN bus respectively with the V2X information exchanges unit, the environment and the vehicle
State aware unit, state of motion of vehicle control unit connection, for according to road environment information, transport condition letter
Breath, controls the state of the vehicle by the state of motion of vehicle control unit.
2. a kind of intelligent network connection control system of electric automobile according to claim 1, it is characterized in that, the vehicle-state control
Unit processed includes engine management system EMS control units, electronic stability ESC control units, electric power steering EPS controls
Unit：
The engine management system EMS control units are used for by the way that the engine is controlled to carry out acceleration control to the vehicle
System；The electronic stability ESC control units are used for by the way that the brake unit is controlled to carry out control for brake to the vehicle；Institute
Electric power steering EPS control units are stated for by the way that the steering unit is controlled to carry out course changing control to the vehicle.
3. a kind of intelligent network connection control system of electric automobile according to claim 1, it is characterized in that, the driving performs list
Member includes engine, brake unit and steering unit.
4. a kind of intelligent network connection control system of electric automobile according to claim 1, it is characterized in that, the road environment letter
Breath includes the obstacle information of vehicle periphery, the traffic information of vehicle front, the light information of current driving environment and other vehicles
Status information；The speed of the vehicle body status information and other car status informations including vehicle, wheel speed laterally accelerate
Degree, longitudinal acceleration and yaw velocity.
5. a kind of intelligent network connection control system of electric automobile according to claim 1, it is characterized in that, the environment and vehicle
State aware unit includes radar sensor and visual sensor；The radar sensor is used to detect the barrier of vehicle periphery
State；The visual sensor is used to perceive the road conditions of the vehicle front and the light of current driving environment.
6. a kind of intelligent network connection control system of electric automobile according to claim 5, it is characterized in that, the radar sensor
Using millimetre-wave radar sensor.
7. a kind of controlling party of the intelligent network connection control system of electric automobile based on according to claim 1-6 any one
Method, it is characterized in that, include the following steps：
1) environmental information of vehicle is detected, obtains vehicle and the running condition information of other vehicles；Obtain road environment information；
2) vehicle transverse and longitudinal kinetic model is established：
Longitudinal dynamics formula is as follows：
Transverse state equation such as following formula：
Wherein,For act on the external force on vehicle along the y-axis direction make a concerted effort,It is vehicle around the sum of each moment of barycenter,
It is front tyre side drift angle,It is rear tyre side drift angle, m is the complete vehicle quality of vehicle, IZFor the rotary inertia of vehicle,It is vehicle
Yaw velocity,For the yaw angular acceleration of vehicle, k1For the cornering stiffness of automobile front-axle, k2For the lateral deviation of vehicle rear axle
Rigidity；uiIt is the longitudinal velocity at the barycenter of vehicle, βiIt is the side slip angle of vehicle,It is the side slip angle speed of vehicle
Degree, δiIt is front wheel angle, a is the barycenter of vehicle to the distance of automobile front-axle, and b is the barycenter of vehicle to the distance of vehicle rear axle；
Under inertial coodinate system oxy, the equation of motion of vehicle centroid in the Y direction：Wherein,For
The longitudinal velocity of vehicle centroid under inertial coodinate system,It is the yaw angle of vehicle, viIt is the lateral velocity at vehicle centroid；
3) binding model establishes state equation x (k+1)=Ax (k)+Bu (k)+d (k),
Wherein
The wherein T simulation step lengths function of time, x (k) are the quantity of state of moment k (k=1,2,3,4...), and u (k) is the control of moment k
Amount processed, d (k) are the discretization difference functions of moment k；
4) object function is established
Wherein,Represent the predicted value of the output quantity at k+i moment in future, yref(k+i | k) represent the k+i moment in future
The reference value of output quantity, Δ u (k+i | k) are following controlled quentity controlled variable sequence, NpFor predicted quantity, NcQuantity in order to control, ρ are weight
Coefficient, ε are relaxation factor, and Q and R are respectively weight coefficient,The respectively formula representation of weighted quadratic summation；
In real vehicle, the front wheel angle of vehicle has certain angle range, uses restraint to the front wheel angle of vehicle, shape
Into the constraint function of object function：Umin(k)≤U(k)≤Umax(k), in formula, Umin(k) it is controlled quentity controlled variable minimum value, Umax(k) it is
Controlled quentity controlled variable maximum；
5) simulated annealing method using Metropolis as criterion is used to be solved to obtain most to the object function in step 4)
Good speed and steering wheel angle, and pass through CAN bus network transmission and give vehicle motion control unit.
8. a kind of intelligent network connection Control of Electric Vehicles method according to claim 7, it is characterized in that, tool in the step 5)
The solution procedure of body is：
51) initial temperature T=T is given0And initial solutionCalculating target function value
52) a new explanation is generated to currently solving random perturbationCalculating target function value
53) increment is calculated
54) increment Delta J is judged, decision condition is Δ J ＜ 0 or exp (- Δ J/T) ＞ rand (0,1)；If the formula is set up,
ReceiveFor new explanation, i.e.,Otherwise carry out in next step；
55) determine whether to reach iterations upper limit value, be carried out if reaching in next step, otherwise return to step 52)
56) determine whether to meet end condition, if meet if computing terminate, export optimal solution；Otherwise Current Temperatures T is reduced, and
Return to step 52).
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| CN108973773A (en) * | 2018-06-08 | 2018-12-11 | 武汉理工大学 | The unmanned controller for pure electric automobile of bimodulus with vehicle body stability contorting |
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| CN109823349A (en) * | 2019-02-01 | 2019-05-31 | 吉林微思智能科技有限公司 | A kind of implementation method interacted for automatic driving vehicle with the external world |
| CN110422176A (en) * | 2019-07-04 | 2019-11-08 | 苏州车萝卜汽车电子科技有限公司 | Intelligent transportation system, automobile based on V2X |
| CN111402582A (en) * | 2020-03-12 | 2020-07-10 | 东南大学 | Control method for electric automobile to borrow lane special for automatic driving in intelligent network connection environment |
| CN111583693A (en) * | 2020-05-07 | 2020-08-25 | 中国农业大学 | Intelligent traffic cooperative operation system for urban road and intelligent vehicle control method |
| CN112596509A (en) * | 2019-09-17 | 2021-04-02 | 广州汽车集团股份有限公司 | Vehicle control method, device, computer equipment and computer readable storage medium |
| WO2021197246A1 (en) * | 2020-03-31 | 2021-10-07 | 长安大学 | V2x-based motorcade cooperative braking method and system |
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