CN114312770B - Vehicle, vehicle running track prediction method and device - Google Patents
Vehicle, vehicle running track prediction method and device Download PDFInfo
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- CN114312770B CN114312770B CN202011073813.5A CN202011073813A CN114312770B CN 114312770 B CN114312770 B CN 114312770B CN 202011073813 A CN202011073813 A CN 202011073813A CN 114312770 B CN114312770 B CN 114312770B
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Abstract
The invention provides a vehicle, a vehicle running track prediction method and a vehicle running track prediction device, and belongs to the field of intelligent vehicles. The method comprises the following steps: acquiring a planned running track of a vehicle as a reference track; calculating the coordinate of a predicted track point corresponding to each predicted period from the predicted starting time, selecting a track point on a reference track as a pre-aiming point of the current predicted period, and calculating a target steering wheel corner required by the vehicle to travel from the starting point of the current predicted period to the pre-aiming point; combining the target steering wheel angle, the vehicle wheelbase, the steering wheel stability factor, the vehicle body speed at the predicted starting moment and the starting point information of the current prediction period to obtain the predicted track point information corresponding to the current prediction period; and selecting a preset point until a set predicted time length is reached or no track point exists on the reference track, and obtaining a predicted running track according to the obtained coordinates of all the predicted track points. The predicted vehicle running track is more consistent with the actual running track of the vehicle.
Description
Technical Field
The invention relates to a vehicle, a vehicle running track prediction method and a vehicle running track prediction device, and belongs to the technical field of intelligent vehicles.
Background
The vehicle driving track planning has important significance for improving the driving safety of the vehicle. When the existing method is used for planning the running track, the running track of the vehicle is planned by combining the vehicle position information, the vehicle motion information and the obstacle motion information around the vehicle, and then the vehicle is directly controlled according to the planned running track of the vehicle, so that the vehicle runs along the planned running track.
However, in the actual control process of the vehicle, it is found that the control target that the vehicle runs along the planned running track is difficult to achieve due to the existence of the error of the vehicle system, that is, it is difficult to control the vehicle so that the actual running track of the vehicle is consistent with the planned running track, even a situation that a large deviation exists between the actual running track of the vehicle and the planned running track occurs, so that if the road traffic capacity of the vehicle is still estimated and collision detection is performed directly according to the running track planned by the existing method, a certain potential safety hazard is needed.
Disclosure of Invention
The invention aims to provide a vehicle, a vehicle running track prediction method and a vehicle running track prediction device, which are used for solving the problem that the actual running track of the vehicle and the planned running track have larger deviation when the running track planned by the existing method is directly used for controlling the vehicle.
In order to achieve the above object, the present invention provides a vehicle travel track prediction method, comprising the steps of:
acquiring a planned running track of a vehicle as a reference track;
acquiring vehicle state information of a predicted starting moment, wherein the vehicle state information comprises a vehicle body speed, a vehicle body course angle, a steering wheel corner and vehicle coordinates;
calculating the coordinates of the predicted track points corresponding to each predicted period from the predicted starting time until the set predicted time length is reached or no track point on the reference track can be selected as a pre-aiming point, and obtaining a predicted running track according to the obtained coordinates of all the predicted track points; taking predicted track point information corresponding to the previous predicted period as starting point information of the current predicted period, wherein the predicted track point information comprises coordinates of predicted track points, a car body course angle and a steering wheel corner at the predicted track points, and the starting point information of the first predicted period is vehicle state information at the predicted starting moment;
the predicted track point information corresponding to the current predicted period is obtained through the following steps:
selecting a track point from the reference track as a pre-aiming point of the current prediction period, and calculating a target steering wheel corner required by the vehicle to travel from the starting point of the current prediction period to the pre-aiming point of the current prediction period;
and combining the target steering wheel angle, the vehicle wheelbase, the steering wheel stability factor, the vehicle body speed at the predicted starting moment and the starting point information of the current prediction period to obtain the predicted track point information corresponding to the current prediction period.
The invention also provides a vehicle running track prediction device, which comprises a processor and a memory, wherein the processor executes a computer program stored by the memory so as to realize the vehicle running track prediction method.
The invention also provides a vehicle, which comprises a vehicle body and a vehicle running track prediction device, wherein the vehicle running track prediction device comprises a processor and a memory, and the processor executes a computer program stored by the memory so as to realize the vehicle running track prediction method.
The invention has the beneficial effects that: when track prediction is carried out, the influence of time lag factors of steering wheel response on the vehicle running track is fully considered by introducing the steering wheel stability factors, the existing planned vehicle running track is corrected based on the influence, the more refined vehicle running track is predicted, and the predicted vehicle running track is more matched with the actual running track of the vehicle, so that the road traffic capacity of the vehicle is estimated by utilizing the vehicle running track predicted by the invention, the running safety of the vehicle can be improved, and meanwhile, the collision detection is carried out by utilizing the vehicle running track predicted by the invention, and the accuracy of the collision detection can be improved. In addition, the invention can be simultaneously applied to structured roads and unstructured roads such as straight roads, curved roads, intersections and the like without distinguishing treatment.
Further, in the vehicle and the vehicle driving track prediction method and device, the process of obtaining the predicted track point information corresponding to the current prediction period includes:
obtaining an actual steering wheel steering angle response increment of a current prediction period according to the target steering wheel steering angle, the vehicle wheelbase, the steering wheel stability factor and the vehicle body speed at the prediction starting moment, and further obtaining the steering wheel steering angle at a prediction track point corresponding to the current prediction period by combining the actual steering wheel steering angle response increment and the steering wheel steering angle at the starting point of the current prediction period;
calculating to obtain the actual yaw rate of the current prediction period by utilizing the steering wheel angle at the prediction track point corresponding to the current prediction period and the relation between the yaw rate and the steering wheel angle;
calculating to obtain a course angle deflection of the current prediction period by using the actual yaw rate, and further obtaining a car body course angle at a prediction track corresponding to the current prediction period by combining the course angle deflection and the car body course angle at the starting point of the current prediction period;
and calculating to obtain a transverse displacement amount and a longitudinal displacement amount corresponding to the current prediction period by utilizing the car body course angle at the prediction track corresponding to the current prediction period and the car body speed at the prediction starting moment, and further obtaining a prediction track point coordinate corresponding to the current prediction period by combining the coordinate of the starting point of the current prediction period and the transverse displacement amount and the longitudinal displacement amount corresponding to the current prediction period.
Further, in the vehicle and the vehicle running track prediction method and device, the actual steering wheel steering angle response increment of the current prediction period is calculated by using a steering angle response increment calculation formula, and the steering angle response increment calculation formula is as follows:
in the formula, delta theta steer Representing the actual steering wheel angle response increment of the current prediction period, v representing the vehicle body speed at the prediction starting moment, L representing the vehicle wheelbase, K representing the steering wheel stability factor, theta steer_target Represents the target steering wheel angle required by the vehicle to travel from the starting point of the current prediction period to the pre-aiming point of the current prediction period, and dt represents the duration of the prediction period.
Drawings
FIG. 1 is a flow chart of a method for predicting a vehicle travel track in an embodiment of the method of the present invention;
FIG. 2 is a graph comparing the predicted effects of the vehicle travel path in an embodiment of the method of the present invention;
fig. 3 is a schematic view of a vehicle travel track prediction apparatus in an embodiment of the apparatus of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Method embodiment:
as shown in fig. 1, the vehicle travel track prediction method of the present embodiment includes the steps of:
step 1, acquiring a planned running track of a vehicle as a reference track;
the planned running track is obtained by using the existing method, for example, a track prediction method disclosed in the invention patent application document with the application publication number of CN110020748A is used for obtaining the planned running track; and uses the planned running track with X= { X 0 ,X 1 ,X 2 ,…,X N N represents the total number of track points on the planned running track, X i ＝(x i ,y i ,v i ) Information indicating the ith trace point, x i Representing the abscissa, y i Representing the ordinate, v i Representing the planning speed.
Step 2, acquiring vehicle state information of a predicted starting time, wherein the vehicle state information comprises a vehicle body speed v start (m/s) vehicle body heading angle A start Steering wheel angle theta start And vehicle coordinates (x start ,y start )；
Step 3, calculating the coordinates of the predicted track points corresponding to each predicted period from the predicted starting time until the set predicted time length is reached or no track points on the reference track can be selected as pre-aiming points (namely the reference track is finished), and obtaining the predicted running track according to the obtained coordinates of all the predicted track points;
in this embodiment, the set prediction duration refers to the time for simulating the running of the vehicle according to the method of this embodiment, specifically, the set prediction duration is set according to actual needs, and the vehicle speed in the whole prediction duration is kept unchanged and is always the vehicle body speed at the prediction starting time; the predicted time length comprises a plurality of predicted periods, and the predicted time length is consistent with the time length of the actual control period of the intelligent vehicle, for example, 0.1s.
Taking the predicted track point information corresponding to the previous predicted period as the starting point information of the current predicted period, wherein the predicted track point information comprises coordinates of predicted track points, and a car body course angle and a steering wheel corner at the predicted track points, namely taking the predicted track point corresponding to the previous predicted period as the starting point of the current predicted period, so that the information of the predicted track point of the previous predicted period is the information of the starting point of the current predicted period; the start point information of the first prediction period is vehicle state information at the prediction start time.
The predicted track point information corresponding to the previous prediction period is respectively expressed as follows: predicting coordinates of a trajectory point (x last 、y last ) Vehicle body heading angle A at predicted track point last Steering wheel angle theta steer_last The method comprises the steps of carrying out a first treatment on the surface of the The starting point information of the current prediction period is respectively expressed as: coordinates of the starting point (x curr ,y curr ) Heading angle A of vehicle body at starting point curr Steering wheel angle theta sterr_curr The method comprises the steps of carrying out a first treatment on the surface of the Taking the predicted track point information corresponding to the previous predicted period as the starting point information of the current predicted period, namely:
the following describes in detail a specific process of calculating predicted track point information corresponding to a current predicted period:
(1) Selecting a track point on the reference track as a pre-aiming point of the current prediction period, and calculating a target steering wheel corner (hereinafter referred to as the target steering wheel corner of the current prediction period) required by the vehicle to travel from the starting point of the current prediction period to the pre-aiming point of the current prediction period;
specifically, determining a pretightening distance according to v×t, and searching a pretightening point of the current prediction period on a reference track by combining a starting point coordinate of the current prediction period and the pretightening distance, wherein v is the speed of the vehicle body at the time of the prediction starting, t is the preset pretightening time, and the preset pretightening time is set according to actual needs, for example, 2s; after the pre-aiming point is selected, calculating the optimal curvature by using a pre-aiming control method, and calculating the corresponding target steering wheel angle according to the Ackerman steering principle, wherein the calculation methods of the optimal curvature and the target steering wheel angle are both the prior art, and are not repeated here.
(2) According to the target steering wheel turning angle, the vehicle wheelbase, the steering wheel stability factor and the vehicle body speed at the predicted starting moment of the current prediction period, calculating by using a turning angle response increment calculation formula to obtain the actual steering wheel turning angle response increment delta theta of the current prediction period steer ：
In the formula, delta theta steer Representing the actual steering wheel angle response increment of the current prediction period, v representing the vehicle body speed at the prediction starting moment, L representing the vehicle wheelbase, K representing the steering wheel stability factor, theta steer_target The target steering wheel angle, dt, representing the current predicted period, represents the duration of the predicted period.
The steering wheel stability factor K is related to the types of vehicles, the actual response conditions of the steering wheels of the vehicles of various types can be obtained by testing the vehicles of different types, and then the steering wheel stability factors of the vehicles of various types are obtained, specifically, K is greater than 1 and indicates steering overshoot, and K is smaller than 1 and indicates understeer. It is easy to understand that, when the method of the embodiment is used for predicting the running track of the vehicle, as long as the model of the vehicle participating in the running track prediction is determined, the value of the steering wheel stability factor is correspondingly determined.
(3) Actual steering wheel angle response delta theta in combination with current predicted period steer And steering wheel angle θ at the start of the current prediction period sterr_curr (i.e., steering wheel angle θ at the predicted trajectory point corresponding to the last predicted period) steer_last ) Obtaining the steering wheel angle theta at the predicted track point corresponding to the current predicted period steer_act ，θ steer_act ＝θ steer_curr +Δθ steer ；
(4) Steering wheel angle theta at predicted trajectory point corresponding to current prediction period steer_act And the relation between the yaw rate and the steering wheel angle (the relation is the prior art), and calculating to obtain the actual yaw rate omega of the current prediction period;
(5) Actual yaw rate ω using current prediction period, body heading angle a at starting point of current prediction period curr Coordinates of the starting point of the current prediction period (x curr ,y curr ) Calculating a predicted track point coordinate (x, y) corresponding to the current predicted period;
in this embodiment, the coordinates (x, y) of the predicted track point corresponding to the current predicted period are calculated by using the vehicle kinematic model, and specifically are as follows:
wherein ω x dt is the course angle deflection of the current prediction period, A is the car body course angle at the prediction track corresponding to the current prediction period, and v is the current prediction periodThe vehicle body speed at the starting point, dt is the duration of the prediction period, x 0 、y 0 Respectively a transverse displacement amount and a longitudinal displacement amount corresponding to the current prediction period; .
As other embodiments, to improve the accuracy of track prediction, other vehicle dynamics models with higher degrees of freedom may be used to calculate the heading angle a of the vehicle body at the predicted track corresponding to the current prediction period and the lateral displacement x corresponding to the current prediction period 0 And a longitudinal displacement y 0 。
As shown in fig. 2, when the vehicle travels to the a position, the planned travel track is used as a reference track, and the predicted travel track obtained by using the method of the embodiment and the actual travel track of the vehicle are respectively shown in fig. 2, it can be seen that, compared with the planned travel track, the predicted travel track obtained by using the method of the embodiment is more identical to the actual travel track of the vehicle, and the error is smaller, so that the road traffic capacity of the vehicle is estimated by using the vehicle travel track predicted by the embodiment, the travel safety of the vehicle can be improved, and meanwhile, the collision detection is performed by using the vehicle travel track predicted by the embodiment, and the accuracy of the collision detection can be improved.
Device example:
as shown in fig. 3, the vehicle travel track prediction apparatus of the present embodiment includes a processor and a memory in which a computer program executable on the processor is stored, the processor implementing the method in the above-described method embodiment when executing the computer program.
That is, the method in the above method embodiment should be understood as a flow of the vehicle travel track prediction method that can be implemented by computer program instructions. These computer program instructions may be provided to a processor such that execution of the instructions by the processor results in the implementation of the functions specified in the method flow described above.
The processor referred to in this embodiment refers to a processing device such as a microprocessor MCU or a programmable logic device FPGA.
The memory referred to in this embodiment includes physical means for storing information, typically by digitizing the information and then storing the information in an electrical, magnetic, or optical medium. For example: various memories, RAM, ROM and the like for storing information by utilizing an electric energy mode; various memories for storing information by utilizing a magnetic energy mode, such as a hard disk, a floppy disk, a magnetic tape, a magnetic core memory, a bubble memory and a U disk; various memories, CDs or DVDs, which store information optically. Of course, there are other ways of storing, such as quantum storing, graphene storing, etc.
The device formed by the memory, the processor and the computer program is implemented in the computer by executing corresponding program instructions by the processor, and the processor can be loaded with various operating systems, such as windows operating systems, linux systems, android, iOS systems and the like.
Vehicle embodiment:
the vehicle of the embodiment includes a vehicle body and a vehicle travel track prediction device, and the vehicle travel track prediction device has been described in detail in the device embodiment, and will not be described here again.
Claims (5)
1. A vehicle travel track prediction method, characterized in that the method comprises the steps of:
acquiring a planned running track of a vehicle as a reference track;
acquiring vehicle state information of a predicted starting moment, wherein the vehicle state information comprises a vehicle body speed, a vehicle body course angle, a steering wheel corner and vehicle coordinates;
calculating the coordinates of the predicted track points corresponding to each predicted period from the predicted starting time until the set predicted time length is reached or no track point on the reference track can be selected as a pre-aiming point, and obtaining a predicted running track according to the obtained coordinates of all the predicted track points; taking predicted track point information corresponding to the previous predicted period as starting point information of the current predicted period, wherein the predicted track point information comprises coordinates of predicted track points, a car body course angle and a steering wheel corner at the predicted track points, and the starting point information of the first predicted period is vehicle state information at the predicted starting moment;
the predicted track point information corresponding to the current predicted period is obtained through the following steps:
selecting a track point from the reference track as a pre-aiming point of the current prediction period, and calculating a target steering wheel corner required by the vehicle to travel from the starting point of the current prediction period to the pre-aiming point of the current prediction period;
and combining the target steering wheel angle, the vehicle wheelbase, the steering wheel stability factor, the vehicle body speed at the predicted starting moment and the starting point information of the current prediction period to obtain the predicted track point information corresponding to the current prediction period.
2. The vehicle travel track prediction method according to claim 1, wherein the process of obtaining the predicted track point information corresponding to the current prediction period includes:
obtaining an actual steering wheel steering angle response increment of a current prediction period according to the target steering wheel steering angle, the vehicle wheelbase, the steering wheel stability factor and the vehicle body speed at the prediction starting moment, and further obtaining the steering wheel steering angle at a prediction track point corresponding to the current prediction period by combining the actual steering wheel steering angle response increment and the steering wheel steering angle at the starting point of the current prediction period;
calculating to obtain the actual yaw rate of the current prediction period by utilizing the steering wheel angle at the prediction track point corresponding to the current prediction period and the relation between the yaw rate and the steering wheel angle;
calculating to obtain a course angle deflection of the current prediction period by using the actual yaw rate, and further obtaining a car body course angle at a prediction track corresponding to the current prediction period by combining the course angle deflection and the car body course angle at the starting point of the current prediction period;
and calculating to obtain a transverse displacement amount and a longitudinal displacement amount corresponding to the current prediction period by utilizing the car body course angle at the prediction track corresponding to the current prediction period and the car body speed at the prediction starting moment, and further obtaining a prediction track point coordinate corresponding to the current prediction period by combining the coordinate of the starting point of the current prediction period and the transverse displacement amount and the longitudinal displacement amount corresponding to the current prediction period.
3. The vehicle travel track prediction method according to claim 2, wherein an actual steering wheel steering angle response increment of the current prediction period is calculated using a steering angle response increment calculation formula, the steering angle response increment calculation formula being:
in the formula, delta theta steer Representing the actual steering wheel angle response increment of the current prediction period, v representing the vehicle body speed at the prediction starting moment, L representing the vehicle wheelbase, K representing the steering wheel stability factor, theta steer_target Represents the target steering wheel angle required by the vehicle to travel from the starting point of the current prediction period to the pre-aiming point of the current prediction period, and dt represents the duration of the prediction period.
4. A vehicle travel track prediction apparatus, characterized in that the apparatus comprises a processor and a memory, the processor executing a computer program stored by the memory to implement the vehicle travel track prediction method according to any one of claims 1-3.
5. A vehicle comprising a vehicle body and a vehicle travel track prediction apparatus, characterized in that the vehicle travel track prediction apparatus comprises a processor and a memory, the processor executing a computer program stored by the memory to implement the vehicle travel track prediction method of any one of claims 1-3.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012214123A (en) * | 2011-03-31 | 2012-11-08 | Equos Research Co Ltd | Vehicle and vehicle control program |
| WO2014115262A1 (en) * | 2013-01-23 | 2014-07-31 | トヨタ自動車株式会社 | Vehicle controller |
| CN106184225A (en) * | 2016-07-08 | 2016-12-07 | 中国第汽车股份有限公司 | Longitudinal automobile speedestimate method that distributed type four-wheel-driven electrical vehicular power controls |
| CN107817790A (en) * | 2017-09-05 | 2018-03-20 | 百度在线网络技术（北京）有限公司 | A kind of method and apparatus for the curvature for calculating track of vehicle |
| JP2018106589A (en) * | 2016-12-28 | 2018-07-05 | パナソニックＩｐマネジメント株式会社 | Vehicle trajectory prediction device, method, program, recording medium, and alarm apparatus |
| CN109727469A (en) * | 2019-01-08 | 2019-05-07 | 南京航空航天大学 | Automatic driving vehicle COMPREHENSIVE RISK DEGREE appraisal procedure under a kind of multilane |
| CN109782754A (en) * | 2018-12-25 | 2019-05-21 | 东软睿驰汽车技术（沈阳）有限公司 | A kind of control method for vehicle and device |
| CN109808687A (en) * | 2019-01-17 | 2019-05-28 | 福瑞泰克智能系统有限公司 | A kind of driving assistance method, device and equipment |
| CN110316193A (en) * | 2019-07-02 | 2019-10-11 | 华人运通(上海)自动驾驶科技有限公司 | Setting method, device, equipment and the computer readable storage medium of preview distance |
| CN110398969A (en) * | 2019-08-01 | 2019-11-01 | 北京主线科技有限公司 | Automatic driving vehicle adaptive prediction time domain rotating direction control method and device |
| CN110703763A (en) * | 2019-11-05 | 2020-01-17 | 武汉理工大学 | Unmanned vehicle path tracking and obstacle avoidance method |
| CN110789528A (en) * | 2019-08-29 | 2020-02-14 | 腾讯科技（深圳）有限公司 | Vehicle driving track prediction method, device, equipment and storage medium |
| CN111038477A (en) * | 2019-11-29 | 2020-04-21 | 苏州智加科技有限公司 | Vehicle control method, device and equipment |
| CN111402580A (en) * | 2020-03-04 | 2020-07-10 | 杭州海康威视系统技术有限公司 | Vehicle running track prediction method and device and electronic equipment |
| CN111547066A (en) * | 2020-04-27 | 2020-08-18 | 中汽研(天津)汽车信息咨询有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2924828B1 (en) * | 2007-12-11 | 2010-09-24 | Airbus France | METHOD AND DEVICE FOR AT LEAST PARTIALLY AUTOMATIC DRIVING OF A GROUNDING AIRCRAFT |
-
2020
- 2020-10-09 CN CN202011073813.5A patent/CN114312770B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012214123A (en) * | 2011-03-31 | 2012-11-08 | Equos Research Co Ltd | Vehicle and vehicle control program |
| WO2014115262A1 (en) * | 2013-01-23 | 2014-07-31 | トヨタ自動車株式会社 | Vehicle controller |
| CN106184225A (en) * | 2016-07-08 | 2016-12-07 | 中国第汽车股份有限公司 | Longitudinal automobile speedestimate method that distributed type four-wheel-driven electrical vehicular power controls |
| JP2018106589A (en) * | 2016-12-28 | 2018-07-05 | パナソニックＩｐマネジメント株式会社 | Vehicle trajectory prediction device, method, program, recording medium, and alarm apparatus |
| CN107817790A (en) * | 2017-09-05 | 2018-03-20 | 百度在线网络技术（北京）有限公司 | A kind of method and apparatus for the curvature for calculating track of vehicle |
| CN109782754A (en) * | 2018-12-25 | 2019-05-21 | 东软睿驰汽车技术（沈阳）有限公司 | A kind of control method for vehicle and device |
| CN109727469A (en) * | 2019-01-08 | 2019-05-07 | 南京航空航天大学 | Automatic driving vehicle COMPREHENSIVE RISK DEGREE appraisal procedure under a kind of multilane |
| CN109808687A (en) * | 2019-01-17 | 2019-05-28 | 福瑞泰克智能系统有限公司 | A kind of driving assistance method, device and equipment |
| CN110316193A (en) * | 2019-07-02 | 2019-10-11 | 华人运通(上海)自动驾驶科技有限公司 | Setting method, device, equipment and the computer readable storage medium of preview distance |
| CN110398969A (en) * | 2019-08-01 | 2019-11-01 | 北京主线科技有限公司 | Automatic driving vehicle adaptive prediction time domain rotating direction control method and device |
| CN110789528A (en) * | 2019-08-29 | 2020-02-14 | 腾讯科技（深圳）有限公司 | Vehicle driving track prediction method, device, equipment and storage medium |
| CN110703763A (en) * | 2019-11-05 | 2020-01-17 | 武汉理工大学 | Unmanned vehicle path tracking and obstacle avoidance method |
| CN111038477A (en) * | 2019-11-29 | 2020-04-21 | 苏州智加科技有限公司 | Vehicle control method, device and equipment |
| CN111402580A (en) * | 2020-03-04 | 2020-07-10 | 杭州海康威视系统技术有限公司 | Vehicle running track prediction method and device and electronic equipment |
| CN111547066A (en) * | 2020-04-27 | 2020-08-18 | 中汽研(天津)汽车信息咨询有限公司 | Vehicle trajectory tracking method, device, equipment and storage medium |
Non-Patent Citations (4)
| Title |
|---|
| 徐兴 ; 汤赵 ; 王峰 ; 陈龙 ; .基于变权重系数的分布式驱动无人车轨迹跟踪.中国公路学报.2019,(第12期),全文. * |
| 朱敏敏 ; .基于模型的电信定位问题研究.电子世界.2017,(第08期),全文. * |
| 罗峰 ; 曾侠 ; .基于多点预瞄的自动驾驶汽车轨迹跟踪算法.机电一体化.2018,(第06期),全文. * |
| 陈威 ; 廖文浩 ; 刘明春 ; .基于MPC的自动驾驶车辆横向路径跟踪控制.南昌大学学报(工科版).2020,(第03期),全文. * |
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