CN116620278B - Unmanned lane keeping method - Google Patents
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- CN116620278B CN116620278B CN202310421107.2A CN202310421107A CN116620278B CN 116620278 B CN116620278 B CN 116620278B CN 202310421107 A CN202310421107 A CN 202310421107A CN 116620278 B CN116620278 B CN 116620278B
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005286 illumination Methods 0.000 claims abstract description 9
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000005516 engineering process Methods 0.000 claims description 19
- 238000013135 deep learning Methods 0.000 claims description 8
- 238000013527 convolutional neural network Methods 0.000 claims description 6
- 230000003631 expected effect Effects 0.000 claims description 5
- 239000002245 particle Substances 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000011217 control strategy Methods 0.000 claims description 3
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 238000013136 deep learning model Methods 0.000 claims description 3
- 230000009977 dual effect Effects 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims description 3
- 230000008447 perception Effects 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 claims description 2
- 238000011161 development Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000001965 increasing effect Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
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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
- B60W30/10—Path keeping
- B60W30/12—Lane keeping
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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
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
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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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
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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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
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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
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
- B60W2050/0008—Feedback, closed loop systems or details of feedback error signal
- B60W2050/0011—Proportional Integral Differential [PID] controller
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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
- B60W2050/0001—Details of the control system
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
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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
- B60W2050/0001—Details of the control system
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
- B60W2050/0052—Filtering, filters
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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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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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
- B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
- B60W2555/20—Ambient conditions, e.g. wind or rain
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Traffic Control Systems (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a method for keeping an unmanned lane, which comprises the following steps: s1, identification and selection: firstly, a high-definition camera is used for identifying and screening reference objects around a vehicle, wherein the reference objects comprise traffic signs, trees and buildings, when the reference objects are identified, a plurality of factors are considered, the illumination condition, the weather condition, the vehicle speed and the road surface state, and the unmanned vehicle is also provided with sensor equipment such as a laser radar, so that the identification accuracy and the stability of the surrounding environment are improved. According to the invention, the high-definition camera is utilized to identify and screen the reference objects around the vehicle, the identified reference objects are graded and are linked with the vehicle, the link between the vehicle and the reference objects is further optimized by utilizing the fuzzy PID algorithm, the reference objects are gradually selected and removed according to the link grade, the vehicle is ensured to have relatively closer link with the reference objects all the time, and the safety and smoothness of automatic driving lane keeping are improved.
Description
Technical Field
The invention relates to the technical field of unmanned methods, in particular to an unmanned lane keeping method.
Background
As global traffic continues to grow, various traffic problems are increasingly emerging. Problems faced by traditional traffic means include traffic jams, traffic accidents, emission pollution and the like, solutions are expected to be brought to the problems, unmanned vehicles refer to automobiles which can run autonomously without manual operation, road signs, traffic lights, pedestrians, vehicles and other obstacles can be identified by means of advanced sensors, artificial intelligence, machine learning technologies and the like, decision making and vehicle movement control are carried out based on collected data, traffic efficiency can be improved, traffic accidents can be reduced, air pollution can be reduced, traffic safety can be improved, the technologies of unmanned vehicles need to comprise multiple aspects of sensor technologies, artificial intelligence, machine learning technologies, high-precision positioning technologies, communication technologies, safety, security, battery technologies and the like, related regulations and standards are also required to restrain a series of problems of unmanned vehicles, research, development, and development of automobile technologies and companies in the unmanned field, along with continuous development of technologies, and intelligent development of unmanned vehicles are expected to become one of the important directions in the future of the world.
However, at the same time, technical development and application of the unmanned vehicle also face a plurality of challenges, wherein the problems of immature technology, high cost, potential safety hazard and the like are the main problems to be solved at present, a lane keeping method is a part of the whole unmanned technical field, and the existing unmanned lane keeping method has the defects that the vehicle is not closely connected with an external environment reference object when the existing unmanned lane keeping method is operated, and the safety and smoothness of the lane keeping of the vehicle after the vehicle deviates from a lane are insufficient.
Disclosure of Invention
The invention provides an unmanned lane keeping method aiming at the defects in the background technology.
The invention aims to solve the above-mentioned phenomenon, adopt the following technical scheme, a unmanned lane keeping method, the method step includes:
s1, identification and selection: firstly, identifying and screening reference objects around a vehicle through a high-definition camera, wherein the reference objects comprise traffic signs, trees and buildings, when the reference objects are identified, a plurality of factors are considered, the illumination condition, the weather condition, the vehicle speed and the road surface state, and the unmanned vehicle is also provided with sensor equipment such as a laser radar, so that the identification accuracy and the stability of the surrounding environment are improved;
s2, dividing the contact: after the identification and selection are finished, the identified reference objects are subjected to grading and are connected with the vehicle, wherein the grading is based on factors such as importance, reliability, distance and the like of the reference objects, and a traditional target tracking algorithm, a Kalman filter and a particle filter can be adopted for connection with the vehicle;
s3, algorithm control: after the connection is divided, the connection between the vehicle and the reference object is further optimized by utilizing a fuzzy PID algorithm, wherein the fuzzy PID algorithm is a control algorithm, and output signals can be adjusted according to the current state and the target state so as to stabilize the system and achieve the expected effect.
In step S1, when the reference object is identified, the following factors are considered, the illumination condition is improved by adjusting the car lamp, the sun shield and the like, so that the identification rate of the reference object is improved, the weather condition is adapted to the second time, the identification capacity is enhanced by using special sensors such as an infrared camera and the like for severe weather conditions such as rainy days, foggy days and the like, the algorithm adaptability is required to be enhanced, erroneous judgment is prevented, and the third adaptation vehicle speed is that: along with the increase of the speed of the vehicle, the recognition speed needs to be adapted, a multi-path video stream merging mode is adopted, the efficiency is improved by using distributed calculation, the road surface state is perceived, and the sensor data is filtered for the road surface conditions such as pits, fluctuation and the like, so that the error is reduced.
In step S2, the vehicle and the external environment are divided and connected by adopting a plurality of technologies, a deep learning technology is introduced, a deep learning model such as a convolutional neural network CNN and a cyclic neural network RNN is used for sensing and analyzing a road scene in real time, integrated map information is combined with sensor data to more accurately determine the position and the shape of the vehicle, a plurality of different sensor data such as cameras, laser radars and millimeter wave radars are fused by adopting multi-sensor fusion, and in the lane keeping process, the dynamics characteristics, acceleration and steering angular rate of the vehicle are considered, and a control strategy is adjusted in real time according to the information such as the current road condition, the vehicle state and the target driving path.
In step S3, more sensors are added to obtain more comprehensive road condition information in consideration of increasing the perception capability of the vehicle, such data are processed and analyzed in combination with a deep learning algorithm, the recognition and prediction capability of road conditions are improved, a real-time dynamic path planning algorithm is required to be adopted, an optimal driving decision can be made according to the current road condition and traffic condition, adjustment is performed according to the real-time state of the vehicle, and the reliability of the system is improved by adopting a dual backup mechanism, namely a standby computing unit and a standby sensor are added in a vehicle control system, and when the main computing unit or the sensor fails, the standby unit and the sensor immediately take over for work, so that the safe driving of the vehicle is ensured.
According to the invention, firstly, a high-definition camera is utilized to identify and screen reference objects around a vehicle, sensor equipment such as a laser radar and the like can be equipped, illumination conditions are improved by adjusting modes such as a car lamp and a sun shield, so that the identification rate of the reference objects is improved, for severe weather conditions such as rainy days, foggy days and the like, special sensors such as an infrared camera can be used for enhancing the identification capability, algorithm adaptability is required to be enhanced, misjudgment is prevented, the identification speed is adjusted along with the increase of the vehicle speed, a multi-path video stream merging mode is utilized, the efficiency is improved by utilizing distributed calculation, the road surface state is an important factor influencing the identification of the reference objects, for road surface conditions such as pits and waves, sensor data can be filtered, errors are reduced, the identified reference objects are classified and are connected with the vehicle, the classification can be based on factors such as the importance, the reliability and the distance of the reference objects, a traditional target tracking algorithm, a Kalman filter and a particle filter can be adopted for establishing connection with the vehicle, and a deep learning technology is introduced: combining map information with sensor data, fusing various sensor data, further optimizing the relation between the vehicle and the reference object by using a fuzzy PID algorithm, wherein the fuzzy PID algorithm is a control algorithm, and can adjust output signals according to the current state and the target state so as to ensure that the system is stable and achieves the expected effect.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Description of the embodiments
In the following, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a technical scheme that: an unmanned lane keeping method, the method steps comprising the following steps:
s1, identification and selection: firstly, identifying and screening reference objects around a vehicle through a high-definition camera, wherein the reference objects comprise traffic signs, trees and buildings, when the reference objects are identified, a plurality of factors are considered, the illumination condition, the weather condition, the vehicle speed and the road surface state, and the unmanned vehicle is also provided with sensor equipment such as a laser radar, so that the identification accuracy and the stability of the surrounding environment are improved; when the reference object is identified, the following factors are considered, the illumination condition is improved by adjusting the modes of a car lamp, a sun shield and the like, so that the identification rate of the reference object is improved, the weather conditions are adapted to the second time, the identification capacity is enhanced by using special sensors such as an infrared camera and the like for severe weather conditions such as rainy days, foggy days and the like, algorithm adaptability is required to be enhanced, misjudgment is prevented, and the third adaptive vehicle speed is as follows: along with the increase of the speed of the vehicle, the recognition speed needs to be adapted, a multi-path video stream merging mode is adopted, the efficiency is improved by using distributed calculation, the road surface state is perceived, and the sensor data is filtered for the road surface conditions such as pits, fluctuation and the like, so that the error is reduced.
S2, dividing the contact: after the identification and selection are finished, the identified reference objects are subjected to grading and are connected with the vehicle, wherein the grading is based on factors such as importance, reliability, distance and the like of the reference objects, and a traditional target tracking algorithm, a Kalman filter and a particle filter can be adopted for connection with the vehicle; the vehicle and external environment division connection adopts a plurality of technologies, a deep learning technology is introduced, a deep learning model such as a convolutional neural network CNN and a cyclic neural network RNN is used for sensing and analyzing a road scene in real time, integrated map information is combined with sensor data, the position and the road shape of the vehicle are more accurately determined, multiple sensors are adopted for fusion, multiple different types of sensor data, cameras, laser radars, millimeter wave radars and the like are fused, in the lane keeping process, the dynamics characteristics, acceleration and steering angular rate of the vehicle are considered, and a control strategy is adjusted in real time according to the current road condition, the vehicle state, the target running path and other information.
S3, algorithm control: after the connection is divided, the connection between the vehicle and the reference object is further optimized by utilizing a fuzzy PID algorithm, wherein the fuzzy PID algorithm is a control algorithm, and output signals can be adjusted according to the current state and the target state so as to stabilize the system and achieve the expected effect; the vehicle perception capability is increased, more sensors are added to obtain more comprehensive road condition information, the data are processed and analyzed by combining a deep learning algorithm, the road condition recognition and prediction capability is improved, a real-time dynamic path planning algorithm is adopted, an optimal driving decision can be made according to the current road condition and traffic condition, the vehicle is regulated according to the real-time state of the vehicle, the system reliability is improved by adopting a dual backup mechanism, namely a standby computing unit and a standby sensor are added in a vehicle control system, and when the main computing unit or the sensor fails, the standby unit and the sensor immediately take over for work, so that the safe driving of the vehicle is ensured.
In summary, the invention firstly utilizes the high-definition camera to identify and screen the reference objects around the vehicle, can be equipped with sensor devices such as a laser radar, improves the lighting conditions by adjusting the modes such as a car lamp, a sun shield and the like, thereby improving the identification rate of the reference objects, can use special sensors such as an infrared camera to enhance the identification capability for severe weather conditions such as rainy days, foggy days and the like, simultaneously needs to strengthen algorithm adaptability to prevent misjudgment, the identification speed is adjusted along with the increase of the vehicle speed, adopts a multi-path video stream merging mode, utilizes distributed computation to improve the efficiency, the road surface state is an important factor influencing the identification of the reference objects, can carry out filtering processing on sensor data for the road surface conditions such as pits, waves and the like, reduces errors, carries out grading on the identified reference objects, establishes connection with the vehicle, and establishes connection with the vehicle based on factors such as importance, reliability, distance and the like of the reference objects, can adopt a traditional target tracking algorithm, a Kalman filter and a particle filter, and introduces a deep learning technology: combining map information with sensor data, fusing various sensor data, further optimizing the relation between the vehicle and the reference object by using a fuzzy PID algorithm, wherein the fuzzy PID algorithm is a control algorithm, and can adjust output signals according to the current state and the target state so as to ensure that the system is stable and achieves the expected effect.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (4)
1. A method of unmanned lane keeping, the method comprising the steps of:
s1, identification and selection: firstly, identifying and screening reference objects around a vehicle through a high-definition camera, wherein the reference objects comprise traffic signs, trees and buildings, when the reference objects are identified, a plurality of factors are considered, the illumination condition, the weather condition, the vehicle speed and the road surface state, and the unmanned vehicle is also provided with laser radar sensor equipment, so that the identification accuracy and the stability of the surrounding environment are improved;
s2, dividing the contact: after the identification and selection are finished, the identified reference objects are subjected to grading and are connected with the vehicle, the grading is based on importance, reliability and distance factors of the reference objects, and a traditional target tracking algorithm, a Kalman filter and a particle filter can be adopted for connection with the vehicle;
s3, algorithm control: after the connection is divided, the connection between the vehicle and the reference object is further optimized by utilizing a fuzzy PID algorithm, wherein the fuzzy PID algorithm is a control algorithm, and output signals can be adjusted according to the current state and the target state so as to stabilize the system and achieve the expected effect.
2. The method according to claim 1, wherein in step S1, when the reference object is identified, the following factors are considered, the first improvement of the illumination condition is achieved by adjusting the car light and the sun shield to improve the illumination condition, so as to improve the identification rate of the reference object, the second adaptation is weather conditions, the special sensor of the infrared camera is used for enhancing the identification capability for the rainy day and the foggy day bad weather conditions, the algorithm adaptability is required to be enhanced, the erroneous judgment is prevented, and the third adaptation is carried out for the vehicle speed: along with the increase of the speed of the vehicle, the recognition speed needs to be adapted, a multi-path video stream merging mode is adopted, the efficiency is improved by using distributed computation, the road surface state is perceived in the fourth mode, and the sensor data is filtered for the conditions of pits and wavy road surfaces, so that errors are reduced.
3. The unmanned lane keeping method according to claim 1, wherein in step S2, the vehicle is connected with the external environment in a divided manner by adopting a plurality of technologies, a deep learning technology is introduced, a convolutional neural network CNN and a cyclic neural network RNN deep learning model is used for sensing and analyzing a road scene in real time, integrated map information is combined with sensor data to more precisely determine the position and the shape of the vehicle, a plurality of different types of sensor data, a camera, a laser radar and a millimeter wave radar are fused by adopting a multi-sensor fusion, and in the lane keeping process, the dynamics, acceleration and steering angular rate of the vehicle are considered, and a control strategy is adjusted in real time according to the current road condition, the state of the vehicle and the target driving path information.
4. The method according to claim 1, wherein in step S3, the increase of the perception capability of the vehicle is considered, more sensors are added to obtain more comprehensive road condition information, the data are processed and analyzed in combination with a deep learning algorithm, the recognition and prediction capability of road conditions are improved, a real-time dynamic path planning algorithm is adopted, an optimal driving decision is made according to the current road condition and traffic condition, the adjustment is performed according to the real-time state of the vehicle, and the system reliability is improved by adopting a dual backup mechanism, namely a backup computing unit and a backup sensor are added in the vehicle control system, and when the main computing unit or the sensor fails, the backup unit and the sensor immediately take over the work to ensure the safe driving of the vehicle.
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| CN202310421107.2A CN116620278B (en) | 2023-04-19 | 2023-04-19 | Unmanned lane keeping method |
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| CN202310421107.2A CN116620278B (en) | 2023-04-19 | 2023-04-19 | Unmanned lane keeping method |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110641465A (en) * | 2019-10-25 | 2020-01-03 | 长安大学 | Lane keeping system and method based on vehicle speed |
| CN112537302A (en) * | 2020-11-30 | 2021-03-23 | 南通路远科技信息有限公司 | Driverless traffic vehicle lane keeping method and device and traffic vehicle |
| CN112537299A (en) * | 2020-11-30 | 2021-03-23 | 南通路远科技信息有限公司 | Lane keeping method and device based on target object and traffic vehicle |
| WO2022100996A1 (en) * | 2020-11-11 | 2022-05-19 | Volkswagen Aktiengesellschaft | Method for operating a lane keeping assistance system of a vehicle during locomotion of the vehicle along a lane, and electronic driver assistance system and vehicle |
| CN114802278A (en) * | 2022-03-04 | 2022-07-29 | 净豹智能机器人(台州)有限公司 | Vehicle control system and method for unmanned driving in severe weather |
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- 2023-04-19 CN CN202310421107.2A patent/CN116620278B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110641465A (en) * | 2019-10-25 | 2020-01-03 | 长安大学 | Lane keeping system and method based on vehicle speed |
| WO2022100996A1 (en) * | 2020-11-11 | 2022-05-19 | Volkswagen Aktiengesellschaft | Method for operating a lane keeping assistance system of a vehicle during locomotion of the vehicle along a lane, and electronic driver assistance system and vehicle |
| CN112537302A (en) * | 2020-11-30 | 2021-03-23 | 南通路远科技信息有限公司 | Driverless traffic vehicle lane keeping method and device and traffic vehicle |
| CN112537299A (en) * | 2020-11-30 | 2021-03-23 | 南通路远科技信息有限公司 | Lane keeping method and device based on target object and traffic vehicle |
| CN114802278A (en) * | 2022-03-04 | 2022-07-29 | 净豹智能机器人(台州)有限公司 | Vehicle control system and method for unmanned driving in severe weather |
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