CN108545082B - A kind of automobile lane change method for early warning - Google Patents
A kind of automobile lane change method for early warning 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
- G01S13/08—Systems for measuring distance only
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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Abstract
The invention discloses a kind of automobile lane change method for early warning, comprising: Step 1: lane change prior-warning device is opened with turning indicator control；Step 2: sensor detects the speed of this vehicle, the speed of the first associated vehicle, the speed of the second associated vehicle and the distance between first associated vehicle and second associated vehicle；Step 3: the speed of this vehicle that microcontroller is detected according to sensor, the speed of the first associated vehicle, the speed of the second associated vehicle and the distance between first associated vehicle and second associated vehicle, judging whether can be with lane change；Step 4: when microcontroller judgement cannot lane change when, by communication device pass the signal along to car-mounted terminal to driver carry out early warning prompting.Automobile lane change method for early warning provided by the invention can sound an alarm driver, improve the safety of lane change when vehicle is by lane change but unsuitable lane change.
Description
Technical field
The invention belongs to automobile active safety early warning technology field, in particular to a kind of automobile lane change method for early warning.
Background technique
With the rapid development of economy, vehicle guaranteeding organic quantity is increasing sharply, road traffic accident is on the rise.Automobile belt
What it is to people is not only the convenience lived, and there are also security of the lives and property massive losses with caused by.So carrying out traffic safety
Research is very important.
In the past few decades, people mainly concentrate on the passive security in vehicle, such as installation insurance thick stick, peace
Full band, air bag, with the bring injury that cuts down traffic accidents.Although bumper can play a protective role when generating collision, not
The injury to object is hit, the especially protection of pedestrian can be reduced.Different surely give drives in an impact for safety belt, air bag
Member and the due protection of passenger cause more serious injury to passenger instead sometimes, and people increasingly pay attention to pedestrian now
Protection.These passive security measures not can solve vehicle and cause damages in an impact and the problem of to pedestrian protecting.
Wherein, part traffic accident is exactly as caused by automobile lane change.When automobile lane change, if in advance into lane
Vehicle distances are too small or speed is too fast, it is easy to which the traffic accidents such as collide or knock into the back.The prior art generally uses
The vehicle that backsight sem observation back row makes is possible to deposit the collimation error or driver's driving experience not due to visual angle etc.
Foot carries out lane change when being not suitable for vehicle lane change, so as to cause accident generation.
Therefore, a kind of active forewarning device in automobile lane change is designed, i.e. by lane change but when being not suitable for lane change, to driving
The person of sailing is reminded, the generation to avoid traffic accident.
Summary of the invention
The present invention provides a kind of automobile lane change method for early warning, goal of the invention of the invention first is that can be in automobile discomfort
It effectively alarms when closing lane change.
The present invention provides a kind of automobile lane change method for early warning, goal of the invention of the invention second is that by based on BP nerve
Network is monitored and then keeps automobile lane change early warning more accurate.
Technical solution provided by the invention are as follows:
A kind of automobile lane change method for early warning, comprising:
Step 1: lane change prior-warning device is opened with turning indicator control；
Step 2: sensor detect the speed of this vehicle, the speed of the first associated vehicle, the speed of the second associated vehicle and
The distance between first associated vehicle and second associated vehicle；
Step 3: speed, the speed of the first associated vehicle, the second phase of this vehicle that microcontroller is detected according to sensor
The speed and the distance between first associated vehicle and second associated vehicle cut-off, judge whether to become
Road；
Step 4: when microcontroller judgement cannot lane change when, car-mounted terminal pair is passed the signal along to by communication device
Driver carries out early warning prompting.
Preferably, in the step 3, use BP neural network to whether can judging with lane change, including as follows
Step:
Step 1, according to the sampling period, pass through this vehicle speed of sensor measurement V0, the first associated vehicle speed V1, the second phase
Close car speed V2, the distance between the first associated vehicle and the second associated vehicle L, microcontroller determine the first associated vehicle with
The difference DELTA L of the distance between second associated vehicle and gauged distance；
Step 2 successively standardizes parameter, determines the input layer vector x={ x of three layers of BP neural network1,x2,x3,
x4}；Wherein, x1For this vehicle speed coefficient, x2For the first associated vehicle velocity coeffficient, x3For for the second associated vehicle velocity coeffficient,
x4For the difference coefficient of the distance between the first associated vehicle and the second associated vehicle and gauged distance；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；During m is
Interbed node number；
Step 4 obtains output layer vector o={ o1,o2}；o1For automobile lane change coefficient, o2It is described for emergency-stop signal
Output layer neuron value isK is output layer neuron sequence number, k={ 1,2 }；Wherein, work as o1When being 1, automobile can not
With lane change, work as o1When being 0, automobile can be with lane change；Work as o2When being 1, prior-warning device is working properly, works as o2When being 0, prior-warning device work
Make exception, stops working.
Preferably, in the step 2, by this vehicle speed V0, the first associated vehicle speed V1, the second associated vehicle speed
Spend V2And first the distance between associated vehicle and the second associated vehicle and gauged distance difference DELTA L carry out it is normalized
Formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter V0、V1、V2, Δ L, j=1,2,3,4；
Xj maxAnd Xj minMaximum value and minimum value in respectively corresponding measurement parameter.
Preferably, the middle layer node number m meets:Wherein, n is input layer
Number, p are output layer node number；And
The excitation function of the middle layer and the output layer is all made of S type function fj(x)=1/ (1+e-x)。
Preferably, the gauged distance is 5m.
Preferably, the automobile lane change method for early warning further includes acquiring outside temperature by infrared sensor.
Preferably, the infrared sensor is mounted on above the front-wheel of automobile two sides.
Preferably, the gauged distance is corrected according to outside temperature and vehicle model information, the normal pitch after correction
From are as follows:
Wherein, L0For the gauged distance of setting, LcFor length of wagon,For the vehicle body length of the automobile with the same vehicle of this vehicle
Median is spent, W is body width,For the body width median of the automobile with the same vehicle of this vehicle, T is outside temperature, T0
For the normal temperature of setting, e is the truth of a matter of natural logrithm.
The beneficial effects of the present invention are:
Automobile lane change method for early warning provided by the invention, can be when vehicle be by lane change but unsuitable lane change, to driving
Member sounds an alarm.
Automobile lane change method for early warning provided by the invention, this vehicle speed that sensor is acquired in real time, the first associated vehicle
Speed, the difference of the second associated vehicle speed and the distance between the first associated vehicle and the second associated vehicle and gauged distance
Value, is accurately judged using BP neural network algorithm, and when judging to be not suitable for lane change, prior-warning device carries out being not suitable for lane change police
Report, reminds driver, improves the safety of lane change.
Specific embodiment
The present invention is described in further detail below, to enable those skilled in the art's refer to the instruction text being capable of evidence
To implement.
It, can be by the automobile lane change early warning that is mounted on automobile the present invention provides a kind of automobile lane change method for early warning
Device reminds driver when automobile is by lane change but unsuitable lane change.The automobile lane change prior-warning device includes: vehicle
Mounted terminal is arranged in car steering room, and the car-mounted terminal has sound prompt function；The car-mounted terminal utilizes car
Power supply power supply.Microprocessor is arranged in the car-mounted terminal；Radar is mounted on automobile, for monitoring this vehicle speed
And first associated vehicle speed and the second associated vehicle speed；Distance measuring sensor is mounted on automobile, for monitoring the first phase
Close the distance between car speed and the second associated vehicle.Wherein, first associated vehicle and the second associated vehicle are automobile
It is pre- into lane with this vehicle apart from nearest two automobiles, and first associated vehicle is located at second associated vehicle
Front.In another embodiment, further include temperature monitoring module, use infrared sensor, the infrared sensor
It is fixed above the wheel of vehicle right and left two sides, for detecting the outside temperature near wheel；The sensor module difference
Collected information is transmitted into the microprocessor by wireless communication module.
The automobile lane change method for early warning includes the following steps:
Step 1: lane change prior-warning device is opened with turning indicator control, when general automobile lane change, driver can open first to be turned
To lamp, therefore, the switch of lane change prior-warning device is set as linking with turning indicator control.
Step 2: sensor starts, the speed of this vehicle, the speed of the first associated vehicle, the speed of the second associated vehicle are detected
Degree and the distance between first associated vehicle and second associated vehicle；Infrared sensor detects near wheel
Outside temperature；Collected information is transferred to microprocessor by sensor.
Step 3: speed, the speed of the first associated vehicle, the second phase of this vehicle that microcontroller is detected according to sensor
Between the speed cut-off, and first associated vehicle and second associated vehicle that are calculated by microprocessor
The difference of distance and gauged distance, judging whether can be with lane change；The gauged distance is the distance set according to experience.
In the present embodiment, the gauged distance is 5m.At this point, Δ L=L-L0, wherein L is the first of sensor measurement
The distance between associated vehicle and the second associated vehicle, L0For the gauged distance of setting.
In another embodiment, the gauged distance is corrected according to outside temperature and vehicle model information, after correction
Gauged distance are as follows:
Wherein, L0For the gauged distance of setting, unit m；LcFor length of wagon, unit m；For with the same vehicle of this vehicle
The length of wagon median of automobile, unit m；W is body width, unit m；For the vehicle body of the automobile with the same vehicle of this vehicle
Width median, unit m；T is outside temperature, DEG C；T0For the normal temperature of setting, unit DEG C, e is the truth of a matter of natural logrithm；T0
Take 25.At this point, Δ L=L-L '0。
Judging whether can be with lane change method particularly includes: using BP neural network to whether can safe lane change sentence
It is disconnected, include the following steps:
Step 1 establishes BP neural network model.
For the BP network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding
Indicate n monitoring signals of equipment working state, these signal parameters are provided by data preprocessing module.The second layer is hidden layer,
Total m node is determined in an adaptive way by the training process of network.Third layer is output layer, total p node, by system
Actual needs output in response to determining that.
The mathematical model of the network are as follows:
Input vector: x=(x1,x2,...,xn)T
Middle layer vector: y=(y1,y2,...,ym)T
Output vector: O=(o1,o2,...,op)T
In the present invention, input layer number is n=4, and output layer number of nodes is p=2.Hidden layer number of nodes m is estimated by following formula
It obtains:
4 parameters of input signal respectively indicate are as follows: x1For this vehicle speed coefficient, x2For the first associated vehicle velocity coeffficient, x3
For the second associated vehicle velocity coeffficient, x4For the difference of the distance between the first associated vehicle and the second associated vehicle and gauged distance
Value coefficient.
Since the data that sensor obtains belong to different physical quantitys, dimension is different.Therefore, people is inputted in data
Before artificial neural networks, need to turn to data requirement into the number between 0-1.
Specifically, for this vehicle speed V0, after being standardized, obtain this vehicle coefficient x1:
Wherein, V0-minAnd V0-maxThe respectively minimum speed and maximum speed of this vehicle.
Likewise, for the first associated vehicle speed V1, after being standardized, obtain the first associated vehicle velocity coeffficient x2:
Wherein, V1-minAnd V1-maxThe minimum speed and maximum speed of respectively the first associated vehicle.
Likewise, for the second associated vehicle speed V2, after being standardized, obtain the second associated vehicle velocity coeffficient x3:
Wherein, V2-minAnd V2-maxThe minimum speed and maximum speed of respectively the second associated vehicle.
Likewise, for the difference DELTA L of the distance between the first associated vehicle and the second associated vehicle and gauged distance, into
After professional etiquette is formatted, the difference coefficient x of the distance between the first associated vehicle and the second associated vehicle with gauged distance is obtained4:
Wherein, Δ LminWith Δ LmaxRespectively the distance between the first associated vehicle and the second associated vehicle and gauged distance
Minimal difference and maximum difference.
2 parameters of output signal respectively indicate are as follows: output layer vector o={ o1,o2}；o1For automobile lane change coefficient, o2For
Emergency-stop signal, the output layer neuron value areK is output layer neuron sequence number, k={ 1,2 }；Wherein,
Work as o1When being 1, automobile cannot lane change, work as o1When being 0, automobile can be with lane change；Work as o2When being 1, prior-warning device is working properly, when
o2When being 0, prior-warning device operation irregularity stops working.
Step 2, the training for carrying out BP neural network.
After establishing BP neural network nodal analysis method, the training of BP neural network can be carried out.It is passed through according to the history of product
Test the sample of data acquisition training, and the connection weight w between given input node i and hidden layer node jij, hidden node j and
Export the connection weight w between node layer kjk, the threshold θ of hidden node jj, export the threshold θ of node layer kk、wij、wjk、θj、θk
It is the random number between -1 to 1.
In the training process, w is constantly correctedijAnd wjkValue, until systematic error be less than or equal to anticipation error when, complete
The training process of neural network.
(1) training method
Each subnet is using individually trained method；When training, first have to provide one group of training sample, each of these sample
This, to forming, when all reality outputs of network and its consistent ideal output, is shown to train by input sample and ideal output
Terminate；Otherwise, by correcting weight, keep the ideal output of network consistent with reality output；
(2) training algorithm
BP network is trained using error back propagation (Backward Propagation) algorithm, and step can be concluded
It is as follows:
Step 1: a selected structurally reasonable network, is arranged the initial value of all Node B thresholds and connection weight.
Step 2: making following calculate to each input sample:
(a) forward calculation: to l layers of j unit
In formula,L layers of j unit information weighted sum when being calculated for n-th,For l layers of j units with it is previous
Connection weight between the unit i of layer (i.e. l-1 layers),For preceding layer (i.e. l-1 layers, number of nodes nl-1) unit i send
Working signal；When i=0, enable For the threshold value of l layers of j unit.
If the activation primitive of unit j is sigmoid function,
And
If neuron j belongs to the first hidden layer (l=1), have
If neuron j belongs to output layer (l=L), have
And ej(n)=xj(n)-oj(n)；
(b) retrospectively calculate error:
For output unit
To hidden unit
(c) weight is corrected:
η is learning rate.
Step 3: new sample or a new periodic samples are inputted, and until network convergence, the sample in each period in training
Input sequence is again randomly ordered.
BP algorithm seeks nonlinear function extreme value using gradient descent method, exists and falls into local minimum and convergence rate is slow etc.
Problem.A kind of more efficiently algorithm is Levenberg-Marquardt optimization algorithm, it makes the e-learning time shorter,
Network can be effectively inhibited and sink into local minimum.Its weighed value adjusting rate is selected as
Δ ω=(JTJ+μI)-1JTe；
Wherein, J is error to Jacobi (Jacobian) matrix of weight differential, and I is input vector, and e is error vector,
Variable μ is the scalar adaptively adjusted, for determining that study is completed according to Newton method or gradient method.
In system design, system model is one merely through the network being initialized, and weight needs basis using
The data sample obtained in journey carries out study adjustment, devises the self-learning function of system thus.Specify learning sample and
In the case where quantity, system can carry out self study, to constantly improve network performance；
As shown in table 1, given the value of each node in one group of training sample and training process.
Each nodal value of 1 training process of table
Step 3, acquisition sensor operating parameter input neural network obtain automobile lane change coefficient and emergent stopping early warning dress
It sets.
Trained artificial neural network is solidificated among chip, hardware circuit is made to have prediction and intelligent decision function
Can, to form Intelligent hardware.
The initial of BP neural network is obtained by the way that above-mentioned parameter is standardized using the collected parameter of sensor simultaneously
Input vectorInitial output vector is obtained by the operation of BP neural network
Step 4, the automobile lane change situation of monitoring warning device are to carry out prior-warning device emergency shutdown.
According to output layer neuron value o={ o1,o2}；o1For automobile lane change coefficient, o2It is described defeated for emergency-stop signal
Layer neuron value is outK is output layer neuron sequence number, k={ 1,2 }；Wherein, work as o1When being 1, automobile can not
With lane change, work as o1When being 0, automobile can be with lane change；Work as o2When being 1, prior-warning device is working properly, works as o2When being 0, prior-warning device work
Make exception, stops working.
By above-mentioned setting, this vehicle speed V acquired in real time by sensor0, the first associated vehicle speed V1, the second phase
Close car speed V2And the first difference DELTA L with gauged distance of the distance between associated vehicle and the second associated vehicle, it uses
BP neural network algorithm monitors the alarm condition of prior-warning device in real time.When judging to be not suitable for lane change, microcontroller control
Voice module in car-mounted terminal processed, which issues, is not suitable for lane change alarm, reminds driver；When prior-warning device exception, tightly
Emergency stop machine.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (7)
1. a kind of automobile lane change method for early warning, which comprises the steps of:
Step 1: lane change prior-warning device is opened with turning indicator control；
Step 2: sensor detects the speed of this vehicle, the speed of the first associated vehicle, the speed of the second associated vehicle and described
The distance between first associated vehicle and second associated vehicle；
Step 3: the speed of this vehicle that microcontroller is detected according to sensor, the speed of the first associated vehicle, the second phase are cut-off
Speed and the distance between first associated vehicle and second associated vehicle, judging whether can be with lane change；
Step 4: when microcontroller judgement cannot lane change when, car-mounted terminal is passed the signal along to driving by communication device
Member carries out early warning prompting；
Wherein, in the step 3, BP neural network is used to include the following steps: to whether can judging with lane change
Step 1, according to the sampling period, pass through this vehicle speed of sensor measurement V0, the first associated vehicle speed V1, the second phase cut-offs
Speed V2, the distance between the first associated vehicle and the second associated vehicle L, microcontroller determines the first associated vehicle and second
The difference DELTA L of the distance between associated vehicle and gauged distance；
Step 2 successively standardizes parameter, determines the input layer vector x={ x of three layers of BP neural network1,x2,x3,x4}；
Wherein, x1For this vehicle speed coefficient, x2For the first associated vehicle velocity coeffficient, x3For for the second associated vehicle velocity coeffficient, x4For
The difference coefficient of the distance between first associated vehicle and the second associated vehicle and gauged distance；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；M is middle layer
Node number；
Step 4 obtains output layer vector o={ o1,o2}；o1For automobile lane change coefficient, o2For emergency-stop signal, the output layer
Neuron value isK is output layer neuron sequence number, k={ 1,2 }；Wherein, work as o1When being 1, automobile cannot become
O is worked as in road1When being 0, automobile can be with lane change；Work as o2When being 1, prior-warning device is working properly, works as o2When being 0, prior-warning device work is different
Often, it stops working.
2. automobile lane change method for early warning according to claim 1, which is characterized in that in the step 2, by this vehicle speed
V0, the first associated vehicle speed V1, the second associated vehicle speed V2And first between associated vehicle and the second associated vehicle
Distance and the difference DELTA L of gauged distance carry out normalized formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter V0、V1、V2, Δ L, j=1,2,3,4；XjmaxWith
XjminMaximum value and minimum value in respectively corresponding measurement parameter.
3. automobile lane change method for early warning according to claim 2, which is characterized in that the middle layer node number m meets:Wherein, n is input layer number, and p is output layer node number；And
The excitation function of the middle layer and the output layer is all made of S type function fj(x)=1/ (1+e-x)。
4. automobile lane change method for early warning according to claim 1 or 3, which is characterized in that the gauged distance is 5m.
5. automobile lane change method for early warning according to claim 4, which is characterized in that further include being adopted by infrared sensor
Collect outside temperature.
6. automobile lane change method for early warning according to claim 5, which is characterized in that the infrared sensor is mounted on vapour
Above the front-wheel of vehicle two sides.
7. automobile lane change method for early warning according to claim 6, which is characterized in that according to outside temperature and vehicle model information pair
The gauged distance is corrected, the gauged distance after correction are as follows:
Wherein, L0For the gauged distance of setting, LcFor length of wagon,In length of wagon for the automobile with the same vehicle of this vehicle
Between be worth, W is body width,For the body width median of the automobile with the same vehicle of this vehicle, T is outside temperature, T0To set
Fixed normal temperature, e are the truth of a matter of natural logrithm.
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| CN109455178B (en) * | 2018-11-13 | 2023-11-17 | 吉林大学 | Road traffic vehicle driving active control system and method based on binocular vision |
| CN109552289B (en) * | 2018-11-29 | 2020-06-02 | 辽宁工业大学 | Automobile self-adaptive auxiliary braking system and control method thereof |
| CN109584630B (en) * | 2018-12-13 | 2020-12-22 | 辽宁工业大学 | Vehicle lane change early warning method based on Internet of vehicles |
| CN109552245A (en) * | 2018-12-28 | 2019-04-02 | 辽宁工业大学 | A kind of vehicle remote anti-theft monitoring system and method based on electronic digitalizing |
| CN109785628A (en) * | 2019-02-27 | 2019-05-21 | 辽宁工业大学 | Road conditions alarm system and alarm method based on car networking communication |
| CN110111573B (en) * | 2019-05-15 | 2020-09-08 | 辽宁工业大学 | Congestion vehicle comprehensive scheduling method based on Internet of things |
| CN110816534B (en) * | 2019-11-27 | 2020-10-20 | 安徽江淮汽车集团股份有限公司 | Vehicle lane change early warning method, device, storage medium and device |
| CN114379555A (en) * | 2020-10-22 | 2022-04-22 | 奥迪股份公司 | Vehicle lane change control method, device, equipment and storage medium |
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| US9475491B1 (en) * | 2015-06-08 | 2016-10-25 | Toyota Motor Engineering & Manufacturing North America, Inc. | Lane changing for autonomous vehicles |
| KR102004818B1 (en) * | 2015-10-29 | 2019-10-01 | 자동차부품연구원 | Device For Warning Driver Of Vehicle Against Danger Of Overtaking |
| CN106114520A (en) * | 2016-06-30 | 2016-11-16 | 上海卓易科技股份有限公司 | A kind of vehicle drive lane change alert system and method |
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| CN106740457A (en) * | 2016-12-07 | 2017-05-31 | 镇江市高等专科学校 | Vehicle lane-changing decision-making technique based on BP neural network model |
| CN107134173B (en) * | 2017-06-15 | 2023-04-18 | 长安大学 | Lane changing early warning system and method with driving habit recognition function |
| CN107757465A (en) * | 2017-10-18 | 2018-03-06 | 南京双环电器股份有限公司 | A kind of vehicle high-speed lane change aids in intelligent reminding system and method |
| CN107933551B (en) * | 2017-11-27 | 2019-11-26 | 长安大学 | A kind of intelligence fleet lane-change method |
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