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CN110588623A - Large automobile safe driving method and system based on neural network - Google Patents

Large automobile safe driving method and system based on neural network Download PDF

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Publication number
CN110588623A
CN110588623A CN201910942251.4A CN201910942251A CN110588623A CN 110588623 A CN110588623 A CN 110588623A CN 201910942251 A CN201910942251 A CN 201910942251A CN 110588623 A CN110588623 A CN 110588623A
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China
Prior art keywords
automobile
information
distance
safe
driving
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Granted
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CN201910942251.4A
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Chinese (zh)
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CN110588623B (en
Inventor
赵一兵
马振强
郭烈
杨宇
周一飞
吕彦卿
于丰宁
韩治中
刘昌华
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Dalian University of Technology
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Dalian University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/18—Conjoint control of vehicle sub-units of different type or different function including control of braking systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • 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
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • 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
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
    • 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
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/04—Traffic conditions
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/064—Degree of grip
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/112—Roll movement
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/20—Steering systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/06—Combustion engines, Gas turbines
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/08—Electric propulsion units
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/18—Braking system
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems

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  • Engineering & Computer Science (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention provides a safe driving method and a system of a large automobile based on a neural network, which belong to the field of electronic control of large automobiles and aim to solve the safe driving problem of the existing large automobile, and comprise an environment sensing module, an automobile controller, a safe voice prompt module and a safe driving auxiliary control module, wherein the environment sensing module is used for acquiring environment information and automobile driving state information, sending the environment information and the automobile driving state information to the automobile controller as input information, judging the automobile state and the environment information and then outputting the automobile state and the environment information to the safe voice prompt module; setting two safety distance information according to the automobile driving state information, and after the automobile receives the distance information about a dangerous condition, outputting the information to a safety voice prompt module by an automobile controller to remind a driver to react; if the driver makes a response, the driving behavior data of safe avoidance of driving is stored in the database, otherwise, after the specified minimum dangerous distance of safe avoidance is reached, the safe driving auxiliary control module makes auxiliary driving control.

Description

Large automobile safe driving method and system based on neural network
Technical Field
The invention relates to the technical field of large automobile electronic control, in particular to a large automobile safe driving method and system based on a neural network.
Background
With the development of economy and the continuous improvement of the requirements of people on life quality in recent years, the demands of people on transportation are increasing day by day, and further the quantity of automobiles kept in China is increased year by year, so that urban traffic jam is serious, traffic accidents frequently occur, the main object of the frequent traffic accidents is a large-scale freight transportation automobile, and in the driving process of the large-scale automobile, as the driving cabin is too high, a part of the visual field in front of the automobile is shielded, unnecessary potential safety hazards are easy to occur in the driving process of the automobile, so that the problem of safe driving of the large-scale automobile needs to be solved.
The prior art has at least the following problems for safe driving of large automobiles:
1. the problem of vision blind areas of large automobiles due to high automobile bodies is solved;
2. when a large automobile runs, danger can not be effectively avoided when dangerous conditions occur, and the problem that a neural network training set data model is lacked under the variable driving condition is solved;
3. the economic problem of expensive automotive sensors.
Disclosure of Invention
According to the technical problems, a safe driving method and system for a large automobile based on a neural network are provided. The invention mainly utilizes an environment sensing module established by a binocular infrared camera, an ultrasonic sensor and the like to observe environment information, simultaneously detects the current running state information of the automobile and inputs the running state information into an automobile controller for processing, the automobile controller is a neural network model established on the basis of a Linux system, and finally the automobile controller outputs a control instruction to control an execution mechanism of the automobile to carry out safe auxiliary driving.
The technical means adopted by the invention are as follows:
a safe driving method of a large automobile based on a neural network comprises the following steps:
s1, collecting real vehicle running data set information and establishing a database;
s2, building an automobile controller, wherein the automobile controller is a neural network model built on the basis of a Linux system, and trains the neural network model by using the real vehicle running data set information, so as to obtain a mapping relation between various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver;
s3, acquiring environmental information and current automobile driving state information through an environmental sensing module, and taking the environmental information and the current automobile driving state information as input information;
s4, the automobile controller receives the input information, judges the current state and the environmental information of the automobile and outputs the current state and the environmental information to the safety voice prompt module;
s5, the safety voice prompt module sets two safety distance information according to the current driving state information of the automobile, namely the distance information which is judged by combining the current environment information and the driving state information of the automobile and is about to generate a dangerous condition and the minimum distance information of the safe avoidance distance of the automobile;
s6, after the automobile receives the distance information of the dangerous condition, the automobile controller outputs information to the safety voice prompt module to remind the driver to react;
and S7, if the driver reacts at the moment, the automobile controller stores the driving behavior data of safe avoidance in the driving database, and if the driver does not react, the automobile controller controls the safe driving auxiliary control module to perform auxiliary driving control through the input information acquired in the step S2 after the specified minimum safe avoidance distance is reached.
Further, the step S7 is followed by:
s8, the safety voice prompt module receives information transmitted by the automobile controller, prompts distance information of dangerous conditions to be generated to a driver through the voice player, and judges whether the driver reacts between the distance of the dangerous conditions to be generated and the minimum distance of the safe avoidance distance.
Further, the step S8 is followed by:
s9, if the driver does not react between the distance of the dangerous situation and the minimum distance of the safe avoidance distance, after the automobile reaches the minimum distance of the specified minimum safe avoidance distance, the automobile controller judges according to the current environmental information and the automobile state information and outputs corresponding safe avoidance decision data information to be transmitted to the safe driving auxiliary control module, so that safe avoidance and safe driving are achieved.
Further, the acquiring of the environmental information and the current driving state information of the automobile through the environment sensing module specifically includes:
collecting the current running speed, the current running acceleration, the current road adhesion coefficient, the yaw rate, the steering wheel angle, the course angle, the mass center coordinate and the image information transmitted by the camera of the automobile;
the image information comprises surrounding vehicle information, pedestrian information, traffic signal signs, road resistance information and distance information between the vehicle and a front vehicle, pedestrians, traffic signal signs and road resistances; the information of the side of the automobile and the edge of the road is the information of the obstacles on the two sides of the automobile and the distance information between the obstacles, which are detected and transmitted by the ultrasonic sensor.
Further, the specific process of setting two safety distance information in step S5 is as follows:
s51, calculating the braking distance of the automobile, wherein the braking distance of the automobile comprises a brake acting stage and a brake continuous braking stage, and the total braking distance calculation formula is as follows:
wherein S is the braking distance of the automobile according to the current speed, amaxIs the maximum braking deceleration, mu, of the vehicle0Is the initial deceleration of the vehicle during deceleration, t1' time for which the brake reacts, t, due to the clearance existing between the brake shoes and the brake drum1"time required for the braking force to increase continuously from zero to a maximum braking force, t1＝t1'+t1"collectively referred to as the application time of the brake, the application time of the brake is between 0.2s and 0.9 s;
s52, judging the relation between the distance D of the automobile about to generate a dangerous condition and the minimum distance information D of the safe avoidance distance of the automobile according to the distance between the automobile and the front obstacle;
s53, detecting environmental information in front of the automobile and vehicle, pedestrian, traffic signal sign and road resistance information in the environment through a binocular infrared camera in the environment sensing module, and calculating parallax according to a binocular distance measurement principle and a triangular similarity principle to further obtain depth information;
s54, after the distance between the automobile and the obstacle is measured through the environment sensing module, the automobile controller calculates the braking distance of the automobile according to the current speed information and the ground adhesion coefficient information, judges whether the automobile is dangerous or not, sets the distance D of the automobile to be dangerous as 3/2S, and sets the minimum distance information D of the safe avoidance distance of the automobile as S.
The invention also provides a large automobile safe driving system based on the neural network, which comprises an environment sensing module, an automobile controller, a safe voice prompt module and a safe driving auxiliary control module;
the environment sensing module comprises an external environment information sensing module and an automobile current driving state information sensing module; the external environment information sensing module is used for acquiring external environment information of the automobile, and the current driving state information sensing module of the automobile is used for monitoring the current driving state information of the automobile in real time;
the safety voice prompt module comprises a voice player and a controller; after receiving the environmental information transmitted by the environmental sensing module, the controller detects and calculates the distance of the obstacle information in the longitudinal and transverse distance directions, judges the current running state of the automobile, and outputs information to the voice player to remind a driver to react after the automobile receives the distance information about the dangerous condition;
the automobile controller is a neural network model built on the basis of a Linux system, the neural network model mainly comprises a CNN network architecture and an SSD network architecture, and the neural network model is trained by using the real automobile running data set information, so that a mapping relation is formed between various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver;
the safe driving auxiliary control module comprises an accelerator pedal actuator, a brake pedal actuator and a steering wheel actuator; the accelerator pedal actuator and the brake pedal actuator are used for controlling the positions of a brake pedal and an accelerator pedal; the steering wheel actuator is used to control the steering of the steering wheel.
Furthermore, the external environment information sensing module mainly comprises a binocular infrared camera, two side monocular infrared cameras and an ultrasonic sensor;
the binocular infrared camera is arranged in the middle of the front of the vehicle head and used for scanning information in front of the whole vehicle;
the two side monocular infrared cameras are respectively arranged at the rear parts of the two sides of the automobile body and used for observing the environmental information at the sides of the automobile;
the ultrasonic sensors are installed on two sides of the automobile body of the automobile and used for detecting obstacle information on two sides of the automobile and distance information of obstacles.
Furthermore, the current driving state information sensing module of the automobile mainly comprises a steering wheel angle sensor, a steering wheel moment sensor, a vehicle speed sensor, a vehicle acceleration sensor, a yaw rate sensor, a gyroscope, a lateral acceleration sensor, a brake pedal angle sensor, an accelerator pedal angle sensor, a brake pedal angular velocity sensor, an accelerator pedal angular velocity sensor and a ground adhesion coefficient sensor, wherein the sensors are all installed at corresponding positions on the automobile.
Furthermore, the safe driving auxiliary control module can also realize the transverse movement and the emergency braking of the automobile.
Compared with the prior art, the invention has the following advantages:
1. the safe driving method of the large automobile based on the neural network, provided by the invention, not only improves the safety of automobile driving, but also enables the neural network model to be continuously trained in practice, so that the neural network model is more mature and safer; the large automobile safe driving system based on the neural network provided by the invention has the advantages that the problem of safe driving of a large automobile is greatly improved, and the safety of longitudinal and transverse movement of the automobile is improved.
2. According to the large automobile safe driving system based on the neural network, the environment information is observed through the environment sensing module established by the binocular infrared camera, the ultrasonic sensor and the like, the current driving state information of the automobile is detected and input into the automobile controller for processing, finally, the automobile controller outputs a control instruction to control the execution mechanism of the automobile to carry out safe auxiliary driving, and the problem that the large automobile generates a vision blind area due to a high automobile body is solved.
3. According to the large automobile safe driving system based on the neural network, the automobile controller is a neural network model built on a Linux system, and the neural network model is trained by utilizing a large amount of real automobile driving data set information, so that a mapping relation is formed between various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver, and the problems that danger cannot be effectively avoided when dangerous situations occur in the driving process of a large automobile and the neural network training set data model is lacked under changeable driving conditions are solved.
Based on the reasons, the invention can be widely popularized in the fields of electronic control of large automobiles and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of binocular distance measurement according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
The invention provides a large automobile safe driving method based on a neural network, which comprises the following steps:
s1, collecting real vehicle running data set information and establishing a database;
s2, building an automobile controller, wherein the automobile controller is a neural network model built on the basis of a Linux system, and trains the neural network model by using the real vehicle running data set information, so as to obtain a mapping relation between various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver;
s3, as shown in figure 1, collecting environmental information and current automobile driving state information through an environmental perception module, and taking the environmental information and the current automobile driving state information as input information; as a preferred embodiment of the present invention, the acquiring of the environmental information and the current driving state information of the vehicle by the environment sensing module specifically includes:
collecting the current running speed, the current running acceleration, the current road adhesion coefficient, the yaw rate, the steering wheel angle, the course angle, the mass center coordinate and the image information transmitted by the camera of the automobile;
the image information comprises surrounding vehicle information, pedestrian information, traffic signal signs, road resistance information and distance information between the vehicle and the front vehicle, the pedestrians, the traffic signal signs and the road resistance; the information of the side of the automobile and the edge of the road is the information of the obstacles on the two sides of the automobile and the distance information between the obstacles, which are detected and transmitted by the ultrasonic sensor.
S4, the automobile controller receives the input information, judges the current state and the environmental information of the automobile and outputs the current state and the environmental information to the safety voice prompt module;
s5, the safety voice prompt module sets two safety distance information according to the current driving state information of the automobile, namely the distance information which is judged by combining the current environment information and the driving state information of the automobile and is about to generate a dangerous condition and the minimum distance information of the safe avoidance distance of the automobile; as a preferred embodiment of the present invention, a specific process of setting two safety distance information is as follows:
s51, calculating the braking distance of the automobile, wherein the braking distance of the automobile comprises a brake acting stage and a brake continuous braking stage, and the total braking distance calculation formula is as follows:
wherein S is the braking distance of the automobile according to the current speed, amaxIs the maximum braking deceleration, mu, of the vehicle0Is the initial deceleration of the vehicle during deceleration, t1' time for which the brake reacts, t, due to the clearance existing between the brake shoes and the brake drum1"time required for the braking force to increase continuously from zero to a maximum braking force, t1＝t1'+t1Collectively referred to asThe application time of the brake is between 0.2s and 0.9s, and the application time of the brake selected by the embodiment of the invention is 0.9 s.
S52, judging the relation between the distance D of the automobile about to generate a dangerous condition and the minimum distance information D of the safe avoidance distance of the automobile according to the distance between the automobile and the front obstacle;
s53, detecting environmental information in front of the automobile and vehicle, pedestrian, traffic signal sign and road resistance information in the environment through a binocular infrared camera in the environment sensing module, and calculating parallax according to a binocular distance measurement principle and a triangular similarity principle to further obtain depth information; as shown in FIG. 2, P is a point on the object to be inspected, ORAnd OTThe optical centers of the two cameras are respectively, imaging points of a point P on photoreceptors of the two cameras are respectively P and P ', f is a focal length of the cameras, B is a center distance between the two cameras, Z is depth information obtained in the embodiment, and if the distance between P and P' is dis:
dis＝B-XR-XT
according to the triangle similarity principle:
the following can be obtained:
in the formula, XR-XTThe depth information can be obtained by obtaining the parallax.
S54, after the distance between the automobile and the obstacle is measured through the environment sensing module, the automobile controller calculates the braking distance of the automobile according to the current speed information and the ground adhesion coefficient information, judges whether the automobile is dangerous or not, sets the distance D of the automobile to be dangerous as 3/2S, and sets the minimum distance information D of the safe avoidance distance of the automobile as S.
S6, after the automobile receives the distance information of the dangerous condition, the automobile controller outputs information to the safety voice prompt module to remind the driver to react;
and S7, if the driver reacts at the moment, the automobile controller stores the driving behavior data of safe avoidance in the driving database, and if the driver does not react, the automobile controller controls the safe driving auxiliary control module to perform auxiliary driving control through the input information acquired in the step S2 after the specified minimum safe avoidance distance is reached.
As a preferred embodiment of the present invention, the step S7 is followed by:
s8, the safety voice prompt module receives information transmitted by the automobile controller, prompts distance information of dangerous conditions to be generated to a driver through the voice player, and judges whether the driver reacts between the distance of the dangerous conditions to be generated and the minimum distance of the safe avoidance distance.
As a preferred embodiment of the present invention, the step S8 is followed by:
s9, if the driver does not react between the distance of the dangerous situation and the minimum distance of the safe avoidance distance, after the automobile reaches the minimum distance of the specified minimum safe avoidance distance, the automobile controller judges according to the current environmental information and the automobile state information and outputs corresponding safe avoidance decision data information to be transmitted to the safe driving auxiliary control module, so that safe avoidance and safe driving are achieved.
Example 2
On the basis of the embodiment 1, the invention also provides a large-scale automobile safe driving system based on the neural network, which comprises an environment perception module, an automobile controller, a safe voice prompt module and a safe driving auxiliary control module;
the environment sensing module comprises an external environment information sensing module and an automobile current driving state information sensing module;
the external environment information sensing module is used for collecting the external environment information of the automobile and mainly comprises a binocular infrared camera, two side monocular infrared cameras and an ultrasonic sensor; the binocular infrared camera is arranged in the middle of the front of the vehicle head and used for scanning information in front of the whole vehicle; the two side monocular infrared cameras are respectively arranged at the rear parts of the two sides of the automobile body and used for observing the environmental information at the sides of the automobile; the ultrasonic sensors are installed on both sides of a body of the automobile and used for detecting obstacle information on both sides of the automobile and distance information of the obstacles.
The current driving state information sensing module of the automobile is used for monitoring the current driving state information of the automobile in real time, and mainly comprises a steering wheel angle sensor, a steering wheel moment sensor, a vehicle speed sensor, a vehicle acceleration sensor, a yaw angular velocity sensor, a gyroscope, a lateral acceleration sensor, a brake pedal angle sensor, an accelerator pedal angle sensor, a brake pedal angular velocity sensor, an accelerator pedal angular velocity sensor and a ground adhesion coefficient sensor, wherein the sensors are all installed at corresponding positions on the automobile.
The safety voice prompt module comprises a voice player and a controller; after receiving the environmental information transmitted by the environmental sensing module, the controller detects and calculates the distance of the obstacle information in the longitudinal and transverse distance directions, judges the current running state of the automobile, and outputs information to the voice player to remind a driver to react after the automobile receives the distance information about the dangerous condition;
the automobile controller is a neural network model built on the basis of a Linux system, the neural network model mainly comprises a CNN network architecture and an SSD network architecture, and the neural network model is trained by using the real automobile running data set information, so that a mapping relation is formed between the various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver;
the safe driving auxiliary control module comprises an accelerator pedal actuator, a brake pedal actuator and a steering wheel actuator; the accelerator pedal actuator and the brake pedal actuator are used for controlling the positions of a brake pedal and an accelerator pedal so as to achieve the speed information of a target position; the steering wheel actuator is used for controlling steering of the steering wheel, so that safe avoidance and safe running are achieved. The safe driving auxiliary control module can also realize the transverse motion and the emergency braking of the automobile, and specifically comprises the following steps:
if the two side monocular infrared cameras and the ultrasonic sensor detect that the environmental information on the two sides of the automobile can move transversely, the automobile moves transversely;
if barrier information exists on two sides of the automobile or the automobile is dangerous when the automobile is out of the road due to the detection of transverse movement, the automobile adopts the current maximum braking deceleration to brake.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. A safe driving method of a large automobile based on a neural network is characterized by comprising the following steps:
s1, collecting real vehicle running data set information and establishing a database;
s2, building an automobile controller, wherein the automobile controller is a neural network model built on the basis of a Linux system, and trains the neural network model by using the real vehicle running data set information, so as to obtain a mapping relation between various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver;
s3, acquiring environmental information and current automobile driving state information through an environmental sensing module, and taking the environmental information and the current automobile driving state information as input information;
s4, the automobile controller receives the input information, judges the current state and the environmental information of the automobile and outputs the current state and the environmental information to the safety voice prompt module;
s5, the safety voice prompt module sets two safety distance information according to the current driving state information of the automobile, namely the distance information which is judged by combining the current environment information and the driving state information of the automobile and is about to generate a dangerous condition and the minimum distance information of the safe avoidance distance of the automobile;
s6, after the automobile receives the distance information of the dangerous condition, the automobile controller outputs information to the safety voice prompt module to remind the driver to react;
and S7, if the driver reacts at the moment, the automobile controller stores the driving behavior data of safe avoidance in the driving database, and if the driver does not react, the automobile controller controls the safe driving auxiliary control module to perform auxiliary driving control through the input information acquired in the step S2 after the specified minimum safe avoidance distance is reached.
2. The method for safely driving a large automobile based on a neural network as claimed in claim 1, wherein the step S7 is followed by further comprising:
s8, the safety voice prompt module receives information transmitted by the automobile controller, prompts distance information of dangerous conditions to be generated to a driver through the voice player, and judges whether the driver reacts between the distance of the dangerous conditions to be generated and the minimum distance of the safe avoidance distance.
3. The method for safely driving a large automobile based on a neural network according to claim 1 or 2, wherein the step S8 is followed by further comprising:
s9, if the driver does not react between the distance of the dangerous situation and the minimum distance of the safe avoidance distance, after the automobile reaches the minimum distance of the specified minimum safe avoidance distance, the automobile controller judges according to the current environmental information and the automobile state information and outputs corresponding safe avoidance decision data information to be transmitted to the safe driving auxiliary control module, so that safe avoidance and safe driving are achieved.
4. The safe driving method for the large automobile based on the neural network as claimed in claim 2, wherein the collecting of the environmental information and the current driving state information of the automobile by the environment sensing module specifically comprises:
collecting the current running speed, the current running acceleration, the current road adhesion coefficient, the yaw rate, the steering wheel angle, the course angle, the mass center coordinate and the image information transmitted by the camera of the automobile;
the image information comprises surrounding vehicle information, pedestrian information, traffic signal signs, road resistance information and distance information between the vehicle and a front vehicle, pedestrians, traffic signal signs and road resistances; the information of the side of the automobile and the edge of the road is the information of the obstacles on the two sides of the automobile and the distance information between the obstacles, which are detected and transmitted by the ultrasonic sensor.
5. The method for safely driving a large automobile based on a neural network as claimed in claim 1, wherein the specific process of setting two safe distance information in the step S5 is as follows:
s51, calculating the braking distance of the automobile, wherein the braking distance of the automobile comprises a brake acting stage and a brake continuous braking stage, and the total braking distance calculation formula is as follows:
wherein S is the braking distance of the automobile according to the current speed, amaxIs the maximum braking deceleration, mu, of the vehicle0Is initial deceleration t 'of the automobile during deceleration'1Time of reaction of the brake, t, due to the clearance existing between the brake shoes and the brake drum "1Time required for the braking force to increase continuously from zero to a maximum braking force, t1＝t'1+t”1Collectively referred to as the application time of the brake, the application time of the brake is between 0.2s and 0.9 s;
s52, judging the relation between the distance D of the automobile about to generate a dangerous condition and the minimum distance information D of the safe avoidance distance of the automobile according to the distance between the automobile and the front obstacle;
s53, detecting environmental information in front of the automobile and vehicle, pedestrian, traffic signal sign and road resistance information in the environment through a binocular infrared camera in the environment sensing module, and calculating parallax according to a binocular distance measurement principle and a triangular similarity principle to further obtain depth information;
s54, after the distance between the automobile and the obstacle is measured through the environment sensing module, the automobile controller calculates the braking distance of the automobile according to the current speed information and the ground adhesion coefficient information, judges whether the automobile is dangerous or not, sets the distance D of the automobile to be dangerous as 3/2S, and sets the minimum distance information D of the safe avoidance distance of the automobile as S.
6. A large-scale automobile safe driving system based on a neural network is characterized by comprising an environment sensing module, an automobile controller, a safe voice prompt module and a safe driving auxiliary control module;
the environment sensing module comprises an external environment information sensing module and an automobile current driving state information sensing module; the external environment information sensing module is used for acquiring external environment information of the automobile, and the current driving state information sensing module of the automobile is used for monitoring the current driving state information of the automobile in real time;
the safety voice prompt module comprises a voice player and a controller; after receiving the environmental information transmitted by the environmental sensing module, the controller detects and calculates the distance of the obstacle information in the longitudinal and transverse distance directions, judges the current running state of the automobile, and outputs information to the voice player to remind a driver to react after the automobile receives the distance information about the dangerous condition;
the automobile controller is a neural network model built on the basis of a Linux system, the neural network model mainly comprises a CNN network architecture and an SSD network architecture, and the neural network model is trained by using the real automobile running data set information, so that a mapping relation is formed between various complex and changeable emergency situations and behavior data of safe avoidance and safe driving of a driver;
the safe driving auxiliary control module comprises an accelerator pedal actuator, a brake pedal actuator and a steering wheel actuator; the accelerator pedal actuator and the brake pedal actuator are used for controlling the positions of a brake pedal and an accelerator pedal; the steering wheel actuator is used to control the steering of the steering wheel.
7. The large automobile safe driving system based on the neural network as claimed in claim 6, wherein the external environment information sensing module mainly comprises a binocular infrared camera, two side monocular infrared cameras and an ultrasonic sensor;
the binocular infrared camera is arranged in the middle of the front of the vehicle head and used for scanning information in front of the whole vehicle;
the two side monocular infrared cameras are respectively arranged at the rear parts of the two sides of the automobile body and used for observing the environmental information at the sides of the automobile;
the ultrasonic sensors are installed on two sides of the automobile body of the automobile and used for detecting obstacle information on two sides of the automobile and distance information of obstacles.
8. The large automobile safe driving system based on the neural network as claimed in claim 6, wherein the automobile current driving state information sensing module mainly comprises a steering wheel angle sensor, a steering wheel moment sensor, a vehicle speed sensor, a vehicle acceleration sensor, a yaw rate sensor, a gyroscope, a lateral acceleration sensor, a brake pedal angle sensor, an accelerator pedal angle sensor, a brake pedal angular rate sensor, an accelerator pedal angular rate sensor and a ground adhesion coefficient sensor, and the sensors are all installed at corresponding positions on the automobile.
9. The large automobile safety driving system based on the neural network as claimed in claim 6, wherein the safety driving auxiliary control module can also realize the transverse movement and the emergency braking of the automobile.
CN201910942251.4A 2019-09-30 2019-09-30 Large automobile safe driving method and system based on neural network Expired - Fee Related CN110588623B (en)

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111152834A (en) * 2020-01-10 2020-05-15 大连理工大学 Electric automobile electronic differential control method based on Ackerman steering correction
CN111775929A (en) * 2020-06-11 2020-10-16 南京邮电大学 Dynamic safety early warning method for dangerous liquid mobile vehicle-mounted device
CN111899625A (en) * 2020-07-16 2020-11-06 北京理工大学 Intelligent driving assisting development device
CN112172827A (en) * 2020-06-24 2021-01-05 上汽通用五菱汽车股份有限公司 Driving assistance system control method, device, equipment and storage medium
CN112298094A (en) * 2020-09-27 2021-02-02 中国第一汽车股份有限公司 Active front bumper pedestrian protection plate system and control method thereof
CN112590787A (en) * 2021-01-05 2021-04-02 青岛海尔科技有限公司 Driving auxiliary processing method and device
CN114516300A (en) * 2022-02-22 2022-05-20 东风汽车集团股份有限公司 Vehicle trafficability assisting method and vehicle
CN116394965A (en) * 2023-04-26 2023-07-07 深圳市科莱德电子有限公司 Intelligent driving assistance system, control method and device thereof, and storage medium
WO2024138453A1 (en) * 2022-12-28 2024-07-04 华为技术有限公司 Autonomous driving method and device, and vehicle

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104691545A (en) * 2013-12-04 2015-06-10 财团法人车辆研究测试中心 Adaptive vehicle anti-collision method
CN105644557A (en) * 2015-12-30 2016-06-08 苏州安智汽车零部件有限公司 Braking and steering assisting system and method considering collision avoidance intention of driver
CN106864455A (en) * 2015-12-09 2017-06-20 上海渤元信息科技有限公司 Novel automobile intelligent safety system
CN108082184A (en) * 2017-12-19 2018-05-29 戴姆勒股份公司 Collision-prevention system and the method for preventing vehicle collision
CN108657189A (en) * 2018-06-22 2018-10-16 南京航空航天大学 Line operating mode automatic Pilot steering and its control method are moved based on BP neural network and safe distance
CN109591811A (en) * 2017-09-28 2019-04-09 华为技术有限公司 Vehicle braking method, device and storage medium
US20190202450A1 (en) * 2017-12-28 2019-07-04 Denso Corporation Apparatus and system for controlling travel of vehicle

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104691545A (en) * 2013-12-04 2015-06-10 财团法人车辆研究测试中心 Adaptive vehicle anti-collision method
CN106864455A (en) * 2015-12-09 2017-06-20 上海渤元信息科技有限公司 Novel automobile intelligent safety system
CN105644557A (en) * 2015-12-30 2016-06-08 苏州安智汽车零部件有限公司 Braking and steering assisting system and method considering collision avoidance intention of driver
CN109591811A (en) * 2017-09-28 2019-04-09 华为技术有限公司 Vehicle braking method, device and storage medium
CN108082184A (en) * 2017-12-19 2018-05-29 戴姆勒股份公司 Collision-prevention system and the method for preventing vehicle collision
US20190202450A1 (en) * 2017-12-28 2019-07-04 Denso Corporation Apparatus and system for controlling travel of vehicle
CN108657189A (en) * 2018-06-22 2018-10-16 南京航空航天大学 Line operating mode automatic Pilot steering and its control method are moved based on BP neural network and safe distance

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111152834B (en) * 2020-01-10 2021-05-11 大连理工大学 Electric automobile electronic differential control method based on Ackerman steering correction
CN111152834A (en) * 2020-01-10 2020-05-15 大连理工大学 Electric automobile electronic differential control method based on Ackerman steering correction
CN111775929A (en) * 2020-06-11 2020-10-16 南京邮电大学 Dynamic safety early warning method for dangerous liquid mobile vehicle-mounted device
CN111775929B (en) * 2020-06-11 2024-03-19 南京邮电大学 Dynamic safety early warning method for dangerous liquid mobile vehicle-mounted device
CN112172827A (en) * 2020-06-24 2021-01-05 上汽通用五菱汽车股份有限公司 Driving assistance system control method, device, equipment and storage medium
CN111899625A (en) * 2020-07-16 2020-11-06 北京理工大学 Intelligent driving assisting development device
CN112298094A (en) * 2020-09-27 2021-02-02 中国第一汽车股份有限公司 Active front bumper pedestrian protection plate system and control method thereof
CN112590787A (en) * 2021-01-05 2021-04-02 青岛海尔科技有限公司 Driving auxiliary processing method and device
CN114516300A (en) * 2022-02-22 2022-05-20 东风汽车集团股份有限公司 Vehicle trafficability assisting method and vehicle
CN114516300B (en) * 2022-02-22 2023-05-12 东风汽车集团股份有限公司 Vehicle passing assistance method and vehicle
WO2024138453A1 (en) * 2022-12-28 2024-07-04 华为技术有限公司 Autonomous driving method and device, and vehicle
CN116394965A (en) * 2023-04-26 2023-07-07 深圳市科莱德电子有限公司 Intelligent driving assistance system, control method and device thereof, and storage medium
CN116394965B (en) * 2023-04-26 2024-01-23 深圳市科莱德电子有限公司 Intelligent driving assistance system, control method and device thereof, and storage medium

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