CN110816531B - Control system and control method for safe distance between unmanned automobile vehicles - Google Patents
Control system and control method for safe distance between unmanned automobile vehicles Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/18—Conjoint control of vehicle sub-units of different type or different function including control of braking systems
- B60W10/184—Conjoint control of vehicle sub-units of different type or different function including control of braking systems with wheel brakes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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Abstract
The invention discloses a control system for the safe distance between unmanned vehicles, which comprises: the image acquisition devices are respectively arranged in the middle of the front side and the rear side of the automobile; the auxiliary braking system is connected with the brake disc and is connected with the brake braking system in parallel; the auxiliary braking system can brake together with the brake braking system and also can brake independently; the auxiliary acceleration system is connected with a vehicle hub and used for assisting acceleration; the data acquisition module is used for detecting vehicle conditions and road conditions; the data processing module is connected with the data acquisition module and the image acquisition device and is used for processing data; and the controller is connected with the data processing module, the auxiliary braking system and the auxiliary acceleration system and is used for receiving the data of the data processing module and controlling the auxiliary braking system and the auxiliary acceleration system to work. The invention also discloses a control method of the safe distance between the unmanned automobile vehicles, which improves the driving safety.
Description
Technical Field
The invention relates to the technical field of control over a safe distance between vehicles, in particular to a control system and a control method for the safe distance between the vehicles of an unmanned automobile.
Background
The unmanned vehicle is a novel intelligent vehicle, and is characterized in that each part of the vehicle is accurately controlled, calculated and analyzed through a control device (namely, a vehicle-mounted intelligent brain), and finally different devices in the unmanned vehicle are respectively controlled by sending instructions to an Electronic Control Unit (ECU), so that the full-automatic operation of the vehicle is realized, and the purpose of unmanned driving of the vehicle is achieved.
During the debugging process of the unmanned vehicle or during the actual running process of the unmanned vehicle, some manual management mechanisms are needed to ensure that the unmanned vehicle can be manually intervened when the unmanned vehicle is abnormal. The timeliness of manual intervention has very important safety significance for the safety of the vehicle and passengers thereof as well as surrounding vehicles and pedestrians.
However, the significance of the unmanned vehicles is limited by manual intervention, if the unmanned vehicles need to be taken over manually at any time, the unmanned vehicles are difficult to popularize, so that people must be ensured to supervise at any time when the unmanned vehicles run, the labor cost is increased, the application of the unmanned vehicles is not facilitated, the running safety of the unmanned vehicles is most concerned by most people, and therefore, the control of the safety distance between the unmanned vehicles is very necessary.
Disclosure of Invention
The invention aims to design and develop a control system for the safe distance between unmanned vehicles, which is provided with an auxiliary braking system and an auxiliary accelerating system, so that the distances between the vehicles and a front vehicle and a rear vehicle can be adjusted in real time, and the driving safety is improved.
Another objective of the present invention is to further design and develop a method for controlling a safe distance between unmanned vehicles, which can collect environmental conditions, road conditions and vehicle conditions during the driving of the vehicles, and determine the working states of the auxiliary braking system, the auxiliary acceleration system and the alarm system based on the BP neural network.
The invention can also control the acceleration when the vehicle brakes and the acceleration degree when the vehicle accelerates, accurately adjust the distance between the vehicle and the front vehicle and the rear vehicle, and improve the driving safety.
The technical scheme provided by the invention is as follows:
a system for controlling a safe distance between unmanned automotive vehicles, comprising:
the image acquisition devices are respectively arranged in the middle of the front side and the rear side of the automobile;
the auxiliary braking system is connected with the brake disc and is connected with the brake braking system in parallel;
the auxiliary braking system can brake together with the brake braking system and also can brake independently;
the auxiliary acceleration system is connected with a vehicle hub and used for assisting acceleration;
the data acquisition module is used for detecting vehicle conditions and road conditions;
the data processing module is connected with the data acquisition module and the image acquisition device and is used for processing data;
and the controller is connected with the data processing module, the auxiliary braking system and the auxiliary acceleration system and is used for receiving the data of the data processing module and controlling the auxiliary braking system and the auxiliary acceleration system to work.
Preferably, the data acquisition module includes:
a vibration acceleration sensor for measuring the dynamic vibration intensity of the vehicle;
a speed sensor for measuring a vehicle running speed;
a gradient sensor for measuring a gradient of a road surface on which the vehicle travels;
an environmental sensor for detecting an environmental condition.
Preferably, the method further comprises the following steps:
and the alarm system is used for early warning in severe conditions and reminding manual intervention.
Preferably, the auxiliary acceleration systems are hub motors respectively arranged on corresponding hubs.
A control method for the safe distance between unmanned vehicles collects the environment state, road condition and vehicle condition during the running process of the vehicles, and determines the working states of an auxiliary braking system, an auxiliary accelerating system and an alarm system based on a BP neural network, which comprises the following steps:
firstly, measuring an environmental state, a vehicle running speed, a vehicle vibration intensity, a road surface gradient, a vehicle-to-front vehicle distance and a vehicle-to-rear vehicle distance through a sensor according to a sampling period;
step two, determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5,x6}; wherein x is1Is an environmental state, x2As the vehicle running speed, x3As intensity of vibration of the vehicle, x4Is the road surface gradient, x5Distance between vehicle and front vehicle, x6The distance between the vehicle and the rear vehicle;
mapping the input layer vector to a hidden layer, wherein the number of neurons of the hidden layer is m;
step four, obtaining an output layer neuron vector o ═ o1,o2,o3}; wherein o is1To assist the operating state of the brake system, o2To assist the operating state of the acceleration system, o3Being alarm systemsOperating state, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3}, and when okWhen the number is 1, the corresponding system is in a working state, and when the number is okWhen the value is 0, the corresponding system is in a non-operating state.
Preferably, when o1When the acceleration is equal to 1, the auxiliary braking system works to control the vehicle to brake, and the acceleration is as follows:
when d issu-du≤dh-dshWhen the temperature of the water is higher than the set temperature,
when d issu-du＞dh-dshWhen the temperature of the water is higher than the set temperature,
wherein the content of the first and second substances,
in the formula, abAcceleration at the time of braking of the vehicle, duDistance to front vehicle when preparing for braking of vehicle, dsuA safe distance between the vehicle and the front vehicle, dhDistance to rear vehicle when preparing for braking of vehicle, dshIs the safe distance between the vehicle and the following vehicle, v is the vehicle speed when the vehicle is ready to brake, k is a parameter, n is the number of selected objects with equal probability,
is an environmental condition and
g is the acceleration of gravity, e is the base number of the natural logarithm, σ is the wind power level and σ belongs to [0,7 ]]，dsFor visibility, d0Is a safe distance that should be maintained under the current vehicle condition environment.
Preferably, when o2When the vehicle speed is 1, the auxiliary acceleration system works to control the vehicle to accelerate, and the acceleration is as follows:
wherein the content of the first and second substances,
in the formula, aaAcceleration of the vehicle during acceleration, duDistance to front vehicle in preparation for acceleration of vehicle, dsuIs the safe distance between the vehicle and the front vehicle, v is the vehicle speed when the vehicle is ready to accelerate, k is a parameter, n is the number of selected objects with equal probability,
is a weather condition and
g is the acceleration of gravity, e is the base number of the natural logarithm, σ is the wind power level and σ belongs to [0,7 ]]，dsFor visibility, d0Is a safe distance that should be maintained under the current vehicle condition environment.
Preferably, when o3When the alarm is 1, the system carries out early warning on severe conditions and reminds manual intervention.
Preferably, the vibration intensity of the vehicle is:
wherein, VrmsIs the vibration intensity of the vehicle, ViFor the measured vibration velocity value, N is the measured vibration signal sample length.
Preferably, the number of neurons in the hidden layer is 5; the excitation functions of the hidden layer and the output layer adopt S-shaped functions fj(x)＝1/(1+e-x)。
The invention has the following beneficial effects:
(1) the control system for the safe distance between the unmanned vehicles is provided with an auxiliary braking system and an auxiliary accelerating system, so that the distances between the vehicles and the front vehicle and the rear vehicle can be adjusted in real time, and the driving safety is improved.
(2) The control method for the safe distance between the unmanned vehicles can acquire the environmental state, the road condition and the vehicle condition in the driving process of the vehicles, and determine the working states of the auxiliary braking system, the auxiliary accelerating system and the alarm system based on the BP neural network. The invention can also control the acceleration when the vehicle brakes and the acceleration degree when the vehicle accelerates, accurately adjust the distance between the vehicle and the front vehicle and the rear vehicle, and improve the driving safety.
Detailed Description
The present invention is described in further detail below to enable those skilled in the art to practice the invention with reference to the description.
The invention provides a control system for a safe distance between unmanned vehicles, which comprises: the image acquisition devices are generally binocular cameras which are respectively arranged in the middle of the front side and the rear side of the automobile; the auxiliary braking system is connected with the brake disc and is connected with the brake braking system in parallel; the auxiliary braking system can be used for braking together with the brake braking system and also can be used for braking independently; and the auxiliary acceleration system is connected with the vehicle hub and used for assisting acceleration, and generally selects hub motors which are respectively arranged on corresponding hubs. The data acquisition module is used for detecting vehicle conditions and road conditions; the data processing module is connected with the data acquisition module and the image acquisition device and is used for processing data; and the controller is connected with the data processing module, the auxiliary braking system and the auxiliary acceleration system and is used for receiving the data of the data processing module and controlling the auxiliary braking system and the auxiliary acceleration system to work.
The data acquisition module comprises: a vibration acceleration sensor for measuring the dynamic vibration intensity of the vehicle; a speed sensor for measuring a vehicle running speed; a gradient sensor for measuring a gradient of a road surface on which the vehicle travels; an environmental sensor for detecting an environmental condition.
In this embodiment, the method further includes: and the alarm system is used for early warning under severe conditions, and the vehicle cannot be driven automatically completely, so that manual intervention is reminded, and the driving safety is improved.
The control system for the safe distance between the unmanned vehicles is provided with an auxiliary braking system and an auxiliary accelerating system, so that the distances between the vehicles and the front vehicle and the rear vehicle can be adjusted in real time, and the driving safety is improved.
The invention also provides a control method of the safe distance between the unmanned vehicles, which collects the environment state, road condition and vehicle condition during the driving process of the vehicles, and determines the working states of the auxiliary braking system, the auxiliary accelerating system and the alarm system based on the BP neural network, and comprises the following steps:
step one, establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are
opj＝fj(netpj)
Where p represents the current input sample, ωjiIs the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjIs the output thereof; f. ofjIs a non-linear, slightly non-decreasing function, generally taken as a sigmoid function, i.e. fj(x)＝1/(1+e-x)。
The BP network system structure adopted by the invention consists of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the system are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is a hidden layer, and has m nodes which are determined by the training process of the network in a self-adaptive mode; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
In the invention, the number of nodes of an input layer is n-6, the number of nodes of an output layer is p-3, and the number of nodes of a hidden layer is m-5.
The input layer 6 parameters are respectively expressed as: x is the number of1Is an environmental state, x2As the vehicle running speed, x3As intensity of vibration of the vehicle, x4Is the road surface gradient, x5Distance between vehicle and front vehicle, x6The distance between the vehicle and the rear vehicle;
wherein the vibration intensity of the vehicle is as follows:
in the formula, VrmsIs the vibration intensity of the vehicle, ViFor the measured vibration velocity value, N is the measured vibration signal sample length.
The output layer has 3 parameters expressed as: o1For assisting braking systemsOperating state of o2To assist the operating state of the acceleration system, o3For the working state of the alarm system, the neuron value of the output layer is
k is the output layer neuron sequence number, k is {1,2,3}, and when okWhen the number is 1, the corresponding system is in a working state, and when the number is okWhen the value is 0, the corresponding system is in a non-operating state.
And step two, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the hidden layer node j and a connection weight between the hidden layer node j and the output layer node k.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 1.
TABLE 1 output samples for network training
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
In the formula (I), the compound is shown in the specification,
for the weighted sum of the j unit information of the l layer at the nth calculation,
is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),
is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
And is
If neuron j belongs to the first hidden layer (l ═ 1), then there are
If neuron j belongs to the output layer (L ═ L), then there are
(b) And (3) calculating the error reversely:
for output unit
Pair hidden unit
(c) Correcting the weight value:
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of error to weight differentiation, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
When o is1When the acceleration is equal to 1, the auxiliary braking system works to control the vehicle to brake, and the acceleration is as follows:
when d issu-du≤dh-dshWhen the temperature of the water is higher than the set temperature,
when d issu-du＞dh-dshWhen the temperature of the water is higher than the set temperature,
wherein the content of the first and second substances,
in the formula, abAcceleration at the time of braking of the vehicle, duDistance to front vehicle when preparing for braking of vehicle, dsuA safe distance between the vehicle and the front vehicle, dhDistance to rear vehicle when preparing for braking of vehicle, dshIs the safe distance between the vehicle and the following vehicle, v is the vehicle speed when the vehicle is ready to brake, k is a parameter, n is the number of selected objects with equal probability,
is an environmental condition and
including the rain and the snow,
the value varies according to the size of rain and snowWhen the rain and the snow are heavy rain and heavy snow,
when the weather is clear,
g is the acceleration of gravity, e is the base number of the natural logarithm, σ is the wind power level and σ belongs to [0,7 ]]，dsFor visibility, d0Is a safe distance that should be maintained under the current vehicle condition environment.
When o is2When the vehicle speed is 1, the auxiliary acceleration system works to control the vehicle to accelerate, and the acceleration is as follows:
wherein the content of the first and second substances,
in the formula, aaAcceleration of the vehicle during acceleration, duDistance to front vehicle in preparation for acceleration of vehicle, dsuIs the safe distance between the vehicle and the front vehicle, v is the vehicle speed when the vehicle is ready to accelerate, k is a parameter, n is the number of selected objects with equal probability,
is a weather condition and
including the rain and the snow,
the value varies according to the size of rain and snow, and in heavy rain and heavy snow,
when the weather is clear,
g is the acceleration of gravity, e is the base number of the natural logarithm, σ is the wind power level and σ belongs to [0,7 ]]，dsFor visibility, d0Is a safe distance that should be maintained under the current vehicle condition environment.
When o is3When becoming 1, the system carries out adverse circumstances early warning, and the vehicle can't be automatic drive completely, reminds to carry out manual intervention.
The following describes the method for controlling the safety distance according to the present invention with reference to specific embodiments.
10 groups of different road conditions, vehicle conditions and weather conditions are simulated for testing, and specific test data are shown in table 2.
TABLE 2 test data
The control method of the safe distance provided by the invention is adopted for regulation and control, and the regulation result is shown in table 3.
TABLE 3 Regulation and control results
| Serial number | Brake system | Acceleration system | Alarm system | Acceleration of brake | Acceleration |
| 1 | Not working | Not working | Not working | / | / |
| 2 | Work by | Not working | Not working | 0.23 | / |
| 3 | Not working | Work by | Not working | / | 0.5 |
| 4 | Work by | Not working | Not working | 1.30 | / |
| 5 | Work by | Not working | Work by | 1.00 | / |
| 6 | Not working | Work by | Not working | / | 2.5 |
| 7 | Not working | Work by | Not working | / | 1.5 |
| 8 | Work by | Not working | Not working | 0.9 | / |
| 9 | Not working | Not working | Work by | / | / |
| 10 | Not working | Work by | Not working | / | 1.2 |
Through accelerating and braking the vehicle and adjusting for the vehicle can keep safe vehicle distance all the time, and vehicle distance around suitably adjusting avoids with preceding vehicle distance too big or the speed of a motor vehicle slow excessively, influences the normal travel of rear vehicle. The driving smoothness and safety of the vehicle are improved, and the driving safety and the sequence of all vehicles on the road are also improved. When the alarm system gives an alarm, the situation that the road condition is complex and the day is bad is shown, the vehicle cannot be regulated and controlled completely and independently, manual intervention is needed, and the driving safety is further improved.
The control method for the safe distance between the unmanned vehicles can acquire the environmental state, the road condition and the vehicle condition in the driving process of the vehicles, and determine the working states of the auxiliary braking system, the auxiliary accelerating system and the alarm system based on the BP neural network. The invention can also control the acceleration when the vehicle brakes and the acceleration degree when the vehicle accelerates, accurately adjust the distance between the vehicle and the front vehicle and the rear vehicle, and improve the driving safety.
While embodiments of the invention have been disclosed above, it is not limited to the applications listed in the description and the embodiments, which are fully applicable in all kinds of fields of application of the invention, and further modifications may readily be effected by those skilled in the art, so that the invention is not limited to the specific details without departing from the general concept defined by the claims and the scope of equivalents.
Claims (5)
1. A control method for a safe distance between unmanned vehicles is characterized in that in the driving process of the vehicles, environment states, road conditions and vehicle conditions are collected, and the working states of an auxiliary braking system, an auxiliary accelerating system and an alarm system are determined based on a BP neural network, and the method comprises the following steps:
firstly, measuring an environmental state, a vehicle running speed, a vehicle vibration intensity, a road surface gradient, a vehicle-to-front vehicle distance and a vehicle-to-rear vehicle distance through a sensor according to a sampling period;
step two, determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5,x6}; wherein x is1Is an environmental state, x2As the vehicle running speed, x3As intensity of vibration of the vehicle, x4Is the road surface gradient, x5Distance between vehicle and front vehicle, x6The distance between the vehicle and the rear vehicle;
mapping the neuron vectors of the input layer to hidden layers, wherein the number of the neurons of the hidden layers is m;
step four, obtaining an output layer neuron vector o ═ o1,o2,o3}; wherein o is1To assist the operating state of the brake system, o2To assist the operating state of the acceleration system, o3For the working state of the alarm system, the neuron value of the output layer is
k is the output layer neuron sequence number, k is {1,2,3}, and when okWhen the number is 1, the corresponding system is in a working state, and when the number is okWhen the value is 0, the corresponding system is in a non-operating state;
when o is1When the acceleration is equal to 1, the auxiliary braking system works to control the vehicle to brake, and the acceleration is as follows:
when d issu-du≤dh-dshWhen the temperature of the water is higher than the set temperature,
when d issu-du>dh-dshWhen the temperature of the water is higher than the set temperature,
wherein the content of the first and second substances,
in the formula, abAcceleration at the time of braking of the vehicle, duDistance to front vehicle when preparing for braking of vehicle, dsuA safe distance between the vehicle and the front vehicle, dhDistance to rear vehicle when preparing for braking of vehicle, dshIs the safe distance between the vehicle and the following vehicle, v is the vehicle speed when the vehicle is ready to brake, k is a parameter, n is the number of selected objects with equal probability,
is a weather condition and
g is the acceleration of gravity, e is the base number of the natural logarithm, σ is the wind power level and σ belongs to [0,7 ]]，dsFor visibility, d0Is a safe distance that should be maintained under the current vehicle condition environment.
2. The method of claim 1, wherein the time when the distance between the unmanned vehicles is zero is set2When the vehicle speed is 1, the auxiliary acceleration system works to control the vehicle to accelerate, and the acceleration is as follows:
wherein the content of the first and second substances,
in the formula, aaAcceleration of the vehicle during acceleration, duDistance to front vehicle in preparation for acceleration of vehicle, dsuIs the safe distance between the vehicle and the front vehicle, v is the vehicle speed when the vehicle is ready to accelerate, k is a parameter, n is the number of selected objects with equal probability,
is a weather condition and
g is the acceleration of gravity, e is the base number of the natural logarithm, σ is the wind power level and σ belongs to [0,7 ]]，dsFor visibility, d0Is a safe distance that should be maintained under the current vehicle condition environment.
3. The method of claim 1, wherein the time when the distance between the unmanned vehicles is zero is set3When the alarm is 1, the system carries out early warning on severe conditions and reminds manual intervention.
4. A method for controlling a safe distance between unmanned vehicles as claimed in any one of claims 1 to 3, wherein the vibration intensity of the vehicles is:
wherein, VrmsIs the vibration intensity of the vehicle, ViFor the measured vibration velocity value, N is the measured vibration signal sample length.
5. The method of claim 4, wherein the number of neurons in the hidden layer is 5; the excitation functions of the hidden layer and the output layer adopt S-shaped functions fj(x)＝1/(1+e-x)。
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| CN201911154339.6A CN110816531B (en) | 2019-11-22 | 2019-11-22 | Control system and control method for safe distance between unmanned automobile vehicles |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108688631A (en) * | 2018-06-19 | 2018-10-23 | 王佐欣 | A kind of automotive safety guard method and device |
| CN109213148A (en) * | 2018-08-03 | 2019-01-15 | 东南大学 | It is a kind of based on deeply study vehicle low speed with decision-making technique of speeding |
| CN109455178A (en) * | 2018-11-13 | 2019-03-12 | 吉林大学 | A kind of road vehicles traveling active control system and method based on binocular vision |
| CN109552289A (en) * | 2018-11-29 | 2019-04-02 | 辽宁工业大学 | A kind of automotive self-adaptive auxiliary braking system and its control method |
| CN109910865A (en) * | 2019-02-26 | 2019-06-21 | 辽宁工业大学 | A kind of vehicle early warning brake method based on Internet of Things |
| CN110154893A (en) * | 2019-05-29 | 2019-08-23 | 辽宁工业大学 | A kind of vehicle security drive method for early warning based on driver characteristics |
| JP2019159930A (en) * | 2018-03-14 | 2019-09-19 | オムロン株式会社 | Drive support device, drive support method, drive support program, operation control device, operation control method, and operation control program |
-
2019
- 2019-11-22 CN CN201911154339.6A patent/CN110816531B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019159930A (en) * | 2018-03-14 | 2019-09-19 | オムロン株式会社 | Drive support device, drive support method, drive support program, operation control device, operation control method, and operation control program |
| CN108688631A (en) * | 2018-06-19 | 2018-10-23 | 王佐欣 | A kind of automotive safety guard method and device |
| CN109213148A (en) * | 2018-08-03 | 2019-01-15 | 东南大学 | It is a kind of based on deeply study vehicle low speed with decision-making technique of speeding |
| CN109455178A (en) * | 2018-11-13 | 2019-03-12 | 吉林大学 | A kind of road vehicles traveling active control system and method based on binocular vision |
| CN109552289A (en) * | 2018-11-29 | 2019-04-02 | 辽宁工业大学 | A kind of automotive self-adaptive auxiliary braking system and its control method |
| CN109910865A (en) * | 2019-02-26 | 2019-06-21 | 辽宁工业大学 | A kind of vehicle early warning brake method based on Internet of Things |
| CN110154893A (en) * | 2019-05-29 | 2019-08-23 | 辽宁工业大学 | A kind of vehicle security drive method for early warning based on driver characteristics |
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